UpTrajectory Review

Hillary Remy's Inc. piece delivers a bracing corrective to the AI gold rush: the graveyard of failed business technology investments is filled not with victims of vendor fraud, but with operators who bought the pitch before they diagnosed the problem. This is a crucial distinction that gets lost in the breathless coverage of new models and features. Remy argues that the selection process itself is where businesses falter—chasing capability rather than fit, accumulating subscriptions that solve nothing because no one bothered to articulate what needed solving. For small-business operators drowning in demos and LinkedIn hype, this framing is both liberating and demanding: it moves responsibility from the market to the mirror.

The practical stakes here are immediate and painful. A small business running lean cannot absorb the hidden tax of orphaned software—unused licenses, staff time squandered on onboarding tools that get abandoned, the gradual erosion of team trust in leadership's technology decisions. Remy's insight reframes AI selection as an operational discipline rather than a technical one. The operator who needs this most is likely the one already subscribed to three AI tools, using none effectively, who feels the nagging sense of falling behind competitors. That anxiety is the enemy; it produces reactive purchasing that compounds the original problem. What Remy offers is permission to pause and a method for doing so.

What genuinely distinguishes this piece from the flood of AI buying guides is its rejection of feature-comparison matrices. Most coverage assumes the reader's job is to evaluate tools; Remy insists the job is to evaluate need. This is not semantic quibbling. The AI vendor ecosystem profits enormously from the assumption that buyers understand their own workflows well enough to match them to products. They often do not. Remy's skepticism toward the category itself—treating AI as a means, not an end—runs against the grain of publications that treat every product launch as inherently newsworthy. We find this refreshing, though we would push further: the 'clear reason' she demands requires honest accounting of whether a human process is actually broken, or merely feels inefficient because someone read about AI.

The downstream effects of Remy's approach, if adopted, would reshape vendor relationships. Sales cycles would lengthen as buyers resist premature demos. Implementation consultants would face harder questions about measurable outcomes. More consequentially, small businesses that master this discipline would develop institutional muscle for evaluating any technology wave, not just AI. The cost of this rigor is real: it demands time from operators who feel they have none, and it risks the occasional missed opportunity when a genuinely transformative tool arrives before the need is fully articulated. But the alternative—perpetual churn among tools that promise transformation and deliver distraction—is costlier still.

What to watch: whether this 'needs-first' framing survives the next cycle of AI product launches, when the temptation to acquire capability before purpose will intensify. What to do now: audit current AI subscriptions against specific workflow failures, not aspirational use cases. Kill what fails that test. For pending purchases, require a one-page brief that describes the current state, the friction point, and the metric that would confirm success. No metric, no purchase. The businesses that survive this technology cycle with both capital and team cohesion intact will be those that treated AI like any other capital allocation—suspicious until proven necessary.

A final caution: Remy's framework assumes operators can accurately perceive their own operational gaps. This is harder than it appears. The same anxiety that drives tool accumulation can obscure where work actually bogs down. Consider pairing her method with an external perspective—an advisor, a peer group, even candid frontline staff—before concluding that the need is real and the AI solution is apt. Self-diagnosis is only as reliable as the diagnostician.

“Failures rarely come from picking a bad product. They come from picking without a clear reason.” — Inc. Magazine

Takeaway: Audit every AI subscription against a specific workflow failure and a measurable success metric; abandon what fails this test before adding anything new.