For a long time now, being effective in B2B pricing required knowing how to do some pretty esoteric things…
How do we pull all the transactional data together? How do we clean it up and remove the noise? How do we segment the data according to different objective functions? How do we accurately analyze relative performance within relevant segments? How do we estimate value, delivered and perceived?
And how do we identify and quantify an improvement opportunity, build a recommended action plan, and turn the whole mess into something the leadership team can actually understand?
All of this specialized know-how mattered a great deal because a lot of this stuff was genuinely difficult to figure out and execute.
But AI is quickly making the “how” a whole lot easier and more accessible.
AI can now help assemble and structure data. It can write queries, summarize research, identify patterns, generate hypotheses, run analytical routines, build presentations, draft recommendations, and explain complex concepts and analyses in relatively straightforward language.
And it’s only going to get better, faster, and more ubiquitous.
So…what happens when even the most esoteric know-how becomes broadly available? Simply put, the differentiator shifts from knowing “how” to knowing “what, where, when, and why.”
After all, just because you can do anything, that doesn’t mean you should do everything. And commercial teams already have more potential problems to solve than they have time, money, or organizational patience to address.
Should we refine the existing segmentation model or build a new one? Should we tighten discount controls and add new policies? Change the price-list structures and definitions? Deliver better deal guidance to the point-of-sale? Correct particular offer-design problems? Investigate and correct margin erosion in a specific segment? Improve differential value messaging? Train salespeople on new or different topics?
Increasingly, AI can help us figure out how to do any and all of these things.
But it can’t tell us which ones matter most in our specific situation at this particular point in time, and which ones we should pursue first, second, and never.
Those decisions require much more than know-how…they require a high degree of acumen and discernment.
They require understanding the broader commercial situation well enough to define short- and long-term priorities. They require the ability to distinguish between root causes and symptoms. They require knowing when a pricing problem is actually a pricing problem…and when it’s really a targeting problem, an offer problem, a sales execution problem, all of the above, or something else entirely.
And they require an understanding of all the various factors and tradeoffs involved.
This is why the best pricing organizations in an AI-enabled world will not be the ones that use AI to automate the most processes, generate the most analyses, or produce the most recommendations.
The best pricing organizations will be the ones that consistently choose the right things to do with AI in the first place.
And the same applies to individual pricing practitioners.
If AI makes it easier for everyone to pull data, conduct analyses, develop recommendations, and create polished outputs, simply knowing how to perform those tasks becomes far less differentiating over time. So your differential value as an individual practitioner will increasingly need to come from knowing what really matters and why, and where and when to focus first, next, and never.
In an AI-enabled world, the specialized know-how to produce an answer is far less valuable than the acumen and ability to discern whether it’s the right answer to a question that’s worth asking.
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