AI and the future of business leadership

What happens when the business model that built your success can no longer carry you forward? That is the question sitting underneath most conversations about AI and leadership right now, even when nobody says it out loud.

In episode 59 of The Innovation Room, our co-founder Somayeh sat down with Sarah Gillard, CEO of Blueprint for Better Business, to talk about something bigger than tool adoption or automation roadmaps. They talked about paradigm shifts. About what happens when the metrics that made businesses successful for a century stop being reliable guides for the future. And about the role AI plays in speeding that shift up, rather than slowing it down.

For roughly 200 years, Western economies have run on a simple formula. Grow the economy, measure that growth through GDP, and treat rising GDP as a reliable proxy for human wellbeing. For a long stretch, Gillard argues, that formula genuinely worked. It lifted billions out of poverty, cut child mortality, and opened up education to people who had never had access before. The problem is not that the formula was wrong.

The problem is that the problem it was solving has changed. Growth solved poverty. It did not solve what came after: environmental strain, social fracturing, a widening gap between who benefits and who does not. Gillard calls this paradigm one, and she is blunt about where it is heading. It is unsustainable, and by definition, things that are unsustainable do not sustain.

Somayeh raises an interesting counterpoint here. A small number of countries have already tried measuring something other than GDP, from Bhutan's long running focus on national happiness to newer efforts elsewhere to track wellbeing alongside economic output. These experiments stay marginal for a reason Gillard names directly: GDP is genuinely difficult to displace, not because it is a good measure of what matters most, but because entire systems of politics, investment and business reporting have been built around it.

Here is the part that should make leaders pay attention. Gillard's argument is not that AI is bad for paradigm one. It is that AI makes paradigm one work better, faster, at scale, and that is precisely the problem.

Deploy AI inside a system built purely to maximise short term financial return, and you do not get a better version of that system. You get the same system, arriving at its unsustainable edge more quickly. Efficiency without a change in purpose simply accelerates whatever direction a business was already heading in.

That reframes a question a lot of leaders are quietly avoiding. It is not "how do we use AI to do what we already do, faster?" It is "what are we actually optimising for, and does AI make that a good idea or a dangerous one?" Gillard is careful to note that AI itself is neutral. It is a tool, and tools serve whatever intention sits behind them.

Gillard brings in two contrasting ideas about how paradigm shifts occur in practice. The first comes from economist Milton Friedman, who argued that only a crisis, actual or perceived, produces real change, and that when the crisis hits, the response depends entirely on what ideas happened to be lying around at the time.

The second, often attributed to Buckminster Fuller, takes a different route. You do not fight the existing reality. You build a new model that makes the old one obsolete.

Most businesses default to the first path. They wait for the crisis, then scramble for whatever ideas happen to be on hand. Far fewer take the second, harder route of building the better model before pressure forces their hand. Gillard's point here is practical rather than idealistic. If crisis driven change is coming anyway, having good ideas ready in advance is not optional. It is the difference between reacting well and reacting badly when the moment arrives.

This is where Gillard's philosophy meets AI strategy directly. A business that treats AI purely as a way to defend its current model is betting on the first path, hoping the crisis stays manageable. A business that uses AI to genuinely rethink what it offers and to whom is doing something closer to the second. Neither route is easy. But only one of them puts a business in control of its own timeline.

The hardest part of this conversation is not philosophical. It is personal. Somayeh puts it plainly in the episode: imagining a better future for a business sometimes means imagining a version of that business, and your role within it, that looks nothing like today.

That is a genuinely difficult ask. Few people are naturally equipped to say, my authority, my responsibilities, and my current way of adding value might need to change for the business to move forward. It is far easier to assume the status quo will simply hold. But Gillard's counterpoint is worth sitting with. The assumption that today's position of advantage will remain intact indefinitely is looking shakier by the year, especially once AI is added to a system that was already showing strain.

For leaders weighing AI investment, this shows up as a practical question rather than an abstract one. Beyond faster processes and lower costs, what value is the business actually trying to create, and for whom?

Gillard has seen first hand how difficult this question is for leaders to sit with, precisely because the honest answer rarely points back to revenue alone.

That question does not have a single right answer. But asking it, before AI locks a business more firmly into its current paradigm, is what separates leaders who shape the shift from leaders who simply get swept along by it. Sarah Gillard and Somayeh's conversation does not offer a tidy checklist for getting this right. It offers something more useful: a clearer way of seeing the choice in front of every business right now, whether they have noticed it yet or not.