UpTrajectory Review
Daniel Marcos, writing for Inc., argues that the hard part of AI is not buying it. The hard part is acting on what it tells you. His piece zeroes in on pricing decisions, where software can surface a signal in minutes but the organization still needs sign-offs from strategy, finance, and whoever owns the P&L. Marcos runs the Scaling Up coaching organization and has spent years around growth-stage companies, so he writes from the operator's side of the table rather than the vendor's. The available text is one sentence, but the argument it compresses is familiar to anyone who has bought an analytics tool and watched it gather dust while the pricing committee schedules its third review meeting.
For a small-business owner, this lands close to home. You do not have a pricing committee. You have you, maybe a bookkeeper, and a gut feeling that your rates have drifted below the market. AI tools promise to close that gap by scraping competitor prices, tracking demand, and flagging the moment to raise a quote. Yet the bottleneck is rarely the data. It is the moment you must actually change the number on the proposal and explain the change to a customer who remembers last year's price. Marcos's point, read generously, is that the tool does not remove the courage requirement. It only makes the decision harder to postpone, because now you know.
What is genuinely useful here is the framing of AI as an accountability device rather than an oracle. Plenty of coverage treats AI pricing as a technical problem: better models, better data, better forecasts. Marcos treats it as an organizational problem, which matches what we see in small firms. The owner who hesitates to raise prices will hesitate whether the suggestion comes from a spreadsheet or a chatbot. The under-reported angle is that cheap AI tools can actually worsen this dynamic by flooding the owner with recommendations that all demand action. A tool that tells you to reprice weekly is not a gift if your contracts lock rates for a year.
The second-order effect cuts two ways. On one side, owners who build a habit of acting on small pricing signals will out-earn peers who wait for certainty, because margins compound faster than revenue. On the other side, firms that automate pricing without a clear owner for the decision will blame the tool when a customer pushes back. We are skeptical of any claim that AI makes pricing objective. Prices encode relationships, and a small business lives on repeat customers who notice a sudden 12 percent bump. The tool can suggest; only the owner can absorb the relational cost of the change.
Watch for two things. First, whether your AI vendor shows its work: a price recommendation without the reasoning is a demand for blind trust, and blind trust is how owners get burned. Second, whether you have named a decision-maker and a deadline before you turn the tool on. If the answer is "we shall review it at the next leadership meeting," you have bought a subscription to insight you will not use. Start smaller. Pick one service line, set a floor and a ceiling, and let the tool operate inside that band. You still own the exceptions, but the routine moves stop waiting for your signature.
“Buying AI tools is easy. The harder part is acting on the insights when strategy, pricing, and accountability still wait for sign-offs.” — Inc. Magazine
Takeaway: Name a decision-maker and a deadline before you switch the pricing tool on, or you have bought insight you shall never use.
Excerpt from the original — Inc. Magazine
Buying AI tools is easy. The harder part is acting on the insights when strategy, pricing, and accountability still wait for sign-offs.