Over the last few months, we’ve conducted a couple of sessions on artificial intelligence as it applies to B2B pricing functions…
The first session was a bit of a primer or fundamentals session, where we explored the basic history and evolution, the different flavors of AI technology available to us and how they’re being accessed and utilized in commercial environments. The most recent session delved deeper into a wide range of real-world pricing applications to better understand and expose what separates high-value, high-impact AI use cases from other applications that are little more than slideshow candy.
In both sessions, we highlighted an issue that’s worth calling out as many times as it takes.
Right now, we’re living through an “AI-everywhere-all-at-once” moment. Boards of directors are asking about it. Executive teams are mandating it. Vendors are putting the AI label on anything they can. It feels urgent. It feels transformative. And everything about it feels shiny and new.
But the issue I’m talking about? It’s not new at all. In fact, we’ve been dealing with this issue since business process automation started to gain traction in the late 1960s and early 1970s:
We automate processes that really shouldn’t exist.
A surprising number of pricing processes and workflows only exist because of some deficiency somewhere else. We build approval loops because it’s more expedient than fixing our shallow volume schedules. We build exception workflows because it’s more approachable than fixing our generic pricing recommendations. We build reporting layers to monitor selling behaviors because that’s less daunting than fixing the incentives that are actually encouraging those behaviors.
And now, AI is lowering the development barriers so dramatically that it’s easier than ever to automate all those pesky processes and workflows. In fact, we may no longer need a bulletproof business case, elaborate IT justification, or even a budget override to get it all done. AI makes process automation so accessible that we can skip any and all critical thinking…and therein lies the problem.
Because there’s nothing particularly innovative or transformative about using AI to automate workarounds that really shouldn’t be there in the first place.
So before we use AI to automate any of our pricing processes and workflows, it’s a good idea to take a step back and answer some diagnostic questions:
- When was this process or workflow implemented and what problem was it supposed to mitigate or eliminate?
- Does this process exist because our prices are too generic or imprecise for the realities of our marketplace?
- If our pricing recommendations were more specific to the deals at hand, would the volume of exceptions drop?
- Are we using approvals and reviews to compensate for a lack of confidence in the pricing structures or schedules?
- Is this workflow trying to manage or curb behaviors that our compensation or incentive plans are encouraging?
- If this process disappeared tomorrow, what upstream pricing deficiency would immediately be exposed?
- How will automating this workflow or process contribute to improved financial results vs. saving time?
- Is this process about adding-on judgment and insight that really should be baked into the pricing structure itself?
- Are we trying to treat symptoms (exceptions, overrides, delays) instead of addressing why they’re occurring?
- How much would addressing the underlying root cause reduce the need for this process or workflow?
- If we rebuilt our pricing architecture from scratch today, would we intentionally design this process back in?
To be clear, I’m not against using AI for pricing, or any other commercial function, for that matter. Quite the opposite, in fact. Used properly, artificial intelligence can help us do all sorts of impactful things that we either couldn’t afford to do before, or that weren’t even possible by any other means.
What I’m cautioning against is using AI to further entrench legacy processes and workflows that may no longer be needed, rather than putting AI to much better and more valuable uses.
Always remember that the height of process efficiency isn’t making a workaround faster or less painful…it’s eliminating the need for it in the first place.
Making Sense of AI for B2B Pricing
There's no doubt that artificial intelligence will transform nearly every aspect of sales and revenue operations. How do we cut through the hype and harness AI to generate improved pricing results and business outcomes?
Pricing Process Improvement
In this session, we discuss the critical differences between process types, how to use end-to-end process mapping for diagnostics and prioritization, pricing technology considerations, and common mistakes to avoid.
Valuable AI Uses Cases for B2B Pricing
Some popular AI use cases barely deliver marginal gains, while others have a massive impact on results. How can B2B pricing teams figure out which is which and put their limited resources to the best use?
