{"id":1278,"date":"2026-06-12T14:42:45","date_gmt":"2026-06-12T14:42:45","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=1278"},"modified":"2026-06-12T14:42:45","modified_gmt":"2026-06-12T14:42:45","slug":"your-ai-is-already-setting-prices-the-real-question-is-who-sets-the-rules","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=1278","title":{"rendered":"Your AI is already setting prices. The real question is who sets the rules"},"content":{"rendered":"
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Three months later, sales were down 40%.<\/p>\n

This story is becoming common. And the problem it reveals is not technical.<\/p>\n

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Most executive teams today are asking the right questions about AI: What can it automate? Where does it generate productivity? Which functions can it augment? These are important questions. But they miss the more consequential one: What is your AI already deciding on your behalf and do those decisions reflect the company you want to be?\u00a0<\/p>\n

Read more SpaceX\u2019s first employee, Tom Mueller, thinks the historic IPO is just the beginning<\/a><\/p>\n

The real risk of AI pricing is not that machines will set the wrong price. It is that companies will accidentally turn pricing policy into an optimization problem and discover too late that the algorithm has made strategic, ethical, and political choices the leadership team never debated.<\/p>\n

For most of the past decade, AI in business operated as a copilot. It recommended, flagged, and ranked options. Humans decided. This architecture, however imperfect, preserved a clear line of accountability.<\/p>\n

That architecture has quietly changed. Agentic AI systems, the next generation now being deployed across pricing, customer management, and revenue optimization, don\u2019t recommend. They act. They pursue objectives autonomously, remember past interactions, learn from outcomes, and adjust continuously without requiring human validation at each step.<\/p>\n

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In pricing, this shift is already visible. Salesforce has made the first move. With\u00a0Agentforce, it introduced action-based pricing: Customers can pay not just for access to software, but for work an AI agent completes. The unit of value is moving from the human seat to the machine-executed action. This is not an incremental optimization. It is a fundamental change in what gets decided, by whom, and at what speed.<\/p>\n

McKinsey\u2019s latest AI survey\u00a0shows that while AI use is now widespread, most organizations remain in experimentation or pilot mode, and only a minority are scaling agentic AI. That makes governance even more urgent: Companies are giving machines more autonomy before they have mature routines for deciding when human validation is required.<\/p>\n

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\u00a0Pricing agents now operate at speeds that outpace any human review cycle. Prices can shift multiple times per day based on signals (demand, inventory, competitor behavior, time of day) that no human team could process in real time. The decisions are individually defensible. But their cumulative effect on customer trust, brand positioning, and long-term margins is rarely audited at the same frequency.<\/p>\n

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\u00a0Agentic systems can create, in practice, a different market for each customer. Two people visiting the same platform at the same moment may see different prices, different bundles, different urgency signals calibrated to what the system has inferred about their willingness to pay. The revenue upside is real. So is the exposure: when customers compare notes, the gap between \u201cpersonalized offer\u201d and \u201cdiscriminatory pricing\u201d can collapse overnight.<\/p>\n

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\u00a0Unlike earlier AI tools, agentic systems build a cumulative model of each customer relationship. Past decisions shape future ones. A pricing agent that learned six months ago that a particular client segment accepts 15% premiums will keep applying that logic, refining it, extending it, unless someone explicitly changes the objective. No individual decision triggers a review. The drift is invisible until it isn\u2019t.<\/p>\n

Ticketmaster is the cautionary tale, and shows it is not new.\u00a0A U.S. Senate investigation\u00a0found that between 2019 and 2022, the number of dynamically priced tickets sold by Ticketmaster for North American concerts rose by more than 700%. The system did not fail because it could not optimize. It failed because optimization collided with consumers\u2019 sense of fairness.<\/p>\n

The subcommittee opened an inquiry into Ticketmaster\u2019s role in rising ticket prices. The algorithm hadn\u2019t malfunctioned. It had performed exactly as designed\u2014and no one had defined how far was too far.<\/p>\n

The commercial cost of perceived unfairness is not theoretical.\u00a0Bain has long argued\u00a0that online retailers need to offer customers a fair, not necessarily the lowest, price, and pricing research shows that fairness perceptions and loyalty are deeply intertwined. Yet the companies building the most sophisticated pricing engines are often the least equipped to explain, defend, or correct what those engines are doing. The precision of the machine outpaces the governance of the institution.<\/p>\n

Read more Current price of Bitcoin for June 12, 2026<\/a><\/p>\n

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The failure mode is almost never a rogue algorithm. It is an organization that delegated decisions without defining their limits.<\/p>\n

Not all pricing decisions carry the same institutional weight. A useful distinction separates transactional decisions\u2014frequent, local, reversible\u2014with bounded impact, from institutional decisions, those that touch brand promise, perceived fairness, long-term relationships, or the social license to operate.<\/p>\n

Transactional decisions are ideal candidates for full automation. Adjusting prices based on real-time inventory, local demand signals, or channel dynamics falls squarely in this category. Speed and consistency genuinely outperform human judgment here.<\/p>\n

Institutional decisions are different. They are the ones where getting the optimization right but the relationship wrong produces outcomes like Ticketmaster. For these, a useful test is what we call the 4Rs. Before delegating any pricing decision to an AI agent, executives should ask four questions: Is it\u00a0reversible<\/em> quickly without creating a toxic precedent? Is it sufficiently\u00a0routine<\/em>\u00a0to be standardized? Is it\u00a0rule-bound<\/em>\u00a0by explicit internal rules? And is any error\u00a0repairable<\/em>\u00a0without a public crisis? If the answer to any of those questions is no, the decision should not be fully delegated. When a single one of these conditions fails, full delegation to the machine becomes a governance risk, not a productivity gain.<\/p>\n

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The deeper question is structural. Every agentic pricing system encodes a vision: of what matters, what can vary, what is negotiable, what is not. Companies that have not made those choices explicitly have made them implicitly by default, through the objective functions their data scientists optimized for last quarter.<\/p>\n

The executives we have spoken with who have navigated this transition most successfully share one characteristic: they treat AI pricing governance not as an IT question but as a leadership question. They can articulate, without consulting a dashboard, what their pricing agents are permitted to do on their own, and what requires a human decision.<\/p>\n

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They have, in effect, written a pricing constitution: the rules that will produce all future prices, before those prices are produced. Such a constitution should define which variables an agent may use and which ones are off-limits, how much price variation is acceptable across customer segments, when a human must approve a change, and which long-term metrics like trust, churn, repeat purchase or complaints can override short-term margin.<\/p>\n

The question is not whether your AI will make pricing decisions. It already is. The question is whether the rules it is following are the ones you would stand behind, this in front of your customers, your board, and, if necessary, a congressional hearing.<\/p>\n

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If you cannot answer that today, the time to define those rules is before the next Monday morning notification arrives.<\/p>\n

Francois\u00a0Candelon<\/em>\u00a0is a partner at private equity firm Seven2 and executive fellow at the HBS AI Institute\u00a0(formerly known as the D^3 Institute)<\/em>.\u00a0Read\u00a0<\/em>other\u00a0<\/em>Fortune\u00a0columns by Fran\u00e7ois Candelon<\/em>.<\/em><\/p>\n

Paul-Louis Andres is a director at Seven2.<\/em><\/p>\n

Augustin Manchon is a professor at HEC and Paris-Dauphine and Pricing Strategy and Governance advisor to CEOs at Manchon & Company.<\/em><\/p>\n

Read more Current price of oil as of June 12, 2026<\/a><\/p>\n

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The rules your AI is following need to be rules you would stand behind\u2014in front of your customers, your board, and even a congressional hearing.<\/p>\n","protected":false},"author":1,"featured_media":1277,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[134],"tags":[],"class_list":["post-1278","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-automation"],"yoast_head":"\nYour AI is already setting prices. The real question is who sets the rules - Cross Country Moving Team<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/crosscountrymovingteams.com\/?p=1278\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Your AI is already setting prices. 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