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How to be an AI-native business

David Buxton · · 4 min read

A five-rung ladder from personal productivity to a self-driving system, illustrating how to become an AI-native business, in warm Harriet tones

How do you actually become an AI-native business? Start by dropping the idea that “AI-native” is a badge you either have or you don’t. It’s more like self-driving cars: there are levels. Knowing which one you’re on tells you exactly what to do next.

First, what is an AI-native business? The 5 levels

An AI-native business sits somewhere on a five-level spectrum, from individuals quietly using AI to a system that runs work on its own.

Level 1 — personal productivity

People use ChatGPT or Claude to draft emails, summarise meetings, brainstorm, maybe build a dashboard nobody asked for. Useful, sure, but the workflow lives entirely with the individual. Good luck measuring the ROI.

Level 2 — team workflow

Sales has its own AI prospecting setup, support has AI triage, HR has AI-assisted hiring. Every team is building its own little stack, and none of them talk to each other.

Level 3 — organisational infrastructure

Here it gets interesting: infrastructure that sees across the whole company. It can query contacts, compound context, and work across functions instead of staying trapped in one team’s tools.

Level 4 — a system that learns

This is where things start to compound. The company isn’t just using AI — the system learns from previous work. It improves workflows over time and lets non-engineers build real shared skills without waiting on an eng team.

Level 5 — the self-driving endpoint

The system notices something important on its own, synthesises context, decides what to do, takes action, escalates when it needs a human, and updates a shared memory so the next decision is better than the last.

The level that actually matters in 2026

It’s a genuinely useful framework, and figuring out where you sit on it is a good chunk of any honest AI diagnostic. But most companies should not be fixating on Level 5 right now. It isn’t realistically on most people’s roadmap for 2026.

The actual bottleneck for almost everyone is getting from personal productivity (Level 1) to real team and cross-functional execution (Level 3). That’s the gap that matters this year — not the sci-fi endpoint.

How to be an AI-native business: 5 steps

You don’t leap to AI-native. You climb. Here’s the shortest path that holds up.

  1. Diagnose your level. Be honest about where you actually are on the five levels above. Most companies discover they’re a scattered Level 1 pretending to be higher.
  2. Aim for Level 3, not Level 5. Cross-functional infrastructure is the prize this year. Autonomy can wait until the foundation exists.
  3. Put AI where the work already happens. Resist the rush to build a pile of specialised internal apps. In practice that creates brittle tools and fragmented permissions — every new app is another place to decide who gets access to what data, who administers it, and who maintains it when the underlying model or API changes. Multiply that by every team and you’ve built a maintenance problem, not an AI strategy.
  4. Route each job to the right model. Send the routine 80 percent to a cheap model and the hard work to a frontier model, under one set of cost controls and one bill. (It’s the same reason you shouldn’t bet the company on a single model — lock-in is lock-in.)
  5. Close the gap between insight and action. Dashboards and data lakes help you see the work; they don’t move it. The unlock is AI that acts, not just reports.

The real unlock: from insight to action

Our take at Harriet is that AI should operate inside the workspace you already use — your docs, spreadsheets, browser, calendar, internal systems — rather than inventing yet another place for it to live. Visibility alone isn’t the win.

The real unlock isn’t more visibility. It’s closing the gap between insight and action.

That’s the whole reason we built the AI control plane: governed AI that works across functions, on infrastructure IT already trusts. See how it fits together on the platform, or book a call to work out which level you’re actually on — and the fastest way up.

Common questions

What is an AI-native business?
An AI-native business isn't a yes-or-no label — it's a spectrum with five levels, like the levels of self-driving cars. It runs from Level 1 (individuals using AI for personal productivity) to Level 5 (a system that notices something, decides what to do, acts, escalates to a human when needed, and updates a shared memory so the next decision is better).
How do you become an AI-native business?
Become an AI-native business in five steps: (1) diagnose which of the five levels you're on; (2) aim for Level 3 (cross-functional infrastructure), not Level 5; (3) put AI inside the tools you already use instead of building a pile of new internal apps; (4) route each job to the right model to keep spend predictable; and (5) close the gap between insight and action so AI moves work forward, not just reports on it.
What are the levels of AI adoption?
Level 1 is personal productivity (individuals drafting and summarising). Level 2 is team workflow (each team builds its own AI stack). Level 3 is organisational infrastructure that works across functions and compounds context. Level 4 is a system that learns from past work and lets non-engineers build real internal tools. Level 5 is the self-driving endpoint, where the system acts autonomously and improves over time.
What level of AI maturity should a company aim for in 2026?
For almost every company the real goal in 2026 is getting from Level 1 (personal productivity) to Level 3 (cross-functional execution), not chasing Level 5. Level 5 autonomy isn't realistically on most roadmaps this year; the gap that actually moves the business is turning scattered personal use into AI that works across the whole organisation.
Should we build internal AI apps for each team?
Be careful. Building a pile of specialised internal apps tends to create brittle tools and fragmented permissions — every app is another place to decide who can access what data, who administers it, and who maintains it when a model or API changes. A better approach is AI that operates inside the tools you already use rather than inventing new places for it to live.