Council Post: The Enterprise AI Gap: Why Personal AI Success Doesn’t Scale
Matt Waxman, Chief Product Officer at Precisely.gettyAlmost every executive I talk to has a personal AI success story. Summarizing meetings in seconds. Drafting documents in minutes. Automating work that used t...
Matt Waxman, Chief Product Officer at Precisely.
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Almost every executive I talk to has a personal AI success story. Summarizing meetings in seconds. Drafting documents in minutes. Automating work that used to take hours. The productivity gains are real, and they’re happening fast.
What’s also real is what organizations discover when they try to scale that individual experience across the enterprise.
At a CPO round table earlier this year, someone described it exactly: AI had completely changed their personal workflow, so they rolled it out to the broader organization. The experience was inconsistent, the outputs were unreliable, and people stopped using it. They couldn’t figure out why it worked so well for one person yet failed at scale.
The answer was almost always data.
When an individual uses AI to summarize a meeting or draft a document, the context is clean, specific and immediate. The person knows what they’re asking, understands the background and can quickly recognize when something is off.
When an enterprise deploys AI across thousands of users and dozens of systems, that protection disappears. The AI operates across data that was entered inconsistently, maintained irregularly and siloed across platforms that were never designed to share it. The model doesn’t know what it doesn’t know. It fills gaps with plausible-sounding outputs, and unlike a human, it doesn’t flag its own uncertainty.
This is the pattern I see playing out across most enterprise AI initiatives right now. Organizations invest in tools and assume adoption will create value. Then they discover that the tools have surfaced every latent problem in their data environment that humans were compensating for all along. A human analyst adjusts for a data anomaly intuitively. An agent acting on that same data won’t, and it won’t tell you it missed anything, either.
Enabling enterprise AI to scale requires establishing a governed, high-quality data foundation that agents can act on without silently failing or amplifying issues that already exist.
Here are three questions to ask before expanding enterprise AI:
1. Do we know where our data is wrong, and by how much? Most organizations have a vague sense that their data has quality issues. Fewer have a clear picture of where those issues are, how frequently they appear and which workflows they affect most.
2. Who’s accountable when an AI output is wrong? At the individual productivity level, the user is accountable. At enterprise scale, that accountability needs to be defined in advance, or it defaults to no one.
3. Are we measuring AI outcomes or AI activity? Tool adoption and prompt volume aren’t the same as business impact. The organizations making real progress are tying AI outputs to specific business results and building feedback loops to catch when the outputs drift.
There are no best practices for enterprise AI yet, only practices, and most organizations are still learning what works as they go.
The companies moving fastest are the ones that dealt honestly with their data foundation before they tried to automate on top of it. That honesty is what turns AI from an experiment into something the business can trust.
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