AI Readiness Is Not a Technology Problem

AI Readiness Is Not a Technology Problem

The Situation

When organisations talk about “AI readiness,” the conversation usually turns technical within minutes.

Do we have the right infrastructure? Is our data clean enough? Do we need to hire machine learning engineers? Should we build or buy? Which vendor has the best model?

These are reasonable and important questions. They are also, in most cases, the wrong starting point.

In the majority of Nigerian organisations we have worked with, technical capability is not the bottleneck. The infrastructure exists or can be acquired. The tools are available. The vendors are eager to help.

The bottleneck is upstream. It is organisational, not technical. And until that bottleneck is addressed, no amount of technical investment will produce meaningful results.

What this means for leaders

The harder questions about AI readiness have nothing to do with technology:

Who is responsible for AI decisions? In most organisations, this question has no clear answer. IT thinks it is a business decision. Business thinks it is a technology decision. Legal thinks someone should have consulted them earlier. The result is either paralysis or chaos — decisions made by whoever moves first, with accountability assigned after the fact.

What risks are acceptable — and which are not? AI systems make errors. They produce biased outputs. They expose data in ways that may violate regulations. Before deploying any tool, leadership needs to decide what failure modes are tolerable. Not in abstract terms, but in specific scenarios: What happens if the system recommends we reject a loan application incorrectly? What if it shares customer data with a third party? What if it produces content that embarrasses the organisation?

How do we decide not to use AI when pressure is high? Every organisation faces pressure to adopt AI — from boards, competitors, vendors, and enthusiastic staff. But not every use case makes sense. The ability to say no, to wait, to choose deliberately, is as important as the ability to say yes. Without a framework for refusal, organisations adopt AI reactively rather than strategically.

Without clear answers to these questions, AI adoption becomes improvised. Tools are introduced before intent is defined. Teams are expected to figure it out on their own. Policies are written after precedents are set.

The pattern that emerges

We see this pattern repeatedly:

Enthusiasm at the top. Leadership reads about AI. They attend conferences. They hear competitors are piloting projects. They want to move. The intent is genuine, but it is often disconnected from operational reality.

Uncertainty in the middle. Middle managers receive the mandate to “do something with AI.” They are not sure what success looks like. They are not sure who to involve. They are not sure what resources they have. So they commission pilots, hire consultants, or request proposals — activity that feels like progress but may not be.

Quiet resistance on the ground. Staff who will actually use AI tools often have legitimate concerns. Will this replace my job? Will I be blamed when it fails? Will I have to learn a new system without adequate support? These concerns are rarely voiced in meetings but shape adoption in profound ways.

The result is familiar: projects that start with fanfare, drift for months, and quietly disappear from the priority list. Not because the technology failed, but because the organisation was never aligned on what it was trying to achieve.

Practical takeaway

True AI readiness is organisational, not technical. It requires alignment across three dimensions:

• Leadership alignment. The executive team needs a shared understanding of what AI is for in your organisation. Not a vague commitment to innovation, but specific agreement on priorities, boundaries, and success criteria.
• Operational alignment. The people who will implement and use AI tools need clarity on expectations, resources, and support. They need permission to surface problems early without career risk.
• Governance alignment. Legal, compliance, risk, and HR need to be involved before deployment, not after. They need to understand AI well enough to provide useful guidance rather than reflexive restrictions.

This alignment takes time. It requires conversations that feel slow when pressure is high. But without it, AI initiatives will remain fragile, regardless of how advanced the technology appears.

The organisations that succeed with AI are not necessarily the ones with the best tools. They are the ones that did the organisational work first.

Risks or limitations

There is a risk in this framing: using organisational readiness as an excuse for inaction. Some leaders will hear “you are not ready” as permission to delay indefinitely. That is not the message.

Readiness is not a destination. It is not something you achieve and then maintain forever. It is a process of continuous alignment as capabilities evolve and context changes.

The goal is not to be perfectly ready before taking any action. The goal is to be ready enough — to have sufficient clarity on intent, responsibility, and boundaries that you can proceed without creating more problems than you solve.

That threshold is lower than many organisations assume. But it requires asking the hard questions first, not after.