Right now, every boardroom is asking: ‘What is our AI strategy?’ Yet the real question should be ‘Who controls the future of your business – is it you, or someone else?’

AI is not just another innovation. It’s more like the introduction of money, electricity or the internet – a foundational, general-purpose revolution that is rewiring the entire architecture of society and business. 

And, fundamentally, AI doesn’t need to be ‘sentient’ to be successful. What exists already is enough to revolutionise business[1]. Even when the AI bubble does burst[2] [3], much like digital before it, it’s still going to change everything, just not as quickly as the stock market hopes. So, the true cost of inaction isn’t lost market share – it’s losing your place in the future. 

Too many companies do not truly understand this. Yet, only by recognising the implications of the new AI value chain will they position themselves to outperform the competition in an AI future. 

What we’re really talking about is the commodification of intelligence

For businesses everywhere, we are now in a new world where compute, data and cognitive capabilities are the fundamental drivers of growth. The rules of competition are being rewritten in real-time – reshaping what firms are, what workers do and where value flows.

The raw material for all this? Data intelligence – extracted from everything, instructing every interaction and overseeing every process. It is now enabling AI to make decisions, choose outcomes and dictate success on a vast scale and at a previously unimaginable speed. 

In response, you must do more than develop an AI strategy – you must create a strategy for your place in an AI future. The difference lies in a core understanding of what AI actually is, how it works and what an AI-driven world will look like – both as a business and as an employer.

Everything has changed (but some things never change…)

Data and governance sit at the core, not the margins. Few companies are willing (yet) to entrust AI with root permissions to their revenue and reputation[4]. And with good reason. The new risk lies in a ‘lethal trifecta’ of exposing private data, ingesting untrusted content and allowing uncontrolled external communication[5]. Managing these is mandatory: only with clean, integrated systems and well-governed data can you realise AI’s full potential.

Human labour must be reimagined to enable what we call ‘high agency’ – the ability to initiate, synthesise and commercialise operations in an AI-augmented world.

Businesses must learn to prioritise the valuable over the visible: high-impact workflows, Business Process Outsourcing (BPO) replacement and decision automation.

AI risks – both direct and indirect – must be actively managed. Not just well-known concerns like data security, bias and misinformation, but also the growing environmental footprint of large-scale models: their energy demands, water consumption and hardware dependency, not to mention the reputational overhead. 

As AI systems scale, so too do the hidden costs. Leaders will need to balance ambition with accountability – ensuring the infrastructure that powers intelligence doesn’t quietly undermine sustainability goals or operational resilience.

In light of all this, hesitation is understandable, but the fear of becoming a replaceable cog in someone else’s intelligence supply chain is real. So, doing nothing is not an option. 

Adjusting to perpetual disruption 

The Coming Wave [6] of disruption is already washing over us. It’s not just about technology – the fundamentals of the old order are in flux. A volatile mix of technical, socio-political, cultural, environmental and economic disruptions is converging, as often happens at historical moments of change.

  • AI investment bubbles, with sky-high price-to-earnings ratios and the flight to safe havens like gold, mask deeper structural change.
  • The US is Brexiting the global world order, so a fight over the void is now seemingly unavoidable. 
  • Capital, talent and favourable business conditions are reclustering outside Silicon Valley.[7]
  • Climate collapse is reshaping supply chains. 
  • Trust in institutions (and even knowledge itself) is evaporating.
  • Influencers, not experts, are commanding attention as algorithms amplify voices that value reach over rigour.

For traditional businesses, this is all understandably overwhelming. Events and technology innovation are moving faster than many can absorb – or trust. In this new reality, AI-first enterprises and traditional businesses must consider what capital allocation and core strategy now look like. 

Closing the hype–reality gap

For all the noise, the frothy headlines and token initiatives – AI isn’t magic[8]. The get-rich-quick investor behaviour and make-me-rich-quick demands of boardrooms everywhere are missing the point. The unicorn universe promises unimaginable human advancement[9]. Its grand showcase? Prime time ad slots where AI’s crowning achievement involves saving Hollywood stars from a soggy restaurant table – a laughably trivial use case held up as a revolution.

Meanwhile, giving every employee Copilot access is fuelling a new epidemic: AI workslop – low-effort, plausible-sounding output that creates more work than it resolves. It clogs inboxes, stalls decisions and drains time in endless review loops. On the surface, it looks like productivity; in reality, it’s pure performance.

This isn’t sustainable. And for those who’ve made a career of hiding in plain sight – ducking decisions, parroting consensus and prizing polish over substance – the endgame is approaching. There will be nowhere left to hide once the tools that gave them cover start to expose the shallowness of their contribution.

So, no, it’s not surprising that Gerard from marketing hasn’t changed the fortunes of the company when his slick new slide deck took a mere 14 minutes to create using ChatGPT and a generic slide builder agent.

While businesses continue to promote eye-catching prototypes, the genuinely game-changing opportunities remain underexploited[10]. According to a recent, much-cited MIT report, 95% of organisations are seeing zero measurable P&L impact from GenAI investment[11]. There has been a 40% reported deployment of tools, but primarily to enhance individual productivity, not P&L performance. 

The remedy: Engineering Organisational Intelligence

This is your opportunity to come out ahead of all this radical upheaval and remain relevant in the new and evolving future. The essential transformation will require a fusion of human and machine intelligence, so your business can take control of its destiny in the new economic order. 

Disruption is inevitable, but those who take the initiative – by building and managing their own intelligence supply chain – will prevail. Above all, you must learn to do what we call Engineering Organisational Intelligence – combining AI and systems thinking to resolve complexity, create opportunities and generate innovative, intelligent, unfair competitive advantage.

That means having a strategy for an AI world that: 

  • focuses your business on data intelligence.
  • uses AI to control narratives, not just make content. 
  • hyperscales your products and services (not just adding novelty chatbot interfaces with hidden support and reputational costs).
  • retrains human labour to enable high agency.
  • leverages AI and critical thinking to accelerate real-world value creation.

Control or be controlled

AI demands a radical restructuring. Its adoption isn’t just about enterprise subscriptions or sandbox trials – it’s a significant structural shift. The real winners in this new economy will be those who see AI as a bottom-up restructuring, not just another round of top-down board initiatives. 

This is a rare and unprecedented opportunity – not merely to seize a temporary edge, but to secure a lasting, compounding advantage over the long term. So, as we approach this new mode of capitalism, the big question to ask yourself is this:

Do you want to be in control of your future AI business?

Or be controlled by the future of AI business?

Sources & References

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