AI adoption: making it work in your business

By Connor Whitehouse, Senior Technical Consultant

A business can spend months deciding where AI could add value, choosing the right technology and putting a strategy in place. None of that guarantees people will actually use it.

That is where many AI projects start to struggle. The technology has been approved, the business case makes sense, and the supplier has demonstrated what it can do. But once the tool is introduced, the results are not always what was expected.

Some employees use it regularly, others avoid it altogether and a few continue working exactly as they did before. Before long, the business is paying for technology that is only being used by a handful of people.

Introducing AI is relatively straightforward. Getting it adopted properly takes more thought.

Start with something worth changing

One of the biggest mistakes businesses make is trying to introduce AI too widely, too quickly.

There is often pressure to demonstrate progress, particularly when competitors are talking about AI and suppliers are constantly promoting new capabilities. That can lead to several projects starting at once, without enough consideration of whether the business is ready.

A better approach is to start with a specific process where there is a clear opportunity to improve how work gets done.

That might be reducing the time spent preparing reports, handling repetitive administration or helping employees find information more quickly. The important thing is that the problem already exists and the benefit of solving it can be understood.

Starting small also gives the business an opportunity to understand what works before introducing the same technology elsewhere.

The people using it need to be involved

Technology decisions are often made by people who will not be using the system every day.

Management identifies an opportunity, IT assesses the technical requirements and a supplier demonstrates the product. By the time employees are involved, the decision may already have been made.

That creates problems when the way a system is expected to work does not reflect what actually happens in practice.

A process that looks straightforward in a demonstration may involve exceptions, manual checks or decisions that are not immediately obvious. The people carrying out that work are usually the ones who understand those details best.

Involving them early helps identify where AI could make a difference and where it might create more work than it removes. It also gives employees an opportunity to influence how the technology will work.

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"Buying an AI tool is not the same as getting people to use it."

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Training needs to go beyond the demonstration

Most AI products are designed to be easy to use. That can create an assumption that employees will not need much training.

Being able to enter a prompt, however, is different from understanding how to use AI properly in a business environment.

Employees need to know what the tool is intended for, what information they can share and when its output needs to be checked. An AI-generated response can look convincing while containing incorrect or incomplete information.

Training should therefore be relevant to the work people actually do.

Showing a finance team examples designed for marketing is unlikely to be useful. Employees need to see how the technology applies to their responsibilities and have enough time to become comfortable using it.

That does not mean everybody needs to become an AI expert. They simply need to understand how to use the tools available to them safely and effectively.

Do not overlook the existing process

Introducing AI into a process does not automatically make that process better.

If information is difficult to find, responsibilities are unclear or employees are already working around problems in an existing system, adding AI may simply introduce another layer of complexity.

For example, a business might want to use AI to produce management reports more quickly. But if the information comes from several systems and nobody is confident which figures are accurate, the AI tool is unlikely to resolve those underlying issues.

There is also the question of how the technology fits into everyday work. If employees have to copy information between systems and manually transfer the results back, the time saved may be considerably less than expected.

Sometimes the better investment is improving the process or making better use of technology the business already owns.

Measure whether it is working

Once an AI tool has been introduced, the business needs to understand whether it is delivering the expected benefit.

The number of licences purchased or employees given access does not tell you much about whether the business is getting value from it.

If the aim was to reduce the time spent on a task, measure how long that task took before and after introducing AI. If the intention was to improve customer response times, look at whether those times have changed.

Quality matters as well. Completing something more quickly is not necessarily an improvement if employees then spend additional time correcting mistakes.

It is also worth speaking to the people using the tool. They will often identify problems that are not visible in usage reports.

Not every AI project will deliver what was originally anticipated. Recognising that early gives the business an opportunity to make changes or stop investing in something that is not producing enough value.

Adoption needs ongoing attention

There is a tendency to treat technology implementation as a project with a clear finish date.

The system goes live, employees receive training and responsibility moves into normal business operations. AI is not quite that straightforward.

The technology continues to change, suppliers introduce new features and employees find different ways of using the tools available to them.

Businesses therefore need someone responsible for checking that the technology remains useful, appropriate and properly supported.

That includes reviewing how it is being used, keeping guidance current and making sure employees know where to raise questions or suggest improvements.

Without that ongoing attention, even a well-planned implementation can gradually become disconnected from how the business operates.

Making AI part of everyday business

Successful AI adoption is not about introducing as many tools as possible or expecting every employee to change the way they work overnight. It is about choosing useful opportunities, involving the people affected and making sure the technology delivers something worthwhile.

Some projects will produce immediate benefits. Others will need more time, changes to existing processes or further investment. There will also be occasions when the right decision is not to proceed.

The important thing is that those decisions are based on what the business needs and what the technology can realistically deliver.

An AI strategy provides the direction, but the value comes from what happens when people start using it.

For some businesses, managing that process internally will be straightforward. For others, bringing in independent technology leadership can help oversee implementation, support adoption and make sure investment in AI delivers measurable business benefits.

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This article has been prepared for information purposes only. Formal professional advice is strongly recommended before making decisions on the topics discussed in this release. No responsibility for any loss to any person acting, or not acting, as a result of this release can be accepted by us, or any person affiliated with us.

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