Every company is racing to put an AI agent in every employee’s hands. It feels like progress. It feels like amplification. Give each person their own copilot, and the whole org blasts off to the moon right?
Here’s the counterintuitive part: although AI is great at breaking down silos that have long prevented technology’s true adoption and impact… it’s often just as adept at building new ones.
We’ve spent years trying to kill the silo. Tirelessly trying to get different areas of the org speaking the same language, working from the same systems, aiming at the same goals. Now we’re handing everyone their own license to build, and calling it alignment. Without a holistic strategy, it’s the opposite. When everyone creates their own solution, in their own way, trained on their own context, you don’t get one company thinking faster. You get individuals pulling in different directions, with different definitions of sucess, faster.
Sound familiar?
Ten prompts, ten proposals, zero consistency
Ask ten salespeople to use AI to build a proposal for the same deal type, and you’ll get ten different documents. Different structure, different tone, different look, different messaging. Each one might be ‘good’, some might even get the job done, but none of them are the company’s proposal — they’re individual answers wearing company letterhead (maybe).
Multiply that across every function at the speed of agentic. Support writes ten different versions of the same playbook. Marketing ships ten different campaigns with different messaging (and not on purpose). Nobody collaborated on it, no one created a consistent process, they simply each had a chat window open. Fire, ready, aim. That’s not scale. That’s noise with slightly better grammar.
The knowledge exhaust lives in the chat, not the company
Every prompt someone writes, the context they include, the tweaks they make, and the version that finally gets the accurate result – all represent incredibly valuable institutional knowledge. The problem is, most of it lives in one person’s chat history where no one else can benefit from it. In this structure a unified purpose driven model isn’t being coached, trained and captured. A single instance purchased on an employee’s personal credit card doesn’t become the I.P. of the company – and when that person leaves, it leaves with them. We spent over a decade building enterprise grade tech stacks so that companies could create living repositories for knowledge and avoid siloed data dependencies. Individual AI autonomy is akin to winning a battle, only to lose the war.
So what does ‘winning’ actually look like?
If the root problem is simplified down to ‘everyone building their own answer’, the fix looks like building the company’s answer once, and giving everyone access to it. In practice, that looks like three things.
- A library of approved AI skills. Not a shared folder full of random prompts, but a vetted, version-controlled set of tools the company trusts employees to use, from proposal builders and call summaries to pricing guidance and objection handling. Built once by people who know what good looks like, tested against real outcomes, and published for every employee to use, the same way you’d publish a template or a playbook. When someone finds a better way to do something, it gets contributed back into the library, not buried in their own chat history. That’s the difference between ten people getting slightly better at their job and a company that compounds.
- A shared design and messaging library. Whether AI is drafting a proposal, an email, a presentation, or a customer response, the output should reflect the company’s voice and standards, not the individual preferences of whoever wrote the prompt. That means baking your voice, your visual identity, your positioning, and your point of view directly into the tools people use, so consistency is the default, not a review step someone has to enforce after the fact.
- Live connections into the systems that actually drive decisions. An agent that only knows what a single employee trained it on will still output a highly confident answer. On the other hand, an agent wired into your CRM, your product usage data, your finance system, and your customer history while being further refined by ingesting all employee/customer interactions will output a highly confident answer that’s also correct. That’s the unlock….comprehensive and real-time context, not asynchronous prompts from a single individual. A recommendation is only as good as the experience informing it.
Someone has to own it
None of this happens by accident, and it definitely doesn’t happen by handing out licenses and hoping for the best. Every company rolling out AI seriously needs a serious AI committee. A small, cross-functional, vision-aligned group with the authority to approve what gets built, decide what goes into the skills library, own the design and voice standards, and actually drive adoption instead of leaving it to whoever’s curious enough to figure it out on their own.
This isn’t a compliance exercise – it’s the basics of scale. There are two facets to solving any problem. Finding the solution itself, and then sharing that knowledge with others so they don’t have to spend valuable time uncovering the solution again. The committee’s job isn’t to slow things down; it’s to make sure the fast thing and the right thing are the same thing.
This is the gap we deal with, and remediate, all the time in early-stage startups In Revenue. Individual empowerment feels like momentum, it feels good, but without a company-level system underneath it you’re not building a company. You’re building a system that requires everyone to be a hero – and if that sounds like a decent solution I have unfortunate news for you… heroes are hard to find and in short supply.
If you’re a founder or operator leveraging AI today, the question worth sitting with isn’t “does everyone have access?” Instead, a more effective query to ponder is, “if ten people asked their AI the same question tomorrow, would the company like ten different answers — and who’s actually responsible for making sure that doesn’t happen?”
