The arrival of AI in the commercial function usually leads to the same first step: equipping the sales organisation to sell faster and with a sharper focus. That answers the question of how the company sells. AI, and the wave of digitalisation that comes with it, reopens the deeper questions beneath it: what the company sells, to whom, how the buyer chooses, and how you determine your pricing. Anyone who merely accelerates the sale is optimising a commercial model whose foundations are already shifting. What is at stake is not the speed of the sale, but whether the company will still be selling the right thing, to the right market, on a footing that holds.
Take an aircraft engine manufacturer. For decades, the proposition was clear: sell the engine, then earn on parts and maintenance. With "Power by the Hour", Rolls-Royce turned that around. The customer no longer buys the engine plus separate maintenance bills, but pays a fixed amount per hour flown for guaranteed availability, while sensors continuously measure the engines and Rolls-Royce carries the maintenance risk. What is sold is no longer a product, but an outcome.
AI brings that same leap within reach of ever more companies: from a one-off product to a continuously measured, guaranteed performance. Research into outcome-based business models shows that this requires a thorough rethink of how value is defined, delivered, and captured (Sjödin et al., 2020).
If the offering changes, so does where it can be sold. A provider that could once serve only large customers profitably, because each one required specialists for advice and service, can now use AI to deliver that same level to thousands of small customers at almost no cost: a long tail that was previously out of reach becomes a market. The flip side is just as sharp: an entrant can now offer that level to the incumbent's best customers for a fraction of the price. In short, AI significantly erodes both the cost of entry and the barriers to it.
Cost is only one wall. AI also lowers the barrier of human capital: the productivity gain is greatest among the least experienced employees (Brynjolfsson et al., 2023), which narrows the skill advantage that long protected established markets. Both walls fall in both directions at once, and that changes the CCO's task: not defending a single market, but managing the portfolio of markets that AI opens and closes. The limit of what the company can serve is no longer determined by what its people can reach, but by what its machines can handle. And that limit is far more mobile.
If the offering and the market shift, so does where the battle is decided. In B2B, the deal is often sealed with a handshake, but the buyer walks into the meeting with their research already done: with AI, they have surveyed the market, compared alternatives, and often already drawn up a shortlist before speaking to a salesperson.
The decisive phase therefore lies before the first conversation. The buyer sits at the table better prepared than ever: they know the market, the alternatives, and the price range, and the salesperson is no longer the one who informs them. If informing and persuading, long the heart of the sales role, largely fall away, the commercial organisation, as currently structured, loses part of its rationale. The question only the CCO can answer is where the sales organisation still adds value: which part of the human effort shifts from informing to the complex, advisory work a buyer cannot do for themselves, even with AI, and how the commercial function is redesigned around it. A cautious hypothesis: now that information is easily and readily available to everyone, the differentiator shifts to the human, and relationships, trust, and judgement come to matter more, precisely where machines cannot reach.
If all of this shifts, so does the basis beneath the price. Most companies price on cost or effort: per hour, per user, or per licence. That worked as long as effort was a reasonable reflection of value, but AI breaks that assumption by significantly lowering the marginal cost of software and knowledge work (Shapiro & Varian, 1998). Per-licence software then loses its basis as soon as an organisation can have disposable software built for it, or a single agent does the work of many users. And billing advisory or accountancy work by the hour leads to a perverse outcome: if AI does the work in a fraction of the time, then working more efficiently lowers revenue, and the hourly rate penalises precisely the productivity gain that makes it possible.
The way out is to anchor the price to what it delivers to the customer rather than to what it cost to make: per outcome or per unit of value (Sjödin et al., 2020). That touches the core of the revenue model, and is therefore squarely the domain of the CCO, though the road is long: 'value-based pricing' has been advocated for decades and is still far from common practice.
The common thread is that AI does not displace commercial questions but poses them anew: the question is not how the company sells, but what it sells, to whom, how the buyer chooses, and how you determine your pricing. That very model is the domain of the CCO.
The following strategic questions offer a starting point for that discussion:
