Most companies announcing an “AI transformation” are running a procurement exercise. Licenses get bought, dashboards get configured, an internal memo gets sent, and somewhere in the org chart a Head of AI gets named. Six months later, usage data tells the real story: a handful of power users, a long tail of curious employees who tried it twice, and a leadership team that still forwards screenshots instead of querying anything themselves.
The gap between buying AI and building a culture that uses it is wider than most executives are willing to acknowledge. Tools are easy. Behavior is not.
Sabeer Nelli, CEO of Zil Money, has spent the last several years navigating that gap from the inside. Zil Money has processed over $100 billion in transactions for more than one million US businesses, and much of that scale has been built without the bloated org structures that usually accompany this kind of growth. The reason, in Sabeer’s framing, is not that the company found better tools. It is that the company changed what was expected of every person using them.
The Habit Leaders Outsource
There is a quiet pattern in most enterprises: the more senior the executive, the less they personally touch the AI tools their company has purchased. Strategy decks get built about AI. Roadmaps get approved. But the actual prompting, the actual experimentation, the actual sitting-with-a-blank-cursor moment, gets delegated downward.
Sabeer does the opposite. He explores new AI tools personally, often before recommending them to anyone else. When he finds one that works, he does not send an announcement. He uses it in front of his team, in meetings, in workflows, until the behavior becomes visible.
“You cannot ask a team to live in the world of tools if you live above it,” he has told colleagues. The phrase is unceremonious, but it captures something most AI rollouts miss. Adoption is not a training problem. It is a modeling problem.
When the Founder Uses It, the Floor Moves
A useful example: when Sabeer introduced a voice-to-text AI tool into his own workflow, he did not pilot it through a department. He used it himself, in plain view, and the time savings became impossible to ignore. The tool spread through the organization without a mandate. Today it is in the hands of nearly every team at Zil Money, and the cumulative hours recovered are difficult to estimate with any precision.
The mechanism here is worth naming. Most companies treat AI rollouts as a curriculum: training sessions, certifications, internal champions. Sabeer treats them as a culture transfer. When the CEO is the first heavy user, the implicit message to the rest of the company is that this is not optional and not experimental. It is how work gets done now.
There is also a secondary effect. Because Sabeer pays for the premium tier of every tool the company adopts, employees are not working with crippled trial versions. They are working with the same capability he is. This eliminates a quiet excuse that plagues most AI adoption efforts: the gap between what leadership demos and what the rank-and-file actually has access to.
Prompt Fluency as a Baseline Skill, Not a Specialty
The conventional view treats prompt engineering as a niche capability, something a small team learns deeply while everyone else picks up the basics. Sabeer’s view is closer to the opposite. He treats prompt fluency the way an earlier generation of managers treated spreadsheet fluency. It is a baseline.
This shows up in hiring conversations, in performance reviews, in how new projects get scoped. The question is no longer whether an employee uses AI. The question is how well they shape the inputs they give it. He has been explicit that even technical staff, who could write code to solve a problem, are expected to engage with AI tools rather than route around them. The reasoning is straightforward: the next decade of work will not reward people who can do the thing manually. It will reward people who can direct tools to do it at scale.
This is the part of “AI culture” that procurement cannot fix. You can buy the licenses. You cannot buy the habit of opening the tool first.
No Assumptions, No Shortcuts
The most distinctive element of how Zil Money operates is also the least visible from the outside. There is a rule, mostly unwritten, that no decision is made on assumption. Whether it is a customer-impacting business call or a small internal process choice, the same standard applies. The reasoning gets checked, the data gets pulled, and personal intuition does not carry the day on its own.
This is where AI quietly becomes structural rather than cosmetic. When a culture refuses to run on assumption, AI stops being a productivity gimmick and becomes infrastructure. It is what teams use to verify claims, surface counter-evidence, and stress-test their own thinking before a decision moves forward. The tool is no longer a writing assistant. It is part of how the company protects itself from its own confidence.
Most organizations cannot operate this way because their leaders quietly rely on assumption to move faster. Sabeer’s bet is the inverse: that removing assumption from the workflow is the thing that actually creates speed, because rework, reversals, and politics get expensive in ways that rarely show up on a P&L.
The Workflow Redesign Nobody Talks About
Buying AI tools is a one-time event. Redesigning workflows around them is continuous, and it is the part most companies skip. At Zil Money, the redesign has been incremental rather than announced. Meeting notes are no longer typed. Research that used to take a day takes an hour. The composition of a typical workday for many employees has quietly shifted toward higher-judgment work, because the lower-judgment work has been absorbed by tools.
None of this required a transformation initiative. It required a CEO who used the tools first, who refused to let the company operate on assumption, and who treated prompt fluency as a normal expectation rather than an advanced skill.
The companies struggling with AI adoption tend to share a profile. Leaders who have not personally adopted the tools. Workflows that have not been touched since the licenses were purchased. A culture that still rewards intuition over verification. The fix is not another platform.
The fix is the work the leader was supposed to do in the first place.
