Insights from 30+ senior pricing leaders on the real barriers to AI adoption—and what needs to change.

By: Kirk Jackisch, President & Benjamin Garden, VP, Pricing Analytics, Iris Pricing Solutions

We know. Another article about AI.

The buzz is constant. The content is everywhere. It’s easy to feel like every possible angle on artificial intelligence has already been covered—and covered again. But here’s the thing: when it comes to AI in pricing, we’re still not seeing the story that really needs to be told. Bluntly, the adoption just isn’t there yet.

This article cuts through the noise by grounding the conversation in the lived experiences of over 30 senior pricing executives from Fortune 500 B2B companies across a range of industries. These are leaders responsible for driving pricing strategy inside some of the world’s largest organizations—many of whom are still grappling with whether, when, or how AI fits into their roadmap.

In our conversations with this group, one theme came up again and again: the gap between AI’s promise and its actual application in pricing functions is wider than the market wants to admit. We explore the reasons behind that divide—and offer a clear-eyed view of what’s needed to bridge it.

 

AI Adoption in Pricing Is Still Rare
Only a small handful of pricing leaders—about 13%—shared that their companies have developed a secure, internal AI platform for commercial use. The vast majority remain cautious, citing data sensitivity and organizational readiness as key blockers.

Security: The Elephant in the Room

If there’s one issue that consistently comes up as a blocker to AI adoption, it’s data security. Pricing teams often deal with highly sensitive commercial data, and the idea of feeding that information into public tools like ChatGPT raises red flags across legal, compliance, and executive teams.

Only a small handful of executives—about 4 out of the 30+—shared that their company had developed an internal, secure AI platform for commercial use – of which costs millions. Most of these were large, well-resourced pricing functions with 30+ team members and strong executive sponsorship. But for the majority, internal safeguards simply aren’t there yet.

Unless data security is addressed explicitly—and early—AI will continue to be seen as a risk, not a solution.

Maturity and Resourcing: A Question of Readiness

Many pricing functions are still in the early stages of digital maturity. Processes are manual, data is siloed, and systems lack integration. For teams still operating out of Excel spreadsheets, the idea of “intelligent automation” can feel distant, if not irrelevant.

Even among billion-dollar organizations, only a few executives said their company was truly AI-ready. Most pricing leaders we spoke with were operating in lean environments—sometimes as a team of one or two—without the infrastructure or resourcing to support meaningful AI exploration.

This also connects to our previous work on pricing and storytelling, which explored how the structure of pricing teams—whether they report into sales, finance, or sit independently—impacts their ability to drive innovation (See article). The leaders furthest along in AI experimentation were often those whose pricing teams were embedded within strategic functions or had a direct line to the C-suite.

 

Half of Leaders See a Generational Divide Around AI
Another key blocker is comfort with the tools themselves. Roughly half of the leaders we engaged said that internal discussions about AI were shaped by a generational divide. Many senior leaders are still catching up on what AI can (and cannot) do. There’s often hesitation around concepts like machine learning, large language models, and data-driven decision-making.
 Generative AI tools, in particular, are misunderstood, not as a standalone concept, but in its use case in the pricing function. While a few forward-thinking teams shared use cases around productivity and early experimentation, most said their peers still view tools like ChatGPT as novelties—or worse, threats.

Basic AI education and enablement for senior staff is essential to build buy-in and close the trust gap. Otherwise, even the best pilot ideas risk being shut down before they’re tested.

Beyond the tech itself, there’s a broader challenge: how do you simplify complexity? AI can produce rich pricing insights, but if those insights can’t be distilled into clear, actionable messages for stakeholders, they won’t gain traction. As we’ve discussed in past storytelling-focused work, effective pricing leadership means being able to translate data into narrative—and AI should support that, not complicate it.

Start Small: From Vision to Pilot

The industry has done a good job selling the vision of AI. What’s missing is a practical roadmap.

Rather than chasing big-ticket transformations, pricing teams should focus on small-scale pilots that are achievable with existing resources. Clean up internal pricing data. Use generative AI to test storytelling capabilities. Launch one simple automation workflow. Then measure the outcome.

Early, measurable wins are crucial to securing broader buy-in. They also help reframe AI not as a magic solution, but as a pragmatic tool—one that supports smarter, faster, and more strategic decision-making.

For companies unsure where to start, partnering with a consulting team to scope and run a small-scale pilot can help accelerate progress while minimizing risk. It’s a lower-lift way to begin proving value before investing in enterprise-wide change.

Elevating Pricing in the Enterprise

At the heart of this discussion is a deeper question: Is pricing viewed as a strategic function within your organization?

The companies furthest along in AI adoption are often the ones where pricing already has a seat at the table. In these organizations, Pricing is not a “nice to have” but a tool for unlocking growth, competitive advantage, and commercial excellence.

Again, this echoes our earlier research on pricing team structure and visibility—where pricing sits in the org chart matters. Teams embedded in revenue, strategy, or C-suite decision-making circles are better positioned to champion AI and attract internal investment.

Only a few leaders described their pricing function as having more than 30 people. The rest operated in hybrid or centralized models, with varied reporting lines into sales, marketing, finance, or operations. Unsurprisingly, the teams with clearer executive visibility also had the clearest path to AI investment.

The Bottom Line

AI’s potential in pricing is real—but potential alone doesn’t drive transformation. Until security, maturity, and strategic alignment are addressed, most pricing teams will remain stuck in the space between interest and implementation.

The call to action for senior leaders isn’t just to adopt AI—it’s to build the foundational systems, structures, and trust that make adoption possible. That starts with:

  • Cleaning and centralizing pricing data
  • Investing in pilot use cases that show measurable ROI
  • Training leaders on what AI can actually do
  • And repositioning pricing as a strategic priority, not a reactive function

As a strategic pricing partner, we work with organizations at all stages of this journey—whether you’re just beginning to think through AI’s role in your function, or you’re ready to build a roadmap for adoption. From pricing analytics to strategic enablement, our team (including experts who sit at the intersection of pricing and AI) can help you take the next step with clarity and confidence.

If your team is wrestling with how to bridge this divide, now is the time to act. The technology is here—but the readiness has to catch up.