How Far AI Has Come (Since Last Year’s M2 AI Summit)

I attended the M2 AI Summit yesterday, and one thing was immediately clear: the pace of change in AI over the past 12 months has been staggering.

Last year, much of the conversation centred around potential, pilots, proofs of concept, and “what if.” This year, it’s different. We’re now firmly in the era of application. AI is no longer just something organisations are experimenting with. It’s something they’re actively building into how work gets done.

From Experiments to Workflows

One of the biggest shifts is the move toward automated, stitched-together workflows.

Instead of standalone tools, we’re seeing ecosystems:

• AI agents handing tasks off to other agents
• Tools integrating seamlessly into CRMs, ATS platforms, and internal systems
• End-to-end workflows being automated, not just individual steps

What used to take multiple systems (and people) can now be orchestrated with surprisingly little friction.

The Rise of “Vibe Coding”

Another theme that stood out was what many are calling “vibe coding.”

This isn’t traditional development. It’s:

• Rapid prototyping with AI
• Building solutions by describing intent rather than writing full codebases
• Iterating in real time with AI as a collaborator

The barrier to creating useful tools has dropped significantly. You no longer need to be a specialist developer to stitch together a working solution. You just need clarity on the problem you’re solving.

That’s a massive unlock.

But… Not Everyone Is Seeing ROI

Despite all this progress, there was also a sobering undercurrent.

A number of organisations are still struggling to see real return on investment from AI.

Why?

Because in many cases, it’s still:
AI for the sake of AI.

Common pitfalls include:

• Implementing tools without clear use cases
• Focusing on novelty rather than outcomes
• Lack of alignment with actual business problems

The message was clear: AI isn’t the strategy. It’s the enabler.

Without a clear “why,” even the best tools won’t deliver value.

Bringing People With You

Perhaps the most important takeaway wasn’t technical at all. It was human.

The organisations seeing success are the ones that are:

• Bringing their people along for the journey
• Investing in education and experimentation
• Creating safe spaces to try, fail, and learn

Because no matter how advanced the tech gets, adoption still comes down to people.

Sharing Success (and Building Momentum)

One simple but powerful idea that came up repeatedly was the importance of sharing AI success stories internally.

Not in a top-down way, but through:

• Forums or internal showcases
• Regular “show and tell” sessions
• Highlighting practical, real-world wins

When people see how their peers are using AI to save time or improve outcomes, it becomes real, and momentum builds organically.

AI Wikis, Skills, and Institutional Knowledge

Another emerging best practice is the creation of internal AI knowledge hubs:

• AI wikis
• Shared prompt libraries
• Documented workflows and “how-to” guides
• Skill repositories for different roles

This helps organisations move from isolated experimentation to repeatable capability.

Instead of everyone reinventing the wheel, knowledge compounds.

Final Thought

If last year was about possibility, this year is about practicality.

AI has moved from hype to utility, but the gap between those getting value and those not is widening.

The difference isn’t the tools.

It’s:

• Clarity of purpose
• Focus on real problems
• A commitment to bringing people along for the ride

The organisations that get this right won’t just use AI.

They’ll build it into how they operate.