Where to Start With AI: Six Practical Patterns That Work

We’ve been working with a range of organizations lately — large private equity firms, public companies, and growth-stage startups. Some with over $10B in AUM or market cap.

Across all of them, one question keeps coming up from leadership:
“Where should we start with AI?”

There isn’t a single right answer. But there are patterns that consistently work. Here’s how we think about it.

1. Start Where Change Is Already Happening

The easiest place to introduce AI is inside teams already evolving.

If a group is upgrading their CRM, improving internal tools, or rethinking a workflow, it’s the right moment. You’re not forcing transformation into a stable system. You’re adding energy to something already in motion.

Change builds momentum. Piggyback on that momentum.

Example:
We’ve seen companies integrate AI during a CRM migration. Instead of just replicating old processes in new software, they built AI copilots into the rollout. The adoption was smoother because the team was already open to learning.

2. Start Where You Have Influence

AI transformation is easier when you can make decisions without friction.

For private equity firms, that often means starting with a portfolio company where they hold majority control. For larger enterprises, it’s about finding a trusted partner — a team leader or exec who’s ready to push forward.

Influence creates speed. It also limits the slowdowns caused by approvals, politics, and board cycles.

Tip:
Start with a single, motivated team where leadership is curious and willing to experiment.

3. Start Where You Can Leapfrog Legacy Systems

Sometimes the best opportunities are in places that haven’t yet modernized.

We worked with a $10M+ pool supply company that had almost no internal systems. That turned out to be an advantage. Instead of retrofitting AI into legacy workflows, we built entirely new AI-first tools for scheduling, customer service, and supply management.

When you start from zero, you skip the friction of technical debt and outdated habits.

4. Start Where There’s Scale

AI has the most visible impact in large teams with repetitive, coordination-heavy work.

At Kit, for example, we started with their sales team. It had the most moving parts — multiple people, data sources, and constant communication. That complexity made it the perfect starting point for automation and AI augmentation.

The math is simple:
More people + more inefficiency = more leverage from AI.

5. Start With the Right People

Politics always matter.

Some leaders are cautious. Others are ready to experiment. Focus on the ones who are curious, influential, and aligned with the company’s vision.

Those internal champions create momentum. Once their teams show visible wins, the rest of the organization follows.

Insight
AI adoption spreads faster from inside wins than from top-down mandates.

6. Start Where Regulation Isn’t a Blocker

In heavily regulated industries, like healthcare or finance, choose functions that don’t touch sensitive data first.

Instead of diving straight into patient records or financial systems, start with supporting processes: marketing, HR, or operations. Prove the model, show results, then move closer to core systems once compliance guardrails are clear.

You build trust by showing that AI can be applied safely and effectively.

Closing Thought

AI adoption doesn’t start with a massive transformation plan. It starts with one practical, well-timed project that builds momentum and trust.

Start where there’s motion. Start where you can decide. Start where the outcome matters.

The rest follows.