In our last post we dove into how analytics must drive source to pay, because most of source to pay should be automated and touch free as most of the source to pay process is straight forward (and capable of being automated for the last decade), non-strategic, and low to medium value.

Strategic Sourcing is an activity that should be focussed on high risk, high complexity, and/or high value categories and occasionally focussed on medium risk, medium complexity, and/or medium value categories where there is incomplete information or insufficient product/category history, atypical turbulence in the market, or highly particular requirements that just came into effect as a result of new regulations. That’s a minority of products/categories, not a majority.

Procurement should only be focussed on significant exceptions. And, with proper, modern, systems with proper e-document integration and exchange, most of the documents should be arriving in standardized digital formats, and most of the processing should, thus, be fully automated. And most of what is non-standard will be PDF in relatively standard formats that LLMs will be able to process to 95% accuracy and only require a few human verifications and field completions. The days of 20 people invoice processing team should be long gone, as the tech, even for standardized PDFs, has been in production by the leading players for over 8 years. Invoice discrepancies can be auto-identified, suppliers auto-notified, suggested corrections auto-included, one-click acceptance emails/screens for the suppliers included, and most contingencies accounted for. Only in the rare situations where suppliers refuse to accept a correction, invoices are in very non-standard or handwritten format, payments don’t go through, etc. should a human need to get involved. However, 95% to 99% of all documents and transactions that flow through Procurement should be 100% automated.

But most of this doesn’t need experimental Agentic AI or Gen-AI. Classic RPA will do just fine. For most of the rest, Adaptive RPA, with a bit of Machine Learning / Auto-Suggestion based on human-based exception processing, will do the trick nicely. If you look closely at current generation (A)RPA, Machine Learning, Optimization, and Predictive Analytics and walk through the full source-to-pay process, there is very little that can’t be automated without Gen-AI LLMs or experimental Agentic Systems. Sourcing — there are many standard (seven step) processes that can be completely automated based on data analysis, data-based risk assessments, goal definitions, and optimization. RFX (including e-Auctions) can be fully automated and, from the time you specify a product/category to source, everything can be automated to the award (including the demand pull/calculation from other systems).

When it comes time to contract, if you have standard templates or a large clause library, the system can automatically create the contract from the template and RFP responses, integrate DocuSign, and auto-execute it. If you don’t, or if you have to use the supplier’s paper, then you might use an LLM to create a draft for human review and/or analyze the supplier’s paper for terms, pricing (to make sure it matches the bid) and potential risks, as well as suggested revisions, before you sign. Gen-AI/LLMs unnecessary, but useful on a point-basis if you don’t have a good historical equivalent of a solution like Coupa Exari or iCertis.

Supplier onboarding can be fully automated with RPA powered dynamic workflows and third party data ingestion, as can risk and compliance analysis — no modern Agentic solutions needed.

Then we get to automatic invoice monitoring and point-based re-orders, receipt creation from inventory integration, and invoice processing in e-Procurement which has all been around for at least a decade. Automated approvals subject to tolerances, rules and pre-approvals — as well as predictive analytics on payments for new or one-time suppliers/orders or (slightly) out-of-tolerance invoices can automate the entire invoice-to-pay process.

We can get through the entire process on best-of-breed, classically oriented, RPA tech with some machine learning that processes human decisions in exception management, alters or augments the rules (and guardrails), and auto-processes the same type of situation next time. We quickly get to 95%+ throughput for any task that should be mostly automated, and a top human employee with BoB (A)RPA solutions and some augmented intelligence packages for analytics and research becomes 10 to 20 times as productive as they would have been in the past.

That’s the real future of Procurement. Small, top-talent teams (mentoring small emerging top-talent teams) doing the work of teams five to ten times their size, doing it better, and delivering more value than anyone would have believed possible with best-of-breed tools. Not error-prone, hallucinatory, agentic systems that work well in demos and a few select categories, and go all over the place in reality (and then try to hide their mistakes like Nick Leeson [who single-handedly collapsed Barings Bank] until they do a modern equivalent of the 2005 J-Com trade and cost you hundreds of millions of dollars on your key billion dollar product line).

So while you need to modernize at all costs, you don’t need to go full Agentic on unproven solutions. Get 90% of the way on tech that has been proven where you can control the automation level until you get comfortable with automation and learn where you can safely hand tightly boxed “decisions” to the machine (where well-defined calculations would determine your decision the majority of the time) and where you can’t. Otherwise, you’ll just end up being another member of the 94% AI failure camp. That’s not a statistic you want to be part of, especially given the cost of this tech today (and the increased cost tomorrow as energy grids start to break and the compute costs for modern AI tech goes through the proverbial roof).