Generative AI

Europe’s AI Act is setting the stage for a tone shift in how enterprises will experiment, deploy and monitor generative AI. 

The EU AI Act, which is expected to go into effect in May or June, will "force companies to be adults about developing and deploying this technology,” Rayid Ghani, distinguished career professor in the Machine Learning Department and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University, told CIO Dive. “There’s very little evidence that the majority of companies are doing any of this responsibly, and if they are, they can now prove it.”

Passed earlier in March, the AI Act emphasizes the use cases and corresponding risks of AI systems, separating unacceptable use cases from those that are high, medium or low risk. 

Unacceptable use cases, including biometric categorization systems, emotion recognition in the workplace and AI that manipulates human behavior, are banned. 

The international regulatory changes will require businesses exploring generative AI adoption to rigorously evaluate use cases through a risk lens. Organizations already deploying AI will require thorough audits to ensure compliance and avoid potential penalties.

The AI Act will go into effect 20 days after its publication in the EU's official journal. Businesses will have to comply with most rules by 2026, while rules requiring compliance with bans on prohibited practices and obligations for providers of general-purpose AI models will apply after six or 12 months.  

Organizations that introduce or operate AI systems in the EU market, regardless of their home base, are subject to the rules.

It’s not the first time U.S. organizations were disrupted by overseas rules on tech. The EU's General Data Protection Regulation, which took effect in 2018, set the standard for how businesses treat personal data, even if they operate outside of Europe. 

Experts expect the AI Act to have a similar international influence. “We know that a lot of regulators in the U.S., Brazil and other countries have looked at the GDPR and made their own versions,” said Enza Iannopollo, principal analyst at Forrester. 

There was an appetite for regulation then, and there’s an arguably larger appetite for regulating AI now, according to Iannopollo. 

If U.S. organizations work to meet the EU’s AI Act requirements, the hope is that tech leaders will likely find themselves on par with or close to compliance with regulatory mandates down the line, Iannopollo said. 

Hunger for regulation

While GDPR is a model, it's not a perfect comparison. 

“Regulating AI will be much more difficult than it has been regulating personal data, which also is very difficult, by the way,” Iannopollo said. 

The rollout of GDPR wasn’t necessarily pretty, either, and many companies struggled to meet compliance targets before audits began. 

Even companies that met the standards felt unprepared. More than two-thirds of businesses feared they couldn’t sustain compliance, according to a 2020 Tanium report, which was released nearly two years after GDPR took effect.

Some critics have also pointed out that fines don’t always curb the behavior of wealthy organizations. 

“Europeans are very good at writing the rules, but they are not very good at enforcing the rules and I think that is true, especially when it comes to the size of the fines,” Iannopollo said. 

The AI Act will levy fines ranging from 7.5 million to 35 million euros ($8.2 million to $38 million) for non-compliant organizations. To adequately enforce the rules, the EU will need to create different entities at the EU and member-state level, Iannonpollo said. 

“There is a real challenge in how long it will take for these networks to appear and be able to work,” Iannopollo. 

EU compliance officers will need time to cultivate AI skills, a snag that could slow down implementation.

The AI talent gap is widespread, and while generative AI jobs aren’t common, related postings are proliferating. 

U.S. regulators have already acknowledged a need for upskilling to craft rules for the new technology. Congressional leaders held insight forums and subcommittee hearings to learn about the potential impact of AI and explore policy recommendations from stakeholders and tech CEOs

What does it all mean for CIOs?

Though the U.S. has tried to manage the rapid onset of AI with a targeted executive order, regulatory frameworks and efforts to deter businesses from overpromising and under delivering on AI, it will fall to Europe to kick-start AI enforcement. 

CIOs can start prepping their organization for the AI Act’s enforcement by getting a clear sense of what tools employees are using and for what purpose, Ghani said. 

Amassing a tool inventory and identifying specific use cases will help organizations classify risk.

In some cases, seemingly low-risk use cases can actually be much more complex, Iannonpollo said, especially when tech leaders start to consider how much personal data is actually being exposed. 

Some organizations are already thinking about AI and generative AI deployment with a use-case-focused lens rather than governing with a one-size-fits-all strategy. 

Venkat Achanta, EVP, CTO and chief data and analytics officer at TransUnion, is bullish about what AI can do for the business.

TransUnion unveiled its AI and ML platform OneTru earlier in March. Sitting at the core of the business, the platform provides a single environment for employees to solve complex data issues, improve identity resolution and enhance collaboration among teams. 

But that doesn’t mean the company is embedding generative AI into every business function, department or solution. Careful consideration for the use case is vital.

"We don't use the same techniques in credit because it has to be very explainable," Achanta said. "We use some explainable ML techniques, but we're not comfortable with a black box or generative AI ... whereas, in fraud, you can go after an objective function, even if it is less explainable.”

American Honda was quick to grasp the business potential of generative AI.

Early last year, as the LLM goldrush gathered momentum, the automotive company outlined a five-pronged strategy to bring the organization’s innovation engine up to speed, test drive off-the-shelf models and identify ways to embed the technology enterprisewide.

“In January of 2023, when Microsoft made their huge announcement of investment in OpenAI, that was really an attention getter for me,” Bob Brizendine, VP of IT at American Honda, said during a CIO Dive panel. “It made this extremely real in terms of the capability that’s there.”

Brizendine also recognized the manifold challenges ahead, from navigating model performance issues to upskilling workforce throughout the company.

“The breadth of use cases has increased exponentially,” as has the number of users, Brizendine said.

The road to AI adoption leads directly through the heart of an organization’s data estate. To drive value and unlock the technology’s transformative potential, IT must first clear clean pathways to its data stores, erect robust guardrails and install reliability checkpoints.

Brizendine said he was particularly wary of predictive degradation known as model drift and of users being caught off guard by the linguistic eloquence of LLM chatbots.

With that in mind, American Honda made data central to its broader AI strategy.

“We've given data a promotion in the company,” said Brizendine. “We've risen it to a top-level position in the IT organization … putting it on the same plane as our chief information security officer or our chief privacy officer.”

Democratizing AI

Companies like American Honda expect big gains from the processing power and speed LLMs afford. But broad accessibility is one of the more immediate changes wrought by ChatGPT and its natural language processing compatriots.

Brizendine described the democratization of AI capabilities as “a watershed moment in almost every facet of both our AI and our data initiatives.”

While analytics were already baked into the business at American Honda, the tools were mainly in the hands of data scientists.

Now, the company is embracing an “AI for all” approach, according to Brizendine.

“The big initiative is going to be training our entire workforce on practical application of these capabilities,” Brizendine said. “We think that's going to yield the highest overall value for the company as we do prepare for the digitally enabled workforce.”

The goal has put additional strain on IT, which still has cloud deployments, application modernization and other technologies to attend to.

“There are a few areas where our previous approach of maturing data quality simply cannot keep pace with this AI-for-all mantra that we're promoting,” Brizendine said.

Brizendine estimates his team spends roughly 60% of its time on routine work and, if they’re lucky, the other 40% on innovation.

“We are not a startup company,” Brizendine said. “We have a lot of legacy cost. We have a lot of legacy systems that are at play. So previously, the complexities of our data models, or even the inconsistencies in our data quality, these were largely unseen from the broader workforce.”

Unlocking unstructured data

Legacy companies like banks and insurers carry the burden of technical debt even as they migrate to cloud. But they are also repositories for troves of largely untapped data locked away in manuals, contracts, memos and other unstructured formats.

Salesforce and Snowflake are two enterprise software vendors that have targeted solutions at unlocking unstructured data. American Honda is one company that’s cognizant of the value.

“Some of the best sources of high-quality data for generative AI and specifically the large language models are not found necessarily in your structured data, but they're in what we would have historically considered to be our unstructured datasets,” Brizendine said.

American Honda has opened up its owners manuals, policy and procedure documents and other sources of unstructured but well curated proprietary data to LLMs for ingestion. The company is also leaning on its technology and cloud providers to connect models to data sources.

“We're taking the AI to the data,” Brizendine said. “If the data is cloud-based data on Amazon, we're going to utilize a lot of AWS capabilities. If it's Azure-based we’re going to use a lot of the Microsoft-based capabilities for some of these more advanced models.”

As with cloud and enterprise modernization more broadly, preparing unending streams of data for model consumption is not a one-and-done endeavor — it’s part of the day-to-day management of AI operations.

“Our data foundation currently is, and it may forever be, a continuous work in process,” Brizendine said.

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Generative AI helps companies reduce costs, create workplace efficiencies and improve the customer experience. How do they take the next step of putting their AI strategy into operation?

No longer simply a technology, artificial intelligence (AI) has quickly evolved into a culture expectation. Regardless of the industry or size of a company, employees and customers now expect organizations to use AI throughout their products, processes and interactions. A Forbes Advisor survey found 64% of business owners believe AI will improve customer relationships. 

Companies across the spectrum have spent the past 18 months creating a strategy for using generative AI to reduce costs, create workplace efficiencies and improve customer experience.  These organizations have the budget needed to invest in AI technology. However, many are struggling to take the final and critical step: putting their strategy into operation. 

Why does the gap between strategy and operation exist?

Many organizations make the common mistake of viewing AI as being comprised of one bucket — the large language models, such as Chat GPT and Bing. However, AI includes two additional buckets: private foundational models and workflow/processes. Companies that overlook these are often unable to identify the best use cases to transform the organization. Without the right use cases, they are unable to take the important step from strategy to operation.

When companies don’t bridge that gap, they unnecessarily spend significant money that could be saved through AI use cases. Additionally, organizations not embracing AI are not going to disrupt their business model with new ways of growing revenue. As their competitors successfully bridge the gap, those organizations stuck in strategy mode will quickly fall behind — and it’s not likely that they will be able to catch up. 

Best practices for bridging the gap

After working with clients of all sizes and industries, we’ve developed core best practices that help organizations successfully move into the operations phase of using AI to transform their business. 

• Create a prioritization framework: The first step is to determine what outcomes the organization wants from AI. Are you looking for cost savings? Incentives? To improve customer experience? Disrupt your business model? Generate revenue? By establishing a list of desired outcomes, the organization then can create a prioritization framework. 
• Evaluate use cases: Using the framework, the organization can review potential use cases to determine the best projects to deploy. C-level leaders can then use their understanding of their business, without needing extensive knowledge of AI, to decide where they need to focus the company’s resources to help solve their most pressing business problems. 
• Evaluate the data state: The results of an AI project largely depend on the quality and structure of the organization’s data. By getting the organization’s data ready in terms of data management and processes, you can create a data model. The team should make the data context-aware for industry knowledge as it relates to the relationship with the foundational model. Additional access rights to specific data for users may also need to be provided.
• Work with model orchestration: By using a line chain to create more than one large language model, the team can then orchestrate each model, such as for prompt engineering or multi-agents. At this point in the process, the team also addresses the relevancy and validation of the model. 
• Consider scalability: The true benefits of AI are seen when it’s used across an organization to disrupt multiple processes. Throughout the process of moving from strategy to operations, scalability must stay front of mind. For example, if you are using an AI-dependent use case, then a high volume of calls to the API increases the costs. 

Preparing for the future of AI

Each time a conversation regarding AI begins, the biggest concern typically revolves around AI replacing humans. As we’ve moved into the adoption phase of AI, it’s increasingly clear that this fear will not become a reality. Humans will always have a significant role in all aspects of business. AI simply cannot replace humans, even on a small scale. However, the humans who use AI are apt to replace the humans who do not embrace the AI technology. 

Rather than replacing humans, AI provides the innovation and ability to process large volumes of data so it can be used to successfully disrupt current and long-standing business models. However, these results only come to reality if companies focus on moving from conceptual ideas to concrete utilization. By bridging this gap, companies can transform their business by reducing costs, creating workplace efficiencies and improving the customer experience

The AI craze of 2023 featured a vendor-led, hype-filled cycle of ambitious technology announcements. This year, regulators will look more closely at those claims.

The U.S. Securities and Exchange Commission Chair Gary Gensler cautioned companies about exaggerating or misstating how they use AI in their businesses at an event last month, echoing other enforcement officials’ warnings from earlier last year. 

Keeping AI claims in check is expected to be a continued focus for regulatory agencies this year, according to analysts, and it’s something CIOs will have to keep an eye on as well.

AI washing describes marketing efforts in which vendors make false or misleading claims about a solution's AI capabilities.

“Whatever it can or can’t do, AI is important, and so are the claims you make about it,” said Michael Atleson, attorney at The Federal Trade Commission division of advertising practices, in a blog post last February. “You don’t need a machine to predict what the FTC might do when those claims are unsupported.”

The potential risks of marketing gimmicks require technology leaders to press vendors on the specifics of their tech, ensuring generative AI tools work in the way they’re intended. Vendors can’t hide behind the black-box nature of the technology. Instead, they should provide adequate proof of their claims. 

“When we’re talking about a company, like an advisor or broker-dealer or regulated securities intermediary of some type, the regulators are really unsympathetic to the idea that there’s a black box,” said Amy Caiazza, partner, fintech and financial services at Wilson Sonsini Goodrich & Rosati. 

But vendors aren’t alone in bearing the responsibility. Businesses are also accountable for the solutions they deploy. 

“If a business is really dependent on a third party company that is AI washing, it could have potentially ancillary responsibility for that, because it’s the company’s duty to understand what that risk might be and to disclose it in filings,” said Caiazza. 

The FTC banned Rite Aid from using AI-based facial recognition technology for surveillance for five years as part of a court case settlement announced in December. The agency’s lengthy complaint raised Rite Aid’s obligation to ensure third-party service providers had appropriate safeguards, noting the company failed to obtain adequate documentation of vendor claims. 

The AI solution also didn’t work as intended. Rite Aid collected tens of thousands of images of individuals to create a database of people the company believed engaged or attempted to engage in criminal activity at store locations, according to the FTC. The system generated thousands of false-positive matches that disproportionately impacted Black and Asian communities.

“If you say your AI is bias-free and you don’t have substantiation for that statement, that could be a deceptive practice,” said Maneesha Mithal, partner, privacy and cybersecurity at Wilson Sonsini Goodrich & Rosati. “Companies run the risk of regulatory action and reputational harm in the marketplace.”

While Rite Aid addressed the allegations in a statement saying the tech was piloted in a limited number of stores and deployment was stopped three years ago, the settlement demonstrates the FTC’s desire to regulate unsubstantiated AI claims and subsequent impacts.

“Regulators follow trends in the marketplace, and because of the explosion of AI, regulators have turned their attention,” Mithal told CIO Dive. “I think there’s some feeling among U.S. regulators that they need to keep up.”

Increased regulatory interest comes as employee generative AI use at work grows and technology leaders feel the weight of turning hype into clear deliverables.

“While leaders may be under immense pressure to quickly implement AI solutions, they must prioritize the due diligence of ensuring these tools deliver what their business needs,” Juniper Networks SVP and CIO Sharon Mandell said in an email. “Given how AI technologies work, and how dependent they are on context and data, one should seriously consider proofs of concept with clearly articulated outcomes in mind, preferably those that are measurable and quantifiable.”

Enterprise armor for AI washing

Enterprise leaders hoping to protect their business against AI washing can look to their procurement practices

“AI is one of those things that when it’s done well can be so transformational and when done poorly can be very damaging,” Bret Greenstein, data and analytics partner and generative AI leader at PwC said. “Because of that, IT leaders need a fundamental education when they’re making these decisions, so they’re not just buying it because of the marketing and the hype.”

Asking the right questions, consistent monitoring of solutions and fact-checking AI claims are only possible with the right expertise.  

If CIOs rely on procurement teams, leaders need to ensure that teams have the necessary skills. The process to vet AI solutions varies from other technologies, such as SaaS applications, and those in charge of securing a solution have to ask more questions about data privacy, copyright liability and model limitations. 

“If you don’t live and breathe in the AI space, you could miss those things,” Greenstein said. “We always tell people: 'This is one of those times to never go alone.' Always go with someone who’s been there, people who’ve done it before.”

Enterprises must be aware of red flags during vendor discussions, from avoiding questions to failing to provide adequate proof of claims. However, other caution signs are more veiled. 

“Consider their history with technology and AI,” Maxime Vermeir, senior director of AI strategy at ABBYY, said in an email. 

Vermeir said companies must look closely at how novel a provider's interest in AI is, and whether it has conveniently aligned with the rapid rise of AI popularity over the last 18 months.

“Also examine the logic behind their use of AI," he said. "An unclear use case is a strong indication that the company is just as confused about how AI fits within their service as you are.”

Due diligence is not a one-step or one-time process. The AI market is fresh with new solutions emerging daily, requiring technology leaders to keep a pulse on what’s changing. But with new versions of models, improvement is not always guaranteed. CIOs have to watch the evolving market, while also keeping an eye on solutions they’ve already vetted. 

“You can’t say something on January 1, and assume it’s still correct on June 1,” Caiazza said. 

Greenstein recommends enterprises assess model behavior monthly, checking performance by use case.

With the list of things to keep track of expanding, tech leaders may find it beneficial to delegate some of the responsibilities to skilled employees to ensure nothing slips through the cracks. 

“Eventually, it’ll be automated, but, for now, people have to watch and validate and revalidate that it’s performing where you want it to,” Greenstein said.

As enterprises move forward on generative AI, CIOs should know that even the latest models available today will quickly become outdated.

By 2028, more than half of enterprises that built custom large language models will abandon initiatives due to costs, complexity and technical debt, according to Gartner research. 

“The potential for technical debt is really high for companies that move fast,” Rita Sallam, distinguished VP analyst at Gartner, said. New techniques and models emerge to lower the cost and improve the accuracy nearly daily, she said. 

There’s acute technical debt potential for enterprises that have started from square one, but even those that lean on vendor solutions should prioritize being nimble. 

As teams build applications with the models of today, CIOs must pick architectures that lend themselves to quick API updates as new ones emerge, Sallam said. 

OpenAI pioneered the enterprise generative AI conversation with GPT-3.5, which powered ChatGPT as the tool launched in November 2022. Less than six months later, the startup debuted its next model, GPT-4. By January, OpenAI previewed an updated GPT-4 Turbo model. The company is reportedly working on its next iteration, Q*. 

OpenAI isn't the only provider moving quickly to the next model release. Anthropic rolled out its generative AI chatbot Claude in March 2023 with its first-generation model set. The startup is now on its third generation model set with Claude 3, which it released this month. 

“Not only are there thousands of models right now, but there are tens of thousands, hundreds of thousands of models coming,” Marc Benioff, CEO at Salesforce, said during the company’s earnings call in February. “All the models that are available today will be obsolete 12 months from now.”

Pros and cons of moving fast

The idea of technical debt might make CIOs recoil, but early efforts to build LLMs aren’t in vain.

Fast-moving enterprises have the upper hand in upskilling initiatives and acquiring skilled talent to support AI aspirations, Sallam said. 

“When you move fast, you’re most certainly going to incur more technical debt along the way, but that’s not always a bad thing,” Sallam said. “If your AI ambition is to be a disrupter in your industry, that sort of technical debt can be viewed as a cost of investing in an option that will pay off other strategic dividends.”

While early movers are at risk of technical debt, generative AI efforts may help in that area, too. Gartner predicts that by 2027, enterprises will use generative AI tools to create appropriate replacements for legacy apps, shrinking modernization costs by 70%.

“Your generative AI tech debt will potentially be less of an acute problem than it was in the past, particularly if you’re architecting knowing that there’s going to be stuff you’re going to change,” Sallam said.

The prospect of dwindling tech debt lures enterprises with a wealth of legacy code, even in highly regulated industries. Banks are hoping generative AI can release engineers from the time drain of COBOL.

Vendors are happy to assist on the mission, releasing tools that target legacy code. IBM’s watsonX can translate COBOL into Java, a capability released last summer. Accenture created a tool using GPT-4 and a vector database to help developers with COBOL technical and functional documentation to accelerate modernization. 

Balancing the organizations’ AI goals with risk tolerance will guide next steps, Sallam said. Using AI for coding, content generation or productivity via vendor solutions is relatively easy to procure and deploy, offering a lower risk for tech debt for enterprises.

But there’s a catch. “The competitive moat is, if you have any at all, very short-lived,” Sallam said.

Kraft Heinz is building an internal generative AI tool for its employees, the company confirmed to CIO Dive. Helen Davis, SVP, head of North America operations at the company, spoke about the development of KraftGPT, the company’s colloquial name for the tool, initially at a conference earlier this month. 

“We’re currently in the development phase for the app, working through things like language translation tables for seamless communication with and understanding by the platform,” Davis said in an email. “We do already have an internal website platform that is up and running as well as user groups testing and training the platform.” 

The company’s goal is to use the technology to help gather real-time insights and information across its supply chain to inform employees while making strategic decisions, Davis said. 

Kraft Heinz is in pursuit of a familiar goal associated with generative AI adoption: expand data-driven insights to employees in real time. 

The promise of boosting employee efficiency and reducing tedious tasks is alluring to tech leaders and employees. Nearly 3 in 4 workers use generative AI tools for data analysis and visualization, according to an Insight Enterprises survey.

“Anytime you can make people more productive and solve problems right away without having to go get a data scientist or some type of tech person, it’s better for the organization as a whole because those experts can now focus on the next big thing,” said David McCurdy, chief enterprise architect and CTO at Insight Enterprises.

Kraft Heinz data scientists are using Microsoft Azure OpenAI tools and internal data in conjunction with proprietary AI models, recommendation systems and risk classification systems, according to Davis. 

The company is in the early stages of testing and has no definitive timeline for the rollout. 

Kraft Heinz joins a growing group of enterprises exploring how generative AI can benefit business operations. More than 2 in 5 executives say their organization is piloting generative AI, an October Gartner survey found. Another 10% of organizations are in the production stage.

Generative AI efforts at Walmart last year centered around discovery, David Glick, Walmart’s SVP, Enterprise Business Services, told CIO Dive. But this year it’s all about execution.

Most enterprises can relate. 

The arrival of ChatGPT spurred a feeding frenzy, with businesses and consumers rushing to use the technology. But most organizations struggled to move past the generative AI experimentation stage, running into implementation obstacles that slowed progress toward achieving scalable, quantifiable benefits for the business. 

More than 2 in 5 executives say their organizations are piloting generative AI, according to an October Gartner survey. Only 10% of organizations are reaching the production stage.

Enterprises are investing heavily in employee reskilling, training, restructuring infrastructure, solution selection, experimentation and pilots tied to generative AI. Now, the pressure is on to bring ROI.

Walmart took a “diverge, then converge” approach to generative AI, Glick said. By first surveying the landscape and getting a pulse on a plethora of possible use cases, leaders then turned their attention to picking a few for a deeper dive. 

The retailer launched a generative AI-powered assistant for employees to use through its internal app in August. Since its launch, My Assistant has helped employees draft content, eased information gathering for internal help desks and sped up the process for creating financial documents for quarterly reports, Glick told CIO Dive.

“We’ve seen associates on their own use it for this kind of thing, and now we’re trying to … scale that across the organization, taking these ideas that we’ve crowdsourced and scaling up,” Glick said.

In the year ahead, the company will expand employee access to My Assistant, taking the tool global to 10 more countries, Walmart announced earlier this week. 

“What we’ll see both at Walmart and in the industry is people will pick a few things and go deep on those rather than just sort of talking about lots of different things,” Glick said. 

Identifying use cases to scale

Most enterprises approached generative AI experimentation with a wide net, creating dedicated space for employees to explore where generative AI could benefit them the most. 

Having employees solve their own problems is a big benefit of generative AI tools, according to Vincent Yates, chief data scientist at Credera and founder of the AI Global Council. 

It does, however, create a roadblock. 

“How do you actually give them the guardrails to have this thing scaled to the size it needs to scale, have the right measures in place to ensure security, privacy, data consistency and all of the other things you need to do as a CIO?” Yates said. “That will be the thing that they have to start wrestling with next year because I suspect every organization will have an explosion of these types of applications.”

As they enter the next stage of adoption, CIOs will establish clear goals and metrics to track indicators of success. 

They’re not alone. CEOs are tasking a majority of leaders from across the business with identifying the return on investment of implementing generative AI, according to an Insight Enterprise survey of more than 600 decision-makers. 

Part of the recipe for making any implementation project or initiative successful is knowing the end goal. It’s up to leadership to determine what kind of ROI the business needs from generative AI. 

Leaders are focusing on measuring customer satisfaction gains, enhancing service availability, reducing operational costs and improving productivity, according to an Insight Enterprise survey.

“What I’m really focused on internally at Insight is, how do I take something that maybe took two days to do and now can be done in five minutes and add that into the workflow,” said David McCurdy, chief enterprise architect and CTO at Insight Enterprises. 

Once businesses identify what they want to come from generative AI adoption, leaders can select solutions and identify use cases with those goals as a guiding light. 

Glick said Walmart always starts evaluations with the user experience in mind. 

“It has to pay dividends and has to make financial sense, but starting with the experience that our associates get on a day-in, day-out basis and how we can make that better is where I always start,” Glick said. “Then we can say, ‘What’s our ROI on this? Is there a cost-benefit analysis? Is this going to save us money, make us more money, or allow us to provide higher quality services to both associates and customers?’”

From strategy to action

Since generative AI tools hit the scene in late 2022, people have used these tools for research, coding, image generation and more. 

The most common way employees access generative AI is through chatbot-style tools and many organizations are deploying conversational tools across their workforce.  

PwC laid out plans for a broad internal rollout of the company’s generative AI tool ChatPwC in August. JLL gave its 103,000-member workforce access to its proprietary generative AI tool, JLL GPT, in the summer as well. 

P&G rolled out a generative AI chatbot-style tool chatPG to employees in September. And Deloitte began giving 75,000 employees access to PairD, an internal generative AI platform, for coding and researching in January. 

Zillow, another enterprise early and eager to embrace AI, experimented with off-the-shelf solutions and built capabilities in-house early last year. The experiments resulted in the broad implementation of an internal conversational generative AI tool using OpenAI’s technology, which is open to all employees, and a code generation tool for its developers. 

 “As a leader, one of the top things that I’m thinking of is how do we improve our developer experience and improve our productivity,” Lakshmi Dixit, VP of tech engineering and operations at Zillow. “There’s a lot of tools that are coming out, and our engineering team is actively exploring, but we also want to make sure it seamlessly integrates with our Zillow ecosystem.”

Zillow’s ZG Chat is open to all employees with use cases ranging from task prioritization to meeting scheduling and writing content. The code-generation tool has an acceptance rate close to 50%, according to Dixit.

“The key thing for us is the ability to scan our code repository, so we are definitely looking at what works best for our unique use case and how do we make sure that our employees or our developers find the most value,” Dixit said.

Walmart is expanding employee access to the company’s proprietary, internal generative AI tool, CEO and President Doug McMilon said at the Consumer Electronics Show in Las Vegas.  

In the year ahead, workers in 10 more countries will gain access to My Assistant, an internal tool Walmart employees access via the Me@Campus app. The expansion will give around 25,000 additional employees access to the tool, a spokesperson told CIO Dive.

Employees will be able to use the tool in their native language to speed up text drafts, summarize large documents and enhance creativity, Walmart said.

When Walmart first introduced My Assistant to 50,000 employees in August, the retailer joined a growing list of enterprises equipping workers with generative AI capabilities customized to internal systems. 

Now, that list is even longer, featuring more recent entrants such as P&G and Deloitte. 

Even with a multitude of solutions hitting the market, many enterprises are leaning on a central conversational assistant as the primary way to scale generative AI capabilities across the business. The versatility of these tools, and the ability to fine-tune models to internal datasets, gives businesses the flexibility and control they need.

With the expanded international rollout, around 75,000 Walmart workers across 11 countries will have access to generative AI, though, it's still a sliver of its 2.1 million employees worldwide.

One-quarter of CEOs globally expect to trim headcount by up to 5% because of generative AI's impact, according to a PwC report published. The firm surveyed 4,700 CEOs. 

The technology will likely take the greatest toll on media and entertainment companies. In that sector, nearly one-third of CEOs expect a 5% workforce reduction. By contrast, just 14% of tech CEOs expect similar cuts.

Despite the expected trims in pockets of industries, 2 in 5 CEOs say the technology's adoption will actually add to their workforce by 5% or more, according to the survey. 

The generative AI rush of 2023 is giving way to a more concerted effort around implementation this year. But companies have yet to determine the definitive impacts of generative AI to enterprise workforce needs. 

Most CEOs believe generative AI will deliver customer service improvements, PwC found. But benefits won’t come without change.

"This is the biggest transformational shift in decades and will require new skills for front-line workers, end users, AI practitioners and leaders," said Bret Greenstein, PwC partner and AI leader, in an email. 

CEOs' focus on outcomes and growth goals suggests corporate interest in investing in creating and developing an AI-savvy workforce. 

"Additionally, the CEO Survey suggests that CEOs are leaning towards a net addition of employees in some very new roles, indicating a potential increase in demand for tech talent in the market," Greenstein said.

More than half of executives say AI will save them money in 2024, and nearly three-quarters increased planned tech investments this year in search of productivity gains, according to a Boston Consulting Group report published last week. 

Despite executive enthusiasm, it's early days for generative AI adoption at scale. Just one-third of CEOs say generative AI was deployed companywide in the last year.

More than half of C-suite leaders expect AI to save them money in 2024, according to a Boston Consulting Group survey of more than 1,400 executives across 14 industries published this week. 

Of those executives, around half expect cost savings in excess of 10%, BCG found. Executives anticipate the ROI to come primarily through increased productivity in operations, customer service and IT. 

Nearly three-quarters of executives are increasing tech investments this year, up from 60% in 2023. Nine in 10 organizations plan to up spending on AI and generative AI. 

Generative AI dominated tech conversations in 2023. The talk has now translated into spending. 

Nearly 9 in 10 C-suite leaders rank AI and generative AI among their top three tech priorities. But a gap remains between aspirations related to the technology and how enterprises have been able to execute.

Training initiatives have become a key point of enterprise generative AI plans even as some fall short of reaching their upskilling goals. 

Only 6% of companies have trained more than one-quarter of employees on generative AI tools so far, BCG found. Leaders will need to brush up on AI skills as well, with more than half of organizations saying they have limited or no confidence in their executive team’s proficiency in generative AI. 

The lack of skills threatens enterprise ambitions for transformative benefits to user experience, productivity and accessibility.