Beyond the Obvious

As intelligence becomes embedded in products, machines, industrial processes and energy systems, we may be approaching an economic transformation far more significant than the current enthusiasm for generative AI suggests.

Yet conventional approaches to AI governance risk missing the bigger picture, and businesses risk overlooking an entirely new market in the process.

Much of today’s regulatory and corporate attention focuses on the trustworthiness of AI models; accuracy, explainability, bias, alignment and responsible behaviour. Important considerations, certainly, but insufficient for the economy now emerging.

Consider industrial machinery independently procuring maintenance, electric vehicles negotiating energy prices, buildings trading electricity, and autonomous agents selecting suppliers and executing commercial transactions. These are no longer simply intelligent tools. They are becoming economic participants, exercising delegated authority with real financial and operational consequences.

For business leaders, this raises questions extending well beyond technology governance. Who controls these commercial relationships? Who owns the customer interface when the customer is a machine? Who carries liability when an autonomous purchasing decision goes wrong? And who captures the value generated by billions of machine-to-machine transactions?

The governance challenge shifts fundamentally. It is no longer sufficient to ask whether an AI can be trusted. We must establish what it is authorised to do, under which conditions, and how those boundaries are independently enforced.

More troublingly, what happens when thousands of individually compliant systems interact and collectively produce an undesirable outcome? No malicious intent, no compromised model, perhaps not even a malfunction. Simply interconnected systems optimising against competing objectives, but the risk also illuminates an extraordinary commercial opportunity. This is a new generation of Business Logic risk = systems that are technically secure, correctly authenticated and fully authorised, yet independently interpret and execute business objectives in ways that produce unacceptable outcomes. The resulting challenge is not simply securing AI, but assuring the integrity of autonomous decisions, delegated authority and business outcomes, as I wrote about earlier ‘Don’t Trust the Model. Architect the Authority‘.

As intelligence becomes increasingly commoditised, trust could emerge as one of the most valuable services in the machine economy. Autonomous commercial participants will require verifiable identities, enforceable authority, validated suppliers, contractual assurance and trustworthy evidence of transactions.

However, building this market is not simply a technology or regulatory challenge. It is potentially one of the greatest brand and marketing opportunities of the coming decade. Established consumer brands with millions of customers already possess something AI startups and frontier laboratories cannot readily manufacture: longstanding relationships, established distribution and accumulated consumer trust. Banks, insurers, retailers, telecommunications operators and other household brands could leverage that position to introduce trusted personal agents, autonomous purchasing services and machine-mediated commercial platforms under brands customers already recognise.

The proposition is commercially compelling. Rather than asking consumers to place their financial and personal affairs in the hands of unfamiliar AI providers, established brands can extend existing relationships into agentic services, provided they underpin their promises with demonstrable security, accountability and independent assurance.

This could help the market cross its trust horizon, a term I use to reference the point at which consumers become sufficiently confident to delegate meaningful decisions and commercial authority to AI and agents and birth the machine customer economy. Beyond that threshold, adoption could accelerate towards an economic escape velocity, as agent adoption attracts suppliers, services and further investment. Much as payment networks and trusted financial institutions enabled digital commerce, familiar consumer brands could provide the confidence and distribution needed to accelerate the machine economy.

For boards and investors, the opportunity therefore extends beyond productivity gains. It is about capturing new customer relationships, recurring service revenues and entirely new markets.

My takeaway is the winners may not be those developing the smartest AI models, but those combining brand trust, customer reach and independently enforceable authority to make autonomous commerce commercially viable.

Perhaps the greatest oversight in today’s AI governance debate is that we are concentrating on making intelligence trustworthy, while overlooking the larger commercial prize, making intelligence safe to do business with and recognising that trust is not merely a governance obligation, but potentially the most powerful route to market in the agentic economy.