Among the topics we discussed: 

• Let’s talk about integration technology in the enterprise. How does it work in terms of, say, integrating cloud and legacy in-house apps?
• What are the challenges in integration? The typical headaches? How do you recommend companies handle these challenges?
• How is Jitterbit addressing the integration needs of its clients?
• The future of tech integration in the enterprise? Will it ever get easy?

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How so? On the upside, they accurately, if simplistically, describe the technical and market dynamics of a forward-focused industry that is quick to develop innovations and products designed to enhance performance and user experience.

But on the downside, the terms reflect the industry’s longstanding obsession with building, marketing and profiting from new products to the point of claiming, often without evidence, that they are superior to solutions already residing in client data centers.

Most important, they continually enhance existing solutions and platforms to ensure that they remain relevant to the needs of modern enterprises. IBM’s new watsonx Code Assistant for Z is a good example of one such effort.

Modernization vs. Legacy Hype

That “new” doesn’t automatically translate to “better” is a bit of practical wisdom that is seldom, if ever, seen in tech industry ad copy. Instead, vendors tend to hype shiny new things—claiming the innate superiority of this year’s gear over previous generation systems and platforms.

Certainly, new or next gen CPUs, storage media, interconnects and other technologies typically deliver better and/or more efficient performance. However, the value of ripping out existing or older systems and replacing them with new hardware is usually vastly overrated, often resembling a case of “fixing what isn’t broken.” The process is also expensive for customers, sometimes hugely so, due to costs related to system integration, software upgrades and retraining and certifying IT personnel.

In addition, generational shifts can make it increasingly difficult for businesses to find new system administrators, developers and technicians as existing staff members age-out. As is true in most other industries, younger workers typically prefer to explore and use new and emerging technologies.

That is a scenario that IBM plans to mitigate and avoid with its new watsonx Code Assistant for Z.

What is it? According to the company, the new solution is a generative AI-assisted product that is designed to enable faster translation of COBOL to Java on IBM Z, thus saving developers time and enhancing their productivity. It also joins IBM watsonx Code Assistant for Red Hat Ansible Lightspeed (scheduled for release later this year) in the watsonx Code Assistant product family.

Both solutions leverage IBM’s watsonx.ai code model, which the company says will employ knowledge of 115 coding languages learned from 1.5 trillion tokens. According to IBM, at 20 billion parameters, the watsonx.ai code model will be one of the largest generative AI foundation models for computer code automation.

Why is this important? First, because of the sheer pervasiveness of COBOL. Enterprise developers and software engineers have written hundreds of billions of lines of COBOL code. Plus, due to its notable flexibility and reliability, COBOL is still widely used, reportedly supporting some $3 trillion in daily financial transactions. In other words, COBOL is literally “business critical” to tens of thousands of large enterprises, millions of smaller companies and billions of consumers.

Also see: Top Digital Transformation Companies

COBOL Meets Watsonx Code Assistant for Z

Despite its vital position in transaction processing, COBOL is hardly a favorite among young computer professionals. Though COBOL and other mainframe programmers earn premium salaries (according to IBM, some 20-30 percent more than their peers), employers struggle to fill available positions.

That’s where IBM’s watsonx Code Assistant for Z comes in. The company notes that the new solution is designed to make it easier for developers to selectively choose and evolve COBOL business services into well architected, high-quality Java code.

Plus, IBM believes watsonx generative AI can enable developers to quickly assess, update, validate and test the right code, allowing them to efficiently modernize even large scale applications.

The Java on Z code resulting from watsonx Code Assistant for Z will be object-oriented and is designed to be performance-optimized versus comparable x86 platforms. IBM is designing the solution to be interoperable with the rest of the COBOL application family, as well as with CICS, IMS, DB2 and other z/OS runtimes. Lastly, IBM Consulting’s deep domain expertise in IBM Z application modernization makes it a prime resource for clients in key industries such as banking, insurance, healthcare and government.

Final Analysis

Though marketing professionals may feel comfortable with portraying modern and legacy technologies as a simplistic “new vs. old” conundrum, business owners, IT management and knowledgeable staff, including developers, understand the complexities of the modern/legacy dynamic. Rather than age, the larger issue is relevance: why an organization began employing a particular technology and how or whether that solution remains relevant to its owner’s needs.

It is not unlike how people and organizations remain relevant. Industries, companies, markets and larger economies are in a constant state of evolution. People and organizations succeed by adapting to those changes, by learning new skills, exploring new opportunities, and remaining vitally relevant to customers and partners. IBM’s new watsonx Code Assistant for Z demonstrates that what is true for people can also be true for information technologies.

Read next: Digital Transformation Guide

The post Modernizing the Mainframe—IBM Introduces Watsonx Code Assistant for Z appeared first on eWEEK.

Reshoring’s primary goal is to regain control over the entire end-to-end supply chain—it’s about manufacturing products on local soil, and it’s a process that’s been gaining traction from companies worldwide.

From a North American perspective, the picture is no different. Many U.S. companies have begun the shift away from globalization as default, with research suggesting that nearly 350,000 jobs were re-shored to the U.S. in 2022—a notable increase when compared to the 2021 figure of 260,000.

The movement has also seen companies become less reliant on China. Now, many economies, including the U.S., India, and the European Union, are looking to establish a roadmap that will balance supply chains and increase resiliency. The China Plus One Strategy is an approach adopted by a number of businesses looking to include sourcing from other destinations. Already, numerous companies have turned to Vietnam and India as alternatives, with both countries reporting an uptick in investment from U.S. companies that have built plants there.

According to the Reshoring Initiative IH 2022 Data Report, supply chain gaps, the need for greater self-sufficiency, and a volatile geopolitical climate are major factors driving reshoring. The report found that 69% of companies cited supply chain disruptions as the primary reason for reshoring.

There is now movement on a national level to strengthen supply chains and promote domestic manufacturing with the introduction of the bipartisan National Development Strategy and Coordination Bill in December 2022. This bill highlights the importance of manufacturing reshoring to national economic development going forward into 2023.

Sustainability and Tech in Reshoring

Recent research commissioned by IFS, polling senior decision-makers working for large enterprises globally, found that 72% have increased their usage of domestic suppliers, compared to international suppliers.

From a sustainability perspective, there are huge benefits to be gained. In fact, reshoring is giving manufacturers a golden opportunity to look hard at their manufacturing processing and how they can develop more sustainable processes.

For example, it can minimize CO2 emissions as transport is reduced and spur a deduction in wasteful overproduction as supply chains are brought closer together. As the whole world strives to act more sustainably in the race to net-zero, environmental benefits will play a huge role in driving new sourcing strategies.

However, the raw materials, components, and products that they source from suppliers are likely to become more expensive, especially as inflation continues to gather pace globally. As a result, 53% have considered increasing the proportion of materials/components they produce in-house. But again, these measures and others like them that organizations are now taking to mitigate risk are likely to add cost, complexity, and waste to the supply chain.

Therefore, reshoring is not the silver bullet to mitigating supply chain disruption entirely. Often, companies underestimate the sheer level of effort, costs, and logistical planning required to make reshoring a success.

But for many U.S. companies, the extra costs to manufacture within the country are definitely outweighed by the savings in customs and shipping costs and the additional sustainability benefits associated with offshore operations.

It’s here organizations need the helping hand of technology—in fact, it can be a key facilitator for solving supply chain, labor, and production challenges associated with reshoring.

For 94% of respondents in a recent McKinsey study, Industry 4.0 helped keep operations running during the COVID-19 pandemic, with another 56% claiming Industry 4.0 technologies had been critical for efficient responses.

A new IDC InfoBrief, sponsored by IFS and entitled Shaping the Future of Manufacturing, shows an active correlation between digital maturity and profit. According to the research, manufacturers reporting an optimized level of digital transformation saw profits increase 40%, while those with less advanced digital transformation maturity suffered bigger reductions in profit in the last fiscal year.

Tech has been quick to respond to the call to deliver the agility and fast “Time to Insight” (TTI) that manufacturers need to better forecast demand and provide a more detailed view of sustainability across product supply chains. Exceptional supply chain management will be a vital part of the move to reshoring. The IFS study showed supply chain management was now seen by 37% of respondents as one of the top three priorities their organization is trying to solve through technology investment.

Reshoring in Action: Will the Benefits Be Worth It?

In a recent Kearney index on manufacturing reshoring, 92% of executives expressed positive sentiments toward reshoring. And that’s no surprise when you consider the additional benefits on offer. As well as a more protected supply chain ecosystem, there are also positive societal benefits from the move to reshoring.

According to the U.S. Reshoring Initiative, in 2021 the private and federal push for domestic U.S. supply of essential goods propelled reshoring and foreign direct investment (FDI) job announcements to a record high.

From a broader perspective, there are many profitable and supply chain benefits at stake for manufacturers. For example, research found that 83% of consumers in the U.S. are willing to pay 20% more for American-made products, with another 57% claiming that the origin of a product would sway their purchasing decision.

From a management standpoint, control over operations has significantly increased. Bringing operations all to one centralized location gives businesses tighter control over processes. Manufacturers will also benefit from shorter supply chains as much of today’s manufacturing is spurred by IoT, AI, and machine learning capable of performing monotonous tasks around the clock.

On a day-to-day level, on-site teams will experience increased collaboration as reshoring drastically reduces the time difference between headquarters and the manufacturing plant.

Tech Needs to Drive Reshoring

It’s easy to see why the appeal of reshoring is prompting a move toward U.S.-based manufacturing initiatives. By addressing reshoring now with the right technology, efficiently and cost-effectively, manufacturers will put themselves in a great position to not only survive but also thrive long into the future.

Of course, as with any major transformation, there are hurdles to overcome. But the long-term results of reshoring, from increased employment to tighter manufacturing control, look as though it’s a journey worth embarking on. As more and more companies around the world look to reshore operations on home soil, manufacturers will need the guiding hand of a flexible and agile software platform to make reshoring a reality at scale.

About the Author:

Maggie Slowik is the Global Industry Director for Manufacturing at IFS.

The post Reshoring Alleviates Supply Chain Issues – But It Needs Tech to Control Costs appeared first on eWEEK.

This varies significantly according to industry, region and commercial markets. Plus, it is commonplace for ESG programs to evolve as priorities and circumstances change.

Recently, Dell published its new ESG Report for FY2023, updating its achievements and overall strategy. Let’s consider how the company has progressed – but first let’s take a brief look at the state of enterprise ESG issues today.

Also see: Top Digital Transformation Companies

Today’s Corporate ESG Issues

It is worth noting the importance of ESG programs. The issues covered in these programs affect all of our lives and are closely tied to organizations’ relationships with stakeholders, including customers and strategic partners. Empowering disadvantaged groups of customers and businesses is just good for business.

That is especially true in the U.S. where despite their myriad benefits, ESG policies have become bugaboos of “wokeness” among some politicians and groups. Many of those individuals and alliances are also attempting to dial-back broader environmental and social justice advances but are encountering resistance from progressive organizations and individuals, as well as from seemingly unlikely organizations. Those include large corporations, pension funds, insurers and investment firms.

Why would those disparate players actively protect ESG programs? A couple of issues are top of mind. First, disadvantaging specific groups of consumers and businesses to appease politicians and special interest groups is simply bad for business.

Equally important are the negative impacts that anti-ESG efforts can have on promising businesses and industries. Consider that earlier this year, 19 Republican state governors signed an open letter warning of the “direct threat” posed by ESG proliferation. Some connect the ‘E’ in ESG to renewable energy technologies and programs, such as hydroelectric, wind power and ethanol subsidies for farmers. Since many or most of the governors who signed the letter lead states that benefit from renewable energy initiatives, their anti-ESG rhetoric seems ironic in the extreme.

Finally, and perhaps most importantly, is the value that ESG programs and strategies offer to companies doing business globally. Environmental, social and governance issues vary widely in importance and scope from place to place. The variety of ESG subject matter means that organizations can craft programs to maximize value for the customers and partners they believe are most in need.

Far from being the direct threat that some U.S. state governors and other politicians and groups imagine, ESG continues to deliver substantial, welcome benefits to businesses, state institutions and consumers worldwide.

Dell’s FY 2023 ESG Report

Dell Technologies has emphasized the importance of ESG-related issues since 1998 when the company published its initial Environmental Progress Report.

Beginning in 2002, the company shifted to annual reports charting its focus on and progress in key areas, including the environment, sustainability and corporate social responsibility. The company has maintained these commitments through recent political headwinds because it understands these priorities are not only good for business but also for the communities in which they operate.

What are some of the key highlights in Dell’s new FY2023 ESG report?

First, the company refined the goals included in the FY2022 report and condensed its 25 top-level goals to:

• Achieve net zero greenhouse gas (GHG) emissions across Scopes 1, 2 and 3 by 2050.
• Reuse or recycle one metric ton of materials for every metric ton of products Dell customers buy by 2030.
• Make or utilize packaging made from recycled or renewable material for 100 percent of Dell products by 2030.
• Leverage recycled, renewable or reduced carbon emissions materials in more than half of the products Dell produces by 2030.
• Employ women as 50% of Dell’s global workforce and 40% of the company’s global people leaders by 2030.
• Employ people who identify as Black/African American or Hispanic/Latino as 25% of Dell’s U.S. workforce and 15% of its U.S. people leaders by 2030.
• Improve the lives of 1 billion people through digital inclusion by 2030 through efforts such as supply chain training and initiatives aimed at girls and women, or underrepresented groups.
• Provide support for and participation in community giving or volunteerism by 75% of Dell team members by 2030.

Additionally, in 2022 Dell began framing a trust model centered on security, privacy and ethics. Given the importance of those areas in terms of establishing and maintaining trusted relationships, the company is emphasizing “Upholding Trust” with the goal of having customers and partners rate Dell Technologies as their most trusted technology partner.

Finally, the company demonstrated its continuing commitment to diverse supplier spend by doing over $3 billion in business with small and diverse companies. Plus, for the 13th consecutive year, Dell was recognized by the Billion Dollar Roundtable (BDR), which celebrates corporations that spend at least $1 billion annually with minority- and women-owned businesses.

Further details, background information and customer/partner examples can be found in the full Dell Technologies ESG Report for FY2023.

For more information, also see: What is Data Governance

Final Analysis

Transformation is a concept and process that permeates the technology industry, but it also has many guises. For example, there’s the “digital transformation” strategies and solutions that so many vendors emphasize aim to help customers improve business outcomes by maximizing compute performance and data efficiency. Other efforts include process transformation, such as leveraging automation and logistical efficiencies to improve supply chain performance.

One topic less commonly discussed is corporate cultural transformation. This is when an organization continually and proactively evolves to adapt and benefit from changes in commercial markets, business practices and demand forecasts, as well as shifts in politics, economies and the environment. In my opinion, this type of transformation holds a central role in Dell Technologies’ ESG strategy and its annual ESG reports.

Many of the practical steps the company is taking—expanding the use of recycled and renewable materials, for example—simply make good business and financial sense. Others, including achieving net zero GHG emissions, reflect the company’s deep understanding of and intention to practically address climate change and other environmental issues.

Some goals enumerated in the new FY2023 report may appear aspirational but are far more practical than one might expect. At a Dell Technologies World session a few years ago, Michael Dell noted (I confess to paraphrasing here) that, “A company should look like its customers and partners.”

That is a particularly profound statement, not to mention being highly applicable to business and a wide range of public and private organizations and institutions. Without having such a vision and investing in efforts to achieve it, individuals, businesses and governments will inevitably find their vision blurring, their frontiers shrinking and their opportunities dwindling.

By embracing cultural evolution through supporting and advancing the careers of underrepresented groups, by actively improving communities and the lives of a billion people and by working to become the vendor that customers and partners trust the most, Dell Technologies will further grow its own outlook, relevance and potential for success.

Is there a greater or more important goal for any organization?

For more information, also see: Digital Transformation Guide

The post Dell’s 2023 ESG Report: Evolving Corporate Culture appeared first on eWEEK.

As far back as 2001, at data warehousing firms like my old haunt of Daman Consulting, we touted the value of “operationalizing” business intelligence. The idea was to leverage BI-derived insights within operational systems dynamically, and thus directly improve performance.

Though embedded analytics have been around for decades, it’s fair to say that most BI solutions in this millennium have focused on the dashboard paradigm: delivering high-level visual insights to executives via data warehousing, to facilitate informed decision-making.

But humans are slow, much slower than an AI algorithm in the cloud. In the time it takes for a seasoned professional to make one decision, AI can ask thousands of questions, get just as many answers, and then winnow them down to an array of targeted, executed optimizations.

That’s the domain of applied intelligence, a closed-loop approach to traditional data analytics. The goal is to fuse several key capabilities – data ingest, management, enrichment, analysis and decisioning – into one marshaling area for designing and deploying algorithms.

There are many benefits to this approach: transparency, efficiency, accountability; and most importantly in today’s market? Agility. During times of great disruption, organizations must have the ability to pivot quickly. And when those decisions are baked in via automation? All the better.

It also helps in the crucial domain of explainability, the capacity to articulate how an artificial intelligence model came to its conclusion. How explainable is a particular decision to grant a mortgage loan? How repeatable? What are the biases inherent in the models, in the data? Is the decision defensible?

On a related topic: The AI Market: An Overview

Take It To the Bank

The rise of fintech startups and neobanks, coupled with rapidly changing interest rates, has put tremendous pressure on traditional financial market leaders to innovate rapidly but safely. Rather than embrace a rear-guard strategy, many firms are looking to AI to regain momentum.

As CPTO for FICO, Bill Waid has overseen a wide range of banking innovations. UBS reduced card fraud by 74%, while Mastercard optimized fraud detection in several key ways, including automated messaging to solve the omni-channel conundrum of communications.

The Mastercard story demonstrates how a large financial institution is now able to dynamically identify, monitor, and manage client interactions across a whole host of channels – and fast enough to prevent money loss. A nice side benefit? Less-annoyed customers.

In a recent radio interview, Waid explained another situation where collaboration improves marketing. “In banking, from a risk perspective, one of the most profitable products is credit card. So if you were to ask somebody from risk: which would you push, it would be the credit card.”

But other departments may disagree. “If you ask the marketing person, they have all the stats and the numbers about the uptake, and they might tell you no, it’s not the credit card, at least not for this group (of customers), because they’re actually looking for a HELOC or an auto loan.”

The point is that you can drive away business by making the wrong suggestion. Without collaborating around common capabilities from a centralized platform, Waid says, that mistake would have likely gone into production, hurting customer loyalty and revenue.

With an applied intelligence platform, he says, key stakeholders from across the business all have their fingers in the pie. This helps ensure continuity and engagement, while also providing a shared baseline for efficacy and accountability.

Think of it as a human operating system for enterprise intelligence, one that’s connected to corporate data, predictive models, and decision workflows, thus achieving cohesion for key operational systems. In the ideal scenario, it’s like a fully functioning cockpit for the enterprise.

This transparency leads to confidence, a cornerstone of quality decision outcomes: “That confidence comes in two dimensions,” he says. “The first is: can you understand what the machine is doing? Do you have confidence that you know why it came to that prediction?

“The second element is that in order for the analytic to be useful, it’s gotta get out of the lab. And many times, I see that the analytic comes after the operationalization of a process, where there is more data, or a flow of data that’s well warranted to an analytic.”

For more information, also see: Best Data Analytics Tools

Bottom Line: The Analytic Becomes an Augmentation

This is where rubber meets road for applied intelligence: the analytic becomes an augmentation. And when the business has that transparency, they get comfortable, and they adopt the insight into their own operational workflow. That’s when the intended value out of the machine is felt.

“Platforms provide unification: bringing process, people, and tech together,” Waid says. And as AI evolves, with Large Language Models and quantum computing closing in, it’s fair to say that the practices of applied intelligence will provide critical stability, along with meaningful insights.

Also see: 100+ Top AI Companies 2023

The post Navigating the Perfect Storm with Applied Intelligence appeared first on eWEEK.

Among the topics we covered: 

• Based on your latest research into Digital Transformation, what technologies are bubbling to the top?
• What key trends are you seeing in Observability? Why is it getting so much attention?
• AI is everywhere, and creeping into IT Ops too. How are ML and AI impacting IT Ops and DevOps today? What about the near future?
• Looking ahead, what is the Next Big Thing for Ops?

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The post Sageable CTO Andi Mann on Observability and IT Ops appeared first on eWEEK.

Chuck Whitten joined Dell Technologies in 2021 where he became co-Chief Operating Officer in partnership with Jeff Clarke. Together, Whitten and Clarke own company-wide strategy and execution. Whitten oversees the day-to-day financial and operating plans and performance, as well as long-term planning for emerging technology areas like Cloud, Edge, Telecom and as-a-Service.

Prior to joining Dell, Whitten worked at Bain and Company for over two decades where he served as the managing partner of Bain Southwest and was a two-time elected member of Bain’s Board of Directors.

Also see: Top Digital Transformation Companies

Technology and Business

Pund-IT (Charles King): A couple of decades ago, vendors spent a lot of their time explaining and proselytizing the importance of technology to businesses. Today, that’s a common understanding or belief. So, you might say that Dell’s approach has benefited from that evolution and maturation of the links between technology and business.

Whitten: For sure. Technology has never been more essential to our customers and to society. That is also why there’s been this blurring of technology and business budgets, because it’s one and the same. You’re either born a technology company or you evolve a technology-led strategy very quickly, or you go away. There’s also the realization and the profound impact of digital transformation sweeping across industries. I think you’d be hard pressed to find a board or a C suite anymore that doesn’t talk about the criticality of technology to their business and their future. Dell is in the business of helping them solve whatever those problems are.

Dell’s Management Profile

Pund-IT: Since you and Jeff Clarke share the co-COO title, can you talk about that relationship and how your individual responsibilities work?

Whitten: When I joined Dell in August 2021, it was for two reasons. The first and most important was to share responsibilities for better coverage and speed decision making to accelerate our growth potential and deliver outcomes for customers.

The second was to capitalize on these big opportunities in front of us. We are facing a pivotal time in our company’s history, and expanded leadership capacity helps us to do that. The COO scope at Dell is really broad, so my first job was to jump on the moving train, listen and learn. Then, over time, we began to create executive capacity to divide and conquer responsibilities where it makes sense for our customers.

Pund-IT: How does that work practically?

Whitten: So today, Michael, Jeff and I share responsibility for the strategic direction of the company, as well as the talent agenda. Jeff tilts his time more toward technical strategies and the architectures that are going to define our next era. That is logical given his unmatched experience as an engineer. He also drives the critical ecosystem relationships that he’s built over decades at the company. My responsibilities are the day-to-day execution of Dell’s business like delivering for the quarter and the year, taking solutions to market, making sure we’re supporting customers in the here-and-now, and driving shareholder value.

Pund-IT: That sounds like a sensible division of labor.

Whitten: Look, Jeff and I are quite complementary. It works, I think, because we have years of deep trust, and we stay in constant communication. We’re also agile when and where we need to be. Jeff likes to say there are some problems where we need, “Four eyes, four arms and two brains.” We dig in together. It’s a model that works very well in Dell’s culture, but it takes a lot of communication and having a shared DNA. All Jeff and I want to do is win for our company and our customers, and we tend to sort out things as needed with that as the foundation.

For more information, also see: Digital Transformation Guide

The Dell Company Culture 

Pund-IT: You mentioned being attracted to the fact that Dell is a founder-led company. Does that make it a different kind of organization or entity than other vendors?

Whitten: I think founder-led businesses are differentiated in that company culture is ultimately traced back to the founders. So, the principal behaviors and practices that are instituted in the early days of a company carry forward to the present. I credit our innovative, customer-centric culture to Michael and the consistent way he’s led over the years. We have a common and real sense of purpose because of Michael—to create the technologies that drive human progress. It’s aspirational and it’s also very clear.

Pund-IT: Michael is also, to my mind, one of the most fully engaged leaders in the tech industry.

Whitten: Michael is definitely not hands-off. He is an entrepreneur to the core with incredible instincts. Look, we all admire his leadership. He has correctly predicted where technology is headed at pivotal moments in history and made some very different bets on shaping the company. That’s a real gift. I think what I’m most grateful for, and what I think differentiates founder-led companies from other enterprises, is just the long-term orientation.

Pund-IT: In what sense?

Whitten: Michael is focused on building an enduring business. We never feel pressure to drive short-term profits. We’re encouraged to wade into all these unsolved, hard problems of technology, like multicloud, security, artificial intelligence, the edge. That is a founder’s mentality at work. That’s asking, “Hey, what are the big opportunities and big problems that we can support our customers on?”

Pund-IT: We’ve talked quite a bit about culture. However, it’s a term that has become something of a bromide among some vendors. But I agree with you about the importance of culture and the value of leveraging what a company innately is to drive strategy and execution.

Whitten: Absolutely. I believe it is one of our top differentiators. We have a culture code that traces back to Michael which we all adopted when we joined Dell. It defines who we are, what we believe, how we work and how we lead our teams. It’s somewhat simple: We believe that customers and innovation are the foundation of success in the technology industry. If you never lose sight of that, you will win as long as you act with integrity and commitment. That’s what we ask our team members to embrace.

Pund-IT: What do employees receive in return?

Whitten: In return, we commit to our team members. We believe people should have fulfilling and full careers, as well as fulfilling lives. That’s culturally where we strive to build achievement inside of Dell, but also balance life at home, connections, diversity and inclusion. None of that happens without having a CEO at the top, like Michael, who says, “This matters.” We want to build a people-centric company that delivers technology and innovation for our customers as well as our team members. That’s easy to say and very hard to do.

Pund-IT: In your years working both as a Bain advisor and a Dell executive, what has impressed you or surprised you the most about the company?

Whitten: I think the biggest thing is our agility. Look, $100 billion companies are not supposed to be able to move quickly. But we do. IBM’s Lou Gerstner famously said elephants aren’t supposed to be able to dance. We dance and sing and do backflips like a smaller-scale company.

Pund-IT: What are some examples of that?

Whitten: You saw it in our performance during the last few years as we navigated the pandemic boom in PCs and supporting work-from-home (WFH), followed by the boom in infrastructure. All that time, we were supporting our own WFH employees and navigating global supply chain shortages and doing so better than the rest of the industry. We also saw the current economic caution in businesses faster than anyone else in a position to be relevant to customers. I also think you see that agility in our innovation. The rate of progress we’re making in places like telecom and AI and multicloud is astonishing. Last year in our Infrastructure Solutions Group business, we had a period of 30 major launches in 13 weeks. The ability to move that fast is a really formidable thing.

Also see: Top Cloud Companies

Dell Technology and the Future 

Pund-IT: Is there anything about Dell that you wish people outside the company knew or understood more clearly?

Whitten: I think it’s appreciated but it bears repeating. At Dell, we’ve built something that looks really different than the rest of the technology industry. We’re number one in all our core markets, and we have what we call “durable competitive advantages” that help us continue to win and reinvent ourselves. Our end-to-end portfolio puts us in the center of customers’ agendas, has the largest go-to-market and channel ecosystem in the industry, offers leading services, scale and capabilities, and benefits from the industry’s best supply chain with unmatched scale. And then, as we’ve discussed, we have a culture that I think is unmatched in the industry. Because of that and what we’ve built, we sit at the center of these great challenges our customers are facing.

Pund-IT: Such as?

Whitten: What’s the future of work and how does the PC unlock collaboration? How can companies make multicloud architectures work seamlessly? How can security be more intrinsic to infrastructure and not something that you add on after the fact? How can we accelerate AI? And how can we do it smartly and securely and ethically? How do we unlock innovation at the edge? Technology has never been more essential to customers, and never been more essential to addressing the broad problems of society. We’re in the center of all of that. I think we have the capabilities and the culture to make a difference. Building on that, we can plan and pursue our future-focused goals.

Pund-IT: Speaking of that, where do you see Dell in four or five years?

Whitten: We’ve reinvented ourselves over multiple decades, and we’re just going to continue to reinvent ourselves. There are short-term economic headwinds, but we’ve seen those before. What feels different in this cycle is that technology has never been more essential to our customers and we, as a company, have never been better positioned and prepared to help them solve those problems. Five years from now, I believe we will continue to be at the center of customers’ technology agendas, whatever those agendas are. On the way there, we’re going to help them solve the most pressing challenges that we’re seeing today so we will be at the center of whatever customers are doing. I think that’s the enduring institution that Michael built and that we’re all building together now.

Also see: Top Edge Companies

The post Dell’s Chuck Whitten on Dell Company Culture appeared first on eWEEK.

Of course, this quick integration is no easy task. Especially considering that over the last two years, one in two companies rapidly adopted new technologies and transformed their business in record time, according to new research from Accenture. In fact, the average company now has well over 500 software applications, from almost as many vendors, with 81 percent planning to add more over the next two years.

At a time when budgets are tightening, enterprises now need to focus on untangling their applications to ensure they work cohesively to enable agility and provide ongoing business value.

Also see: Top Cloud Service Providers and Companies

Cracking the Code

We’ve found that one in three companies have cracked the code and have managed to make their enterprise technologies work together.

Last year, these companies with high interoperability grew revenue six times faster than their peers with low operability and they are poised to unlock an additional five percentage points in annual revenue growth. This is a huge financial advantage.

To put it in perspective, if two organizations start with $10 billion in revenue today, the organization with high interoperability stands to make $8 billion more than its peer with low interoperability over the next five years.

So, how does high interoperability create such a powerful impact? By integrating enterprise applications, businesses can move from siloed technologies to connected solutions that enable better data sharing, enhanced employee productivity and improved customer experiences.

GN Group, a global audio solution manufacturer, is a prime example of how high interoperability can enable organizations to take advantage of opportunity. Following a 42 percent rise in headset sales in 2020, the company braced for further demand surges due to remote school and work. When sales jumped 82 percent in the first quarter of 2021, company leaders knew they needed to unite employees and technology under a common strategy to meet the increased demand—and fast.

They turned to Microsoft’s cloud-based enterprise solutions to connect functional applications – like supply chain operations and finance – so that employees across the organization could make decisions based on a single source of trusted data.

In this way, the sales team could check if procurement had the available components for a large incoming order. Similarly, vendors and suppliers, who were previously late to learn of new demand, could also make informed inventory decisions.

By removing data silos and creating a common language across critical applications and systems, GN enabled parallel, rapid transformation in multiple business areas.

Also see: Top Digital Transformation Companies

Long-Term Value Without a Big Price Tag

The concept of interoperability isn’t new, but the ability to manifest it is. As companies move to the cloud and have access to improved and inexpensive applications, interoperability not only becomes a source of long-term value—it becomes low-cost too.

Leading companies are achieving high interoperability with just 2-4 percent higher IT and functional budgets directed at applications. The investment is helping them outperform their peers across industries and economic cycles.

Consider life sciences, an industry that grew rapidly with the global demand for vaccinations. Companies with high interoperability grew revenue by almost 10 percent on average, while those with low/no operability only managed a five percent gain.

In the travel industry, which was hit particularly hard by the pandemic, saw revenue decline by four percent, on average, in low operability companies. In contrast, high interoperability companies were able to quickly pivot their business models and grow revenue by two percent.

For more information, also see: Digital Transformation Guid

The Three C’s

Across industries, interoperability is a common denominator for success. There are three best practices for getting to high interoperability in an era of compressed transformation.

1. Leverage the Cloud: By moving existing applications to the cloud and adopting cloud-based enterprise applications, companies can connect data and experiences, which helps to standardize processes and drive change across the organization in parallel. Seventy-two percent of companies with high/medium interoperability have adopted public cloud and have already migrated 30% of their data and workloads.
2. Use Composable Tech: Moving away from a technology architecture of static, monolithic and standalone parts to creating one comprised of composable pieces helps to boost agility. Repeatable solutions that can be configured and reconfigured to rapidly develop new capabilities enables companies to build flexibility into the core of their business. These solutions can be curated for specific industries and functions and act as a form of future proofing—giving organizations the dexterity to quickly adopt the technologies of tomorrow. With data flowing between connected applications, companies can easily share information with the entire organization so everyone is on the same page.
3. Meaningful Collaboration: Interoperable applications are only one part of the equation. Interoperability supports meaningful collaboration by allowing functions and people to work together seamlessly toward a common goal. Real-time data, analytics, and AI, together with new ways of working, can unlock the value of technology and empower people and achieve better outcomes. Companies with high interoperability have an unwavering focus on improving human connections.

It’s clear today, more than ever, that companies must anticipate uncertainty in all its forms. By leveraging the cloud, using composable tech, and focusing on collaboration, companies can improve interoperability to overcome obstacles and outpace competitors in growth, efficiency and resiliency.

For more information, also see: Cloud and AI Combined: Revolutionizing Tech 

About the Author: 

Brian McKillips is Senior Managing Director and Growth and Strategy Lead for Enterprise & Industry Technologies, Accenture

The post Interoperability: Thriving in Uncertain Times appeared first on eWEEK.

Among the topics we covered: 

• Enterprise IT spending is forecast to be up modestly this year. But inside that figure are larger shifts in how companies are spending their IT budget. Overall, what do you see?
• CIO budgets: Much of a CIO’s budget is allocated before the year begins. Does a CIO truly allocate funds?
• CIO as service provider: As a CIO, what’s your approach to being a service provider to your organization?
• CIO as traffic cop: Shadow IT has been – and still is – a major force in tech. How do handle “policing” the tech infrastructure management in your company?
• The future of enterprise spending? What trends do you see evolving going forward?

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Artificial intelligence, or AI, is a combination of sophisticated algorithms, computing, and data training methods that allow machines and computers to mimic human knowledge and behaviors.

In some ways, artificial intelligence is the opposite of natural intelligence. While living creatures are born with natural intelligence, man-made machines can be said to possess artificial intelligence. In fact, one of the early pioneers of AI, John McCarthy, defined artificial intelligence as “the science and engineering of making intelligent machines.”

In practice, however, artificial intelligence companies use the term artificial intelligence to refer to machines doing the kind of thinking and tasks that humans have taken to a very high level.

Also see: Top Generative AI Apps and Tools

Artificial Intelligence: Table of Contents

• What is Artificial Intelligence in Simple Terms?
• What is Generative AI?
• Artificial Intelligence vs. Machine Learning
• Machine Learning vs. Deep Learning
• Types of Artificial Intelligence
• Short History of AI
• AI Uses Cases: What Can AI Do?
• Pros and Cons of Using AI
  
• The Limitations of AI
• Ethics and AI
• Bottom Line: The Future of AI
• Artificial Intelligence: Additional Resources

What Is Artificial Intelligence in Simple Terms?

Computers are very good at making calculations — taking inputs, manipulating them, and generating outputs as a result. But in the past, they have not been able to do other kinds of human tasks, such as understanding and generating language, identifying objects by sight, creating art, or learning from past experiences.

Today, many computer systems have the ability to communicate with humans using ordinary speech. They can recognize faces and other objects. They use machine learning techniques, especially deep learning and neural networks, in ways that allow them to learn from the past and make predictions about the future. 

Much of this technology is still being developed and advanced every day, but now, even the average consumer can access AI models to generate content, solve problems, and handle a number of other advanced tasks.

Also see: Generative AI Companies: Top 12 Leaders 

What Is Generative AI?

Generative AI is a specific, emerging form of artificial intelligence that relies on big data training sets, neural networks, deep learning, and some natural language processing to create original content outputs. Although the most commonly used generative AI tools currently generate text and code, generative AI solutions can also generate images, audio, and synthetic data, among other outputs.

Generative AI is perhaps the most popular and fastest-growing type of AI today, especially with the global popularity of OpenAI’s ChatGPT and GPT-4. Other popular examples of generative AI include Google Bard, Jasper, Stable Diffusion, DALL-E, Microsoft and GitHub Copilot, and DreamStudio.

Learn more: What is Generative AI? 

Artificial Intelligence vs. Machine Learning

At the simplest level, machine learning (ML) is a subset of artificial intelligence. While the greater artificial intelligence umbrella is dedicated to all kinds of approaches to human-like problem-solving, machine learning involves developing a specifically trained model that focuses on teaching machines to complete focused tasks and identify data patterns. In many cases, machine learning is used in conjunction with other forms of artificial intelligence.

Further the comparison: AI vs. ML: Artificial Intelligence and Machine Learning Overview 

Machine Learning vs. Deep Learning

Just as machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning. Machine learning as a whole is about deriving insights from big datasets and making decisions based on the information these solutions find. It is an algorithmic, data-driven approach to decision-making. Deep learning is also an algorithmic approach to decision-making, but it’s a bit more complex; instead of working with one or a small number of algorithms, deep learning models work with multiple layers of algorithms — known as a neural network. This structure is designed to help deep learning models mimic the functions of human brains.

Get a closer look: Machine Learning vs. Deep Learning

Types of Artificial Intelligence

Computer scientists have proposed different ways to classify the types of AI. One popular classification uses three categories:

1. Artificial Narrow Intelligence

Artificial Narrow Intelligence (ANI) is designed to complete one task or set of tasks with high competence and skill. Apple’s Siri, IBM’s Watson, and Google’s AlphaGo are all examples of Narrow AI. Narrow AI is fairly common in the world today.

2. Artificial General Intelligence

Artificial General Intelligence (AGI) is a form of AI that performs many intellectual tasks on par with a human. Many researchers are currently working on developing general AI. One of the best early examples of AGI is GPT-4, which is able to solve a variety of problems and has performed well on a number of standardized human tests.

3. Artificial Superintelligence

Artificial Superintelligence (ASI), which is still theoretical, has intellectual capacities that far outstrip those of humans. This kind of artificial intelligence is not yet close to becoming a reality.

Also see: How AI is Altering Software Development with AI-Augmentation 

Another popular classification uses four different categories

1. Reactive Machines

Reactive machines take an input and deliver an output, but they do not have memory or learn from past experiences. The bots you can play against in many video games are good examples of reactive machines.

2. Limited Memory

Limited memory machines can look a short distance back into the past. Many vehicles on the road today have advanced safety features that fall into this category. For example, if your car issues a backup warning when a vehicle or person is about to pass behind your car, it is using a limited set of historical data to come to conclusions and deliver outputs.

3. Theory of Mind

Theory of mind machines are aware that human beings and other entities exist and have their own independent motivations. Most researchers agree that this kind of AI has not yet been developed, and some researchers say that we should not attempt to do so. However, some of the latest generative AI models are performing well in theory of mind tasks and tests.

4. Self-Aware

Self-aware machines are aware of their own existence and identities. Although a few researchers claim that self-aware AI exists today, only a handful of people share this opinion. Developing self-aware AI is highly controversial.

While these classifications are interesting from a theoretical standpoint, most organizations are far more interested in what they can do with AI.

Also see: Three Ways to Get Started with AI 

A Short History of Artificial Intelligence

1950s and 1960s

Many people trace the history of artificial intelligence back to 1950 when Alan Turing published “Computing Machinery and Intelligence.” Turing’s essay begins with “I propose to consider the question, ‘Can machines think?’” It then lays out a scenario that came to be known as the Turing Test. Turing proposed that a computer could be considered intelligent if a person could not distinguish the machine from a human being.

In 1956, John McCarthy and Marvin Minsky hosted the first artificial intelligence conference, the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI). The conference convinced computer scientists that artificial intelligence was an achievable goal, setting the foundation for several decades of further research. Some of the earliest forays into AI technology, developed bots to play checkers and chess, emerged as a result of this conference.

The 1960s saw the development of rudimentary robots and several problem-solving programs. One notable highlight was the creation of ELIZA, a program that simulated psychotherapy and provided an early example of human-machine communication.

1970s and 1980s

In the 1970s and 80s, AI development continued but at a slower pace. The field of robotics in particular saw significant advances, such as robots that could see and walk. Additionally, Mercedes-Benz introduced the first (extremely limited) autonomous vehicle. However, government funding for AI research decreased dramatically, leading to a period some refer to as the “AI winter.”

1990s and Early 2000s

Interest in AI surged again in the 1990s. The Artificial Linguistic Internet Computer Entity (ALICE) chatbot demonstrated that natural language processing could lead to human-computer communication that felt more natural than what had been possible with ELIZA. The decade also saw a surge in analytic techniques that would form the basis of later AI development, as well as the development of the first recurrent neural network architecture. This was also the decade when IBM rolled out its Deep Blue chess AI, the first to win against the current world champion.

The first decade of the 2000s saw rapid innovation in robotics. Roombas began vacuuming rugs and robots launched by NASA explored Mars. Closer to home, Google was working on a driverless car.

2010s

The years since 2010 have been marked by unprecedented increases in AI technology. Both hardware and software developed to a point where object recognition, natural language processing, and voice assistants became possible. IBM’s Watson won Jeopardy. Siri, Alexa, and Cortana came into being, and chatbots became a fixture of modern retail. Google DeepMind’s AlphaGo beat human Go champions. And enterprises in all industries have begun deploying AI tools to help them analyze their data and become more successful.

Perhaps most significant to today’s generative AI landscape, in 2017, Google released a research paper that first identified a neural network architecture concept called the Transformer. The transformer has since become one of the foundational technologies for developing generative AI models.

2020s

AI is beginning to evolve past narrow and limited functions into more advanced implementations, some of which are accessible to the general public; indeed, this decade seems to place more focus on AI democratization than ever before. The early years of this decade have seen the rise of generative AI, with more complex models created for enterprise users and simplified, low-cost versions available to all users. 

The greatest and most popular advancements of AI took off in late 2022 when OpenAI first launched its ChatGPT chatbot and large language model (LLM). Many competitors and similar models have since emerged to support text, code, audio, video, image, and synthetic data generation requirements.

Beyond content generation in its various forms, AI advancements of the 2020s include AI-powered search and virtual assistants in web browsers and various business applications, AI-powered medical and pharmaceutical research, and more advanced instances of AI-powered computer vision for AR, VR, and XR experiences. Increasingly, AI is being regulated and its ethics and environmental impact are being discussed.

Also see: The History of Artificial Intelligence 

AI Use Cases: What Can AI Do?

The possible AI use cases and applications for artificial intelligence are nearly limitless. Some of today’s most common AI use cases include the following:

Content generation

Generative AI models are being used to generate content in a variety of formats: not just text but also code, synthetic data, audio and music, images, video, and voice. Content generation models are currently applied to a variety of industries and use cases, including marketing and sales, customer service, employee coaching, cybersecurity, computer vision, healthcare and pharmaceuticals, entertainment and gaming, and legal and government.

More on this topic: Generative AI Examples

Recommendation engines

Whether you’re shopping for a new sweater, looking for a movie to watch, scrolling through social media, or trying to find true love, you’re likely to encounter an AI-based algorithm that makes suggestions. Most recommendation engines use machine learning models to compare your characteristics and historical behavior to people around you. The models can be very good at identifying preferences even when users aren’t aware of those preferences themselves.

Natural language processing

Natural language processing (NLP) is a broad category of AI that encompasses speech-to-text, text-to-speech, keyword identification, information extraction, translation, and language generation. It allows humans and computers to interact through ordinary human language (audio or typed), rather than through programming languages. Because many NLP tools incorporate machine learning capabilities, they tend to improve over time.

Sentiment analysis

AI can not only understand human language, but it can also identify the emotions underpinning human conversation. For example, AI can analyze thousands of tech support conversations or social media interactions and identify which customers are experiencing strong positive or negative emotions. This type of analysis allows customer support teams to focus on customers that might be at risk of defecting and/or extremely enthusiastic supporters who could become advocates for the brand.

Voice synthesis and assistance

Many of us interact with Siri, Alexa, Cortana, or Google on a daily basis. While we often take these assistants for granted, they incorporate advanced AI techniques, including natural language processing and machine learning. Several new generative AI solutions offer voice synthesis and assistance as well.

Fraud prevention

Financial services companies and retailers often use highly advanced machine learning techniques to identify fraudulent transactions. They look for patterns in financial data, and when a transaction looks abnormal or fits a known pattern of fraud, they issue alerts that can stop or mitigate criminal activity.

Image recognition

Many of us use AI-based facial recognition to unlock our phones. This kind of AI also enables autonomous vehicles and automates processing for many health-related scans and tests.

Predictive maintenance

Many industries like manufacturing, oil and gas, transportation, and energy rely heavily on machinery, and when that machinery experiences downtime, it can be extremely costly. Firms are now using a combination of object recognition and machine learning techniques to identify in advance when equipment is likely to break down so they can schedule maintenance at a time that minimizes disruptions.

Predictive and prescriptive analytics

Predictive algorithms can analyze just about any kind of business data and use that as the basis for forecasting likely future events. Prescriptive analytics, which is still in its infancy, goes a step further and not only makes a forecast but also offers recommendations as to what organizations should do to prepare for likely future events. These AI-powered approaches to analytics are used across a variety of industries but are particularly gaining steam in quote-based industries like insurance.

Autonomous vehicles

Most vehicles in production today have some autonomous features, such as parking assistance, lane centering, and adaptive cruise. And while they are still expensive and relatively rare, fully autonomous vehicles are already on the road, and the AI technology that powers them is getting better and less expensive every day.

Robotics

Industrial robots were one of the earliest implementations of AI, and they continue to be an important part of the AI market. Consumer robots, such as robot vacuum cleaners, bartenders, and lawnmowers, are becoming increasingly commonplace.

Of course, these are just some of the more widely known use cases for AI. AI technology is seeping into daily life in so many ways that we often aren’t fully aware of.

AIOps

AIOps — artificial intelligence for IT operations — is increasingly being used to simplify workflows and workloads for skilled tech workers. AI can be used to complete tasks related to service and performance management and data management and analysis.

Also see: Best Machine Learning Platforms 

Pros and Cons of Using AI

AI is permeating every corner of the business and home, but, much like with any other new and fast-changing technology, artificial intelligence presents both considerable pros and cons.

Pros

• High levels of accuracy: Simplifies workflows and reduces the potential for human error.
• Increases employee availability: Takes repetitive task loads off human workers, allowing them to focus on more meaningful tasks.
• Advanced content generation capabilities: Fast and affordable content generation and data analysis are possible with many AI models.
• New research and discoveries powered by AI-driven analysis: AI developments are leading to sophisticated analysis advancements in fields like medicine and pharmaceuticals.

Cons

• Often expensive and power-hungry: High costs and energy requirements are often part of running AI models.
• User privacy and security shortcomings: User privacy and security concerns are paramount, especially with how much data is required to train AI models; with the latest generative AI models, there’s also concern that the models will learn from, retain, and share user inputs without authorization.
• Workforce disruption: Advanced AI tools could potentially take jobs away from human workers.
• No sense of self: AI has no self-awareness or self-driven creativity; everything is programmed and can lead to bias or inappropriate/dangerous outputs.

On a related topic: The AI Market: An Overview

The Limitations of AI

AI is limited both by the data it’s trained with and the environment in which it’s operating. Here are a few examples of the limitations of AI:

• AI bias: If training data is not robust, accurate, and varied, the model can suffer from inaccurate or partial outputs. AI bias is a major concern, as machines that are trained on biased data may not serve the needs of more diverse populations and use cases.
• No emotions or creativity: AI continues to grow in its conversational and creative capabilities, but it is still an algorithmic model that does not “think” but rather operates on its training. As such, AI solutions cannot fully replace the emotional intelligence and sparks of creativity that humans have.
• Limited recall and contextual understanding: Although some of the latest generative AI models and other AI models can pull from their recent history, many AI tools can only handle inputs without considering any additional context while generating outputs.
• Limited timeliness: Not all AI models have real-time access to the internet and other resources with updated information. They may generate inaccurate responses or fail to learn from recent mistakes as a result.
• Compute power requirements: Most AI models require significant compute power, which can be expensive and energy-intensive to implement. That is why many AI models are limited to large enterprises that have the resources to run this kind of AI.

The Importance of AI Ethics

Ai ethics have been discussed in a more theoretical sense for many years, but especially as AI has become more mainstream and capable, AI ethics discussions have become more important than ever before. AI ethics is incredibly important to the long-term health and development of AI because ethical issues with AI can cause businesses to lose customers, reputation, legal battles, and money. In some cases, unethical AI instances could even lead to the loss of human life.

With a strong ethical AI framework in place, AI companies and users can expect the following benefits:

• Avoids harmful biases: AI ethics focuses heavily on creating tools that work well for everyone, including users across the globe and of varying races, genders, cultural backgrounds, and disabilities.
• Protects user privacy: AI requires massive amounts of data to run successfully, and sometimes, that data encroaches upon personal privacy. AI ethics train models to more carefully handle user inputs, but also their payment information, their image/identity, etc.
• Encourages responsible environmental impact: Many AI models use a lot of energy, which is already having negative consequences on the environment. Some of the foremost AI companies in the world are working to incorporate responsible energy consumption and other environmental considerations into their AI ethics.

• Increases human safety features: AI ethics encourage AI developers to create tools that put user and human safety at the forefront; autonomous vehicles, for example, must be tested and vetted thoroughly before they can be operated by humans or driven near pedestrians.

More on this topic: AI Ethics: An Overview

 The Future of AI

So what does the future of AI look like? Clearly, AI is already reshaping consumer and business markets, but it has a ways to go before it truly matches human knowledge and capabilities.

The technology that powers AI continues to progress at a steady rate. Future advances like quantum computing may eventually enable major new innovations, but in the near term, it seems likely that the technology itself will continue along a predictable path of constant improvement.

What’s less clear is how humans will adapt to AI. Many early AI implementations have run into major challenges. In some cases, the data used to train models has allowed bias to infect AI systems, rendering them unusable.

In many other cases, businesses have not always seen the financial results they hoped for after deploying AI. The technology may be mature, but the business processes surrounding it are not. Therefore, enterprise AI’s future will rely heavily upon the investments businesses make in the technology.

“Successful AI business outcomes will depend on the careful selection of use cases,” said Alys Woodward, senior director analyst at Gartner. “Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders.”

Finally, and perhaps most significantly, there have been mixed reactions from the general public when it comes to artificial intelligence developments. While many users are excited about new AI tools like generative AI models, others are nervous about losing their jobs or their personal information to the technology. Others are concerned about the future implications of tools that are only growing “smarter” and more capable.

High levels of adoption have propelled certain forms of AI forward, while others have languished in obscurity. In a very real sense, the future of AI may be more about people than about machines.

Also see: The Future of Artificial Intelligence

Artificial Intelligence: Additional Resources

In an AI market that’s constantly changing, it can be difficult to keep up with the latest news and trends. We’ve gathered several of our top resources in one place to help you stay current on artificial intelligence:

Top Resource: 100+ Top AI Companies

Also see: 

• • The AI Market: An Overview 
  • Cloud and AI Combined: Revolutionizing Tech
  • Guide to Algorithms in AI
  • Generative AI Startups in 2023
  • Generative AI Companies: Top 12 Leaders
  • Top 9 Generative AI Applications and Tools
  • Generative AI Landscape: Current and Future Trends
  • Generative AI and Cybersecurity: Advantages and Challenges
  • The Benefits of Generative AI
  • AI Courses: Learn AI With These Top 10 Courses
  • Top 10 AI Detector Tools for 2023
  • AI Careers: How to Build a Career in AI
  • ChatGPT vs. Watson Assistant
  • ChatGPT vs. Google Bard: Generative AI Comparison

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