The Destination Problem

For thirty years, advantage in consumer industries came from execution. Better forecasting, tighter supply chains, sharper campaigns, faster checkout. The winners were the ones who ran the machine best.

With the onset of AI and agents, that machine is now starting to run itself, today in parts, tomorrow in full. And when execution becomes something you can buy, running it well stops being the advantage. The advantage moves to the one decision the machine cannot make: where to point it.

I call this the Destination Problem. AI is solving the driving problem. The choices that decide who wins are destination choices, and they belong to leaders, not the machine.

The consumer enterprise is becoming self-driving

Look at what crossed consumer leaders' desks in just the past few months.

Walmart is rebuilding its assistant into a system that plans the family's week of meals, fills the basket, and completes the purchase. Kroger has put a hard number on its AI storefront: roughly $400 million of e-commerce operating-profit improvement in 2026. Amazon packaged the engine behind its multibillion-dollar Alexa shopping business into a kit any retailer can rent and stand up in weeks. Marriott and IHG opened their inventory to autonomous booking agents, and Booking.com's agent crossed 50 million monthly users. General Mills has banked tens of millions from AI orchestrating its supply chain, not from chatbots.

Read together, these are not isolated technology announcements. They are the same shift, arriving from every direction at once: demand sensed rather than surveyed, supply chains that rebalance themselves, agents transacting with other agents, and increasingly, agents doing the shopping. This is the largest redesign of how consumer companies run since ERP. The consumer enterprise is becoming self-driving.

A self-driving enterprise does not choose its destination

Here is the tension every leadership team is about to feel.

A self-driving car still needs a person to say where to go. A self-driving enterprise is no different. As AI takes over how the business runs, advantage shifts to the choices only leadership can make. The machine gets faster at getting there. It never decides where there is.

And the timing is what makes this urgent. The capabilities are commoditizing in real time. When Amazon will rent your competitor the same shopping engine it rents you, the engine stops being the moat. Execution is going abundant. Judgment is becoming the scarce resource, and the scarce resource is where the margin goes.

The three destination questions

Strip away the technology and every consumer leader faces three destination choices. Not one of them can be handed to the platform or the pilot team.

1. Which demand do you own when the shopper is an algorithm?

The journey is collapsing to a single turn. When most purchases follow one search and an agent builds the shortlist, you are no longer winning the shopper's eye. You are winning the agent's pick. Shelf psychology does not travel into that layer; machine-legible value does. The choice is which intent you optimize to own, and you encode it into your data, your ranking logic, and your retail-media bids, not your brand deck. I worked through one version of this in Both sides of shopping just went commodity.

2. What does your brand mean to a machine, and to the human behind it?

An agent reads your brand in milliseconds as price, availability, ratings, and substitution rules. Two brands running the same engine, both straight out of the box, are interchangeable: same answers, same logic, same refusals. The differentiator is what your brand refuses to do, the judgment the box cannot supply. Adoption without a point of view does not differentiate you. It commoditizes you faster.

3. Where does your pricing power live when execution is instant?

When everyone can point the same revenue-growth optimizer at the same syndicated data, prices and promotions converge and margin drains to the floor. The edge is the proprietary read on how your shoppers actually respond, and the tradeoff rules you engineer into the system: what you refuse to discount, where you defend premium, how you weigh margin against loyalty. PepsiCo has gone as far as naming the muscle. It calls it Decision Engineering, the subject of Decision Engineering: AI's first win is in the pricing room.

Strategy is no longer in the deck. It is encoded.

Here is the part most leaders miss.

In a self-driving enterprise, strategy stops living in the slide and starts living in the system. It gets compiled into the pricing agents, the supply-chain policies, the demand algorithms. Walmart's choice of which agentic rail to join is strategy. Marriott's decision to expose its inventory through an API is strategy. The single number a company tells its AI to move is strategy. Choosing where to go and building the machine that gets there are becoming the same act. I traced this through three companies in The number came first.

This is why the destination cannot be delegated to a transformation office. It is a leadership decision that gets compiled into code, and once it is compiled, it runs at machine speed whether or not anyone chose it on purpose.

Why most will get this wrong

Analysts expect a large share of agentic AI projects to be abandoned over the next two years. The reflex is to read that as the technology failing. It is not.

Starting a pilot requires no decision. Naming the destination it serves, the P&L line it answers to, and the choices it is allowed to make, forces one, and most organizations quietly decline. The projects that die rarely had a destination to begin with.

So the trap is not moving too slowly. It is moving fast and deploying the default. Two competitors on the same engine, both running the out-of-the-box configuration, end up in the same place: average, faster. The leaders pulling away are not the ones with the most agents. They are the ones who decided where the machine should go before they switched it on.

What this looks like in your industry

The pattern is the same everywhere, but it lands differently by sector.

In consumer goods, the first durable win is not on the shelf. It is in revenue-growth management, where AI can move net revenue realization at a granularity no human team could ever hold. The moat is the decision you encode, not the model you license.

In retail, the contest is shifting from winning the shopper to winning the agent that shops for the shopper. Whoever makes their assortment, price, and availability legible to that agent owns the basket. Everyone else becomes a substitutable line item.

In travel and hospitality, every category is about to re-fight the disintermediation war it thought it had lost, this time against an agent rather than an online travel agency. Make your brand natively legible in the AI layer, or be resold inside it and hand over both the margin and the customer relationship.

The questions to put on the table

If you lead a consumer business, the board question this year is not whether you are using AI. Everyone is. It is narrower and more uncomfortable:

Which demand are we choosing to own when the algorithm is the shopper?
What will we never let our agents do, in order to protect what the brand means?
Which line on our P&L is the AI actually moving, and did we choose that line on purpose?

Answer those honestly and you have your destination. Leave them to the machine and the machine will choose one for you.

Where I sit

I have spent twenty years on every floor of this building. Strategist at BCG, builder at Accenture, operator running a nine-digit consumer P&L, and now leading Wipro's global consumer consulting business across CPG, retail, travel and hospitality. The pattern from inside these transformations is consistent. The winners are not the ones with the best models. They are the ones who decided where the machine should go.

Execution is becoming abundant. Judgment is becoming the moat. The only question that still belongs entirely to you is the destination one.

What is the destination question on your desk right now?

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