You bought the AI tools. Your team still works the old way. If that sounds familiar, the instinct is to assume you picked the wrong tools, or that you need one more. The instinct is wrong. The problem is rarely too few tools. It is too many, with nothing underneath them tying the work together.
The scale of the problem
This is not a hunch. It is the defining pattern of AI right now. McKinsey’s 2025 global survey of nearly 2,000 organizations found that 88 percent now use AI in at least one business function, yet only 7 percent have fully scaled it across the enterprise (McKinsey, 2025). Almost everyone is buying. Almost no one is scaling. The single biggest reason, in McKinsey’s own data, is that most organizations are layering AI tools on top of their existing processes rather than redesigning the work underneath, and bolting more tools onto a broken process does not fix the process. It just adds tools.
Boston Consulting Group found the most direct evidence that more is worse. In their study of a thousand companies, the laggards spread themselves across six or more AI use cases per workflow, while the leaders pursued only about three and went deeper, persistently rethinking the end-to-end workflow rather than deploying scattered tools (BCG, 2024). Twice the tools, less value. That is the pattern in one line.
Every application you already use is shipping an AI feature. A new standalone tool launches every week with a louder promise than the last. Buying more of them feels like momentum and produces the opposite, because each new tool is one more thing to learn, integrate, secure, train your team on, and eventually abandon. The pile grows, the progress does not.
What my research found underneath the tools
I studied a version of this problem for my doctorate, a decade before AI made it urgent. My 2014 research was a qualitative descriptive multiple-case study built on in-depth interviews with 19 people across adopting and resisting Fortune 500 organizations, looking at what separated leaders who adopted new collaboration technology from those who stalled. One theme that emerged was the effect of sheer range. When participants were asked about the technology available to them, the answers spanned everything from email to complex multi-use platforms, and that wide dispersion of options correlated with a lower understanding of what any of it actually did. The variety itself created confusion for many of the resisters.
But here is the part I want to be precise about, because it is easy to oversimplify. The adopters in my study were not the people with the fewest tools. They had often engaged with more technology, not less. What set them apart was not a short tool list. It was that they understood the system underneath the tools, the unified communication layer the individual pieces were meant to serve. They were not collecting software for its own sake. They grasped the thing all the software was actually for, and they could see how the pieces were supposed to connect into one workflow. The resisters saw a pile of disconnected options. The adopters saw a system.
That distinction is the whole game with AI. The teams getting real value are not the ones with the most subscriptions. They are the ones who decided what their underlying system should do first, then added the smallest number of tools that made it work. This is one of the five patterns I write about in the 5 drivers of AI adoption, and it connects directly to a second one: you cannot build a coherent system out of tools you do not understand, which is the subject of you cannot adopt what you do not understand.
Why workflow beats tools
The most important finding in the McKinsey data points the same direction. Of all the changes linked to real financial return from AI, fundamental workflow redesign ranks highest, yet only about one in five organizations using generative AI has actually redesigned any workflows (McKinsey, 2025). The other four out of five are doing exactly what does not work: adding AI on top of the way they already operate and hoping the tool carries the change by itself. It never does. BCG quantifies the fix in what it calls the 10-20-70 rule: successful AI transformation puts only 10 percent of effort into algorithms and 20 percent into technology, while 70 percent goes to people and process (BCG, 2024). The tool is the small part. The system around it is the work.
A tool is an answer to a question. If you have not defined the question, which is the workflow you want to exist, then every tool you add is an answer to nothing, and answers to nothing pile up as cost and complexity. The reason more tools makes you slower is not the tools themselves. It is that buying them lets you skip the harder work of designing the system they are supposed to serve, and that skipped work is the only thing that actually produces results.
The move
Stop adding tools. Start with the workflow you want to exist, the one where the busywork runs itself and your people do only the work that needs them. Map that system first. Then choose the smallest set of tools that makes it real, and cut everything that does not serve it. One system you understand and own beats ten tools you rent and do not. The order is the whole point: system first, tools last.
If you want to see where your own setup stands, whether you have a system or just a pile, the Omnine AI Readiness Assessment takes about three minutes and shows you the first place to focus.
References
Goodwin, M. R. (2014). A qualitative descriptive multiple-case study: Fortune 500 leaders’ social business platform adoption (Doctoral dissertation, University of Phoenix). ProQuest Dissertations Publishing (UMI No. 3648813).
McKinsey & Company. (2025). The state of AI in 2025: Agents, innovation, and transformation. McKinsey Global Survey on the state of AI.
Boston Consulting Group. (2024). Where’s the value in AI?