Leadership1 distinct publisher2 min readPublished
Its CEO expected pilots and internal champions, and got neither. What he got was per-seat spend nobody had totalled and outputs nobody had checked.
The Board Room · Leadership desk
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Take the per-seat rate at both ends, multiply by 1,200 heads and twelve months, and full coverage costs between $288,000 and $576,000 a year [11]. That is the visible half of the bill and the easier half to manage, because a per-seat invoice at least arrives in one place. Duplication does not. When one employee swears by one model and someone two desks away insists a competitor is better at the same task [c3b], the second licence is a line on somebody's expense claim, not a decision anyone made.
The accuracy problem generates no invoice at all. John Davie's account is that nobody was tracking outputs, their accuracy, or how they surfaced in critical business decisions [4]. He reaches for a global figure of $67.4 billion in losses from consequential hallucination errors in 2024, attributed to a Four Dots report citing AllAboutAI [9], which is a number to treat as an order of magnitude rather than a measurement of anything. His own version is more damning and needs no third party: as usage climbed, he asked which tools were earning their keep and how carefully people were checking what they leaned on, and nobody could answer him [5].
Then there is the asset that never reached a balance sheet. The refined prompts and the workarounds that actually moved work along lived and died inside third-party tools, with no way to capture them or pass them around [7]. That work was done on company time, on company output, and stored in an account the company does not hold. If his workforce resembled the population Microsoft and LinkedIn surveyed, roughly 936 of the 1,200 would be working through personal accounts [12]. That is an extrapolation across two different populations rather than a count of his own staff, and he does not claim otherwise.
Read the closing third with the byline in view. Davie says nothing offering the flexibility and oversight he wanted was available on the market [c6b], so his organisation built CollectivIQ, which he also runs [1][10]. The diagnosis survives the conflict of interest; the market conclusion is the oldest premise in enterprise software and should be checked against actual vendor pricing before anyone repeats it. What is worth keeping is the smaller finding. The persuasion work leaders had budgeted for never arrived [3]. The governance work they had not budgeted for is now the job, and it starts from a position where the employer knows less about its own tooling than any individual employee does.
Ranked by verification strength, evidence, and original report placement.
Davie writes that Buyers Edge Platform's 1,200 employees were using different, sometimes overlapping AI models, each with their own workflows, pricing and what he calls dangerous levels of inaccuracy.
Davie expected the usual rollout of pilot programmes, internal champions and a lot of convincing; none of that happened, and people just started using AI on their own.
One employee swore by one model while someone two desks over insisted a competing platform was better for the same task; some liked how Gemini wrote, and some developers trusted Claude for coding.
From Davie's viewpoint, thousands of quiet decisions were being made with no one tracking the outputs, their accuracy, or how they showed up in critical business decisions.
As usage climbed, Davie asked questions nobody could answer: how much the company was spending across all the tools, which ones were earning their keep, how carefully people were checking accuracy of outputs used for decisions, and where the visibility was for long-term value.
Davie says AI vendors were charging steep markups on top of model costs, often bundled into per-seat licences priced at $20 to $40 a month per user regardless of how much an employee actually used the tools.
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Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Single self-authored account, secondhand statistics
One source: a contributor essay written by the CEO of the company described and of the platform proposed as the remedy. The operational claims are first-hand but wholly unaudited, with no inventory, spend total, accuracy measurement or dates. The two quantitative external anchors travel secondhand (Microsoft/LinkedIn 78%; Four Dots citing AllAboutAI $67.4 billion) with no methodology, sample or link, and nothing in the cluster corroborates them or the assertion that no usage-based, controllable alternatives existed.
One self-reported company, no verified scale
Real adoption is disclosed but narrow: employee-driven use of multiple commercial AI models across a single 1,200-person firm, plus an internal build that the author says solved the problem. There are no per-tool user counts, no spend figures, no CollectivIQ rollout scale or dates, and no second organization in the cluster, so the phenomenon is evidenced only as a first-person anecdote at one company.
Overstated: vendor-authored problem framing
The strong assertions - 'dangerous levels of inaccuracy', $67.4 billion in hallucination losses, and no available solution with usage-based pricing and oversight - outrun what the piece measures, which is nothing: no error rates, no spend total, no alternatives evaluated, no post-CollectivIQ results. The gap is directional rather than extreme because the core operating observations (organic adoption, per-seat pricing untied to usage, prompt knowledge trapped in third-party tools) are plausible, specific and consistent with widely reported enterprise experience, and the author's conflict is disclosed in the byline rather than hidden.
Author sells the described remedy
The narrator is simultaneously CEO of the company whose problem is described and CEO of CollectivIQ, the platform presented as the answer, published in an invitation-only contributor community that carries no independent editorial verification. The essay's structure - escalating problem, assertion that no adequate solution existed, introduction of the author's product - aligns exactly with a commercial interest. The conflict is disclosed in the byline, which is why this is not scored at the ceiling.
Clear provenance, single conflicted source
Confidence in this assessment is moderate: the source text is complete, unambiguous and self-disclosing about the author's roles, so classifying the emphasis, the conflict and the missing measurements is straightforward. What limits confidence is that a single-source cluster provides no way to test whether the operating account is representative, whether the cited statistics hold, or whether the claimed market gap is real.
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1 article · August 26, 2026