Are You Transforming with AI or Just Optimising?

And so as promised, here is my piece on AI and transformation …

Back in 2018, I wrote an article titled ‘Are You Digitally Transforming or Just Optimising?‘ The central argument was simple, many organisations claim transformation when they are really just making incremental improvements to existing ways of working.

Eight years later, as organisations race to adopt Artificial Intelligence (AI), I find myself asking exactly the same question – ‘Are you transforming with AI, or are you simply optimising what already exists?‘ History may not repeat itself but it often rhymes as the saying goes.

Throughout every major technology shift, a familiar pattern emerges. Early adopters often focus on using new technology to do old things slightly better. The true winners are usually those who reimagine what is possible altogether.

Consider cloud computing. Many organisations began by lifting and shifting applications from their own datacentres into the cloud. They achieved some infrastructure savings and operational efficiencies but little fundamentally changed. In some cases the costs actually went up … but that is another tale. Others refactored applications, embraced cloud-native architectures, adopted new operating models and redesigned entire business processes. They did not simply move workloads; they transformed capabilities.

The same lesson can be seen with smartphones. Imagine buying one of the most powerful hand held computing devices ever created and using it solely for voice calls. It would technically work, but you would be realising only a fraction of its potential. The real value came from entirely new behaviours, navigation, digital banking, social collaboration, photography, video streaming, commerce and countless applications that were never possible or even dreamt of let alone feasible on traditional mobile phones. The smartphone injected a transformation leap, not a linear shift or change, but an exponential leap that sent household names to the wall and boosted unheard of and struggling companies into the mainstream, remember Apple in its doldrum days?

The internet followed the same trajectory. Many businesses initially treated websites as digital brochures. Others reinvented supply chains, customer engagement, retail models and entirely new industries. Amazon continuous to bankrupt the high-street which continues its legacy battle with neighbouring brands for footfall. What a different world it would be if they had unified against the common new threat when Amazon was a new kid on the virtual block. A bit late now, but therein lies a lesson. 

We can also harp back to the advent of Electricity which provides perhaps the most famous example. Early factories simply replaced steam engines with electric motors while retaining the same layouts and processes. It took years before industrialists realised that electricity allowed factories themselves to be redesigned. Productivity gains arrived not from electrification alone, but from organisational reinvention.

Today, AI presents a similar crossroads. Many organisations are deploying AI to write emails faster, summarise meetings, generate reports or automate existing tasks. These are valuable improvements but they are largely optimisation plays. They make current processes more efficient without fundamentally changing how value is created.

Transformation begins when leaders stop asking ‘How can AI help us do what we already do faster?‘ and start asking  ‘If intelligence were abundant, how would we design this organisation from scratch?‘

That is a profoundly different question and class of leader. It shifts the conversation from automation to augmentation, from productivity to capability, from incremental gains to entirely new operating models and more often than not today’s laggard for tomorrow’s leader.

The challenge is that true transformation requires experimentation. It requires uncertainty. It requires leaders to accept that not every initiative will succeed. Yes a class of leader that is rare but more often than not found in startups.

Unfortunately, many organisations remain trapped by what might be called ‘executive paranoia of failure’. Leaders understand intellectually that innovation requires experimentation, yet governance structures, budgeting processes and cultural incentives often punish failure more than they reward discovery. It is even worse for listed companies in the public spotlight and handcuffed to declaratory behaviour that deters such experimentation.

As a result, how long will we have to witness organisations gravitating toward the safest AI initiatives, low-risk optimisation projects that deliver predictable but limited outcomes. This is understandable but experience suggests it is rarely where the greatest value accumulates. Manifesting yet another example for the Gartner Hype cycle fan base, when I truly believe that painful journey is avoidable.

The companies that ultimately define technology eras are seldom those that simply improve existing processes. They are the ones that challenge the assumptions behind those processes altogether.

That does not mean blindly embracing every AI claim or accepting every prediction of an AI driven future. History also teaches us to be cautious of technological hype cycles. Not every forecast becomes reality. Not every disruption unfolds as expected.

Importantly, we should avoid the trap of selective recollection, looking backwards only for examples that reinforce our existing beliefs about the future. The cloud revolution, the internet boom, mobile computing, electrification and countless other innovations each unfolded differently. The economic winners, losers, timelines and outcomes varied significantly. History does not provide a blueprint. What it provides is perspective.

The lesson is not that AI will inevitably follow the same path as previous technology revolutions. It most certainly will not. With AI the time compression of change is exponential and will exceed the cognitive whit of leaders who have not started re-tuning their minds and attitudes to handle the exponential leaps in disruptive technologies that AI will trigger as it gains velocity and starts chaining complementary innovation silo’s that we cannot even imagine today and with that innovation create whole new industries and roles for a displaced labour force.

The lesson is that transformative opportunities often emerge when people stop viewing new technology through the lens of old constraints.

The greatest risk facing many organisations today is not that they will fail with AI. It is that they will become exceptionally efficient at doing things that may no longer matter.

The leaders who thrive will be those willing to see with new eyes, challenge inherited assumptions and question the status quo. They will balance prudence with experimentation, governance with exploration and operational discipline with imagination and boldly go … because when intelligence becomes abundant, the question is no longer whether technology can change your organisation. The question is whether your organisation is willing to change itself.

Thank you for getting this far and taking the time to read these thoughts. The future of AI remains uncertain and none of us can predict precisely how it will unfold. What we can do is remain curious, challenge our assumptions staying open to possibilities that may not yet be obvious. If this article encourages you to look beyond optimisation and consider what genuine transformation might mean for your organisation, then I hope it has added some value.