In this guide, learn about some of the best AI video generator tools on the market today, their pros and cons, pricing, and what you should be looking for if you’re interested in using AI to improve your video workflow.

Table of Contents: Top AI Video Tools

• Top AI Video Generators: Comparison Chart
• Synthesia: Best for AI Avatars
• AI Studios: Best for Text-to-Speech Quality
• InVideo: Best for Social Media and Content Marketing Videos
• Pictory: Best for AI Video Summaries and Highlights
• Lumen5: Best for Ease of Use
• Key Features of AI Video Generation Tools
• How We Evaluated the Best AI Video Generation Tools
• Bottom Line: Top AI Video Generators and Tools

Top AI Video Generators: Comparison Chart

Synthesia: Best for AI Avatars

Synthesia is a leading generative AI startup that helps users transform text scripts and instructions into videos. The solution is particularly well-known for its AI avatars: Users can choose between more than 140 AI avatars — or create their own avatars — and generate audio in more than 120 languages.

Additionally, users have the option to customize backgrounds and branding colors, add soundtrack music and other audio elements, and insert markers to create simple animations.

Users often select Synthesia for its ease of use, full slate of features, natural-sounding AI avatars and extensive artificial intelligence avatar library, and collaborative elements.

For example, once a draft of a video is complete, the creator can easily share that internal link with other users and receive feedback directly on the video platform’s feed. From there, they can embed, download, or otherwise move the video to a new location once it’s ready to be shared more widely.

Pricing

The following two subscription plans are available for Synthesia users:

• Personal: $22.50 per month, billed annually, or $30 billed monthly for one seat and 10 minutes of video per month.
• Enterprise: Pricing is customized based on the number of seats users need. Prospective buyers should contact Synthesia directly for pricing information.

Users also have the option to create one basic demo video for free or to receive a free personalized demo if they are interested in Synthesia for a larger enterprise.

Key Features

• More than 140 stock AI avatars and options for custom and branded avatars.
• AI script assistant and screen recorder.
• More than 65 stock templates as well as branded video template options and a media library.
• Auto-generated closed captions.
• MP4 downloads and video embeds.

Pros

• Live customer support is available to all users.
• More than 120 stock languages and voices are available to users, which is more than nearly any other competitor.
• Customers overwhelmingly consider this an easy-to-use and easy-to-setup tool.

Cons

• Personal plan users are limited to 10 minutes of video per month and one seat.
• Personal plan users cannot access branded templates or AI avatars.
• Some users have had difficulties with speech and audio quality in generated content, especially for less commonly used languages.

Also see: Best AI Writing Tools 

AI Studios: Best for Text-to-Speech Quality

AI Studios from DeepBrain AI is a competitor of Synthesia for text-to-video and/or audio-to-video content generation. It has been particularly well received for its speech and audio quality, giving users the ability to easily mix audio and adjust tones and accents for AI avatars that better reflect what sounds natural to their audience.

DeepBrain also does a good job of showcasing how the tool can be used for different enterprise use cases, including education, sales, news and media, entertainment, retail and commerce, and financial services.

In financial services in particular, AI Studios and its avatars have been used by major enterprises to create virtual finance analysts for personalized videos and virtual lobby assistants for bank kiosks in South Korea.

Pricing

AI Studios is available in three subscription options:

• Starter: Between $24 and $144 per month, billed annually, or $30 and $180 billed monthly. Pricing adjusts based on the number of video minutes subscribers select per month. Users can choose either 10, 20, 40, or 60 minutes of video per month.
• Pro: Between $180 and $480 per month, billed annually, or $225 and $600 billed monthly. Pricing adjusts based on the number of video minutes subscribers select per month. Users can choose either 90, 120, 180, or 240 minutes of video per month.
• Enterprise: Custom pricing. The plan includes up to 50 scenes per video, custom AI avatars, and 24/7 priority customer support.

Key Features

• More than 100 AI avatars in more than 55 languages.
• More than 500 prebuilt video templates for marketing, social media, and other business needs.
• Drag-and-drop video editor with closed captioning.
• Compatible with text and audio scripts.
• API access for Pro and Enterprise users.

Pros

• Strong audio editing features for volume, pitch, pronunciation, and audio mixing needs.
• Text-to-video content generation can be completed in five minutes or less for most videos.
• Pricing is incredibly modular and allows users to select how many minutes of video they want to pay for each month.

Cons

• Import maximums, especially for PowerPoint, can be limiting; in general, presentation tools are somewhat lacking.
• Extra video minutes do not roll over at the end of each month.
• Compared to some of its competitors, AI Studios is not as easy to set up and use from the outset.

Also see: AI Detector Tools

InVideo: Best for Social Media and Content Marketing Videos

InVideo is a video-making platform with AI features that support everything from script generation and avatar generation to slideshow design and YouTube video editing. Its template library is one of the most extensive in the market, covering topics and format types for advertising, slideshows, memes, YouTube, Instagram, music videos, breaking news, and logo videos.

With InVideo AI, users can make content that is tailored to specific platforms, such as YouTube, or to specific looks and feels that match a brand’s identity or goal for the video. Additionally, users can input a target audience when generating a video, ensuring the AI picks design elements and other features that fit that buyer persona’s expectations.

Pricing

InVideo is available in three subscription plans:

• Free: $0 per month for unlimited team members and some limited features.
• Business: $15 per month, billed annually, or $30 billed monthly.
• Unlimited: $30 per month, billed annually, or $60 billed monthly.

Key Features

• More than 5,000 project templates; additional premium templates for Business and Unlimited plan users.
• AI script generation.
• Team sharing and shareable links.
• Customizable intros and outros.
• Event and social-media-calendar-driven project templates.

Pros

• All users are given access to branding tools for brand presets, intros, and outros.
• Even InVideo’s free plan supports unlimited team members and users, making it a great tool for affordable collaboration.
• The Social Media Calendar feature is a unique way to make videos based on the most popular social media holidays; in general, this platform offers a range of useful features for social media content creation.

Cons

• Free plan users are not able to export any of their video projects or access the mobile app.
• Some users have commented on difficulties when working with customer support.
• While the template library is extensive, it lacks useful search and save functionalities, making it harder for users to find and save the templates they want to use most.

Also see: Best Artificial Intelligence Software

Lumen5: Best for Ease of Use

Lumen5 is an AI video generation tool that is most commonly used for text-to-video content generation from long-form blogs and news articles. Companies with limited digital marketing prowess frequently select this tool because it is easy to use, offering users a drag-and-drop editing tool, maker templates and tools, and the ability to automatically generate videos from blogs and RSS feeds.

While the platform may not be the best for a larger creative team that wants to make videos collaboratively, it is a strong contender for individuals and solopreneurs who are managing content for multiple brands.

Users have the option to upload multiple brand kits with certain plans; create multiple workspaces; and upload custom colors, fonts, and watermarks. The free Community plan is also a generous option, giving individuals the bandwidth to create up to five videos per month at no cost.

Pricing

Lumen5 is available in five plan options:

• Community: $0 for up to five videos per month.
• Basic: $19 per month, billed annually, or $29 billed monthly.
• Starter: $59 per month, billed annually, or $79 billed monthly.
• Professional: $149 per month, billed annually, or $199 billed monthly.
• Enterprise: Custom pricing.

Key Features

• Smart Summarization feature for blog-to-video content transformations.
• Automatic language detection and matching.
• Built-in photo, video, audio, and AI voiceover media library.
• Automated video generation from RSS feeds.
• Custom branding and brand kits.

Pros

• Although it has limitations, the free Community plan still gives users the opportunity to work on up to five videos per month, which is much more than most free plan and trial options. All paid plans allow users to create unlimited videos per month.
• Lumen5 is generally considered an easy-to-use tool; its video tools and maker tools walk users through the steps for varying drag-and-drop video editing tasks.
• While many similar platforms only support one brand per account, the Professional and Enterprise plans for Lumen5 allow users to work with up to three brand kits.

Cons

• Template options are somewhat limited for Lumen5 users; users also cannot access AI avatars or most speech synthesis tools.
• Only the expensive Lumen5 Enterprise plan supports more than one user per account.
• The Starter, Professional, and Enterprise plans are fairly expensive compared to similar packages from competitors.

Also see: The Benefits of Generative AI 

Pictory: Best for AI Video Summaries and Highlights

Pictory is another AI video generation platform that is best suited for content marketing and social media video projects. It is a particularly effective solution for creating micro-content, or shorter clips and highlight reels from existing long-form content.

The platform is designed to automatically generate these shorter snippets, making it possible for users to get more content, engagement, and reach from a single project.

While the platform is most frequently used by digital creators and marketers, it can also be used by e-learning teams, coaches, and other users who need an accessible video format. Its auto-generated summaries are especially helpful to teams that want to offer more digestible ways to consume video content.

Pricing

Pictory is available in three subscription plan options:

• Standard: $19 per month, billed annually, or $23 billed monthly.
• Premium: $39 per month, billed annually, or $47 billed monthly.
• Teams: $99 per month, billed annually, or $119 billed monthly.

The platform can also be tested through a free trial option, which gives users the opportunity to create up to three video projects, each up to 10 minutes long.

Key Features

• AI summaries and transcriptions; users can also remove filler words from scripts and spoken audio.
• AI voice narration and voiceovers with support from ElevenLabs.
• Highlight reel and short clip creation.
• Academy for video marketing masterclasses and other learning resources.
• Hootsuite integration for social media projects.

Pros

• Users can easily transform a blog post or other long-form written content into video content that removes filler words and any text that doesn’t make sense in the new format.
• In addition to highlight reels and video summaries, videos can be auto-captioned and auto-transcribed, making this a great tool for greater accessibility.
• The vendor offers video marketing masterclasses, case studies, a blog, and a creator community to give users the resources they need to make more engaging video projects.

Cons

• Pictory does not offer AI avatars to its users.
• No account option allows more than three users per account, which can be incredibly limiting for video team requirements.
• Compared to many other competitors, Pictory is fairly expensive.

Also read: Generative AI for Business: Top 7 Productivity Boosts

Key Features of AI Video Generation Tools

Text-to-Video Content Generation

Many users do not have coding experience and/or the time to learn the finer details of video software.

That’s why many AI video generation tools include text-to-video content generation capabilities, allowing users to easily turn their scripts or other written text into video content that may include human-looking AI avatars, AI voices, and other elements that create a professional-looking and sounding video.

Video Templates

A number of AI tools now give users a baseline video template from which to build their video content. These templates may focus on specific industry use cases, a certain social media or digital platform, or a video format with animation or transitional elements.

Users can often customize these templates and add their own branding, but the video template gives them the creative ideas and basic design to get started.

AI Avatars

Many businesses do not have the budgets to pay actors or employees to act as talking heads for brand videos, but they nonetheless want the personal touch of an onscreen personality. AI avatars can be developed to match different appearances, genders, and other expectations, with personalities, tones, accents, and other unique features added to synthetic voices.

The best AI avatar solutions create natural-looking avatars and give users the ability to custom-create their own avatars. These avatars can be used for personalized sales and marketing videos, e-learning and training videos, and other forms of media that benefit from a friendly face.

Speech and Audio Synthesis

With an AI video tool, AI speech and audio synthesis make it so any number of videos can be made without a human actor or voice. These platforms take text or rough spoken recordings and transform them into video-ready voices that are synthetically generated.

These voices should be available in a variety of languages and, in most cases, users are able to adjust the tone, pitch, accent, and other elements of speech to make the voice sound more believable.

Low-Code/No-Code Usability and Video Editing

Regardless of how much work AI features can do to put together the final product, users typically want some hands-on control over the video creation process.

To make this as simple as possible, most AI video generation tools provide a low-code/no-code user interface, giving customers the ability to move around slides, audio and visual elements, and other pieces of their video with a drag-and-drop editing tool.

On a related topic: What is Generative AI?

How We Evaluated the Best AI Video Generation Tools

Ease of Use – 30%

Most people using an AI video generation tool have little to no experience with traditional video software and its complex features. AI should handle the most difficult aspects of video generation, especially for users looking to create a range of videos at scale; this is why we’ve decided to give “ease of use” a larger weight in our evaluation.

Customer reviews that comment on ease of use, strong customer support, low-code/no-code interfaces, and strong collaboration features all contribute to the ease of use score for each of these tools.

Enterprise Use Case(s) – 30%

The best AI video generation tools give users the extensive capabilities and features necessary to create enterprise-ready marketing, sales, training, and customer service videos.

Although many other AI video generation tools exist for casual mobile users and use cases, we mostly steered away from those tools in favor of platforms that offer enterprise features such as video embeds and exports, useful integrations, business templates, advanced AI avatars and audio synthesis, and other features that support an enterprise video-making workflow.

Video and Audio Quality – 20%

To do enterprise-level projects and create humanoid voices and avatars, it’s important to invest in AI video generators that emphasize high video and audio quality.

While compiling our list, we searched for tools that supported high-resolution video uploads and downloads and audio mixing and synchronization capabilities. We also looked for tools that received favorable customer reviews for AI avatar and sound quality performance and that include basic transition, animation, and intro and outro functionalities.

Cost – 20%

When considering the cost of video generation tools, we looked for both affordability and a range of subscription options. In terms of affordability, we sought out tools that offered free versions and/or useful demos and trial periods.

However, we paid even closer attention to tools that offered a range of subscription options, from free or low-cost to enterprise-level subscriptions and features. We focused heavily on tools that came at a variety of price points, identifying solutions that both small businesses and enterprises could use, as well as tools that smaller businesses could stick with even as their needs scale up.

Also see: Generative AI Companies: Top 12 Leaders

Bottom Line: Top AI Video Generators and Tools

AI video generators simplify a digital marketing and communication practice that has long been considered the realm of expert creatives: video editing and creation.

With the help of an AI video tool, users can now take on many of the most complicated video tasks, ranging from editing footage and audio to creating shorter clips and summaries from long-form content.

Especially with so many affordable AI video generators on the market today, marketing and sales professionals in particular should incorporate this type of software into their digital toolset for accessible video creation and better customer engagement results.

Read next: Top 9 Generative AI Applications and Tools

The post 5 Best AI Video Generators 2023 appeared first on eWEEK.

Read on to learn how generative AI is being used in contact centers now and how it can benefit contact centers that adopt the technology with security, ethics, and other best practices in mind.

Table of Contents: Contact Centers and Generative AI

• How Is Generative AI Being Used in Contact Centers Today?
• Benefits of Using Generative AI in the Contact Center
• Generative AI in the Contact Center: Tips and Best Practices
• Bottom Line: Contact Centers Supported by Generative AI Technology

How Is Generative AI Being Used in Contact Centers Today?

Voice Generation for Customer Service and Client Calls

Though most people think of generative AI in the context of text or image generation, the technology has also come a long way in the areas of audio generation and voice synthesis.

With the right tools, contact centers can use artificial voices that are trained to sound human-like and take calls, answering complex questions and/or triaging calls to more experienced reps just as a human would. For contact centers where video calls are more common, artificial intelligence avatars can also be generated that both look and sound like a human employee.

Off-hours Customer Service Support

AI agents are particularly effective for international businesses, healthcare organizations, and contact centers that have trouble staffing their centers after regular business hours.

AI chatbots and agents can be on the clock 24/7 and never experience the fatigue or frustration that a human employee might feel if they worked these extended hours. As generative AI capabilities for these AI assistants and service reps continue to advance, they are becoming increasingly capable of handling complex tasks and customer requests without human intervention.

Also see: Top Generative AI Apps and Tools

Service Representative Coaching and Live Assistance

Generative AI contact center tools are frequently used to monitor human rep calls and give them feedback on how their tone is coming across, how customers are receiving that information, and other factors that may positively or negatively impact call outcomes.

Additionally, these tools are designed with administrators’ and managers’ needs in mind: although artificial intelligence can now handle most coaching tasks on their own, they also transparently share information about:

• Contact center rep evaluations.
• Previous coaching sessions.
• The number of evaluations each employee has received.
• Other data in a digestible dashboard format so managers can intervene and provide further coaching or disciplinary action if necessary.

Call Recording and Summarization

Generative artificial intelligence platforms go beyond simply recording contact center calls and use their algorithmic training to help contact centers extract deeper intelligence and insights from call transcripts.

For example, if a customer service rep has a 30-minute call with a customer that touches on several issues and shifts tone throughout the conversation. In this case, certain generative AI solutions can quickly summarize this conversation in key bullet points, assess buyer sentiment at different points in the conversation, and make recommendations for how or if a rep from the contact center should follow up with this individual.

In many cases, these tools are also able to supplement information from the current conversation with past conversations, buying or patient history, and other data that informs employees about who this individual is and what they expect from the brand.

Omnichannel Content and Communication Enrichment

The modern contact center rarely sticks to traditional phone calls, often giving users the option to communicate with their reps via email, chatbot threads, and social media messages. When contact centers opt to use generative AI-driven chatbots and analytics tools, they can more easily embed intelligent assistance into all of the channels where customers choose to interact with them.

Because of this omnichannel approach enabled by modern AI, chatbots and AI assistants are frequently able to use their natural language skills and advanced search capabilities to extend useful learning resources and knowledge base materials to customers, helping to avoid the need for further contact center or customer service escalation.

Also see: Best Artificial Intelligence Software

Sentiment Analysis and Real-time Analytics

Live call monitoring used for real-time analytics across a variety of demographic and customer data points. Can determine how customers are feeling and make recommendations for how to interact with individuals and customers as a whole better in the future.

Automated Follow Ups and Touchpoints

Contact centers have traditionally required reps to manually handle repetitive tasks, like accepting customer calls and messages, recording and reviewing transcripts, and following up with customers at regular intervals.

With the help of generative AI tools, many of these tasks, including reminders for these tasks, can be automated so employees are able to focus on more complex customer experience tasks. In the meantime, AI tools set up calls, emails, and other types of follow ups to ensure customers feel taken care of and are reached at key points in the customer lifecycle.

Also see: 100+ Top AI Companies

Benefits Generative AI in the Contact Center

Generative AI in the contact center helps businesses to better organize, automate, and respond to customer service needs. Some of the most important benefits that come from using generative AI in contact center settings include the following:

• Fill in the Gaps for Employee Shortages: Artificial intelligence can take on open roles for certain contact center tasks, like answering phones and preparing and analyzing call transcripts; generative AI agents are especially useful for 24/7 phone coverage and international availability.
• Real-Time Detailed Data Insights: Live coaching and performance tweaking opportunities are made more feasible with the help of generative AI data analytics. Both predictive and prescriptive analytics, with AI-driven recommendations, can be used to improve future call cadences and behaviors.
• Employee Training and Guidance: Employees can be trained during their calls to make better decisions and improve their conversational skills immediately with the help of AI coaches. Taking this approach doesn’t require human managers to listen to recordings and give feedback at a later time, when that feedback may be more difficult to apply.
• Learn and Improve After Each Customer Interaction: Generative AI tools themselves are trained to take customer queries and other data inputs and give customers the best possible answer. Beyond simply answering customer questions, these generative AI agents are trained to use this conversational data to improve how they interact with customers in the future.

Generative AI in the Contact Center: Best Practices

Using generative AI in the contact center can improve workflows for employees and outcomes for callers. But because of the nature of these tools and how they are trained, there are many AI-related cybersecurity and ethical considerations about AI that should be weighed when implementing contact center AI tools.

To ensure your team follows best practices that consider the wants and needs of the client, follow these tips:

• Adhere to Relevant Data Privacy and Usage Laws: This is especially important if you’re working in a highly regulated industry’s contact center, like a healthcare facility. For a better customer experience and to prevent legal action, go beyond the basics of data compliance and privacy laws and follow AI privacy best practices that put the customer first.
• Integrate and Embed Generative AI into Existing Tool Stacks: Contact centers should run like well-oiled machines, so it’s a good idea to invest in contact center tools that natively include generative AI capabilities or that integrate smoothly with generative AI tools like ChatGPT and GPT-4.
• Use Proven and Purpose-Built Contact Center Tools: Look for leading solutions from proven AI leaders and well-funded, innovative AI startups. Examples of leading generative AI contact center and e-commerce solutions include the following: Gridspace, Salesforce Einstein, Microsoft Copilot, Cresta, and Observe.ai.
• Monitor Customer Feedback Pre- and Post-AI Implementation: Generative AI agents, chatbots, and coaches may slip up occasionally, especially as they are learning how your customer base works. To catch their mistakes and train them for better outcomes in the future, continue to monitor their performance and how customers feel about AI-powered interactions.

Bottom Line: Contact Centers Supported by Generative AI Technology

Regardless of what tools you choose to use in your contact center, customers should not feel like they’ve been “left to the robots.”

For more complex call center scenarios or high value customers who are dissatisfied, humans should always be available to give them the personal touch they require. It’s also a good idea to ensure that human employees are continually vetting and updating pretrained AI responses to fit the times, buyer and/or patient trends, and other changing customer expectations.

Ultimately, generative AI offers exciting new opportunities for customer engagement and automation for contact centers, but this technology must be used responsibly and thoughtfully. It’s most important that customers and employees feel that the contact center is being supported by AI rather than taken over by AI.

Read next: Generative AI Companies: Top 12 Leaders

The post Generative AI in the Contact Center: Uses, Benefits, Best Practices appeared first on eWEEK.

In this guide, we’ll cover some of the most common use cases of generative AI in e-commerce today and we’ll touch on how today’s benefits may lead to further AI growth in this industry.

Table of Contents: Generative AI and E-Commerce

• Generative AI Use Cases in E-Commerce
• Leading Generative AI Solutions in the E-Commerce Space
• Bottom Line: The Benefits of Generative AI in E-Commerce

Generative AI Use Cases in E-Commerce

Generative AI can be used to supplement or even supplant various components of a company’s e-commerce workflow.

Across the e-commerce sector, artificial intelligence benefits both the vendor and its employees as well as customers and prospective buyers. Learn more about generative AI’s use cases in e-commerce below:

AI-Driven Analytics

E-commerce of course involves less face-to-face interaction with customers than traditional commerce, so it can be particularly challenging to gauge customer satisfaction across different parameters and data points.

Online businesses have used data analytics tools for many years now to better understand how customers engage with their brands. But these tools have been limited in the quantity and quality of data they collect as well as the speed with which data is updated.

In contrast, generative-AI-driven data analytics tools give business leaders and data scientists more contextual customer data, updated in real time and across different shopping, channel, and demographic data points.

These generative AI tools can collect data in various unstructured formats, including customer service queries, social media posts and comments, ad clicks and engagements, and other data that has historically been difficult to capture.

Additionally, these tools often go beyond predictive analytics, offering insights into what’s happening now and making prescriptive recommendations for what vendors can do to achieve better results, products, and services in the future.

Other ways users can combine generative AI and data analytics for better e-commerce outcomes include the following:

• Quickly assess products, websites, and other customer-facing assets to determine if they are meeting quality requirements.
• Make more accurate and timely supply chain predictions.
• More effectively manage inventory and demand forecasting needs.

Customer Service Chatbots, Agents, and Coaches

With generative AI chatbots and virtual agents, businesses can handle chat conversations 24 hours a day that are designed to feel like a real human is engaging with the customer.

Many brands have had 24/7 operational chatbots in the past. But without the content generation and predictive capabilities of generative AI, these previous chatbots have relied heavily on human-built workflows and prebuilt responses that don’t always meet customer needs. Their limited training on limited amounts of data severely impacted their ability to interact with customers. They can’t solve problems in the face of unique customer experience scenarios.

When a customer service problem needs to be escalated, generative AI agents can also be used to triage more complex communications to human customer service reps, giving them all of the contextual information they need to follow up with the customer effectively.

Additionally, these AIs can be used to coach human customer service reps on how to have better customer interactions that match the tone and needs of that individual.

AI Search for Customers

Generative AI tools have made it possible for businesses to quickly scale up their online knowledge bases in a way that answers a variety of customer questions that may not have previously been considered or adequately covered in early versions of online resources.

This database of knowledge can then be embedded in brand websites and apps, and in some cases, may also be connected to the internet for real-time search capabilities and more targeted ads.

AI-driven search not only gives customers the best search results for their queries but also frequently offers contextual information, suggested next searches, and other information that may assist them in their buying experience.

Product Descriptions and Content Writing

Businesses of all backgrounds are experimenting with generative AI for content creation, and the e-commerce world is filled with opportunities where AI can fill the gaps. For example, generative AI tools can be used to quickly write product descriptions, product guides and white papers, marketing and sales blogs, emails and marketing campaigns, chatbot responses, and targeted ad content.

Because of the speed and scale at which generative AI content writing tools can work, businesses can quickly create and rework content while also using these tools to detect customer sentiment in queries and respond accordingly.

Especially in marketing and communications campaigns, many of these content generation tools can also be trained and set up to automatically reply and follow up with customers when appropriate.

Back-Office Operational Support

A growing number of AI tools focus on creating a one-stop shop for e-commerce back-office operational tasks, including content generation, task management, storefront management, and ad management needs.

An example is Shopify Magic, a set of generative AI capabilities that is built directly into the Shopify commerce platform. Its latest feature is Sidekick, a Shopify assistant that helps vendors manage their task lists while answering specific questions about everything from customer interactions to what’s needed to prepare for an upcoming sale.

Virtual Customer Experiences

Though this area is still fairly early in its development, virtual customer experiences like virtual try-on for clothing e-tailers are quickly growing and gaining a loyal customer base.

Clothing retailers are primarily benefiting from this new innovation, but other VR/AR experiences are also in the works that allow users to have remote shopping experiences that “feel” real. Additionally, a growing number of customer-facing apps now exist that combine different generative AI elements, such as AI assistants, chatbots, guided search, and catered product recommendations, to create a smoother shopping experience for users.

For more on a similar topic, read AI in Retail: What You Need to Know.

Leading Generative AI Solutions in the E-Commerce Space

A number of generative AI solutions have popped up to solve for different e-commerce use cases, including ad and product content creation, communication and customer service, and more.

Below, we’ve gathered information about some of today’s leading e-commerce-focused generative AI solutions:

• OpenAI’s ChatGPT and GPT-4: OpenAI’s ChatGPT and GPT-4 are being used by many other generative AI vendors as a foundation for embeddable chatbots, virtual assistants, and other tools that improve the customer experience on e-commerce websites and virtual storefronts.
• Jasper: Jasper’s library of tools covers a range of e-commerce needs, including generative AI chatbots, image generation, brand-driven content generation, and automated marketing campaigns.
• Gridspace: A contact center tool that supports e-commerce needs like virtual agents to take calls and detailed conversational analytics to coach and improve future conversation outcomes.
• Veesual: VR/AR-driven customer experiences for clothing shopping and virtual try-ons.
• Shopify Magic: A back-office assistant that can help vendors with task management and content generation types of tasks.
• SolidGrids: Product image and content generation, including banner generation and SEO optimization features.
• Describely: Product descriptions, catalog content, and other types of product-driven content generation.
• Cresta: A contact center solution for virtual contact automation, rep coaching, and real-time call analytics.
• AdCreative.ai: Creative ad content generation platform.
• Shulex VOC: A ChatGPT-driven AI assistant that makes suggestions, answers questions, and automates various e-commerce tasks.
• Phrasee: A content generation platform that works across email, SMS, web, app, and social channels and focuses on customer loyalty outcomes.
• Kore.ai: A platform with both employee experience and customer experience tools for conversational AI, AI-driven assistance, and smart search.

For more information on these solutions and others from emerging generative AI startups, read about the 50 Generative AI Startups to Watch in 2023.

Bottom Line: The Benefits of Generative AI in E-Commerce

The generative AI and e-commerce market is ripe with new tools and features emerging on a near-daily basis to address the different wants and needs of e-commerce vendors, business stakeholders, employees, and customers.

While certainly not everyone is open to artificial intelligence taking over tasks that have traditionally involved human-driven customer service, more and more people are growing comfortable with this development. It provides new conveniences, particularly to retailers that are struggling with worker shortages.

With generative AI in a supporting role, e-commerce companies can confidently move forward and grow their businesses in a way that feels sustainable and innovative.

Read next: Top 9 Generative AI Applications and Tools

The post Generative AI’s Impact on E-Commerce appeared first on eWEEK.

The everyday consumer is now using AI apps to simplify everything from crafting an email to editing a photo to improving their fitness with the support of AI assistants and mobile AI apps.

In this guide, we’ll cover some of the top AI apps for Android and iOS users today and how these apps are transforming everyday life.

Table of Contents: Best AI Apps for Android and iOS Users

• Top 5 AI Apps for Conversational AI and AI Assistants
• Top 5 AI Apps for Health, Safety, and Transportation
• Top 7 AI Apps for Entertainment
• Top 3 AI Apps for Education
• Bottom Line: AI Apps for Mobile Users

Top 5 AI Apps for Conversational AI and AI Assistants

ChatGPT

The mobile app version of ChatGPT, the popular large language model AI chatbot from OpenAI, is available for both Android and iOS users.

With ChatGPT on mobile, users can sync up their ChatGPT data with all other devices that they use. They can also benefit from the Whisper integration, which enables them to use voice inputs while engaging with the ChatGPT app.

Though more advanced features may be available to ChatGPT Plus subscribers, the basic mobile app is free to use and gives users the ability to generate various types of content in response to their inputs.

Pi

Inflection’s Pi generated buzz before anyone had even heard the name of the app due to the pedigree of the company’s founders. The app has proven itself a worthy competitor of ChatGPT, giving users the ability to have human-like conversations, seek advice, and get other interesting responses from the Pi chatbot.

Pi is currently only available to iOS users, though there are plans to eventually release the app in the Google Play store.

Replika

Replika is a conversational AI app that allows users to interact with a virtual friend in a way that feels like speaking with a real human.

Depending on the user’s needs, this Replika can be set up to feel like a friend, a personal or professional mentor, or even a romantic interest. Users also have the option to virtually share moments with their Replika through augmented reality. Replika is available to iOS, Android, and Oculus device users.

Otter.ai

Otter.ai is an assistive AI app that primarily helps with audio transcription, note-taking, and live summaries.

Although it can just as easily be used by individuals, the app is best suited for collaborative content, as different users can edit the transcript and add relevant comments and highlights as necessary. Otter.ai is available for iOS, Android, and Slack and is also available through a Chrome extension.

Cleo

Cleo is an AI assistant that helps users with personal finance management tasks. Users can engage with the Cleo app for help with managing a personal budget, saving, avoiding certain types of purchases, and borrowing money.

Cleo also brings a certain amount of personality to the personal finance experience, giving its users the option to be “roasted” or “hyped” for their spending habits and how they align with their overall goals. Cleo is available for both iOS and Android users.

Also see: Top 9 Generative AI Applications and Tools

Top 5 AI Apps for Health, Safety, and Transportation

Wysa

Wysa is a mobile app that takes a hybrid approach to mental health care, using both artificial intelligence and humans to give users the care they need.

Users have the option to engage with the conversational AI agent on demand, and this agent can then assign curated care guides, programs, and additional support to users who need it. If a user is in a clinical program with Wysa, they may also receive regular check-ins from the AI agent, which can escalate to human coaches and therapists if necessary. Wysa is available for both iOS and Android users.

Youper

Youper is another AI-driven mental health app that relies on Cognitive Behavioral Therapy (CBT) best practices and methodologies.

With Youper, users first complete a detailed assessment to give Youper the data it needs to curate a more personal experience. From there, its conversational AI has on-demand conversations with the user. This is an exciting app option for users who want to track their mood or symptoms over time, as Youper offers detailed symptom monitoring and analytics features with helpful visualizations. Youper is available to both iOS and Android users.

Seeing AI

Seeing AI is an app that’s primarily designed for the visually impaired and blind to get detailed audio descriptions of their surroundings.

The app can be used to translate documents, products, handwriting, and currency into audio descriptions, and even more abstract concepts like colors, people, and scenes can be transformed into auditory narrations. Seeing AI is only available to iOS users, though similar apps are available for Android users.

FitnessAI

FitnessAI is an AI-driven fitness training app that generates personalized workouts for users based on their personal goals, fitness levels, and health backgrounds.

Additionally, if users keep up with their workout logs, the AI can assist them with deciding on the optimal rest period and workout difficulty levels for future workouts. FitnessAI is only available for iOS users, though similar AI fitness and nutrition apps are available for Android users as well.

Waze

Waze is a popular navigation and transportation app that uses a combination of AI and machine learning to optimize user routes and predict their typical driving patterns and styles.

It also uses Google Cloud’s AI Platform to develop a network for Waze Carpool, a sustainable way for users to share their trips with each other when following similar routes and schedules. Waze is available for both iOS and Android users.

Also see: AI Detector Tools

Top 7 AI Apps for Entertainment

Lensa AI

Lensa is an AI photo editor and retoucher that also allows users to create videos, add special effects to images, and create avatar characters in different styles.

Its range of features and editing styles make it a popular image-editing tool for the everyday consumer. Lensa AI is available for both iOS and Android users.

Wonder

Wonder is an AI-generated art app that allows users to submit text prompts to create art in different styles.

No coding experience is necessary, as the tool creatively generates the art based only on user requests and descriptions, as well as on submitted photos. Wonder is available for both iOS and Android users.

Spotify

Spotify is one of the most popular music and podcast streaming apps on the market, and part of this popularity comes from the curated experiences it creates for users through AI.

Its latest AI addition is the AI DJ, which pulls data from user listening history and other factors to create curated set lists and explanations of the music that gets played. Spotify is available for both Android and iOS users.

Facetune

Facetune by Lightricks has been a popular photo and video editing app since the earliest days of Instagram, owing to its easy-to-use interface and wide variety of photo-editing features.

More recently, the company has added various AI-enhanced and AI-powered features, including the AI Selfie Generator, AI avatars, and the AI Photo Enhancer. With the selfie generator tool, users can generate different image styles for their selfies with their own prompts or they can use Facetune’s own library of AI-generated presets. Facetune is available to both Android and iOS users.

Character AI

Character AI is an AI and generative AI tool that allows users to have realistic conversations with AI avatars and characters.

These characters can be created for more serious purposes, like preparing for a job interview or imagining what it would be like to speak with a dead relative, but they can also be used for more fun conversations, like pretending to chat with your favorite celebrity. Character AI is available to both iOS and Android users.

AI Dungeon

AI Dungeon is a generative AI tool that allows users to simulate and create characters, adventure prompts, and virtual worlds for virtual gaming experiences.

Although users have full freedom to create scenarios from their imagination, the tool also comes with a few foundational prompts if anyone needs assistance getting started. AI Dungeon is available to both iOS and Android users.

Photoleap

Photoleap is another image editing app from Lightricks that focuses on AI-powered image generation and photo editing.

Users can submit text prompts to create unique images and can also benefit from a tool for AI avatar generation. Photoleap is available to both Android and iOS users.

Also see: Generative AI Examples

Top 3 AI Apps for Education

Socratic by Google

Socratic is an education-focused application that is powered by Google AI.

Students in subjects ranging from science and math to literature and social studies are able to submit questions — either through typed text, voice prompts, or homework screenshots — and receive detailed responses that blend text explanations, diagrams and images, and internet results to give users a more well-rounded response. Socratic is available to both iOS and Android users.

ELSA

ELSA is a conversational AI application that focuses primarily on English language-learning initiatives, both in schools and company settings.

Users are given access to ELSA’s AI coach, which is able to detect progress in their speech patterns and pronunciation. AI within the system is also used to create customized curricula for students that fit their specific performance and behaviors over time. ELSA is available to both iOS and Google users.

Grammarly

Grammarly is a writing assistance tool that can help users with simple misspellings and punctuation errors but also goes beyond basic proofreading to offer AI-powered suggestions for improved correctness, clarity, and delivery for particular audiences.

The tool also more recently added generative AI assistance to support brainstorming, content creation, and rewrites of existing content. A version of the Grammarly tool — the Grammar Keyboard — is available to both Android and iOS users.

Also see: Generative AI Companies: Top 12 Leaders 

Bottom Line: Top AI Apps

This guide to the best AI apps only scratches the surface of an exciting AI mobile market, especially as new players emerge on a near-daily basis.

The four overarching categories we’ve covered above are where most AI apps for mobile users are focused today, but that’s quickly expanding. More AI developers are launching tools for things like virtual shopping and entertainment experiences, telehealth, and project management, to name a few categories out of many.

Whether you’re looking for a tool that can support key business initiatives or simply want an AI solution for personal use cases, the AI mobile market is a great place to start for user-friendly and low-cost AI applications.

Read next: Best Artificial Intelligence Software

The post 20 Best AI Apps 2023 appeared first on eWEEK.

Businesses everywhere are adopting the practice because, in theory, it should make their jobs easier. However, AIOps is a complex process that, when executed without a clear plan in place, can lead to more inefficient and convoluted IT processes.

To achieve the best results when first implementing AIOps in your organization, it’s important to understand not only how AIOps can benefit your team but what AIOps best practices you should follow from the outset.

AIOps Best Practices: Table of Contents

1. Invest in an AIOps Platform That Integrates With Your Existing Tool Stack

2. Define Concrete AIOps Operations and Goals in Advance

3. Identify Relevant Data and Data Sources

4. Maintain High Data Quality Standards

5. Don’t Skimp on Data Security Best Practices

6. Complete an AIOps Test Project

7. Establish an AI Use Policy for Your Organization

8. Regularly Monitor Your Network and AIOps Workflows

9. Document AIOps Processes as They Are Established

10. Don’t Limit AIOps Best Practices to IT Use Cases

Bottom Line: Implementing AIOps Best Practices

The Top AIOps Best Practices

1. Invest in an AIOps Platform That Integrates With Your Existing Tool Stack

AIOps is all about making your current artificial intelligence and IT processes more efficient, and that only happens if your AIOps tool(s) integrate with all of the other most important resources, data sources, and business applications in your tool stack.

For the best possible results, look for AIOps platforms with deep native integration libraries or a capable API that makes it easy to connect your AIOps platform across your IT ecosystem. Aim for minimal customization and fine-tuning requirements, at least at the start, so you can keep the AIOps platform running efficiently even as your other tools change and scale.

2. Define Concrete AIOps Operations and Goals in Advance

AIOps is only effective if you know what you’re using it for and what you’re hoping to improve with AI-driven automation and workflows.

Every organization’s goals and KPIs are different when it comes to AIOps, but regardless of how the individual goals look, you’ll achieve better outcomes if your goals are framed as part of larger IT, operations, and business objectives.

For business leaders who are not sure where to start, consider setting goals around the following IT operational tasks and improvements:

• Improved mean time to resolution (MTTR)
• Improved mean time between failures (MTBF)
• Reduction of IT incidents and outages, particularly for critical services
• Resource optimization and allocation across cloud and on-premises resources
• Proactive issue detection and incident mitigation
• Reduction of false positives and false negatives
• Resource provisioning time
• Capacity utilization across usage metrics
• Quality and frequency of change impact predictions
• Number of policy and compliance violations over time
• IT operational expenses over time

3. Identify Relevant Data and Data Sources

Once you’ve completed initial tool research and goal setting, you’ll need to get a handle on all relevant data sources that need to be accessed, integrated, and migrated for effective AIOps.

Consider the different formats of data involved, how this data is used, and the different location(s) where this data currently lives in your network. Most important, determine if this data exists in different formats across your organization; if that’s the case, you’ll want to clean and standardize that data before adding it to your AIOps system.

4. Maintain High Data Quality Standards

AIOps relies heavily on data for both AI/ML training and operational queries. It’s important to hold your organization to high data quality standards so all data that you need is accessible, understandable, and in an actionable format.

Some of the steps you can take to improve data quality include using data cleansing, data mapping, data preparation, and data integrity management tools. You can and should also invest in data governance tools, as these tools can help improve data quality, security, and compliance while also assisting IT teams in monitoring and addressing changes in data quality over time.

5. Don’t Skimp on Data Security Best Practices

Data security best practices and tools are necessary for smooth operations, regulatory compliance, and maintaining business reputation and customer trust. Beyond investing in data governance tools as mentioned above, your AIOps teams should adhere to the following data security best practices as well:

• Use only secure and trusted databases and data sources.
• Keep your data storage systems updated and make sure all data storage practices align with regulatory and security requirements.
• Set up mobile device use and data management policies for employees.
• Designate a multi-tier access and authorization system for your most critical assets.
• Whenever it’s necessary for larger groups of people to access or use sensitive data, look for additional ways to protect that data, such as data encryption, anonymization, and/or masking.

Bear in mind that if your organization is interested in using generative AI in its AIOps workflows, additional data security rules and best practices may be necessary.

Also read: Generative AI and Data Analytics: Best Practices

6. Complete an AIOps Test Project

Before setting up the entire network and several automations within an overarching AIOps framework, you should first complete a test run with a smaller project as a proof of concept. During this test run, you can monitor how the infrastructure performs and also make note of how the team handles this new workflow. From there, you can make adjustments to infrastructure, team training, cybersecurity tooling, and the AIOps plan accordingly after the test project is over.

7. Establish an AI Use Policy for Your Organization

Any organization, department, or team that uses AI needs to be held to an AI use policy and receive training on the technology and its role in operations.

The policy should particularly address questions about how AI is being used in the organization, individual roles and responsibilities when using AI, and how to maintain data security and integrity while using AI. If you’re looking for an example AI policy to use as a foundation for your own, this generic artificial intelligence ethics policy may be a good place to start.

8. Regularly Monitor Your Network and AIOps Workflows

Your team should be monitoring network performance at all times, but it’s especially crucial when implementing a new operational practice like AIOps.

On a regular basis, you’ll want to use monitoring tools with features that support analytics and deeper dives into AI and ML performance. On a less frequent basis, your team or an objective third party will need to conduct deeper network audits to assess how each piece of the AIOps workflow is performing and how or if it’s impacting the rest of your network’s performance.

Some monitoring tools simply notify users when a possible issue arises, while others offer suggestions or take corrective action. You’ll want to select a monitoring tool that complements your team’s skill sets, budget, and other team requirements.

9. Document AIOps Processes as They Are Established

Documenting AIOps processes after they are quality-tested and approved is one of the surest ways to protect the integrity of the overall operation.

Concrete, detailed documentation also supports change management efforts, giving business leaders the resources they need to keep things running smoothly as team members, tools, and other components of the workflow change over time. Documentation can be kept in traditional data storage and database systems for easy access, but many businesses will benefit from storing this documentation in an additional, off-network or highly secure location.

10. Don’t Limit AIOps Best Practices to IT Use Cases

AIOps is technically focused on IT operations, but these best practices can be applied across an organization, especially as AI and advanced business technologies become a normal part of enterprise operations outside of IT departments.

Making sure all members of your organization across all departments understand how AI can automate their workflows and create new efficiencies could help your overall organization get roles and responsibilities, tool sprawl, and inefficient daily operations under tighter control.

Learn more about why AIOps best practices should be implemented across different business departments.

Bottom Line: Implementing AIOps Best Practices

AIOps is still a fairly new IT operations strategy, but because of the automations and new efficiencies it introduces to team workflows, it has quickly gained traction across industries and teams.

Despite its advantages, IT professionals need to remember that setting up AIOps can be a complex, multi-faceted process and getting it wrong may create even bigger operational headaches than existed before, not to mention potential new security and compliance issues.

If your team takes the extra time necessary to properly set up automated workflows with appropriate expectations and rules from the beginning, your AIOps plan stands a better chance for success in the long run.

Read next: Best Artificial Intelligence Software

The post 10 AIOps Best Practices appeared first on eWEEK.

This guide is for these consumers to build a deeper knowledge base about AI’s privacy features. Additionally, it is a guide for business owners and leaders to better understand their customers’ concerns and how to use AI in a way that protects privacy without sacrificing functionality.

Table of Contents: AI and Privacy

• Issues with AI and Privacy
• How Is AI Data Collected?
• Solutions for AI and Privacy Concerns
• Bottom Line

Issues with AI and Privacy

Little regard for copyright and IP laws

AI models pull training data from all corners of the web. Unfortunately, many AI vendors either don’t realize or don’t care when they use someone else’s copyrighted artwork, content, or other intellectual property without their consent.

This problem grows much worse as models are trained, retrained, and fine-tuned with this data; many of today’s AI models are so complex that even their builders can’t confidently say what data is being used and who has access to it.

Also see: AI Detector Tools

Unauthorized incorporation of user data

When AI model users input their own data in the form of queries, there’s the possibility that this data will become part of the model’s future training dataset. When this happens, this data can show up as outputs to other users’ queries, which is a particularly big issue if users have input sensitive data into the system.

In a now-famous example, three different Samsung employees leaked sensitive company information to ChatGPT that could now possibly be part of ChatGPT’s training data. Many vendors, including OpenAI, are cracking down on how user inputs are incorporated into future training, but still, there’s no guarantee that sensitive data will remain secure and outside of future training sets.

Limited regulatory bodies and safeguards

Some countries and regulatory bodies are working on AI regulations and safe use policies, but no overarching standards are currently in place to hold AI vendors accountable for how they build and use artificial intelligence tools.

A number of AI vendors have already come under fire for IP violations and opaque training and data collection processes. But in most cases right now, AI vendors get to decide their own data storage, cybersecurity, and user rules without interference.

Also see: Top Generative AI Apps and Tools

Unauthorized usage of biometric data

A growing number of personal devices use facial recognition, fingerprints, voice recognition, and other biometric data security in place of more traditional forms of identity verification. Public surveillance devices also frequently use AI to scan for biometric data so individuals can be identified more quickly.

While these new biometric security tools are incredibly convenient, there’s limited regulation regarding how AI companies can use this data once it’s collected. In many cases, individuals don’t even know that their biometric data has been collected, even less that it is being stored and used for other purposes.

Covert metadata collection practices

When a user interacts with an ad, a TikTok or other social media video, or pretty much any web property, metadata from that interaction and the person’s search history and interests can be stored up for more precise content targeting in the future.

This method of metadata collection has been going on for years, but with the help of AI, more of that data can be collected and interpreted at scale, making it possible for tech companies to further target their messages at users without their knowledge of how it works. While most user sites have policies that mention these data collection practices, it’s mentioned only briefly and in the midst of other policy text, so most users don’t realize what they’ve agreed to and subject themselves and everything on their mobile devices to scrutiny.

Limited built-in security features for AI models

While some AI vendors may choose to build in baseline cybersecurity features and protections, many AI models do not have native cybersecurity safeguards in place. This makes it incredibly easy for unauthorized users and bad-faith actors to access and use other users’ data, including personal identifiable information (PII).

Extended data storage periods

Few AI vendors are transparent about how long, where, and why they store user data, and the vendors who are transparent often store data for lengthy periods of time.

For example, OpenAI’s policy says it can store user input and output data for up to 30 days “to identify abuse.” However, it’s not clear when or how the company is justified to take a closer look at individual users’ data without their knowledge.

Privacy and the Collection of AI Data

Web scraping and web crawling

Because it requires no special permissions and enables vendors to collect massive amounts of varied data, AI tools often rely on web scraping and web crawling to build training datasets.

Content is scraped from publicly available sources on the internet, including third-party websites, wikis, digital libraries, and more. In recent years, user metadata has also become a large portion of what’s collected through web scraping and crawling. This metadata is usually pulled from marketing and advertising datasets and websites with data regarding targeted audiences and what they engage with most.

User queries in AI models

When a user inputs their question or other data into an AI model, most AI models store that data for at least a few days. While that data may never be used for anything else, it has been proven that many artificial intelligence tools not only collect that data but hold onto it for future training purposes.

Biometric technology

Surveillance equipment, including security cameras, facial and fingerprint scanners, and microphones that detect human voices can all be used to collect biometric data and identify humans without their knowledge or consent.

State by state, rules are getting stricter regarding how transparent companies need to be when using this kind of technology. Yet for the most part, they can collect this data, store it, and use it without asking customers for permission.

IoT sensors and devices

Internet of Things (IoT) sensors and edge computing systems collect massive amounts of moment-by-moment data and process that data nearby in order to complete larger and quicker computational tasks. AI software often takes advantage of an IoT system’s database and collect relevant data through methods like data learning, data ingestion, secure IoT protocols and gateways, and APIs.

APIs

APIs give users an interface with different kinds of business software so they can easily collect and then integrate different kinds of data for AI analysis and training. With the right API and setup, users can collect data from CRMs, databases and data warehouses, and both cloud-based and on-premises systems.

Public records

Whether records are digitized or not, public records are often collected and incorporated into AI training sets. Information about public companies, current and historical events, criminal and immigration records, and other public information can be collected with no prior authorization required.

User surveys and questionnaires

Though this data collection method is more old-fashioned, using surveys and questionnaires is still a tried-and-true way that AI vendors collect data from their users.

Users may answer questions about what content they’re most interested in, what they need help with, how their most recent experience with a product or service was, or any other question that gives the AI a better idea about how to personalize interactions with that person in the future.

Also see: 100+ Top AI Companies

Solutions for AI and Privacy Concerns

With a handful of best practices, tools, and additional resources, your business can effectively use artificial intelligence solutions without sacrificing user privacy. To protect your most sensitive data at all stages of AI usage, follow these tips:

• Establish an appropriate use policy for AI: Internal users should know what data they can use and how and when they should use it when engaging with AI tools. This is particularly important for organizations that work with sensitive customer data, like protection health information (PHI) and payment information.
• Invest in data governance and security tools: Some of the best solutions for protecting AI tools and the rest of your attack surface include extended detection and response (XDR), data loss prevention, and threat intelligence and monitoring software. A number of data-governance-specific tools also exist to help you protect data and ensure all data use remains in compliance with relevant regulations.
• Read the fine print: AI vendors typically offer some kind of documentation that covers how their products work and the basics of how they were trained. Read this documentation carefully to identify any red flags, and if there’s something you’re not sure about or that’s unclear in their policy docs, reach out to a representative for clarification.
• Use only non-sensitive data: As a general rule, do not input your business’s or customers’ most sensitive data in any AI tool, even if it’s a custom-built or fine-tuned solution that feels private. If there’s a particular use case you want to pursue that involves sensitive data, research if there’s a way to safely complete the operation with digital twins, data anonymization, or synthetic data.

For additional tips related to cybersecurity, risk management, and ethical AI use when it comes to generative AI in particular, check out these previous best practice guides:

• Generative AI Ethics: Concerns and Solutions
• Generative AI and Cybersecurity
• Risks of Generative AI: 6 Risk Management Tips

Bottom Line: AI and Privacy Issues

AI tools present businesses and the everyday consumer with all kinds of new conveniences, ranging from task automation to guided Q&A to product design and programming. But as much as these tools can simplify our lives, they also run the risk of violating individual privacy in ways that can damage vendor reputation and consumer trust, cybersecurity, and regulatory compliance.

It takes extra effort to use AI in a responsible way that protects user privacy, but it’s well worth it when you consider how privacy violations can impact a company’s public image. Especially as this technology matures and becomes more pervasive in our daily lives, it will become crucial to follow AI laws as they’re passed and develop more specific AI use best practices that align with your organization’s culture and customers’ privacy expectations.

Read next: Best Artificial Intelligence Software

The post AI and Privacy Issues: What You Need to Know appeared first on eWEEK.

Some version of AI has been part of the e-tail and e-commerce experiences for several years, but particularly with the advent and growth of generative AI, digital retail experiences have become much smoother and less reliant on human intervention.

In this guide to AI’s role in retail, learn how AI is currently being used and how it impacts everyone from business leaders to hourly workers to customers.

Table of Contents: AI in Retail

• How Does AI in Retail Work?
• Benefits of Using AI in Retail
• Examples of AI in Retail
• Uses of AI in Retail
• Bottom Line: AI in Retail

How Does AI in Retail Work?

The way artificial intelligence works in the retail industry all depends on how and where business leaders choose to focus their AI efforts.

For most retail organizations, AI models are fine-tuned to act as digital retail platforms or are embedded into existing retail platforms, ERP systems, CRM software, and/or business websites. These models are trained to handle a variety of behind-the-scenes and customer-facing tasks, including helping to manage inventory, supply chain processes, customer interactions, and other features of the retail life cycle.

Retail AI deployments are trained to view every customer interaction, click, and movement of inventory as a unique data point. Often, this data is absorbed into the model’s training set and is used to further specialize and fine-tune the model’s ability to interact with humans and provide the support they need.

A variety of AI types can be used in retail, but these three broad categories cover most areas of the AI-retail process today:

• Machine learning: Machine learning algorithms are trained to recognize and act on the differences between different users and data points, including ad clicks, purchases, and customer service conversations; this type of AI is particularly useful for making predictions about overall brand sentiment, what users most want to purchase, and how purchase trends will change over time.
• Generative AI and natural language processing: Generative AI is best for building chatbots, virtual assistants, and other AIs that help customers directly by generating content in response to their queries. Generative AI models can directly answer customer questions and even make purchase suggestions based on their past purchases or preferences.
• Robotics: AI-powered robotics, which can power physical machines, may be used to scan, update, or shift inventory from warehouse to storefront; drive delivery vehicles and drones; or physically interact with customers in stores.

For more information, also see: Top Robotics Startup

Uses of AI in Retail

AI chatbots

AI chatbots with access to varying degrees of information can be embedded into customer facing e-commerce sites, social media, and other applications. Going a step beyond chatbots that rely on a few manual workflows for specific conversational topics, AI chatbots are trained on a variety of subjects — often including an organizational database or knowledge base — so they can more effectively understand and respond to diverse customer questions and requests.

Live customer service coaching

AI vendors like Nuance, Gridspace, and Zenarate don’t take over customer service interactions altogether, but instead provide live coaching and suggestions to human customer service representatives. These live suggestions are often combined with detailed customer dashboards that give reps both the tools and the language they need to have a more product-focused conversation with customers on the phone.

Customer sentiment analysis

While your organization may already be taking in and reviewing customer feedback on a regular basis, it’s often difficult to compare these sentiments across users and at scale, and even more difficult to apply that data to necessary business model improvements.

With the help of AI models, all customer feedback and interactions are automatically collected and analyzed, giving retailers a faster and better understanding of how customers feel about the brand. Particularly with large language models and generative AI models, unstructured data like text can be collected from multiple different sources, and at the same time, spammy reviews can be filtered out so they don’t muddy the waters of your analysis.

Beyond developing a better understanding of customer sentiment analysis, AI enables retailers to apply these new sentiment insights to more personalized customer experiences, including catered ads and more precise audience segmentation.

Also see: The Benefits of Generative AI

Demand forecasting and prescriptive analytics

AI-driven data analytics give users — including non-data scientists — a better understanding of all the different kinds of data at their disposal.

These models can analyze data in different formats and in large quantities, looking at historical and current purchasing metadata across different categories to more accurately forecast demand. For example, business owners may use AI analytics to learn that the sweaters they sold last year not only sold quickly but were reviewed favorably in customer reviews and conversations.

They may also learn that while the sweater sold well in the Midwest, Northeast, and most of Europe, it did not perform as well in other U.S. regions or most of Asia. Armed with this data, the retailer will know that it should bring back this product but that some markets require more stock while others require less.

For the non-data scientist, AI analytics tools are particularly effective at offering prescriptive analytics, or analytics that make recommendations for how to adjust business tactics in the future based on current data. The natural-language approach these AI tools can take help business users across departments and areas of expertise deploy this data for better results.

More on a similar topic: Generative AI and Data Analytics: Best Practices

Recommendation AI

Based on individual users’ metadata, past purchases, ad engagement, sentiment analysis, and other data-driven inputs, AI in retail can now recommend products and services to customers that they may not have otherwise considered purchasing but are likely to want.

Indeed, recommendation AI is one of the fastest-growing AI-retail areas because of how well it connects with and monetizes customer preferences.

Automated inventory management

AI-supported demand forecasting is one of the ways retailers are now more accurately predicting how much inventory they need and where and when it should be stocked. AI-driven data analytics may also help retailers determine when prices should be changed, how seasonal purchases impact inventory storage and supply chain movement, and where customer returns require more frequent inventory shifts.

In a more tangible sense, AI-driven robotics can also be used to support automated inventory management. A growing number of retail warehouses are relying on AI assistive robots to scan inventory and monitor stock levels and then restock or remove stock as needed.

Intelligent, personalized display ads

Human marketing and ad managers work behind the scenes to analyze how ads are performing and then decide what changes should be made to get more audience engagement. AIs are taking over this task on a widespread scale and are making more accurate targeting decisions, primarily because they are able to analyze a greater quantity of engagement data points more quickly.

AI software in retail also more frequently notices data patterns that humans may overlook, and can draw decisions based on past data, whether that’s updating the ad copy based on user sentiment or changing where the ad falls on a webpage based on previous heatmap data.

AI can also target ads to individuals based on their metadata, making it so they’re more likely to be interested in and engage with advertising materials. Finally, AI learn and update ads in real time, ensuring ads are always optimized for the current audience.

Also see: Generative AI Companies: Top 12 Leaders 

Simplified checkout experiences

Through a combination of biometrics and AI recognition technology, storefronts are starting to simplify the user checkout experience, including in physical stores.

For example, some stores are now allowing repeat customers to simply grab their items and walk out the door; the store’s AI recognition and scanning technology recognizes the customer and automatically charges them without requiring them to physically check out.

For customers that want a more user-friendly virtual shopping experience, many companies have added AI assistive elements to their retail apps. These features may make purchase suggestions or integrate with virtual wallets for a smoother shopping experience.

Continue reading: Generative AI: Enterprise Use Cases

Examples of AI in Retail

Each of the following enterprise companies has incorporated a different kind of AI into its workflows to simplify retail experiences:

ai.RETAIL

Accenture, the global professional services firm, recently released ai.RETAIL, an AI and data analytics platform that helps retailers better understand their data both at an individual customer and big picture level.

Its features include customizable customer-level views that show historical buying patterns and loyalty, dynamic merchandising for different customer channels, supply network digital twin development, and customer targeting capabilities. Tim Hortons, the Canada-based restaurant chain, has used ai.RETAIL and other products and services from Accenture to set up its customer loyalty app with rewards and data analytics that help them to keep customers engaged with the brand.

IBM watsonx Assistant

IBM’s watsonx Assistant is an AI virtual assistant that can be adapted to various business use cases, including customer service chatbots.

In the case of Camping World, an RV retailer, this AI solution serves as the engine for its custom-built AI chatbot, Arvee. The virtual agent has been designed to handle more straightforward customer service engagements and triage more complex conversations to human customer service representatives through dynamic routing.

It is also able to save detailed information about customer inquiries that occur after-hours, allowing employees to step in and re-engage those customers as necessary once they’re back on the clock. Using IBM’s AI virtual assistant has freed up more time for the company’s employees and also helps them to focus on the most pressing and challenging customer inquiries.

Amazon One and Amazon Go stores

Amazon One and Amazon Go are two AI-driven solutions Amazon has built to simplify the customer-side of each retail interaction with a shopping experience it calls “Just Walk Out” shopping.

With Amazon One, a customer’s payment information is linked to their palm, and AI and machine learning are used to identify that palm and charge the appropriate person’s account when they leave an Amazon Go or other participating store with a purchase. This means that users can quickly pick out their items and leave without going through a traditional checkout process, and Amazon’s AI sensors quickly detect when an item is picked up or put back on the shelf.

Benefits of Using AI in Retail

Many retailers fear that using AI in their business model will cause them to lose their personal touch, but so far, it has offered early adopters a range of benefits — including helping them to create a more customer-friendly experience:

• Emphasis on customer experience: Ads are targeted to what users actually want, chatbots can more clearly answer user questions on a customer’s schedule, and AI-driven apps give users access to new types of shopping experiences that fit their preferences. Although there may be less human-to-human contact in retail as AI is adopted, customers are still on the receiving end of a customer-first experience.
• More automation opportunities: Artificial intelligence can automate conversational workflows, inventory and supply chain management, and other repetitive retail tasks that have traditionally required a human touch. This reduces the chance for human error and frees up employee time for more strategic tasks, both of which can lead to higher levels of organizational productivity.
• Lessened impact of employee-user error: AI often monitors the entire supply chain and inventory management lifecycle and can identify an error as soon as it occurs. This makes it easier to mitigate stocking and shipping errors before they lead to unhappy customers or inventory shortages.
• Optimized digital marketing and analytics: AI-powered analytics tools democratize the analytics process with natural language inputs, contextualized explanations, and more detailed and accurate predictive analytics that are useful to marketing and sales teams. These tools can also analyze greater quantities and different types of data than most traditional marketing analytics tools can.
• Fewer human touch points required: As AI takes over different customer service and inventory management touchpoints, retailers can reduce their staff or focus their attention on more strategic tasks. Especially as more and more of the workforce moves away from retail and service-based industries, these AIs will help to fill in a production gap with limited retraining and recruitment requirements.

Bottom Line: AI in Retail

Retailers are getting creative and finding all kinds of ways to incorporate AI into the work they do. This ranges from allowing AI to directly interact with or indirectly influence customer interactions, restock and monitor inventory across distributed sites, or give business leaders a more detailed glimpse into current performance data and future projections.

If AI is implemented thoughtfully and with appropriate usage policies and safeguards in place, it can benefit both your employees and customers with automation, personalization, and hands-off features that improve the overall retail experience.

Read next: Top 9 Generative AI Applications and Tools

The post AI in Retail: What You Need to Know appeared first on eWEEK.

Larger tech companies like Google, Microsoft, and AWS are working hard to build up their generative AI technologies as well, but many of these tech giants are having trouble keeping up with the agile gen AI startups that are willing to take risks in order to establish their AI niches.

We’ve created a list of the top 50 generative AI startups to watch today and over the next few years. Some of these companies, like OpenAI, have already proven themselves and turned into multi-billion dollar companies. Others have not yet emerged from early rounds of funding. Regardless of where they individually fall in their stages of development, each of these startups has generated enough buzz to earn a spot on our list of the top generative AI startups.

Table of Contents: Top 50 Generative AI Startups

• Top 10 Generative AI Startups: Best of the Best
• Top 2 Generative AI Startups for Developers
• Top 8 Generative AI Startups for Marketing, Sales, and Creative Projects
• Top 10 Generative AI Startups for Healthcare, Pharmaceuticals, and Life Sciences
• Top 4 Generative AI Startups for Synthetic Data and Data Analytics
• Top 3 Generative AI Startups for Customer Service and Customer Experience
• Top 7 Generative AI Startups for Gaming and Entertainment
• Top 4 Generative AI Startups for Project Management, Legal, and Business Operations
• Top 2 Generative AI Startups for Search and Personal Assistance
• Why Is Generative AI Important?
• How Does Generative AI Work?
• Bottom Line: The Generative AI Startups to Watch

Top 10 Generative AI Startups: Best of the Best

1. OpenAI

OpenAI is one of the largest AI startups in the world and is certainly the largest in the generative AI space. Along with its prebuilt AI solutions, OpenAI also offers API and application development support for developers who want to use its models as baselines.

• Founded: 2015.
• Founded by: Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trever Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Jessica Livingston, John Schulman, Pamela Vagata, Wojciech Zaremba
• Category and use cases: Language modeling, content generation, image generation and editing, audio transcription and translation, and custom and embedded model development.
• Products and solutions: GPT-3, GPT-4, ChatGPT Plus and ChatGPT Enterprise, DALL-E, Whisper, and InstructGPT (Ada, Babbage, Curie, and Davinci).

Also read: Top AI Startups

2. Hugging Face

Hugging Face is a community forum, similar to GitHub, that focuses on AI and ML model development and deployment. Some of the community’s main specialties include text classification, question answering, image classification, translation, summarization, audio classification, and object detection.

• Founded: 2016.
• Founded by: Clement Delangue, Julien Chaumond, Thomas Wolf
• Category and use cases: Open-source development community, public submissions and deployments of NLP, computer vision, other AI models.
• Products and solutions: Expert Acceleration Program, Private Hub, Inference Endpoints, AutoTrain, and Hardware.

3. Cohere

Cohere offers NLP solutions that are specifically designed to support business operations. With Cohere’s conversational AI agent, enterprise users can quickly search for and retrieve all kinds of company information without searching through massive applications and databases.

• Founded: 2019.
• Founded by: Aidan Gomez, Ivan Zhang, Nick Frosst
• Category and use cases: Text retrieval, text generation, text classification, enterprise conversational AI agent, and multilingual embedding and language models.
• Products and solutions: Command, Embeddings, Summarize, Generate, Embed, Semantic Search, Rerank, Classify.

4. Anthropic

Anthropic’s Claude platform is similar to OpenAI’s ChatGPT, with its large language model and content generation focus. First released widely in March 2023, Claude is viewed as a more customizable platform with less propensity for rude or inappropriate responses.

• Founded: 2021.
• Founded by: Daniela Amodei, Dario Amodei, Jack Clark, Jared Kaplan, Sam McCandlish, Tom Brown
• Category and use cases: Content generation, coding, customer support, text translation, text classification, text summarization, search, legal document summarization, career coaching, workflow automation, and text editing.
• Products and solutions: Claude, Claude Instant, and Claude Pro.

On a related topic: The AI Market: An Overview

5. Inflection AI

Founded by former leaders from LinkedIn and DeepMind in 2022, Inflection AI’s mission and goals were mostly kept under wraps until Pi, a personal AI that focuses on colloquial conversation and advice, was released in May 2023. Even before its initial release, the company had already received major funding rounds and indicated that it plans to completely transform how humans are able to speak to and communicate with computers; expect to see further developments from them in the near future.

• Founded: 2022.
• Founded by: Karén Simonyan, Reid Hoffman, Mustafa Suleyman
• Category and use cases: Human-to-computer communication in plain language, voice search, brain-computer interface (BCI), conversational AI, AI assistance.
• Products and solutions: Pi.

6. Jasper

Jasper’s core product is designed specifically for business and marketing content generation. Some core areas where Jasper works well include social media, advertising, blog, email, and website content creation. It is a particularly effective tool for establishing a consistent brand voice and managing digital marketing campaigns.

• Founded: 2021.
• Founded by: Chris Hull, Dave Rogenmoser, John Philip Morgan.
• Category and use cases: Long-form and short-form content creation, Dialog-driven content creation and language modeling, AI assistant browser extension, art creation, and multi-language reading and writing.
• Products and solutions: Jasper.

7. Synthesis AI

Synthesis AI is a cutting-edge synthetic data generation startup that creates computer-vision-driven imagery, videos, and human simulations. Its use cases span across industries and focus on ethical AI development, making it one of the most exciting AI startups on our radar today.

• Founded: 2019.
• Founded by: Yashar Behzadi.
• Category and use cases: Synthetic data generation for computer vision, image labeling, image generation, video generation, ID verification, automotive and driver monitoring, pedestrian detection, teleconferencing, security scenarios, virtual try-on, avatar creation, AR/VR/XR, and 3D human models.
• Products and solutions: Synthesis Humans, Synthesis Scenarios, and Synthetic Data Visualizer.

On a related topic: What is Generative AI?

8. Glean

Glean is a generative AI enterprise search company that relies on deep-learning models to understand natural language queries in the context of organizational, departmental, and individual user characteristics. Glean connects to a variety of enterprise apps and platforms, making it easier to set up and maintain access to various business information sources.

• Founded: 2019.
• Founded by: Arvind Jain, Piyush Prahladka, Tony Gentilcore, TR Vishwanath
• Categories and use cases: Cognitive enterprise search, data ingestion and management, knowledge management, and enterprise environment app and data unification.
• Products and solutions: Glean Search, Glean Assistant, Glean Knowledge Management, Glean Work Hub, Glean Connectors, Glean Security.

9. Stability AI

Stability AI is one of the most successful startups in the generative AI space for image and video content generation. Though the company has come under controversy for alleged copyright infringement of artists’ work, Stable Diffusion in particular continues to be a popular solution, operating in the background of many other generative AI startups’ platforms.

• Founded: 2019.
• Founded by: Emad Mostaque
• Category and use cases: Text-to-image generation, image editing, audio and video generation, language modeling, and open-source AI and application development models.
• Products and solutions: Stable Diffusion 2.0 and Stable Diffusion XL, Stable Diffusion Reimagine, Stable Audio, StableLM DreamStudio, Photoshop Plugin, Blender Plugin, and Platform API.

10. Lightricks

Lightricks first gained notoriety with its social-media-friendly image editing app, Facetune. It has since expanded Facetune and its other apps with cutting-edge AI, making it possible to edit and generate new content for videos, photos, and art projects.

• Founded: 2013.
• Founded by: Amit Goldstein, Itai Tsiddon, Nir Pochter, Yaron Inger, Zeev Farbman
• Category and use cases: Text-to-image generation, image editing, video editing, art generation, avatar generation.
• Products and solutions: Facetune, Photoleap, Videoleap, Popular Pays, Filtertune, Beatleap, Motionleap, Artleap, Lightleap, Boosted.

Top 2 Generative AI Startups for Developers

1. AI21 Labs

AI21 Labs creates tools that focus heavily on contextual natural language processing for reading and writing. Third-party developers can build on AI21 Labs’ language models for their own text-based apps and services with AI21 Studio.

• Founded: 2017.
• Founded by: Ori Goshen, Yoav Shoham.
• Category and use cases: Language modeling, application development, content generation and editing, and content summarization.
• Products and solutions: Wordtune, Wordtune Read, and AI21 Studio.

2. Tabnine

Tabnine offers generative AI code assistance for software development. It can be useful for both experienced and novice coders due to its focus on code completion.

• Founded: 2017.
• Founded by: Dror Weiss, Eran Yahav.
• Category and use cases: AI-assisted development, code completion, and code automation.
• Products and solutions: Tabnine and Tabnine Chat.

Top 8 Generative AI Startups for Marketing, Sales, and Creative Projects

1. Rephrase.ai

Rephrase.ai makes it possible for companies and individuals to create custom videos without extensive in-house equipment or experience. Text-to-voice conversion, avatar and template libraries, and campaign analytics combine to create a platform for self-service video production that still has a personal feel to it.

• Founded: 2019.
• Founded by: Ashray Malhotra, Nisheeth Lahoti, Shivam Mangla
• Category and use cases: Video generation and digital avatar creation, marketing customer campaigns, blog-to-video content transformation, and internal communications.
• Products and solutions: Rephrase Studio.

2. Synthesia

Synthesia is a generative AI company that focuses on video creation for personal and enterprise use. Users can rely on AI avatars and voices to communicate in training, marketing, and how-to videos in 120 different languages. Most significantly, professional-looking videos can be generated from users’ text inputs.

• Founded: 2017.
• Founded by: Lourdes Agapito, Matthias Niessner, Steffen Tjerrild, Victor Riparbelli
• Category and use cases: Video generation, AI voice and avatar generation, and video templates.
• Products and solutions: Synthesia.

3. Plask

Plask creates technology to make animation easier and more cost-effective. The tool can be used to create animated or hyper-realistic 3D motion videos; it automates the entire process of creating designs and movement.

• Founded: 2020.
• Founded by: Jaejun Yu, Junho Lee.
• Category and use cases: AI-generated animation, AI motion capture, and 3D character building.
• Products and solutions: Plask (Freemium and MoCap Pro) and Plask API for Enterprise.

4. podcast.ai

podcast.ai, a subsidiary of Play.ht, is a weekly podcast that is entirely created with generative AI voices and transcripts. The podcast covers a different topic each week and has even used Steve Jobs recordings and biographical information to record an episode with “him.”

• Founded: 2016 (Play.ht).
• Founded by: Hammad Syed, Mahmoud Felfel (Play.ht).
• Category and use cases: Podcast content generation, voice generation, and content transcription.
• Products and solutions: podcast.ai.

For more information, also see: History of AI

5. Bertha.ai

Bertha.ai is a content generation solution for WordPress users in particular, though it also works with sites like Shopify, Wix, and Squarespace. It can help with creating written content and imagery for blog posts and other webpages as well as other forms of digital marketing copy.

• Founded: 2021.
• Founded by: Andrew Palmer, Vito Peleg.
• Category and use cases: Content generation, image and illustration creation, blog writing, and product description writing.
• Products and solutions: Bertha AI, Chat, Ask Me Anything, Long Form Content, and Bertha Chrome Extension.

6. Tavus

Tavus is a generative AI company that creates new versions of videos users have already created based on specific viewer qualities and other personalization requirements. The foundational template videos users create give Tavus enough material to generate believable audio and visuals for future videos on different topics, making it possible to record only one video and send custom messages to each of your contacts.

• Founded: 2020.
• Founded by: Hassaan Raza, Quinn Favret.
• Category and use cases: Automated video generation and personalization, voice cloning, media blending, lip sync, video templates, and recruiting and marketing campaign videos.
• Products and solutions: Tavus.

7. Midjourney

Midjourney is a generative AI solution for image and artwork creation that primarily gives users access to its features and community support through Discord. The company has come under fire for using millions of artists’ images without prior consent; users have also taken advantage of the platform to produce deep fakes of politicians.

• Founded: 2022.
• Founded by: David Holz.
• Category and use cases: Natural-language-driven image generation and image enhancements and modifications.
• Products and solutions: Midjourney.

8. Twain

Twain is designed to help sales professionals write content — particularly outreach emails — that works better for sales outreach. It not only has the ability to generate its own content but can also make detailed recommendations for edits to content that a user submits.

• Founded: 2021.
• Founded by: Mohamed Chahin.
• Category and use cases: Content generation, sales outreach, recruitment messaging, and content recommendations.
• Products and solutions: Twain.

Top 10 Generative AI Startups for Healthcare, Pharmaceuticals, and Life Sciences

1. Paige AI

Paige AI uses generative tissue-based AI for optimized cancer diagnostics. The platform currently specializes in breast cancer, colon cancer, and prostate cancer diagnoses but also offers other diagnostic resources for oncology professionals.

• Founded: 2017.
• Founded by: David Klimstra, Norman Selby, Peter Schüffler, Thomas Fuchs
• Category and use cases: Cancer diagnostics, computational pathology, and biomarker detection, AI-driven image viewer.
• Products and solutions: Paige platform, Paige Prostate Suite (including Paige Prostate Detect), Paige Breast Suite (including Her2Complete), and FullFocus.

2. Insilico Medicine

Insilico Medicine is a pharmaceutical research and development startup that uses generative AI and machine learning to create more efficient processes across biology, chemistry, and analytics. It’s focused on reducing the time and cost of drug development, particularly in areas such as immunology, oncology, central nervous system disorders, and fibrosis.

• Founded: 2014.
• Founded by: Alex Zhavoronkov.
• Category and use cases: Novel molecules generation with de-novo drug design and scalable engineering, clinical trial design and prediction, and deep biology analysis engine for multi-omics target discovery.
• Products and solutions: Pharma.AI, PandaOmics, Generative Biologics, Chemistry42, and inClinico.

3. Entos

Entos is a company made up of top scientists, biotechnology experts, and machine learning experts who are working to optimize oncology therapeutics with AI. Their pipeline therapeutics release is actively in the works and is expected to launch within the next two years.

• Founded: 2019.
• Founded by: Fred Manby, Sarah Trice, Thomas Miller.
• Category and use cases: Drug discovery and development, physics-informed AI design, high-throughput experimentation, and oncology therapeutics.
• Products and solutions: Pipeline of oncology therapeutics.

4. Etcembly

Etcembly is a company that is improving T-cell receptor immunotherapies with its machine-learning platform, EMLy. The platform sifts through complex TCR patterns and datasets to discover and identify personalized TCR therapeutic options for patients.

• Founded: 2020.
• Founded by: Michelle Teng, Jacob Hurst.
• Category and use cases: ML database for TCR immunotherapies, AI-driven TCR discovery and identification, computer-assisted engineering, and biotechnology.
• Products and solutions: EMLy.

5. Kaliber Labs

Kaliber Labs focuses on developing AI-powered surgical software for arthroscopic surgery needs. The company also provides solutions to help patients and other members of the surgical team get the information they need more seamlessly.

• Founded: 2015.
• Founded by: Ray Rahman.
• Category and use cases: Digital surgical assistance, AI-labeled patient communication platform, AI-powered feedback for surgeons, and automated surgery stage recognition.
• Products and solutions: Kaliber SaMD platform is in development.

6. Biomatter

Biomatter uses its Intelligent Architecture platform to design and develop proteins for health and sustainable manufacturing. It also goes beyond more traditional human protein expectations and supports use cases across molecular biology, food and beverage, biotherapeutics, and agriculture projects.

• Founded: 2018.
• Founded by: Donatas Repečka, Laurynas Karpus, Rolandas Meškys, Vykintas Jauniskis.
• Category and use cases: Enzyme and protein design.
• Products and solutions: Intelligence Architecture platform.

7. Osmo

Osmo, founded in 2023 as a spinout from Google Research, uses machine learning and has created a map of odor to help computers predict how something smells based on its molecular structure. It hasn’t gone much farther than that at this point, but the vendor has stated its goal to use this technology to support human health and wellness.

• Founded: 2023.
• Founded by: Alex Wiltschko.
• Category and use cases: Olfactory science and computer smelling capabilities.
• Products and solutions: Osmo AI.

8. Activ Surgical

Activ Surgical uses intra-operative surgical intelligence to give surgeons real-time information and better visuals during surgery. With some of the company’s most recent developments, surgeons can also perform surgeries with the help of augmented reality overlays.

• Founded: 2017.
• Founded by: Peter Kim, Seth Teicher.
• Category and use cases: Surgical intelligence and assistance, multimodal advanced visualization, and tissue evaluations.
• Products and solutions: ActivSight Intelligent Light.

9. Aqemia

Aqemia uses AI that includes experimental data in order to scale drug discovery in the pharmatech space. The company touts how it uses both quantum and statistical mechanics algorithms to achieve better outcomes for critical and niche disease categories.

• Founded: 2019.
• Founded by: Emmanuelle Rolland-Martiano, Maximilien Levesque
• Category and use cases: Drug discovery, drug discovery pipeline, and drug design.
• Products and solutions: Launchpad; pipeline in the works.

10. New Equilibrium Biosciences

New Equilibrium Biosciences works with a combination of AI, chemistry, computer science, mathematics, cell biology, and biophysics to optimize drug discovery for intrinsically disordered proteins, or IDPs. With its focus on mutated proteins, the New Equilibrium Platform is particularly effective for cancer and neurodegenerative disorder drug designs.

• Founded: 2019.
• Founded by: Peter Tompa, Virginia Burger.
• Category and use cases: IDP detection, IDP freeze-framing, 3D structural analysis, and drug design.
• Products and solutions: New Equilibrium.

Top 4 Generative AI Startups for Synthetic Data and Data Analytics

1. Synthetaic

Synthetaic’s platform, RAIC, is primarily designed to analyze and ingest unstructured and unlabeled datasets from videos, satellite imagery, and video and drone footage. The company famously tracked the origin of a Chinese balloon in February 2023. The company has also partnered with Microsoft, which will likely lead to new products and use cases in the near future.

• Founded: 2019.
• Founded by: Corey Jaskolski.
• Category and use cases: AI prototyping, geospatial analysis, drone-based monitoring, content moderation, and video security.
• Products and solutions: RAIC.

2. MOSTLY AI

MOSTLY AI’s synthetic data generation platform balances data democratization and app development efficiencies with data anonymity and security requirements. The platform has proven especially useful in the banking, insurance, and telecommunications industries.

• Founded: 2017.
• Founded by: Klaudius Kalcher, Michael Platzer, Roland Boubela
• Category and use cases: Synthetic data generation for AI and software app development, data anonymization, AI and ML development, and testing and product development.
• Products and solutions: MOSTLY AI.

3. Infinity AI

Infinity AI is another top generative AI startup that focuses on synthetic data generation, simplifying the collection and labeling of useful data. Infinity AI’s solutions have been used in fitness, smart retail, robotics, and warehouse safety scenarios.

• Founded: 2021.
• Founded by: Lina Colucci, Sidney Primas, Andrew Weitz, Daniel Hensley
• Category and use cases: Synthetic data generation, debugging, ML data pipeline automation, and automatic data labeling.
• Products and solutions: Infinity API, VisionFit API, and Infinity Core.

4. Syntho

Syntho is another synthetic data generation startup that uses generative AI to create synthetic data twins of actual sensitive data. Syntho’s Syntho Engine is often used for realistic product demos, data analytics, and test data generation.

• Founded: 2020.
• Founded by: Marijn Vonk, Simon Brouwer, Wim Kees Janssen
• Category and use cases: Synthetic data generation, test data generation, and data analytics.
• Products and solutions: Syntho Engine.

Top 3 Generative AI Startups for Customer Service and Customer Experience

1. Gridspace

Gridspace offers solutions for organizations that want to better automate, manage, and analyze contact center and customer interactions. The company offers voice bots and live agent training, making it possible to create a hybrid bot-human agent workforce in healthcare, retail, and other customer-service-driven organizations.

• Founded: 2012.
• Founded by: Anthony Scodary, Evan Macmillan, Nico Benitez
• Category and use cases: Conversational AI, virtual agents and voice bots, virtual contact centers, observability and call monitoring, and customer service.
• Products and solutions: Gridspace Grace, Gridspace Sift Analytics, and Gridspace Pulse.

2. Revery AI

Revery AI offers a virtual dressing room and try-on experience that uses generative AI to help users more accurately visualize how clothing will look on them in real life. The company has partnered with some fashion retailers already to create a more integrated virtual shopping experience for users.

• Founded: 2020.
• Founded by: Jeffrey Zhang, Kedan Li.
• Category and use cases: Virtual dressing room and try-on, virtual reality, and e-tail.
• Products and solutions: Revery AI.

3. Veesual

Veesual is another generative AI startup that uses deep learning and image generation to enable virtual try-ons for fashion and e-commerce. It gives users the ability to select the model that looks most like them and sort through high-res images of different clothing items.

• Founded: 2020.
• Founded by: Damien Meurisse, Eric Gillaume, Maxime Patte.
• Category and use cases: Virtual try-on and image generation.
• Products and solutions: Mix & Match, Switch Model, and Digital Dressing Room.

Top 7 Generative AI Startups for Gaming and Entertainment

1. Latitude.io

Latitude.io is one of the first and foremost providers of AI-generated gaming experiences. With its flagship AI Dungeon, users can enter actions into the game while AI drives the rest of the game narrative forward.

• Founded: 2019.
• Founded by: Alan Walton, Nick Walton.
• Category and use cases: Gaming.
• Products and solutions: AI Dungeon and Voyage (AI Art, Medieval Problems, Loom, and Things).

2. Character.AI

Character.AI is a company that offers creative ways to develop and chat with user-created characters. Though the tool can simply be used for fun conversations with “real” or imagined people, it can also be used to simulate important conversations like job interviews.

• Founded: 2021.
• Founded by: Daniel De Freitas, Noam Shazeer.
• Category and use cases: Character generation with virtual chat and entertainment.
• Products and solutions: Character.ai.

For more information, also see: Top Robotics Startups

3. Charisma Entertainment

Charisma Entertainment provides a plug-and-play platform for various entertainment companies and storytellers to create realistic characters and storylines that adjust to player/user inputs. Examples of media created with Charisma include The Kraken Wakes game and the Will Play virtual learning platform.

• Founded: 2015.
• Founded by: Guy Gadney.
• Category and use cases: AI storytelling, entertainment, gaming, virtual learning, intelligent character development, and dialogue engine.
• Products and solutions: Charisma and Unreal Engine plugin.

4. Replika

Replika is a generative AI solution that creates AI companions for AI-generated chats that have a more personal touch. The interface of this app is designed to not only allow users to have realistic conversations but also to spend time with their Replika characters in augmented reality experiences.

• Founded: 2015.
• Founded by: Eugenia Kuyda.
• Category and use cases: Conversational AI, AI companion/avatar generation, and augmented reality.
• Products and solutions: Replika.

5. Aimi.fm

Aimi.fm is a generative AI music player that generates endless loops of music in different genres for listeners. With Aimi Studio, music producers of all skill levels can access basic music creation functionalities.

• Founded: 2019.
• Founded by: Edward Balassanian.
• Category and use cases: Generative music creation, music production, and content curation.
• Products and solutions: Aimi and Aimi Studio.

6. Inworld AI

Inworld AI is a company that uses generative AI and text-to-character prompts to help gaming and media companies make NPC characters seem more realistic. These characters may appear in traditional video games, VR, training, and other types of digital entertainment and experiences.

• Founded: 2021.
• Founded by: Ilya Gelfenbeyn, Kylan Gibbs, Michael Ermolenko.
• Category and use cases: NPC character generation, gaming, training and education, customer experience agents, and other forms of digital entertainment and interaction.
• Products and solutions: Inworld.

7. SOUNDRAW

SOUNDRAW is a generative AI solution for music composition that can be tailored to different genres, instruments, and other musical variables. It is most frequently used to generate music that can be used in the background of video creations.

• Founded: 2020.
• Founded by: Daigo Kusunoki.
• Category and use cases: Music and audio generation for videos, podcasts, games, social media, TV, radio, and other mediums.
• Products and solutions: SOUNDRAW.

Top 4 Generative AI Startups for Project Management, Legal, and Business Operations

1. Notion

Notion has found much of its success in providing task management and other kinds of daily work management capabilities to creatives and other project teams. Notion AI was released to the public in early 2023 and quickly gained traction as an option for teams that want to summarize notes, create quick lists, and write emails with the help of generative AI.

• Founded: 2013.
• Founded by: Chris Prucha, Ivan Zhao, Simon Last
• Category and use cases: Content generation, content summarization, content suggestions and translations, note taking, email writing, and task management.
• Products and solutions: Notion AI, Wikis, Projects, and Docs.

2. Casetext

Casetext has used AI for many years to support litigators in finding and preparing for the best-fit cases. In early 2023, Casetext released CoCounsel, a generative AI-powered legal assistant that takes Casetext’s legal capabilities a step further. This AI assistant, powered by GPT-4, can help with tasks that include writing, summarizing, researching, and completing other administrative tasks that are necessary to prepare for cases and new contracts.

• Founded: 2013.
• Founded by: Jake Heller, Pablo Arredondo.
• Category and use cases: AI-guided legal research, search and contextual search, AI assistant, legal document review, research memos, deposition preparation, contract analysis, and case identification.
• Products and solutions: CoCounsel and AllSearch.

3. PatentPal

PatentPal is a tool that is specifically designed with patent law requirements in mind. The tool looks at claims that have already been written by the author in order to generate tonally and factually accurate patent specification drafts on its own.

• Founded: 2018.
• Founded by: Jack Xu.
• Category and use cases: Content generation and summarization for patent applications and intellectual property.
• Products and solutions: PatentPal.

3. Adept AI

Adept AI is a new OpenAI competitor that relies on AI and natural language commands to create better interfaces between humans and computers in the workplace. It specifically automates and simplifies workflows in common business tools, including Salesforce and Google Sheets.

• Founded: 2022.
• Founded by: Ashish Vaswani, David Luan, Niki Parmar.
• Category and use cases: Business and software development process automation and in-app task and goal development.
• Products and solutions: ACT-1.

Top 2 Generative AI Startups for Search and Personal Assistance

1. Andi

Andi is a generative-AI-driven search bot that not only helps users search for information across the web but also summarizes and further explains that information. Users appreciate Andi’s clean interface and lack of ads.

• Founded: 2021.
• Founded by: Angela Hoover.
• Category and use cases: AI semantic search, chatbot, and search results summarization.
• Products and solutions: Andi.

2. You.com

You.com is a private and secure search engine that summarizes and personalizes results with generative AI. The generative AI solution is available as a Chrome extension and can also be used in apps like WhatsApp.

• Founded: 2020.
• Founded by: Bryan McCann, Richard Socher.
• Category and use cases: AI-driven search, AI assistant, AI chat, content and image generation, and content summarization and personalization.
• Products and solutions: You.com.

Why Is Generative AI Important?

Generative AI is important because it takes AI in a more mature direction, making the technology more accessible and useful to a larger audience.

Individuals can make use of this technology in their daily lives at little to no cost. More important, breakthrough innovations in areas like medical imaging and drug discovery are now possible to develop at scale because of generative AI.

Finally, this technology can better define, contextualize, and automate business operations tasks than previous types of AI ever could, making this a technology that is already being applied to and ripe for more enterprise use cases.

How Does Generative AI Work?

Depending on what users are trying to generate, generative AI works through different types of large AI large language models that undergo extensive training with massive datasets and deep learning algorithms on an ongoing basis.

This type of training allows generative AI tools to pull data-driven knowledge from all corners of the web and from other resources, which makes it possible for these AIs to generate believable, human-like data and results. The deep learning, neural network design intends to mimic a human brain, which also helps generative AIs to understand context, relationships, patterns, and other connections that have traditionally required human thinking to grasp.

For more information on how generative AI models work and how users can make the most of their capabilities, read this guide: What Is a Generative AI Model?

Bottom Line: The Generative AI Startups to Watch

Ever since the debut of ChatGPT in November of 2022, generative AI – and artificial intelligence in general – has taken a huge leap forward and permeated various industries and business sectors. Business leaders, consumers, and investors have all woken up to the vast potential for generative AI to support or take over countless tasks, freeing up actual humans to do higher-value work.

It’s the young companies on this list that will shape the future of AI, which in turn will shape the future of technology and society at large in many profound ways. Much like any other young and dynamic area of technology, expect these players to shift their products, roles, and impact in the coming weeks and months.

On a related topic: Top Natural Language Processing Companies

The post 50 Generative AI Startups to Watch in 2023 appeared first on eWEEK.

Generative AI can support staffers in managing their existing task loads and, in some cases, these models can be trained to take on entirely new tasks and types of work. In all of these cases, generative AI is helping businesses streamline and automate their processes in repeatable and scalable ways that contribute to business growth goals.

Depending on your business priorities and requirements, generative AI can support your operations in a variety of ways. Read on to learn some of the most common ways generative AI is used to improve businesses today. Also important, learn about generative AI usage best practices that will help you achieve new levels of business success.

Also see: ChatGPT Enterprise: AI for Business

Table of Contents: How Generative AI Can Support Business Operations

• Top 7 Ways to Use Generative AI in Your Company
• 5 Benefits of Leveraging Generative AI in Your Business
• 5 Challenges of Using Generative AI
• What Are Generative AI Best Practices for Business Usage?
• Bottom Line: How to Use Generative AI in Your Business

Top 7 Ways to Use Generative AI in Your Company

Generative AI models are being used for a variety of industry-specific enterprise use cases that range from simple customer service support and coaching to more complex tasks like medical imaging and drug discovery.

But generative AI can also be trained to handle more generic business tasks that are relevant to all businesses, regardless of industry or size. Below are seven of the most useful ways business leaders can incorporate generative AI into their business today:

1) Smart, Secure Data Analytics

Generative-AI-powered data analytics solutions make data analysis smarter, speedier, more scalable, and more secure. Depending on the model type and size you select, you can use generative AI to generate synthetic data for more secure and compliant data analysis and QA testing practices.

You can also use generative AI to democratize data analytics in two important ways:

• Generative AI models allow users to create narrativized explanations of their data, making it easier for non-data scientists to understand the data they have in front of them.
• Generative AI assistants are being embedded and used in common data tools like Power BI, which enables less technical users to comprehend complex datasets and create accurate data visualizations and reports.

Example solutions: SparkBeyond Discovery, Dremio, Narrative BI, Power BI Copilot

2) Customer Service and User Experience

Most businesses have a customer service component that could be improved with more consistent training and customer-first communication and designs.

A variety of generative AI tools are springing up to mentor your existing customer service agents and, in some cases, replace them with chatbot, voice generation technology, and AI-powered search engines that are designed with customer queries and natural language requirements in mind.

In more specialized industries, like insurance, generative AI can also be used to better assess risk and achieve optimal outcomes for customers’ plan rates and claims.

Example solutions: Gridspace, IBM Watson Assistant, UltimateGPT, Zendesk Advanced AI, Forethought SupportGPT

3) Assistive Coding and Product Design

Regardless of your tech teams’ levels of expertise, generative AI can be incorporated to support coding and quality assurance tests for digital product design. Generative AI models like ChatGPT can fix bugs, generate test code, and write documentation for programs.

Perhaps most significant, generative AI models can help developers of all skill levels solve problems; even non-technical team players can generate code through natural language queries.

Example solutions: Code Snippets AI, ChatGPT, Google Bard, Tabnine, MOSTLY AI, Stability AI

4) Content Creation

Generative AI models have already proven their ability to quickly generate natural language content affordably and at scale, which has made these models particularly enticing for organizations that want to outsource content writing.

Several generative AI tools are able to write blog and social media content based on simple prompts. Others are designed to create more specific content, like legal documents or marketing materials. However, the area of generative AI content generation that is growing most quickly is multimedia content, with AI models used to create content like marketing videos with AI-generated voices and avatars.

Example solutions: Jasper, Notion AI, Phrasee, HubSpot Content Assistant

5) Document Summarization

A simple but effective way to use generative AI in your business is to summarize long and complex documents.

This can be a quick and easy way to identify the most important points in a long contract, legal document, conversation transcript, or other long-form content type. While some generative AI tools are standalone content summarization tools, others are embedded in everyday business operations and project management tools.

For example, Notion AI is a feature that enables Notion users to quickly summarize project notes and sales call transcripts.

Example solutions: Cohere Summarize, Anthropic Claude, PatentPal, AI21 Studio

6) Project and Workflow Management

Several of the most popular project management platforms have or are currently adding generative AI assistive capabilities to their features lists. These assistants can help with administrative tasks like note-taking during meetings, email writing, and document summarization, freeing up time for your employees to focus on more strategic tasks.

At a more technical level, these AI assistants can also automate and optimize workflow builds, make task suggestions, and support data integration efforts.

Example solutions: Process Street, Wrike, Notion AI, ClickUp, Asana

7) Cybersecurity Management

Although generative AI can pose major risks to cybersecurity postures, these AI tools are also valuable for automating business cybersecurity management workflows and handling data security requirements.

Some of the most common ways businesses are taking advantage of generative AI in cybersecurity include through smarter threat intelligence and hunting, contextualized security environment recommendations, and attack simulations. Some of these new generative AI security tools are freestanding products, but many of them are either new features or add-on products for existing cybersecurity platforms.

Example solutions: Google Cloud Security AI Workbench, Microsoft Security Copilot, CrowdStrike Charlotte AI, Airgap Networks ThreatGPT

Also see: Top Generative AI Apps and Tools

5 Benefits of Leveraging Generative AI in Your Business

Depending on how and how much you choose to leverage AI capabilities in your organization, expect the following generative AI benefits from your efforts:

• Quicker delivery times on projects: Generative AI models can quickly generate responses, new content, useful data, and other things your organization may need at scale. This leads to more efficient production and allows your team to complete projects on tighter timelines, which is ultimately more cost-effective.
• Fewer hands-on employee requirements: Whether you lack the in-house staff expertise to complete a certain task or find that certain tasks are too tedious to hold employee attention, generative AI tools can pick up the slack and deliver consistent, accurate, and repeatable results.
• Support and QA for employee-driven tasks: Particularly with coding and product development tasks, but also with data analytics and other content forms, generative AI tools can act as quality assurance analysts, checking employee work for errors and sometimes correcting those errors without human intervention.
• Enhanced user experiences: Many generative AI tools are embedded in software that your employees or customers already use. With the help of generative AI, search engines, enterprise knowledge bases, project management platforms, and other user-facing tools are optimized for natural language inputs and an overall better user experience.
• Industry-specific solutions: Several generative AI models have been fine-tuned to address industry- and function-specific requirements across sectors like healthcare and pharmaceuticals, insurance, manufacturing, and more. If you’re operating in a specialized industry — even with strict compliance requirements — there’s more than likely a generative AI model that will address your needs and simplify your workflow.

Also see: Best Artificial Intelligence Software 2023

5 Challenges of Using Generative AI

Many risks come with using generative AI, and especially because this technology is still so new, not all ethical use dilemmas have been worked out. If you choose to use generative AI in your business, be on the lookout for these possible challenges you’ll face:

• Limited regulatory requirements and guidance: Generative AI is not currently regulated on a larger scale, and at this time, there are few comprehensive usage frameworks that cover how your organization can use generative AI compliantly and effectively.
• Lack of data and training transparency: Although many generative AI vendors are beginning to improve in this area, most are still not transparent with the types and sources of the training data they use, which leads to additional compliance and usage concerns.
• Cybersecurity concerns: In the wrong hands or in the hands of an ill-trained employee, sensitive data may be exposed to generative models and become part of the training set. Bad-faith actors can also train these models to work around their rules to complete unauthorized tasks.
• Identifying inaccurate outputs: Generative AI models use natural language so well that it’s sometimes difficult to determine if a model has generated an inaccurate response. Users will need to have the skills and nuanced knowledge to check their own work when using this type of technology.
• Employee usage errors: It’s difficult to regulate and control how employees use generative AI in their work. Coming up with an AI use policy is a good step toward mitigating these errors, but there’s still no consistent way to enforce usage rules, control what data employees use as inputs, or monitor multiple employees’ actions at once.

Also read: Generative AI’s Drawbacks: IP to Ethics

What Are Generative AI Best Practices for Business Usage?

Establishing best practices and procedures for generative AI use is the best way to manage internal business use of these models. Follow these best practices for better generative AI outcomes in your business:

• Use data from verified, credible, and approved sources as inputs.
• Develop an AI use policy and training plan that clearly outlines how and when employees can use generative AI tools.
• Partner with a generative AI vendor building models that can scale or be fine-tuned as your business requirements evolve; it’s also beneficial to identify vendors with products that integrate with your existing tool stack.
• Do your due diligence and research generative AI vendors’ policies, terms, and reputations before committing.
• Consider your budget and what tools fit into that budget; many foundation models are not that expensive but can quickly go up in price depending on your usage volume.
• Consider any generative AI models you use as part of your cybersecurity threat landscape and protect accordingly.

For more on this topic, read these comprehensive guides:

• Risks of Generative AI: 6 Risk Management Tips
• Generative AI Ethics: Concerns and Solutions
• Generative AI and Data Analytics: Best Practices

Bottom Line: Generative AI in Your Business

Generative AI technology is a powerful resource that can be leveraged in businesses of all sizes and backgrounds, especially since so many models come in affordable limited versions that still have extensive capabilities.

The most important thing to remember when using generative AI in your business is that these tools are only as effective as the users, inputs, and procedures that surround them. Make sure all employees are trained and given the resources they need to use generative AI in their work effectively, and you’ll achieve new levels of automation, smart assistance, and productivity in your organization.

Read next: AI Detector Tools

The post Generative AI for Business: Top 7 Productivity Boosts appeared first on eWEEK.

With more consistent access to GPT-4 and a variety of new and enhanced security and collaboration features, ChatGPT Enterprise is an important step toward business adoption of the still new sector of generative AI.

Read on to learn more about what’s included in this latest release and how it could benefit your organizational processes and teams.

Table of Contents: ChatGPT Enterprise Release

• What Is ChatGPT Enterprise? Fast Facts
• New Features Available in ChatGPT Enterprise
• Benefits of Using ChatGPT Enterprise
• Bottom Line: What ChatGPT Enterprise Could Mean for Your Business

What Is ChatGPT Enterprise? Fast Facts

ChatGPT Enterprise already has several customers and advanced generative AI use cases available. Here are some of the highlights of what we know about the tool so far:

• Public release: ChatGPT Enterprise was released to the greater public on August 28, 2023.
• Early release users: Several enterprises were able to use ChatGPT Enterprise through early access, including Asana, Canva, PwC, Quizlet, Ramp, Zapier, and Khan Academy.
• Key features: ChatGPT Enterprise focuses on enterprise-grade security and privacy features, better GPT-4 usability and speed, and increased admin controls, among other features for enterprise-level operations and scalability.
• Cost: Unlike with its Free and Plus plans, OpenAI does not transparently list pricing information for ChatGPT Enterprise. Interested users will need to contact the OpenAI sales team directly for pricing.
• Future enterprise AI roadmap: OpenAI has revealed some of the Enterprise AI features they will release next, including additional application-level customizations, more robust power tools for data analysis, and role-specific features and functions.

Also see: Top Generative AI Apps and Tools

New Features in ChatGPT Enterprise

While certain aspects of ChatGPT Enterprise look like its predecessors, this latest release adds to and expands upon the generative AI model’s security, admin, and data analytics features. Take a closer look at the new features in ChatGPT Enterprise:

Security, Privacy, and Compliance

The main area that OpenAI focuses on in its new release announcement is security and privacy for generative AI. The new enterprise model includes both data encryption at rest and in transit through AES 256 and TLS 1.2+, SOC 2 compliance features and certification, and SSO and domain verification features.

But more significantly, OpenAI has promised that this model will not use customer prompts and queries or any other customer data to train future iterations of the model.

This means organizations can confidently input their data for various business use cases without fear of exposing proprietary business data. This new security and privacy commitment from OpenAI has opened ChatGPT up to new users and industries, especially those that require built-in compliance features and security safeguards to protect either consumer or top-secret business data.

Team Management and Admin Features

ChatGPT Enterprise addresses enterprise needs by giving internal administrators and teams more features and functions. For example, the new admin console gives admins access to a dashboard with a database for bulk member management, easy-to-read analytics and charts for usage insights, configurable admin settings, and identity and provisioning features and actions.

At a team level, shareable chat templates are now available and make it possible for teams to build collaborative, repeatable, and standardized projects and workflows. These features are designed to support a smoother user experience across an enterprise environment as well as easier large-scale deployments for business leaders and IT teams.

Advanced Data Analytics

Advanced data analysis for generative AI are available to ChatGPT Enterprise users on an unlimited basis. These capabilities, previously under the label Code Interpreter, are designed to support more technical data analysis tasks as well as democratized, citizen data scientist projects across product design and testing, financial analysis, ETL, and other analytics and data management use cases.

Speed and Scalability Features

This latest version of ChatGPT has increased its speed and power to meet the requirements of enterprise-level operations. Users of ChatGPT Enterprise receive access to unlimited GPT-4, longer context windows, and higher speeds (depending on system utilization).

OpenAI has built up this version of ChatGPT with 32k context for “4x longer inputs, files, or follow-ups,” which makes it possible for users to manage longer conversational strings and problem-solving scenarios.

Customization Features

ChatGPT Enterprise is one of the most customizable products from OpenAI to date, allowing users to do many tasks through self-service features.

Prebuilt chat templates can be used to simplify and standardize team-wide workflows. And for teams that need customizations that are more specific to their industry or business use case, ChatGPT Enterprise comes with several API credits at no additional cost.

Also see: Best Artificial Intelligence Software 2023

Benefits of ChatGPT Enterprise

ChatGPT Enterprise offers a variety of benefits that previous iterations of the tool did not, including the following:

• Larger and more effective context windows: Because this version of ChatGPT can process four times more input and file content at a time than previous versions of the tool, it can effectively generate useful and contextualized responses based on greater quantities of older input data.
• New customizability opportunities: The admin dashboard and many other features can be customized without much effort. Enterprise users also have access to free API credits if they require additional customizations.
• Better privacy and compliance features and settings: ChatGPT Enterprise has new and enhanced encryption features in place as well as SOC 2 compliance. These features, along with the company’s updated privacy commitments and the tool’s APIs for customizability, make it possible for organizations to rightsize ChatGPT to their privacy and compliance requirements.
• Business data protection: With this model, OpenAI has promised to prevent its model from ingesting and using user inputs for future model training and dataset incorporation.
• Democratized data analysis assistance: Users get unlimited access to OpenAI’s advanced data analysis tools, which are designed to support both traditional data science and low-code/no-code democratized data science initiatives.
• Increased team productivity: Current users have seen their organization’s productivity increase with the help of this tool. According to Jorge Zuniga, the head of data systems and integrations at Asana, “ChatGPT Enterprise has cut down research time by an average of an hour per day, increasing productivity for people on our team. It’s been a powerful tool that has accelerated testing hypotheses and improving our internal systems.”
• Unlimited GPT-4 access: This is the first model that has unlimited access to GPT-4’s higher speeds and greater processing capacity.
• Promising future roadmap: OpenAI has already publicly announced some of its future plans, which look promising for organizations that want more self-service features, stronger data analysis capabilities, function-specific features, and more customizability.

Also see: Generative AI Companies: Top 12 Leaders 

Bottom Line: What ChatGPT Enterprise Could Mean for Your Business

ChatGPT Enterprise has now been publicly released, and OpenAI says it is “onboarding as many enterprises as [they] can over the next few weeks.” Several enterprises have already committed to and praised the tool, explaining how well it addresses their more specific generative Ai enterprise use cases with added security and privacy features.

While the current version of this tool may not fit into the budget or address the needs of a smaller business, OpenAI’s future roadmap indicates that this is only the beginning of self-service-driven, customizable models for them. It will be interesting to see how other AI vendors respond and how the generative AI landscape changes over the next several months to further meet enterprise AI expectations.

Read Next: Top Natural Language Processing Companies

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