The Hidden Prerequisites for AI Leverage

Multipliers don’t create direction. They amplify what already exists

Over the last two weeks, we’ve done something most AI conversations avoid. We’ve slowed down. First we acknowledged the truth founders don’t usually say aloud: AI isn’t a cure. It’s a diagnostic.

In Week 2, Rashmi took us inside real operations and showed where AI breaks first: SOP gaps, unclear inputs, broken handoffs.

This week, I want to address the question that quietly follows both pieces: If AI is not the engine of change, then what actually creates leverage? The answer is uncomfortable, especially in a year obsessed with tools. Leverage doesn’t come from intelligence. It comes from conditions.

Why tools don’t create leverage on their own

Founders often ask me, “Which AI tool should we standardise on?” But that question skips a more important one: “What must already be true in our business for any tool to help?” Because AI doesn’t generate value in isolation. It multiplies whatever system it is plugged into.

If the system is stable, AI accelerates outcomes. If the system is fragile, AI accelerates noise. This is why some businesses see dramatic gains from very simple AI use cases, while others struggle even after large investments. The difference isn’t ambition or intelligence. It’s readiness.

Condition 1: Stable processes, not heroic execution

In many SMEs, outcomes depend on who handled the work, not how the work is designed. The best performer becomes the process. Everyone else “manages somehow.” Humans are surprisingly good at compensating for this. They remember exceptions. They improvise. They fix things quietly. AI cannot.

For AI to add leverage, work must be stable enough that two different people can do it the same way and get roughly the same result. Not perfect … just predictable. This doesn’t require a consulting-grade process manual. It requires one honest answer to a simple question:

“If someone new joined tomorrow, would they know the one right way this is done?” If the answer is no, AI won’t help yet. It will only surface the inconsistency faster.

Condition 2: Clear decision rights, not more information

Most SMEs don’t suffer from lack of data or ideas. They suffer from lack of closure. Decisions float. Approvals are implicit. Founders become the final checkpoint. AI is excellent at generating options. But options without decision rights create paralysis, not speed.

For AI to create leverage, three things must be clear: 1) who decides, 2) on what basis, and 3) when the decision is considered final Without this, AI increases the volume of suggestions, reports, and analysis—but execution still stalls.

Leverage comes not from knowing more, but from deciding faster once the information is good enough.

Condition 3: Data discipline, not “more data”

Most businesses don’t have a data shortage. They have a trust problem. Ask three people the same question about price, delivery date, inventory and you’ll get three answers. Each answer has a story. Each story feels valid. AI doesn’t resolve this. It confidently responds based on whatever inputs it sees.

So, the question is not, “Is our data perfect?” It’s, “Which data must be right for this decision to work?”

AI leverage begins when a business agrees on:

• a small set of critical fields

• a single source of truth

• and clear ownership for keeping those fields accurate

This discipline is boring. It’s also non-negotiable. Without it, AI becomes a well-spoken guesser and founders return to verifying everything themselves.

Condition 4: Rhythm and review loops, not constant monitoring

One reason founders feel exhausted is that the business has no visible rhythm. Everything is urgent. Nothing is truly reviewed. Issues surface only when they become painful. AI doesn’t fix this. In fact, it can make it worse by creating the illusion that “everything is being tracked”.

Leverage comes when a business has:

• a weekly cadence for reviewing key work

• clear checkpoints where output is evaluated

• and predictable moments where problems are surfaced early

When these rhythms exist, AI becomes useful support … summarising, flagging, highlighting. Without them, AI just adds another stream of activity to monitor.

The reframe that matters AI is not an engine that pulls your business forward. It is a multiplier. Multipliers don’t create direction. They amplify what already exists. This is why two companies using the same tools can experience completely different outcomes. One has conditions in place. The other doesn’t. And this is also why rushing to “scale AI” often backfires.

You can’t multiply chaos into clarity. A grounded starting point If you’re wondering where to begin, don’t start with AI strategy. Start with one workflow. Just one. Stabilise it. Clarify ownership. Define inputs. Install a review rhythm. Then and only then introduce AI into that slice of work. When AI works there, you’ll know why. And when it doesn’t, you’ll know what to fix. That’s leverage.

(The writer works with founders and second-generation leaders to design operating systems where growth strengthens people, not exhausts them.)