Xavier recently penned another great piece on Analytics in P2P: From visibility to actionability where he highlighted the failures in analytics in traditional P2P:
- static, backward looking, spend by category, invoice cycle time, approval rates, compliance rates
- insights only after transactions are processed, payments are made, and cycles completed
- late payments multiplying, exceptions accelerating, and supplier risk accumulating
- lack of operational insight
According to Xavier, P2P can only be modernized if the embedded analytics shift from descriptive to diagnostic.
- don’t report KPIs, explain the root causes (which approval paths contributed the most to approval time)
- don’t report exception rates, identify suppliers that consistently cause them
- don’t report spend anomalies, break it down and identify root causes
It’s a great start, but where it needs to get to is actionability. Xavier begins to address this point by stating the next step is “predictive awareness” where the system anticipates likely outcomes within active processes, such as predicting which invoices are likely to miss payment terms, which requisitions are likely to stall in approval or which suppliers are likely to generate disputes based on current patterns as that allows a Procurement professional to intervene before issues arise.
Finally, Xavier gets to the main point — the real inflection point comes when analytics begin to recommend actions and influence execution paths. Prescriptive analytics in P2P requires tight coupling between insight and control. If analytics identify a high-risk transaction, the system must be able to route it differently, apply additional validation or prompt a specific decision. If analytics detect a low-risk, repetitive transaction, the system must be able to reduce friction without manual intervention.
But it needs to go one step further. It must not only route differently, and apply more controls, but it must still do so automatically based on the diagnostic and predictive analytics. It can’t just apply a “one-size-fits-all” approach for automation and kick every exception out for human processing. You can’t always make the default path smarter because there should be different paths depending on the cost of the purchase, the risk associated with the purchase, the discrepancy between the invoice, goods receipt, PO, and/or contract terms and conditions. You need multiple streams that are auto-selected by predictive analytics that support the right actions given the assessment of the conditions.
The reality is this — except for truly exceptional situations, once you’ve made the decision on what to purchase, procurement should be 100% automated. It’s all e-document exchange, analysis, authorizations, and (payment) transactions. Unless something is really off, a buyer should never be involved once all the workflows, rules, and authorizations are setup.
But this automation should extend back into, and through, source-to-contract. Building on the Busch-Lamoureux Exact Purchasing pocket-cube framework, there are categories that are low risk, low value, and low complexity — you should NOT be buying these manually. “Agentic” automation should be taking care of these for you, considering that even a worst-case screw up will be of little impact. Then there are categories of moderate risk, value, and/or complexity which can be fully automated if all of the necessary data is available and there is a cost and supply history to build on, there are no special situations that need to be taken into account, and a worst-case analysis indicates that even a statistically unlikely “bad buy” will be of minimal impact. These should be 90%+ automated from the decision to buy to the recommended award, with extensive analytics and augmented intelligence for human review. And if the buyer likes the default recommendation, it should be just one click for the process to go from award to e-signed contract.
All of this requires very extensive descriptive, diagnostic, predictive, and actionable analytics and intelligence with extensive, adaptive, robotic process automation ([A]RPA) that can automate everything that should be. The reality is that while everything should be sourced (or exactly purchased), when you have all of the (market) intelligence, the standard processes, and the organizational goals encoded, then there’s no reason that the systems shouldn’t do the majority (or the entirety) of the work for you.
While buyers won’t be replaced by agentic systems (despite the over-hyped BS claims of AI Employees), they will be heavily augmented by them when most categories aren’t complex, risky, or strategic enough to require human review or intervention.
