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ZDNET’s key takeaways

• Companies are rolling out AI, but tracking benefits is tough. 
• Over-reliance on AI and potential inaccuracies are top concerns.
• Transparency about AI intentions is key to getting buy-in.

Generative AI is on the boardroom table, and it’s taking up a lot of room. However, gen AI is still a relatively untested technological approach to business development, so companies are careful how they roll it out to their workforces. 

Almost two in five (39%) technology business leaders in a recent IEEE survey said their organizations intend to use generative AI regularly, but selectively, in the months ahead — up 20% from the same research a year ago. 

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Just over a third (35%) said they are “rapidly integrating generative AI, and expecting bottom-line results.” In addition, 91% intend to ramp up their use of agentic AI for data analysis over the coming year.

They have passed the exploration and experimental stage, and it’s time for gen AI to prove its mettle — automating workflows, improving data accuracy, and supporting decision-making, the IEEE authors contend. 

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“We’re entering a period of healthy skepticism that follows the natural progression of technology-adoption cycles,” said IEEE senior member Santhosh Sivasubraman. 

Exploiting AI effectively

Even technologically focused companies are approaching AI and gen AI with both optimism and caution. The challenge is figuring out how to fit AI into the organization in a productive way — to boost people and its processes. 

“AI assistants are now our personal productivity tool,” Carrie Rasmussen, chief digital officer at Dayforce, a human capital management platform, told ZDNET. 

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“It serves as a coach, creator, researcher, collaborator — a magnitude of things. We’re in the process of extending that platform — to the connectors through email, Outlook, SharePoint, and HubSpot.” The next wave of AI will extend to role-based technology, she added. 

There is a wide variety of tasks that business technology leaders want AI to handle, as identified in the IEEE survey: 

• Real-time cybersecurity vulnerability identification and attack prevention (47% — down 1% from prior year)
• Aiding and/or accelerating software development (39% — up 4%)
• Increasing supply chain and warehouse automation efficiencies (35% — up 2%)
• Automating customer service (32% — up 4%)
• Powering educational activities, such as customized learning, intelligent tutoring systems, and university chatbots (29% — down 10%)
• Accelerating disease mapping and drug discovery (23% — down 3%)
• Automating and/or stabilizing utility power sources (22% — down 3%)

The survey also identified where companies are struggling with gen AI. Half of the respondents flagged “over-reliance on AI and potential inaccuracies” as top concerns.  

“Projects often fail because teams assume the models are more reliable than they are,” the report’s authors stated. “The confidence with which chatbots deliver results often leads to an overestimation of their capabilities.” In many cases, simpler analytics would be enough. 

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Measurable productivity from gen AI is mixed, Rasmussen added, citing industry estimates that getting 50% of a workforce to use ChatGPT should equate to a 10% productivity boost. “I’m not sure if I buy it completely, but it’s a lofty goal,” she said. “But first, you have to define ‘What is an active user?’ Is that a weekly or daily user? We’re working on that.”    

Developing skills successfully

Another inhibitor to progress may be concerns about the skills needed in an AI-heavy world. Employees question how much of their work will be replaced by machines. 

“One of the questions I get about AI constantly is, ‘What do I tell my employees?'” said Dayforce’s Rasmussen. “They’re worried about job displacement. Leaders shouldn’t speculate; it only creates fear. The CEO makes a bold statement that creates noise and energy, but sometimes negative energy.”

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Focus on what you can control, Rasmussen advised: “It may mean rewriting a job description because AI requires you to work differently now. It’s a transformation, and new jobs are being created. We need to make sure that all of our employees are well-equipped to take those jobs when we get that transformation. Because I can’t go and hire a two-, three-, or four-year AI veteran, it’s too difficult.”

At this point, Dayforce leverages public LLMs, such as OpenAI’s ChatGPT, rather than developing its own models: “We don’t have LLMs that we’re building,” said Rasmussen. “We’re starting to talk about it. But we’re not going to have a big LLM — we would have small LLMs where certain types of machine learning are going to give us some sort of statistical advantage, such as in sales.”

Dayforce employs an OpenAI foundational model, “to build a RAG-augmented retrieval and search. I think that’s where most people are right now — they want knowledge retrieval.”   

Transparency about AI intentions is key to getting buy-in, said Rasmussen: “We want our employees to be the people who take those jobs, and help us create those jobs, which takes away fear. Focus on what you know, and what’s in front of you. Give employees the tools and the training.”

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Interestingly, AI ethics has surfaced as the top skill in demand for 2026, the IEEE survey found:

• AI ethical practices skills (44% — up 9% from prior year)
• Data analysis skills (38% — up 4%)
• Machine learning skills (34% — up 6%)
• Data modeling skills, including processing (32% — no change)
• Software development skills (32% — down 8%)

At Dayforce, preparing for gen AI means bringing in “AI champions” from across the company to evangelize the technology. “They’re the early adopters,” Rasmussen explained. 

“They’re figuring out how to apply the tools. They’re our storytellers, and the people to go to for help. And we need to ask, ‘What are the agents our employees need? What are the agents our sellers need? Can I get that agent from the tools we have? And are those tools mature?’ We are finding they’re not all ready for primetime.”