Adopting AI: From Curiosity to Commercial Impact

📍Cubo, The Lincoln, Manchester M2 5AD
🕓 31st July | 4–6pm

As AI moves from experimentation to adoption, technology leaders are keen to find out how best to integrate it into existing operations, unlock real business value, and build the right foundations for future innovation.

The Northern AI ecosystem is evolving quickly, and we’re bringing together a handpicked group of senior leaders across AI, Data, and Tech leaders for an open and honest discussion on how to drive practical progress without overcomplicating delivery or losing sight of commercial outcomes.

This session is all about adoption over acceleration, helping leaders set realistic strategies that balance innovation, resource, and return.

Discussion topics included:

💡 Where should you start with AI adoption to unlock tangible value and build internal momentum?

💡 How do you balance quick wins with strategic investment in in-house capability?

💡 What does it take to foster a culture of experimentation and upskill teams to embrace AI?

What is the tech scene saying about adopting AI?

• Human-centred AI is a common north star
  Rather than full autonomy, we're seeing a lot of focus on tools that support teams, intelligent co-pilots, not replacements.

• Cost management is still a puzzle
  AI experimentation is often being funded via shadow budgets. Question remains, how sustainable that is, and how to budget sensibly at scale.

• AI hiring looks a little different to traditional data hiring
  There’s growing demand for engineers with strong software fundamentals. Some challenges in transitioning from classic data science into AI-heavy roles.

• Storytelling makes a difference
  Internal buy-in seems to improve when teams can link AI to real-world outcomes, prototype quickly, and spotlight the people behind the tools.

• Tool fatigue is real
  Healthy scepticism around chasing the latest AI app. Integration into existing systems and solving real problems might be the better bet.

• Culture shapes success
  Tech aside, the biggest blockers are often cultural, mindset shifts, team incentives, and the space to experiment safely.

📝 Key Takeaways:

Navigating Key Stakeholder Buy-In

Planning costs and budgeting for AI projects isn’t easy, with businesses struggling to predict usage and expenses.

Things to consider:

• Keep your business mission central. Any AI tool should align with your overall offering.

• Prioritise security and privacy. Always opt for enterprise-grade solutions, especially with sensitive data.

• Moving from experimentation to operational integration requires organisational change. Clarify what’s changing, how, and who it impacts.

• Investors increasingly ask “What are you doing about AI?” — not just whether you’re dabbling. Be able to articulate your position.

• Define your priorities: walk before you run, human-first, not immediate downsizing or full AI embedding.

Decision-making dynamics:

• CFOs are often as receptive as CEOs to driving AI investment.

• Champions at multiple levels accelerate adoption — AI doesn’t need to be purely top-down.

Human vs. AI

Keeping humans at the centre is vital. Teams are already anxious about job security.

• AI is most successful when used as a multiplier of human capability, not as a replacement.

• Junior team members are often more open to experimentation. Empower them as AI champions.

• Training programs help level understanding, reduce misinformation, and create consistent practices.

Where to Start?

Key questions for adoption:

• Where should you start to unlock tangible value and build momentum?

• How do you balance quick wins with strategic investment in in-house capability?

• What does it take to foster a culture of experimentation and upskill teams to embrace AI?

What Is the Market Saying?

• Most leaders don’t want to be first, but also don’t want to be last. Many aim for the first third of adopters.

• Risks and trends:

  • Generic questions → generic answers. Scope and time matter for useful outputs.

  • Are teams validating AI answers, or accepting them blindly?

  • Cost estimation is challenging: token pricing changes, and multi-language models can be disproportionately expensive.

  • Beware of overengineering.

Stay true to your mission. Don’t lose sight of the problem your business exists to solve — lean into AI where useful, don’t force it.

The Future of AI

AI is here to stay, but the playbook is still a work in progress.

• Stay curious but critical: trial thoroughly, validate outputs, and keep your team in the loop.

• With AI making more decisions, ensure humans remain in control — not the tools.

• Don’t hesitate to bring in outside expertise when needed.

Check out our moderator:

Liam Fulton — Co-Founder & CTO
LinkedIn

🔗 Download the summary sheet

If you’re navigating these themes and would like to hear more about our leadership events, or attend one of our upcoming sessions, register your interest below, or reach out to us with more info here.

If you’re navigating these themes and would like to hear more about our leadership events, or attend one of our upcoming sessions, register your interest below, or reach out to us with more info here.