AI Strategy: Stop Trying to “Adopt AI” and Start Solving Real Problems

One of the biggest mistakes I see is organisations starting with:

“We need an AI strategy.”

Well sure… But more than that… you need a business strategy that includes AI.

AI is not the goal. It’s a lever.

If you don’t anchor it to tangible outcomes, you’ll end up with:

disconnected pilots
duplicated tools
shadow AI usage
and leadership asking why nothing is scaling
no ROI

The shift that works:

Start with:

Where are we losing time?
Where are decisions slow or inconsistent?
Where is knowledge trapped in people or systems?

That’s where AI belongs.

A Practical Starting Framework (That Actually Works)

Forget the 50-slide strategy decks. If you’re leading this, focus on three layers:

1. Envision (But Keep It Grounded)

Define 3–5 high-value use cases, not 20.

Good examples:

Automating [this specific process] repetitive decision-making processes
Enhancing [this specific process] customer interactions (not replacing them)
Reducing manual data handling across [this specific system]
Assisting internal teams with knowledge retrieval for [this specific process]

Bad examples:

“Let’s build a chatbot”
“Let’s use AI everywhere”

If it doesn’t tie to a measurable outcome, it’s a distraction.

2. Control (Before You Scale)

This is where most organisations cut corners (and regret it later).

You need early governance, not retrofitted governance.

At minimum:

Data boundaries: What can and cannot be used by AI
Environment strategy: Where AI solutions live (especially in platforms like Power Platform)
DLP policies: Prevent data leakage across connectors
Auditability: Can you explain what the AI did and why?

If you skip this, you won’t be able to scale safely, and someone will eventually shut it down.

3. Execute (Small, Fast, Iterative)

Your first AI solutions should:

Deliver value in weeks, not months
Be low-risk but visible
Prove ROI quickly

Think:

AI-assisted workflows
Copilot-style augmentations
Internal productivity wins

Not:

Fully autonomous, business-critical decision engines

You’re building trust (not just technology).

Where Most AI Strategies Go Wrong

Let’s be honest about the traps.

❌ 1. Overengineering Too Early

Trying to design the “perfect future-state AI platform” before delivering anything.

You don’t need perfection. You need momentum.

❌ 2. Ignoring the Human Layer

AI doesn’t fail because of technology.

It fails because:

people don’t trust it
people trust it too much
people don’t understand it
people feel replaced by it

If your strategy doesn’t include:

enablement
transparency
accountability
communication
and clear positioning (AI as an assistant, not a threat)

…it will stall.

❌ 3. No Ownership Model

Who owns AI in your organisation?

IT?
Business units?
Innovation teams?

If the answer is “everyone,” the reality is “no one.”

You need:

clear ownership
defined operating model
governance that enables, not blocks

Sometimes multiple aspects can be owned by different teams. IT and Business units. As long as it’s known, clear and defined who’s in charge of what then you’re on the right track.

❌ 4. Treating AI Like Traditional Software

AI is probabilistic. It’s unlikely you can test for every scenario or outcome.

That means:

outputs can vary
accuracy isn’t binary
validation is ongoing

If your QA, testing, and governance models assume deterministic systems, you will struggle.

The Overlooked Risks (That Bite Later)

These are the ones I see catch even experienced teams off guard:

⚠️ Data Leakage Through Convenience

Users will always choose ease over policy.

If governance isn’t built into the tools they use, they’ll:

paste sensitive data into public AI tools
create shadow solutions
bypass controls entirely

⚠️ “Invisible” Technical Debt

AI solutions built quickly without structure become:

impossible to maintain
hard to explain
risky to scale

Especially in low-code platforms, governance matters even more here.

⚠️ False Confidence in Outputs

AI can sound right while being wrong.

If you don’t design for:

human validation
specific prompts for non-hallucination
feedback loops
confidence thresholds

You’re creating risk, not efficiency.

⚠️ Scaling Before Standardising

If every team builds AI differently:

governance becomes chaos
duplication explodes
support becomes unsustainable

Standard patterns early. It pays off massively later.

What I’d Do If I Were Starting Today

No fluff. Just action.

Week 1–2:

Identify 3 high-value use cases
Define success metrics
Align stakeholders

Week 3–6:

Deliver 1–2 pilot solutions
Implement baseline governance (DLP, environments, data rules)
Capture feedback and iterate

Month 2–3:

Establish a Center of Excellence
Define reusable patterns
Start scaling what works

Ongoing:

Invest in enablement (this is non-negotiable)
Continuously refine governance
Measure impact, not activity

Final Thought

AI isn’t about replacing people.

It’s about removing friction from how people think, decide, and act.

The organisations that will win here aren’t the ones with the most advanced models.

They’re the ones who:

stay grounded in business value
move quickly but responsibly
show up to scheduled accountability meetings
and bring their people along with them

Right now, most teams are still in that “bubble” of observing, experimenting and unsure.

The goal isn’t to escape that overnight.

It’s to move forward deliberately with clarity, structure, and intent.

Kim Brian – Power Platform Solution Architect