As organizations rush to spin up AI projects, many IT professionals aren’t sold on the value of these early efforts.
A substantial 36% of IT professionals surveyed by Dice.com believe that the primary purpose of many AI projects they’ve worked on is to show investors, board members, or outside stakeholders that the company is doing something with AI.
To be fair, just over half of IT pros say their organizations’ AI projects are strategically important. Still, it’s a problem if any IT workers believe their companies’ IT projects are just for show, say some IT leaders.
And it’s not just an employee perception issue, says Sriram Nagaswamy, executive vice president for technology at supply chain visibility platform vendor FourKites. Nagaswamy has witnessed several organizations launching AI projects simply to impress board members or investors.
“This trend is concerning,” he says. “AI projects without clear goals or measurable outcomes are unlikely to deliver real value. They’re often poorly planned and executed, making it hard to prove their worth.”
Launching AI projects for show can lead to doubts about the technology, even though AI has great potential in supply chain management and other areas, he adds.
“This can lead to a dangerous cycle where decision-makers become skeptical of AI’s potential, reducing future investment,” Nagaswamy says. “The long-term impact is even more worrying — companies risk falling behind competitors who are implementing AI strategically. Their teams miss out on crucial learning experiences, leaving them ill-equipped to handle genuine AI deployments down the road.”
Misunderstanding AI
A huge part of the problem is a lack of understanding of AI’s capabilities, adds Matt Rosen, CEO of digital consulting firm Allata. In many cases, board members, investors, or executives push for projects that AI is ill-suited to address.
“You don’t have business leaders, or even IT leaders taking some basic AI literacy classes,” he says. “There’s some fundamental misunderstanding about what problems AI solves, and there needs to be a continuous curiosity and learning, not only from the IT professionals, but from the IT leadership and then the business executives that are expecting technology solutions to be delivered.”
In some cases, the confusion about AI may lead to inflated expectations about the capabilities of the technology, while in other cases, corporate leaders push AI projects when a much simpler solution would solve the problem, Rosen says.
“They say, ‘Go do this with AI,’ and it’s totally not a fit,” he adds. “You can probably solve that with an RPA bot, or you could probably solve that with some custom code.”
When executive or board members push for poorly planned AI projects, it can lead to several problems, including data exposure and a loss of customer trust, adds Monica Landen, CIO and CISO at risk and compliance solutions provider Diligent.
“To ensure AI is aligned with strategic goals and poised to deliver measurable impact to customers and stakeholders, executives and boards need to prioritize education around AI,” she says. “By building leadership’s understanding of AI and ethics, they can help avoid investing in low-value initiatives or fostering misleading perceptions.”
CIOs and CAIOs can help with the AI education efforts and push for the ethical deployment of the technology, Landen adds. “This empowers leaders to focus on initiatives that deliver real business value, such as improving operational efficiency, enhancing customer experiences, and driving innovation,” she adds.
Looking for inspiration
Like Landon, Allata’s Rosen has seen organizations struggle to find targeted AI projects. Many of the company’s clients have questions about what AI projects they should launch, he says.
Rosen sees a lot of experimentation without a clear sense of direction, from companies that don’t have a clear idea of what AI projects will match their business needs. The fear of missing out is real.
While there’s some value in allowing employees to experiment with AI, launching badly planned projects can lead to several problems, including unnecessary costs, a misallocation of employee resources, and employee attrition, he says.
The scattershot approach “incurs significant opportunity costs by missing out on developing AI solutions with transformative potential, and can negatively affect employee morale, leading to disengagement among professionals who seek meaningful work,” Rosen adds. “AI projects should align with genuine business objectives and focus on delivering measurable results that support long-term strategic goals.”
Rosen advises that organizations experimenting with AI start with a roadmap, including guardrails for employees and departments.
“The companies that are winning are the ones that are actually looking what we call true business cases,” Rosen says. “There’s something compelling in business value that’s going to give them a return and then really helping them figure out what is the best way to deploy that with what set of data, with what governance, with which model.”
Rosen encourages some level of AI experimentation, however, to get employees familiar with the technology and brainstorming about possible uses.
There’s also value for organizations in launching POCs, even if they don’t ultimately work out, adds Michael Umansky, CEO and co-founder of SmythOS, which assists customers with building AI agents. Some AI projects may get dumped, but the big risk for many organizations is not adopting AI, he says.
“You can’t put the toothpaste back in the tube now,” he says. “This is happening. Businesses need to embrace it.”
AI is progressing so fast that it’s difficult to keep up, he adds. Doing nothing means risking being left behind.
“I really do believe the vast majority of big and small companies are trying to figure it out,” he says. “They’re trying to reskill their people, and they’re trying to understand the data issues. They’re trying to understand where they are going to get the most uplift from this type of technology.”
