AI in Sourcing Optimization Tomorrow [PRO]

Our last article recounted the story of artificial intelligence in optimization today, or, more accurately the lack of AI in optimization today.

While AI in its most basic form of “assisted intelligence” is readily available in many modern procurement and sourcing platforms, as evidenced in our previous briefings (AI in Procurement and AI in Sourcing), it has not yet creeped into optimization. The most advanced platforms have limited themselves to easy constraint creation, data verification and detection of hard constraints that prevent solutions — as in the case of Coupa — or easy data population, wizard-based scenario creation (using standard model templates), and automation — as in the case of Keelvar. In the former case, the underlying statistical algorithms can be found at the heart of some modern machine learning technologies (but aren’t quite there), and in the latter case, the robotic process automation (RPA) is nothing more than an automated, manually defined, workflow.

But that doesn’t mean that AI won’t creep into optimization tomorrow. While it may not with the current vendors on the market (for different reasons with each vendor), that doesn’t mean that the next vendor to bring an optimization solution to the market won’t learn from the oversights of its predecessors and bring some obvious advancements to the table — especially when certain vendors are releasing their platforms with an open API to support an Intel-inside-like model where sourcing or AI vendors can build on leading optimization foundations to offer something truly differentiated.

And what could those differentiators be? We’ll get to that, but first let’s review the premise.

Simply put, in the traditional sense of the abbreviation, there is no AI, or artificial intelligence, in any source-to-pay application today, as there is no AI in any enterprise software today. Algorithms are getting more advanced by the day, the data sets they can train on are getting bigger by the day, and the predictions and computations are getting more accurate by the day — but it’s just computations. Like your old HP calculators, computers are still dumb as door knobs even though they can compute a million times faster.

However, with weaker definitions of the term, we have elements of AI in our platforms today. Assisted intelligence capabilities are beginning to become common in best-of-breed applications and platforms, and “augmented intelligence” capabilities are starting to hit the market for point-based problems. For example, tomorrow’s procurement technologies will buy on your behalf automatically and invisibly, automatically detect opportunities, and even identify emerging categories.

But if AI is going to take root, it has to take root everywhere, and that includes sourcing optimization. So what could we see tomorrow?

Let’s step back and review what optimization does. It takes a set of costs, constraints and goals, and then it determines an award scenario that maximizes the goals subject to the constraints and the costs provided. So where could AI help?

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