Organisations are increasingly asking which artificial intelligence tools to buy, which tasks to automate and how quickly they can realise productivity gains. These are reasonable questions, but they are not the first questions that determine whether AI will improve performance. The more important question is whether the organisation and its people are ready to use the technology well.

Buying a tool is an event. Readiness is a condition that must be built.

AI can summarise documents, analyse information, draft communications and support decisions. Yet its usefulness depends on the work around it: the quality of the information provided, the employee’s ability to question an output, the manager’s understanding of the process, and the rules governing what the tool may access or influence.

Without these conditions, an organisation can introduce AI and still see inconsistent results, duplicated work or new risks.

For Nigerian businesses, readiness deserves particular attention. Organisations operate with different levels of digital capability, infrastructure, data quality and employee exposure to emerging tools. Even within the same organisation, a technology team may be comfortable experimenting while other teams are uncertain about how AI applies to their work. Some employees worry that asking questions will reveal a skills gap; others fear that the purpose of adoption is to make their roles redundant. Ignoring these concerns does not make them disappear. It can push experimentation out of sight and reduce the opportunity to learn safely.

Workforce readiness is not the same as giving everyone a one-day introduction to prompts. It includes the knowledge, judgement, confidence and support people need to use AI for appropriate tasks. Employees must know the tool’s limits, how to verify its output, when to seek human review, and what info to avoid entering into an unapproved system. These are practical capabilities, not abstract technology awareness.

The organisation must also be ready. Leaders need to identify the business problem before selecting a tool. Adding AI to an already unclear or poorly designed process will not necessarily lead to improvement. Managers must determine accountability for decisions, establish protocols for handling exceptions, and identify situations where human judgement is crucial. Policies on privacy, security, bias, transparency and review must be understandable enough to guide daily work, not merely sit in a manual.

This is why AI readiness should be treated as a business transformation issue, not only an IT project. Human resources and learning and development teams have an important role, but they cannot carry the responsibility alone. They can assess capability gaps, design role-specific learning, support reskilling and help managers prepare teams. Business leaders must connect these efforts to priorities, processes and measures of performance. Technology, people, workflows and governance have to move together.

A practical starting point is to examine a small number of real workflows. What is taking too long? Where are errors or delays occurring? Which parts of the task involve repeatable processing, and which require context, ethical judgement or a relationship with a customer or colleague? Once the task is understood, the organisation can decide where AI may assist, what the employee must still check and what training or process change is needed.

The next step is to establish a baseline and define evidence of improvement. The number of licences purchased, employees trained or prompts generated can tell a leader something about activity, but not whether work has improved. More useful indicators might include turnaround time, rework, quality, responsiveness, employee confidence or the consistency of decisions, depending on the task. Results should be reviewed carefully because changes may also be influenced by staffing, systems, workload or other business conditions.

Organisations should begin with bounded pilots, give employees safe ways to test and question outputs, and learn from both successes and mistakes. Managers should make expectations clear and avoid treating every hesitation as resistance. Some tasks will benefit from AI; others may not. Responsible adoption includes knowing when not to use it.

The goal should not be to prove that an organisation is using AI everywhere. It should be to make thoughtful choices about where the technology adds value, prepare people to work with it, and retain appropriate human accountability. When workforce readiness is built alongside technology adoption, AI is more likely to become part of a stronger operating model rather than another disconnected tool.

Nigeria’s AI conversation must therefore move beyond access and acquisition. The decisive advantage will belong to organisations that combine technology with capable people, redesigned work and responsible governance. Adoption gets AI through the door; readiness determines what happens after that.

Ishioma Elora Onah, PhD., MCIPM, works on AI readiness, leadership and workforce transformation.