By Tony Grout, Chief Product and Technology Officer at M-Files.
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The honeymoon is over for enterprise AI.
It wasn’t long ago that AI was the shiny new toy, filled with promise. But as with so many technologies before it, AI is moving along in the hype cycle. We’ve hit the point where leaders are now expecting—and demanding—that AI deliver real, measurable value.
Boards talk about it. C-level executives budget for it. And teams are under increasing pressure to use it and show how it’s delivering on its promise. But underneath the surface in many organizations, AI is still sitting on the sidelines when it comes to decisions that actually matter. There’s a good reason for that: Leaders are still on the fence about how much they trust AI.
It’s an existential question. And until it gets resolved, AI is going to struggle to meet inflated expectations. Sure, it can remain an interesting tool, useful in certain applications. But it will likely remain carefully contained across most functions.
So where do these trust issues come from? It’s actually not the models themselves: The trust issues start with the information that AI is asked to work with.
In a typical enterprise, documents live everywhere. They’re scattered across shared drives, desktops, cloud folders, Slack channels and email inboxes. Document sprawl is very, very real, which means that ownership is unclear. Versions proliferate in different forms, and context fades over time. When this happens, workers often waste more time than they’d care to admit searching, confirming and double-checking that they’re using the right documents.
When AI is introduced into an environment like this, the challenge is obvious. AI is expected to make sense of material that even humans struggle to interpret quickly. Critical context is rarely explicit. Which version of a document is final? Does this clause represent a standard position or an exception? Which project does this refer to and what other contracts for this project might affect this contract? Which approvals reflect real sign-off versus temporary workarounds? Which documents are active, outdated or retained only for audit?
If these details—all critical context—are implied rather than defined, it means that AI must infer the meaning itself based on fragments. That introduces a level of uncertainty that is guaranteed to undermine trust in AI outputs.
So many enterprise systems have changed in recent years, but documents still sit at the heart of how most companies operate. Documents are used to record agreements, outline responsibilities, capture decisions and establish institutional knowledge. Contracts define your organization’s approach to risk. Policies define the way your employees behave. Reports define your company’s performance. At the end of the day, documents are where intent lives. They provide the language and the reason that people (and AI systems) rely on to inform their decisions.
This begs the question: If documents play such a central role, why are they treated as background infrastructure? In many enterprises, documents are stored and governed inconsistently, and are only scrutinized reactively when problems arise, as opposed to being proactively maintained. Over time, they lose the context that gives them meaning, with teams relying on memory to fill gaps. That’s a non-starter if you want AI to be effective because it forces AI to guess. That introduces uncertainty and ultimately makes the output harder to trust.
Context is the secret sauce that helps alleviate that uncertainty by showing how the document fits into the business. Context is what links content to customers, projects, processes and decisions, reflecting where a document sits within a workflow and how the information within it is meant to be used. It gives AI a much-needed frame of reference.
Context also shapes sentiment around AI outputs. When results can be traced back to specific documents and understood within existing decision processes, leaders are more likely to incorporate them into approvals, prioritization and risk decisions. When AI is pulling from the same information people already rely on, teams are far more likely to trust the results.
Many AI initiatives stall out because initial results are underwhelming. This happens because so much context lives in employees’ heads. Employees understand how different documents connect because they’ve worked with them over time. They develop a sophisticated and nuanced understanding of which versions matter—something that’s incredibly hard for AI to replicate. But they’re not writing this information down, which makes it inaccessible to AI because it cannot work off anything that’s not captured explicitly.
This is one of the reasons that scaling AI beyond isolated use cases has become so challenging. It’s not that hard to achieve early success in AI with a narrowly scoped task where context is applied, but broader adoption and real scale would require AI to navigate within the morass of operational complexity. When you ask AI to swim in the deep end like that, you’ll notice gaps in document context will surface quickly. That’s when confidence starts to waver.
For leaders shaping AI strategies over the next year, the biggest decisions are not about which tools or features to deploy; they’re about information architecture. Does the information fueling my AI deployment reflect how my business actually works? That’s the important question. That clarity will be what determines whether AI gets deployed for small projects or if it’s something teams can rely on when decisions carry real weight.
The simple truth is that AI will earn its place when leaders feel comfortable standing behind the results it produces. That confidence is built long before an answer appears on a screen; it’s built through documents that retain their meaning, relationships and relevance over time. As organizations push AI deeper into their operations, trust will decide whether it becomes a dependable part of decision-making or stays at arm’s length.
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