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The Number Everyone Quotes: Data Credibility as a Leadership Asset
CPO Path #19, CW 31 2026
EXECUTIVE PREMISE
Procurement's standing at senior level depends less on the sophistication of its analysis than on whether its figures survive contact with finance. A helpful distinction here is between data that is complete and data that is quotable: the number an executive repeats without attribution is the one that has been reconciled, not the one that is most granular. On that reading, data credibility is a positioning question before it is a systems question.
WHY THIS MATTERS
Episodes #10 and #11 built arguments on margin, cash and risk. Each rests on a layer underneath — spend classification, supplier master data, contract coverage — that other functions rarely inspect until the moment they disagree.
An illustration from this field itself. A widely circulated figure holds that fewer than 40% of procurement leaders believe finance sees them as a strategic partner. It originates in a bylined article by a software vendor's chief executive, and the Deloitte page cited in support does not contain the statistic (Supply Chain Management Review, 2025). It travelled because it was plausible, not because it was traceable — the same reason a procurement number is borrowed once and then quietly dropped.
THE REAL DYNAMIC
The erosion between a reported saving and a realised one is measurable. McKinsey reports that the average procurement savings pipeline loses about one-third of its estimated value during planning and a further 20% in execution, which is why stronger organisations build pipelines more than 60% larger than their committed target (McKinsey, 2025). Bain describes the same effect organisationally: gains negotiated by procurement are frequently absorbed by business units to cover a shortfall elsewhere before they reach the bottom line (Bain, 2019).
The layer beneath is thin. Twenty-one percent of organisations describe their data infrastructure maturity as low, with less than 70% of spend data held in one place; a further 30% call it average, and even those with a single source of truth concede the data inside it is neither cleaned nor categorised (McKinsey, 2024). The enterprise base rate is comparable — across an exercise completed by 75 executives, 47% of newly created data records contained at least one critical error (Harvard Business Review, 2017).
This is also where AI ambition meets its constraint. Data quality is now the most-cited internal risk for generative AI implementation among procurement leaders, named by 44% (Deloitte, 2025), while 43% of organisations are actively pursuing AI deployment and only 12% report large-scale implementation (The Hackett Group, 2026).
A counter-perspective is worth holding alongside this. "Data will never be 100%. If it's 97 or 95%, it's good enough for our buyers," observes Syed Naqvi of The Hershey Company (Sievo, 2026). The distinction that matters may be less between clean and unclean data than between an error that is known and documented, and one discovered by someone else, in a meeting, in front of the person whose budget it moves.
WHAT STRONG LEADERS DO DIFFERENTLY
One practical instrument is a reconciliation one-pager, agreed with finance before it is needed rather than after a figure is challenged. For the five largest categories it records procurement's number, finance's number, the named reason for the difference, the person who owns the definition, and the date from which the joint figure applies. It converts a recurring dispute into a settled convention.
A second is a three-tier readiness view: what is trusted enough to publish or take to the board, what is trusted enough to decide on internally, and what is not yet usable. A governing rule follows — no AI use case draws on the third tier, and anything drawing on the second carries a named human approver, consistent with the decision-rights logic in Episode #16.
The third is ownership. One participant in Deloitte's survey attributes the shift to structure rather than tooling: "Data silos were a huge problem for us. Centralizing our data strategy under one leader broke down those barriers and eliminated duplicate data" (Deloitte, 2025). Note how Deliveroo's results appear in reporting on its transformation — not as savings, but as validated cumulative savings (Procurement Magazine, 2025). The adjective is doing the work.
CPO PATH DIAGNOSTIC
Statement | Fully true | Partly true | Not true |
|---|---|---|---|
Finance and procurement use the same addressable spend figure, and the definition is written down | ☐ | ☐ | ☐ |
I can name who owns each spend and supplier data definition | ☐ | ☐ | ☐ |
Reported savings are traced to a budget line or P&L effect, with finance's agreement | ☐ | ☐ | ☐ |
I know which data is publishable, which is decision-grade, and which is not yet usable | ☐ | ☐ | ☐ |
Our AI use cases draw only on data we would defend in front of the CFO | ☐ | ☐ | ☐ |
Four or five in the first column suggests numbers that travel on their own; zero or one, figures that still need their author in the room.
ONE-LINE VERDICT
The number that carries is rarely the most precise one - it is the one nobody has to defend twice.
Hey {{first_name}}, how is your path going?
I read every single message, and past posts already shaped this space. Reach out to me and tell me what you’re up to and the path you’ve chosen. I read every email.
Talk soon,
Pascal
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SOURCES
Bain & Company (2019). Why Procurement Savings Get Lost in Translation. https://www.bain.com/insights/why-procurement-savings-get-lost-in-translation-cfo/
Deloitte (2025). 2025 Global Chief Procurement Officer Survey: Agents of Change. Survey of more than 250 CPOs across 40 countries. https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2025/us-deloitte-2025-global-cpo-survey.pdf
Harvard Business Review (2017). Nagle, T., Redman, T. C., & Sammon, D. Only 3% of Companies' Data Meets Basic Quality Standards. Based on a self-scored exercise completed by 75 executives. https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards
McKinsey & Company (2024). Mittal, A., Cocoual, C., Erriquez, M., & Liakopoulou, T. Revolutionizing Procurement: Leveraging Data and AI for Strategic Advantage. https://www.mckinsey.com/capabilities/operations/our-insights/revolutionizing-procurement-leveraging-data-and-ai-for-strategic-advantage
McKinsey & Company (2025). Pralong, D., Spaulding Schmidt, J., George, T., & Wirpel, C. Aim Higher and Move Faster for Successful Procurement-Led Transformation. https://www.mckinsey.com/capabilities/transformation/our-insights/aim-higher-and-move-faster-for-successful-procurement-led-transformation
Procurement Magazine (2025). The Procurement Interview: Rob Turner, CPO, Deliveroo. https://procurementmag.com/news/the-procurement-interview-rob-turner-cpo-deliveroo
Sievo (2026). Your Data Doesn't Need to Be Perfect: Seven Real Questions Procurement Leaders Asked About AI. Vendor-published; contributors named, including Syed Naqvi, The Hershey Company, and Brian Murphy, Bain & Company. https://sievo.com/blog/ai-ready-procurement-faq
Supply Chain Management Review (2025). Macfee, S. CFOs vs. CPOs: Why They Clash and How to Bridge the Gap. Bylined vendor commentary; cited here as the origin of an unsupported statistic. https://www.scmr.com/article/cfos-vs-cpos-why-they-clash-and-how-to-bridge-the-gap
The Hackett Group (2026). The Hackett Group Reports Rapid Progress in Procurement's AI Agenda, citing the 2026 Procurement Key Issues Study. Sample size not disclosed. https://www.thehackettgroup.com/the-hackett-group-reports-rapid-progress-in-procurements-ai-agenda/
Thank you for reading,
Pascal Hecker | Editor-In-Chief, CPO Path.