“We’ll just start using AI and see where it goes.”

It’s a common and understandable reaction.

AI is moving fast. Tools change every quarter. Vendors promise the world. So why slow things down by writing a strategy?

Here’s the uncomfortable truth:

The earlier you are in your AI journey, the more you need a strategy — just not the kind you’re used to.

Most leaders associate “strategy” with a fixed target state. Multi-year roadmaps. Detailed architecture diagrams. Commitments that age badly.

That approach fails with AI.

Why?

Because AI is not a system you install. It’s a capability that keeps evolving.

Trying to lock down a three-to-five-year AI target state today is like designing a perfect harbour while the tide, wind, and coastline are still moving.

When organisations begin integrating AI into a core application, three things happen fast.

First, experiments multiply. Teams try copilots, chatbots, RAG, automation, analytics — often in parallel.

Second, risk increases quietly. Data exposure, hallucinations, bias, vendor lock-in, and compliance creep in early.

Third, expectations run ahead of reality. Leaders hear “AI” and assume step-change productivity tomorrow.

Without a strategy, AI adoption becomes tool-led instead of outcome-led. Fragmented across teams. Hard to govern after the fact.

A strategy doesn’t slow this down. It keeps it pointed in the same direction.

What works is not a target state, but a directional strategy.

Think of it as a compass, not a map.

A good early-stage AI strategy answers five simple questions.

Why are we using AI? What problems actually matter?

Where is AI allowed to operate — and where is it not?

How will we balance speed with safety?

What principles guide decisions as tools change?

Who is accountable when AI decisions affect customers, staff, or citizens?

That’s it. No brittle roadmaps. No false precision.

Some organisations say they’re being agile, so they don’t need a strategy.

That’s a misunderstanding.

Agile without direction is just motion.

A directional AI strategy sets guardrails, not handcuffs. It enables teams to move faster safely. It allows decisions to be decentralised but coherent. And it evolves as learning increases.

You can change tactics every quarter without changing intent every month.

AI strategy is less about technology and more about decision clarity.

Senior leaders don’t need to know which model is best this year or which vendor will win.

They do need to be clear on what kind of organisation they are becoming. What risks they will not accept. Where human judgement must remain in the loop.

That clarity is leadership. That is strategy.

If you’re just beginning AI integration into a core app, start here.

Write one page, not fifty. Focus on principles and intent, not architecture. Make it explicitly revisable every six to twelve months. Socialise it with delivery teams early. Use it to say no as often as yes.

If the strategy helps people make better decisions when things are uncertain, it’s working.

AI will keep changing.

Your organisation still needs a steady hand on the wheel.

The goal of an AI strategy is not certainty. It’s coherence while learning.

And that matters most at the very beginning.

Written for KiwiGPT.co.nz — Generated, published and tinkered with AI by a Kiwi