Generative AI Pilots Are Struggling to Deliver for Companies

Generative AI Pilots Are Struggling to Deliver for Companies




Betting Big, Landing Small

Companies are diving headfirst into generative AI, hoping it will revolutionize operations and boost revenue. Yet, for most, it feels like starting a race where everyone trips over the starting line. The latest report from MIT’s NANDA initiativeThe GenAI Divide: State of AI in Business 2025lays out a surprisingly sobering picture: despite all the hype, very few enterprise pilots are actually driving meaningful results.

According to the study, only about 5% of generative AI initiatives produce noticeable revenue acceleration. The other 95%? They linger in place, generating little to no impact on profits. MIT’s team reached these conclusions through a mix of methods: 150 interviews with executives, a survey of 350 employees, and a detailed look at 300 public AI deployments. The numbers reveal a sharp divide: some companies soar while others barely leave the gate.

Learning, Not Just Tools

I talked with Aditya Challapally, the lead author of the report, to unpack these findings. He pointed out something critical: success often isn’t about the AI model itself. “Some large companies’ pilots and younger startups are really excelling with generative AI,” he said. Startups run by 19 or 20yearolds, for instance, have gone from zero to $20 million in revenue in a year. Their secret? Focus. They pick one pressing problem, execute it well, and find the right partners to leverage their solutions.

Contrast that with the 95% of companies that struggle. Here, the problem isn’t regulation or poor models. It’s what MIT calls the “learning gap.” Teams and organizations simply don’t know how to adopt these tools effectively. Sure, executives might blame regulatory hurdles or model limitations, but the research points elsewhere: integration. Generic tools like ChatGPT are flexible and powerful for individuals, but they often fail to mesh with enterprise workflows. Without adaptation, these tools remain curiosities rather than revenue drivers.

Misplaced Priorities

Another pattern MIT observed is how companies allocate their AI budgets. Over half of generative AI spending goes to sales and marketing, yet the highest returns appear in backoffice functions. Automating repetitive tasks, reducing reliance on external agencies, and streamlining operations often yield far more measurable gains than flashy customerfacing projects. It’s ironic: companies chase the visible, sexy applications of AI while missing the quieter, highROI opportunities that could fundamentally change efficiency.

Buying vs. Building




When it comes to actually deploying AI, how a company adopts it matters almost as much as the tool itself. MIT found that buying AI solutions from vendors and building strong partnerships succeeds roughly 67% of the time. Trying to build proprietary systems internally? Success rates drop to about a third.

This trend is especially relevant in industries like financial services, where regulatory concerns push firms toward building inhouse AI. But MIT’s data suggests that these companies often stumble, whereas purchased solutionsdespite the allure of “customization”tend to deliver more reliable outcomes.

Challapally noted that companies are reluctant to disclose failures. “Almost everywhere we went, enterprises were trying to build their own tool,” he said, yet the data showed purchased solutions consistently outperformed internal efforts. This speaks to a broader lesson: sometimes, focusing on execution and integration matters far more than reinventing the wheel.

Empowering the Right People

Success isn’t just about tools or budgetsit’s also about who drives adoption. The report highlights that empowering line managers, not just centralized AI labs, is critical. People on the ground often see where the AI can make the most difference, and giving them agency to experiment and adjust can dramatically improve results. Selecting tools that can adapt to workflows over time, rather than rigid systems, also increases the likelihood of lasting impact.

The Human Factor

Workforce disruption is another piece of this puzzle. AI isn’t causing mass layoffsat least, not yet. Instead, companies are quietly letting positions go unfilled, particularly in customer support and administrative roles that were once outsourced. It’s a subtle shift, but it’s changing the landscape of work in a very real way.

At the same time, shadow AIunsanctioned use of tools like ChatGPTis widespread. Employees experiment in ways companies can’t always track, making it even harder to measure AI’s actual impact on productivity or profit. This shadow adoption underscores both opportunity and risk: AI can empower staff, but it can also create fragmented workflows and unpredictable results.

Looking Forward: Agentic AI

The report ends on a forwardlooking note. Some of the most advanced organizations are experimenting with what MIT calls “agentic AI”systems that can learn, remember, and act independently within defined limits. Think of it as AI with a sense of initiative: it can monitor processes, make adjustments, and even take action without constant human supervision. These pilot programs are still rare, but they hint at the next wave of enterprise AIa phase where technology could move from being a tool to being an active participant in business operations.

It’s not all doom and gloom, though. The companies that succeed tend to do three things well: they focus on one clear problem, integrate AI into workflows rather than forcing employees to adapt, and combine purchased solutions with empowered teams. The rest? They risk being left behind, despite all the hype.

So yes, generative AI has enormous potential, but realizing it isn’t as simple as flipping a switch. It requires focus, alignment, and, perhaps most importantly, a willingness to learnand sometimes failalong the way. For now, most companies are still figuring out that tricky balance.



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Source: Workday

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