How to Get Your Money’s Worth on Workplace AI Tools

Critics and skeptics of artificial intelligence technologies have repeatedly denounced the rising buzz the platforms have generated over the past few years, often deriding it as unfounded hype that ignores apps’ current productivity limitations. Now, a new study from MIT largely supports those doubters, finding that a whopping 95 percent of businesses that have adopted AI have thus far gotten zero return on their investment. That was the headline takeaway from a report by MIT Media Lab’s Project NANDA, which was based on survey results and face-to-face interviews with hundreds of senior U.S. business leaders and employees. Despite the study’s estimate that companies have spent $30 billion to $40 billion developing or purchasing AI platforms in the past two years alone, it said only 5 percent of those firms have reported any return on that investment. “The vast majority remain stuck with no measurable (profit or loss) impact,” it said. Similarly, only two of the eight sectors examined — technology, and media and telecom — reflected any significant changes based on the use or performance of AI. “The outcomes are so starkly divided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors, consultancies) that we call it the GenAI Divide,” the report’s authors wrote. “The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time.” Why AI is falling short Participating business executives in NANDA’s The GenAI Divide: State of AI in Business 2025 report offered two main reasons for the tech falling far short of expectations so far. On the development side, it said only 5 percent of tools designed to fulfill specific company needs or business functions ever reach production. The rest remain stranded on the shoals of ambitious ideas that never sail beyond the drawing board, despite developer promises that they’re speeding toward completion. “We’ve seen dozens of demos this year,” said one unidentified chief information officer during a NANDA interview. “Maybe one or two are genuinely useful. The rest are wrappers or science projects.” That, in turn, means many companies are instead using more generalist AI tools like ChatGPT or Copilot. While those tend to be effective in automating repetitive workplace grunt chores like research, text composition, or marketing work, they fail to generate significant increases of key metrics like productivity, customer acquisition, or profits. As a result, study respondents said most of the previous and current excitement over AI has not been matched by the revolutionary results that its boosters say it will deliver. “The hype on LinkedIn says everything has changed, but in our operations, nothing fundamental has shifted,” said one midmarket chief operating officer quoted in the study. “We’re processing some contracts faster, but that’s all that has changed.” The study identified two additional divides in AI use by businesses. The first was that more than 80 percent of organizations have tested or piloted apps, with about half of those saying those are now being used in workplaces regularly. Startups, small companies, and midmarket businesses were found to be the fastest in that transition. But the vast majority of that experimentation and integration involved platforms like ChatGPT or other general performance AI bots. While those do often help increase individual employee productivity on certain tasks, study participants said, those gains tend to plateau fairly fast because the apps can’t extend them higher. “ChatGPT’s very limitations reveal the core issue behind the GenAI Divide: it forgets context, doesn’t learn, and can’t evolve,” the study said. As a result, human employees still need to oversee the tech’s results and pursue myriad business objectives that apps can’t. But survey participants who faulted the limitations of general apps were even harsher with AI created for and tailored to their companies or specific business applications. “The same users were overwhelmingly skeptical of custom or vendor-pitched AI tools, describing them as brittle, overengineered, or misaligned with actual workflows,” the report said. “They expect systems that integrate with existing processes and improve over time. Vendors meeting these expectations are securing multi-million-dollar deployments within months.” How to make AI fit your business So how can employers adopt AI into their operations without winding up on the wrong side of the divide? For starters, the NANDA study urges companies to build their own AI platforms whenever possible. Those apps, meanwhile, should be based on their particular business needs, which should enable them to provide better outcomes than generalist tools. When necessary, employers can turn to outside providers to design solutions for their specific uses. The report’s authors also advise businesses to allow managers, and even team leaders, to decide the best ways of deploying apps to get the desired results, rather than having the tech department designate a one-size-fits-all use. Over time, executives should also base evolving AI deployment on where it is creating the most profitable gains. “The highest-performing organizations report measurable savings from reduced (business process outsoursing) spending and external agency use, particularly in back-office operations,” the authors wrote. ”Others cite improved customer retention and sales conversion through automated outreach and intelligent follow-up systems.” And finally, employers should base whatever AI platforms they ultimately assemble on tech capable of fully integrating information it acquires during use, and continually evolve and improve itself with those experiences. “Stop investing in static tools that require constant prompting, [and] start partnering with vendors who offer custom systems, and focus on workflow integration over flashy demos,” the report concludes. “The GenAI Divide is not permanent, but crossing it requires fundamentally different choices about technology, partnerships, and organizational design.” On the bright side Did researchers find any positive aspects to AI’s mega-hype and mini-results so far? Perhaps — at least for employees worried about the tech taking over their jobs. The study determined layoffs linked to AI deployment have been minimal so far, and usually concentrated in companies that have been deploying the tech most. Perhaps unsurprisingly, those firms were often found to be subcontractors handling marketing, communications, and customer service support for other businesses — outsourcing which in future employers may decide to handle in-house using their own apps. BY BRUCE CRUMLEY @BRUCEC_INC