AI Adoption in 2026: 10 Lessons for Business Leaders

Where should your business start with AI adoption in 2026?

My advice is to choose one recurring business problem, test where AI can help and measure the result.

On 17 October 2026, I joined Bobby Kerr on Newstalk’s Down to Business as part of a panel discussing AI adoption. We explored what it takes to turn interest in AI into something useful for a business.

At AI Ireland, we have focused on practical AI adoption since 2018. We support organisations through executive briefings, leadership workshops, AI readiness assessments, adoption programmes and practical training.

The AI Awards give us another valuable perspective: examples of what organisations are putting into practice.

This year’s awards attracted 276 applications, with finalists selected across 15 categories. Their projects cover areas including healthcare, cybersecurity, agriculture, manufacturing, public services and customer experience.

Looking across the AI Awards 2026 finalists and submissions, here are 10 lessons for business leaders deciding where to start and what to do next.

1. Start AI Adoption With a Clear Business Problem

The strongest AI projects tend to start with something practical.

A customer retention issue. A slow reporting process. A difficult decision. A fragmented workflow. A lack of access to useful information.

That sounds obvious, but many organisations still start by buying a tool and asking employees to find a use for it.

A better starting question is:

Where is work getting stuck?

One good example is Headon Boxing Academy.

Its Pulse system looks for changes in member behaviour that may indicate someone is at risk of cancelling. The AI helps staff identify who may need a timely conversation.

The business problem is clear: customer retention.

The technology helps the team decide where to focus its attention and when to act.

For leaders, the lesson is to define the problem before choosing the tool. Be specific about what you want to improve and how you will know whether it has worked.

2. Clean, Accurate and Accessible Data Matters

Another clear lesson from this year’s applications is the importance of data.

AI needs relevant, reliable information to produce useful results. If the underlying data is poor, fragmented, outdated or inaccessible, AI will not magically solve the problem.

It can simply help you make bad decisions faster.

This is why business leaders need to bring data strategy and AI strategy into the same conversation.

Herdwatch is a useful example.

Its Herd Intelligence service allows farmers to ask questions about their livestock records and farm information in everyday language.

The value comes from making existing operational information easier to access and use. Information that previously required searching through records becomes something a farmer can ask a question about.

For many organisations, the opportunity starts with making better use of what the business already knows.

You do not need to clean every piece of company data before getting started. You do need to understand whether the information used for your chosen task is accurate, current and appropriate.

What useful information does your business already have that people struggle to find or use?

3. AI Should Improve the Work, Not Just Increase the Output

One of the dangers with AI is confusing volume with value.

More reports. More content. More presentations. More emails.

But more does not necessarily mean better.

The goal should be to avoid becoming “AI stupid”: producing more material while applying less judgement.

That might mean sharing an AI-generated report that is longer but adds no insight. Using an image with obvious errors. Sending a proposal that sounds polished but says little about the customer’s actual problem.

AI should raise the quality of the work.

That means keeping judgement, originality and verification in the process.

Before sharing something created with AI, ask:

• Is it accurate?
• Does it answer the real question?
• Is it relevant to this audience?
• Does it help someone make a decision or take action?
• Have I checked the details that matter?

The question “Can AI produce this?” is becoming less useful.

A better question is:

“Is this actually useful?”

4. AI Adoption Is About Redesigning Work

A lot of organisations are using AI to do existing tasks faster.

That is useful. But it is only the first step.

The bigger opportunity is to ask:

If AI gives us time back, what should we change about the work?

If AI saves your team five hours a week, decide what those five hours are for.

Better customer conversations? Solving recurring problems? Testing a new service?

Without that decision, the time can disappear into more reports, more meetings and more of the same work.

Saving time creates the opportunity. Redesigning the work turns that opportunity into business value.

Take yieldHUB.

Its AI agents support semiconductor testing by monitoring data, analysing performance and supporting operational decisions.

For leaders, the interesting question is what that could change about an engineer’s role.

An engineer who spends less time gathering and reviewing information may have more time to set priorities, investigate exceptions and make decisions.

The opportunity extends beyond producing a report more quickly. It can change how people spend their working day and where they contribute the most value.

AI can change tasks. With thoughtful leadership, it can also improve how roles and teams are designed.

5. Think About Blue Euro and Green Euro

I often describe AI value in two simple ways: Blue Euro and Green Euro.

Blue Euro: Improve the Business You Already Have

Blue Euro is about:

• Saving time.
• Reducing repetitive work.
• Lowering costs.
• Improving efficiency.
• Reducing errors.

It asks:

“How can AI help us run the business better?”

Green Euro: Create New Opportunities for Growth

Green Euro is about:

• New products.
• New services.
• New customer experiences.
• New revenue opportunities.

It asks:

“What could AI help us offer that we could not offer before?”

Both matter.

A business might begin by using AI to reduce the time spent preparing customer reports. That is Blue Euro.

It might then use that capability to develop a new service that gives customers more timely, personalised advice. That creates a potential Green Euro opportunity.

The leadership challenge is to consider both.

Efficiency can improve today’s business. New services and experiences can help shape tomorrow’s.

6. Use AI to Test New Business Ideas

Another theme I discussed on Newstalk was how accessible AI has become.

Smaller teams can now use AI tools to build prototypes, explore service ideas and test different ways of working.

That gives business leaders a practical way to learn before committing to a larger investment.

For example, a small service business could test whether AI helps it turn existing expertise into a useful customer briefing. It could create a prototype, have a person check it and ask a small group of customers whether they find it valuable.

The point is to test the proposition as well as the technology.

Does the customer actually want this?

Does it solve a problem they care about?

Can we deliver it reliably and at a sensible cost?

A quick prototype is only the beginning. A dependable service still needs accurate information, clear ownership and a process that works.

But leaders can start learning through a small, controlled experiment instead of waiting until every part of a larger programme has been decided.

7. AI Agents Create a New Leadership Challenge

For an introduction to how these systems work, read AI Agents for Business: Always-On Automation Explained.

AI agents are an important development for business leaders to understand.

A chat assistant might draft a response for you to review. An AI agent can be given permission to work through several steps, use connected systems and take actions.

That makes its permissions a leadership decision.

One line I shared during the discussion was:

“The risk with agents is not intelligence. It’s authority.”

An agent can be capable and still make mistakes. When it has permission to act, those mistakes can have consequences beyond the screen.

A draft email can be corrected before anyone receives it. An agent with permission to send that email has already changed the situation.

Leaders need clear answers to six questions:

• What can the agent access?
• What can it change?
• What requires human approval?
• What gets recorded?
• How can it be stopped?
• Who remains accountable?

Agents do not need to “go rogue” in a science-fiction sense to cause problems. A misunderstanding combined with too much permission can be enough.

The more freedom an agent has to act, the more carefully its boundaries need to be designed.

Governance needs to be part of the daily workflow, with clear limits, approval points and accountability.

8. AI Adoption Is a People Challenge as Much as a Technology Challenge

Buying software is easy. Changing behaviour takes more work.

One of the interesting examples from this year’s finalists is Musgrave.

Its AI and Automation Factory combines tools, internal AI products, staff training and governance.

That combination matters because successful AI adoption requires more than access to software.

People need to understand where AI can help, how to use it well and when human judgement is required. They also need time to practise and support when something does not work as expected.

Another example is Laya Healthcare.

Its Knowledge Hub helps customer service advisers find policy-specific answers, relevant context and supporting information.

The AI supports the person having the customer conversation. It helps them find information and apply their judgement.

That is a practical place for leaders to look for opportunities:

Where could better support help our people do their jobs more effectively?

Training becomes more useful when it connects directly to those situations.

A team should leave knowing what it can apply to its work, how to check the result and where to get help.

9. Start With One Process and Measure the Result

Waiting for the perfect AI strategy can delay the learning needed to build a useful one.

My advice is to begin with one recurring piece of work.

Choose something specific enough to understand and measure.

For example, “reduce the time spent preparing our weekly operations report” gives a team a clearer starting point than “use more AI.”

Then work through a simple sequence:

1. Understand the current process. What happens, who is involved and where does work get delayed?
2. Record the starting point. How long does it take, what errors occur and what does a good result look like?
3. Test a specific improvement. Use an approved tool and appropriate information, with a person responsible for checking the output.
4. Compare the results. Include time spent reviewing and correcting the AI’s work.
5. Decide what happens next. Improve the approach, expand it or stop if it is not delivering value.

The aim is to learn whether AI makes the work better.

A task completed faster but requiring extensive corrections may not be an improvement. A process that saves time, maintains quality and helps the next person do their job is more promising.

You do not need to transform the whole company in one move. You need a useful first step and a way to learn from it.

10. Ask Better Questions About AI Adoption

If I were sitting with a leadership team today, I would ask five questions:

1. Where are we wasting time?
2. Where could AI help us make better decisions?
3. Where could we improve the customer experience?
4. What information do we already have that people struggle to use?
5. What could we offer customers that we could not offer before?

These questions connect AI to the things leaders are responsible for: performance, customers, people and growth.

They also help teams identify different kinds of opportunity.

Some will save time. Some will improve the quality of decisions. Others may create a new service or a better customer experience.

The next step is to choose one, give someone ownership and agree what success would look like.

Start with a business problem. Test a practical improvement. Measure what changes. Then build from there.

For a framework to apply these ideas, explore my five AI Awards lessons through the 5Ps AI Readiness Framework.

See Practical AI in Action at the AI Awards 2026

The AI Awards 2026, with Microsoft as Principal Sponsor, take place in Dublin on 17 November 2026.

The finalists showcase AI applications across healthcare, cybersecurity, manufacturing, agriculture, education, public services and customer experience.

If you are a business leader trying to understand what practical AI adoption looks like, the awards are an opportunity to explore real examples and meet the people behind them.

Join us to celebrate the work, learn from the projects and hear this year’s winners announced.

View the AI Awards 2026 finalists and access ceremony tickets here.

You are also welcome to message me if you would like to know more.