It is almost universally accepted throughout the business world that artificial intelligence (AI) will transform things. A PWC survey confirmed as much, with 85 percent of CEOs feeling AI will “significantly” alter how they do business in the next five years. 

In B2B pricing, the challenges AI can address for the CEO are twofold: first, there’s the matter of reducing lost opportunity, simply because the wrong price was presented and the customer took their business elsewhere. Increased responsiveness—with the right information—means increased revenue. 

The second challenge is more insidious – and though it’s very easy to intuit the trouble, it is much tougher to measure. Too often, the C-suite is drawn into an ad hoc review and approval process—consuming valuable executive bandwidth as every negotiated deal becomes “strategic.” Most companies find that they actually have multiple, very similar, and fairly moderate impact situations that have been handled recently, but all in a crisis or emergency response mode. Think of the benefits to your commercial team as a consistent tactical application of your agreed-upon strategy frees up time, otherwise spent in these fire drills, to focus on improving organizational performance.

A change that’s already in motion

Like so many almost seismic changes that take place within organizations and markets, the arrival of change is not so much a sudden jolt as it is a tectonic shift, gradual but inexorable. That shift toward artificial intelligence is already well underway, especially when it comes to commercial pricing.  

Solutions incorporating AI for dynamic pricing, intelligent negotiation, and product configuration, cloud-based CPQ (configure, price, and quote), and more are finding adoption at more enterprises. Particularly at those who are global in scope and are on the front lines of contending with markets that are ever-more complex and challenging.

That rising complexity, combined with the perpetual pressure to find ways to be more efficient and to maximize positive outcomes, makes AI adoption a mandate. How else can large organizations pursue commercial excellence amidst shifting market dynamics, unceasing competitive assaults, and rising expectations among B2B customers for B2C levels of speed, precision, and convenience during the purchase process?

AI helps build your process around the buyer

When Gartner reported that 77 percent of B2B buyers said their last purchase was complex or difficult, it should have been a clarion call to all sellers that they need to make the process more centered on user convenience. That may be the paramount benefit of deploying AI in pricing in the years ahead.

What some may not have yet recognized yet? That “complex or difficult” are graded by buyers on a relative scale. What constituted “acceptable” or “convenient” five, ten, or twenty years ago to a different generation of buyers doesn’t fly with modern B2B customers.  They’ve been conditioned by personalization advances—not only in consumer apps but in B2B marketing—to expect the same from a vendor’s pricing process. Achieving such responsiveness and personalization using manual and outmoded pricing approaches is, however, virtually impossible.

So AI in B2B pricing will, as time goes on, be an essential means to the end of markedly improving the user experience for a prospective buyer.  Let’s compare two hypothetical sellers to illustrate the point:

Provider A has a sometimes complex B2B product offering or diversity of offerings; its competitor, Provider B, has a nearly identical product mix. The benefits from either are nearly at parity. 

Provider A, however, relies on “traditional” pricing methods, and is thereby unable to quickly price complex configurations and quote those to its prospects. This also hamstrings them in customer segmentation, as they have limited deal intelligence during negotiations and are unable to develop real-time pricing insights. The result? A process that’s slow, arduous, and imprecise for both seller and would-be buyer. Deals go unwon, especially since there’s a competitor—Provider B—who offers a superior user experience during the pricing process.

Provider B has implemented AI in pricing.  This enables them to quickly supply the buyer with expedited negotiations and highly accurate pricing, even for complex configurations, by drawing on multiple datapoints: Intelligent segmentation, machine learning utilizing data from their previous engagements with that buyer, competitive intelligence, contextual data, and more. The outcome?  Less money gets “left on the table” because this seller is now winning more deals while also optimizing margins.

The real transformation in B2B pricing that AI will deliver, as it finds broader use, is in providing pricing processes that work very nearly in real-time, delivering greater end value for both parties.

An unlimited upside?

It’s validated fact: Companies applying AI and machine learning to pricing and selling processes are seeing, even at this comparatively early date in the evolution of these systems, marked improvements in quoting accuracy, revenue lift, and margins.  

As these solutions and platforms evolve, though, and as enterprises make greater use of them, it’s difficult to predict exactly what the long-term improvements will be. Even today, the immediate iterative benefits of employing AI-powered pricing have been eye-opening for many users who didn’t realize just how severely they were being weighed down by last-gen approaches and processes.

For present-day adopters, the benefits of AI-powered pricing have reliably and predictably continued or escalated steadily, year over year. As these systems are fine-tuned and customized to enterprise needs, are able to leverage greater amounts of data to hone smarter actionable insights, or are applied across more products and services, what will be the ultimate ceiling on outcomes, growth, and profitability for these adopters? It may be virtually unlimited.

© 2021 LeackStat.com