Real Velona

I get this question a lot.

It’s something my kids have asked me about their future. My nine year old asked me in the car the other day whether I was worried about AI and robots taking over, and why Elon Musk wasn’t worried about it.

It’s something my friends have asked me as they worry about their jobs and their future whether their job will be taken by AI, and what they’ll do about that.

It’s something my peers have asked me as they ponder where the services sector is going and whether services is a dead-end industry.

I’ve seen a lot of people claiming that AI is going to take over and that within two years all jobs will be gone.

Like almost everyone else, I’ve taken an active interest in AI since the first launch of ChatGPT. I may have been a little earlier to the party, taking an interest in IBM’s Watson before ChatGPT launched. How did IBM drop the ball on that one?!? That’s a story for another day. I’m not an expert in AI, but I feel knowledgeable enough to discuss the topic and provide some thoughts, and maybe some insight from my experience.

Having lived through the Digital and Agile Ways of Working transformations over the last 20 years, I can see some similarities to the way everyone is approaching AI, which I believe might be informative to help answer that question.

The Pattern We’ve Seen Before

In the early phases of agile transformation, lots of organisations adopted agile from the grassroots. I called this the “dabbling phase.” Organisations dabbled in agile approaches rather than really transforming their ways of working. This was most obviously recognisable because they would just stick “agile” on the front of everything. It was the same thing underneath, but sprinkled with a bit of agile. No real transformation had happened—the organisation really looked and functioned the same way, and well-meaning people figured out ways to work around the system to try and be as agile as they could.

Eventually, some organisations, either through the fear of disruption or through visionary leadership, embarked on true transformations, and this then triggered the wider adoption of agile transformation.

So What Has This All Got to Do With AI?

I believe we are still in the early phase of AI adoption—the one where we sprinkle a bit of AI onto everything. People are using chatbots and co-pilots everywhere, using them to accelerate the pace of their work and organisational velocity. If you measure adoption in this way, people may assume that AI has already flown all the way through the adoption curve, and we are now fully adopted.

The data supports this view. Recent research shows that 78% of organisations now use AI in at least one business function, up from 55% just a year earlier¹, and 39.4% of workers have used generative AI, with 28% using it for their job². However, 74% of companies struggle to achieve and scale value from AI³, suggesting we’re still in the surface-level adoption phase.

Let’s call this transformation the “Human-First AI Transformation” paradigm. I would say that we are well underway within this paradigm, but we are still at the early adopter to early mainstream phase, because patterns are only just starting to emerge in this space. We can’t say we have reached the late mainstream until there are established patterns and methodologies in place.

What Comes Next: The Three Paradigms of AI Transformation

Phase 1: Human-First AI Transformation (Current) This is where we are now, humans using AI tools to enhance their productivity. 78% of companies are using AI in one or more functions, while only 16% are using AI in five or more functions?, this gives you an idea of how far we are through this adoption curve.

Phase 2: Human-Supervised AI Transformation (Near Future) I think, well, I know from my experience working with organisations (mostly insurance companies) who invested in sophisticated automation practices and were starting to work more intelligently with RPA agents, that what comes next is working with semi-autonomous or supervised agents within the organisation. This comes with a whole new set of challenges and requires an entirely new operating model and way of working. Let’s call this the “Human-Supervised AI Transformation” paradigm. I would say the insurers will probably go first here, leveraging the work they’ve been doing with autonomous RPA agents.

Phase 3: Autonomous AI Transformation (Distant Future) At some stage in the future, there will come a time that AI agents may become autonomous, and we evolve enough to be able to implement an autonomous AI transformation paradigm. I don’t believe we could place this in the adoption curve at present anywhere other than the early adopter phase.

The Reality Check: It’s Going to Take Longer Than the Pundits Say

My view is that this adoption will take longer than most pundits are saying right now. Whilst there may be parts of organisations that get disrupted quickly; I would say call centres first; broader organisational functions will not be disrupted as quickly, because we just don’t have the organisational know-how to do this. This is not something that can happen in two or three years, no matter what the “godfather of AI” says.

The research backs this up. Currently, only 14% of workers have experienced job displacement due to AI?, and only 15% of employees say it’s likely that automation, robots or AI will eliminate their job within the next five years?. Even job displacement from AI so far appears negligible, with fewer than 17,000 jobs in the U.S. directly attributed to AI cuts between May 2023 and September 2024?.

While predictions suggest AI could replace the equivalent of 300 million full-time jobs globally by 2030?, these forecasts often overlook the complexity of organisational change and the time required for true transformation.

Why Organisational Change Takes Time

Research on AI implementation has concluded that mere AI adoption alone would not significantly improve workplace performance?. AI needs to be combined with new ways of working, staff reskilling, and a culture that supports these changes. This is exactly what I observed during the agile transformation years—technology adoption without organisational transformation simply doesn’t work.

The insurance industry, which I know well, will likely lead the way in the Human-Supervised AI paradigm because they’ve already done the hard work of building the organisational capabilities around automation and RPA. But even they won’t flip a switch overnight.

The Bottom Line

Are we facing the end of white-collar professions? Not in the way the doom-and-gloom predictions suggest, and certainly not in the next two years. We’re still in the early phases of true AI transformation, similar to where we were with agile adoption 15 years ago.

The organisations that will succeed are those that understand this is fundamentally about organisational transformation, not just technology adoption. Those that think they can sprinkle some AI on their existing processes and call it transformation will find themselves in the same position as those organisations that stuck “agile” on the front of their waterfall processes and wondered why nothing changed.

The future will be different, certainly. But it will unfold more gradually and with more human agency than the headlines suggest. And like the agile transformation before it, the winners will be those who understand that real change takes time, commitment, and a willingness to fundamentally rethink how work gets done.

What’s your experience with AI adoption in your organisation? Are you seeing the “sprinkling” phase, or have you moved into something more transformative? I’d love to hear your thoughts.

References

1. McKinsey & Company. (2025). The state of AI in 2025:
2. Pew Research Center. (2024). AI and the Future of Work. Pew Research Center.
3. Boston Consulting Group (2024). Where’s the Value in AI?
4. McKinsey & Company. (2024). The state of AI in 2024: Get ready for what’s next. McKinsey Global Institute.
5. Pew Research Center. (2024). About 1 in 7 workers have been displaced by artificial intelligence. Pew Research Center.
6. Pew Research Center. (2024). AI and the Future of Work. Pew Research Center.
7. Challenger, Gray & Christmas. (2024). Job Cuts Report. Challenger, Gray & Christmas, Inc.
8. Goldman Sachs. (2023). The Potentially Large Effects of Artificial Intelligence on Economic Growth. Goldman Sachs Global Investment Research.
9. AIPRM. (2024). AI in the Workplace Statistics 2024