Summary
Revenue Management Labs uses this special Pricing Guys episode to announce a major step forward: Gustavo Mendonça is joining as a Partner to lead the company’s new build function. The conversation explores how AI is changing pricing work, making custom tools faster and more accessible, while showing why expert judgment and implementation still matter.
Key takeaways
- AI can make custom pricing tools faster and more affordable, especially for companies that could not justify large transformation projects in the past.
- Generative AI tools such as Claude and Gemini are not reliable substitutes for properly built pricing models.
- Strong pricing results still require three connected capabilities: strategy, tools, and adoption.
- Smaller companies can now access more tailored pricing support without taking on a major technology investment.
- Pricing practices in Europe and North America have more in common than many leaders assume, although some European retailers are moving faster on dynamic pricing.
Why Gustavo joined Revenue Management Labs
Gustavo spent a decade at Kraft Heinz, where he worked in revenue management and digital transformation. He describes the experience as rewarding, with strong brands and talented people. Still, two opportunities pushed him toward RML.
The first was the rapid development of AI. In his view, the last six months alone have changed what is possible in pricing technology. Large companies often need to move carefully because of their size, systems, and approval processes. RML offered him the chance to build and deploy new capabilities more quickly.
The second was the opportunity to work across industries. While Gustavo’s experience is rooted in consumer packaged goods, pricing challenges also appear in software, manufacturing, distribution, healthcare, and business services. The data and commercial models differ, but many of the core questions are familiar: Where is value being created? How should prices respond to demand? What will sales teams and customers actually accept?
That cross-industry perspective fits RML’s approach. The firm combines practitioner-led pricing expertise with AI embedded in custom models, rather than treating technology as a standalone answer.
AI makes custom pricing more accessible
For many companies with less than $1 billion in revenue, advanced pricing tools were once difficult to justify. A major transformation could cost several million dollars, take years to deliver, and require a mature pricing department to manage it afterward.
AI is changing that equation. Custom tools can now be developed more quickly and at a lower cost. More importantly, they can be designed around the company’s actual data, team structure, and commercial decisions.
That flexibility matters. Many pricing platforms are powerful, but they can also be difficult to configure and operate. A company may be asked to upload data in a certain format, navigate complex workflows, and wait days for an output. If the business does not have a dedicated pricing team, adoption can become a problem before the project even gets started.
RML’s build function is intended to address that gap. The goal is not to give every company the same tool. It is to create practical pricing capabilities that match the organization’s needs and can be used by the people making daily commercial decisions.
Why asking AI for elasticity is not enough
One of the biggest mistakes discussed in the episode is treating a conversational AI model as a complete pricing solution. A user might upload data to a tool such as Claude or Gemini and ask for price elasticity, promotional lift, or customer interactions. The output may look convincing. That does not make it dependable.
Large language models are built to produce useful language, not to automatically estimate commercial relationships from messy business data. A proper pricing model needs to account for factors such as:
- Promotional activity and timing
- Competitor pricing
- Distribution and availability
- Product mix and pack sizes
- Seasonality and demand changes
- Customer or channel differences
The model also needs to be tested, interpreted, and connected to a business decision. AI can speed up analysis and help identify patterns, but it still requires someone who understands pricing, data quality, and the commercial context.
The bathroom tiling problem
The hosts compare DIY pricing analysis to tiling a bathroom after watching a few online videos. The basic steps may look simple: spread the material, place the tiles, and finish the job. But a poor installation can crack, shift, or fall apart over time.
Pricing tools have the same risk. A company may produce an impressive spreadsheet or dashboard in a weekend, then struggle to get sales, finance, and commercial leaders to trust it. If users do not understand the logic, or if the recommendations do not fit how the business operates, the tool will sit unused.
This is why RML treats implementation and change management as part of the work, not as an afterthought. A useful pricing capability must be technically sound and practical enough for teams to adopt.
Pricing transformation is still a long-term discipline
AI may shorten the distance between an idea and a working prototype. It does not remove the need for a broader pricing program.
Gustavo describes three connected areas that companies still need:
- Advisory: A clear pricing strategy based on market conditions, customer value, and margin opportunities.
- Build: Custom tools and models that turn the strategy into repeatable analysis and decisions.
- Adoption: Process changes, education, and hands-on support so the capability continues after launch.
The timeline may become shorter, and the technology may become more affordable. But sustainable results still depend on disciplined execution.
Europe and North America: more similar than different
The discussion also looks at pricing in Europe and North America, particularly in CPG. Gustavo avoids broad comparisons because Europe includes many different markets, from the United Kingdom and the Nordics to the countries within the European Union.
Still, the underlying commercial work is often similar. Companies in both regions manage range reviews, portfolio changes, store execution, promotions, and long lead times. They are also responding to many of the same trends, including private-label growth, e-commerce, health-focused products, and rising interest in protein and fiber.
One area where parts of Europe may be moving faster is dynamic pricing. Retailers in markets such as the Netherlands are experimenting with electronic shelf labels and changing prices more than once a day. Store formats, shopping frequency, household sizes, and basket behavior can all influence how practical that approach becomes.
What RML’s London presence means
Gustavo’s move also supports RML’s growing European presence, including a London office. For European clients, that creates a stronger local connection while preserving the firm’s broader experience across markets.
The larger message is clear: AI is opening pricing transformation to more companies, but technology alone will not create better margins. Leaders still need a customized strategy, reliable models, and a plan for getting decisions adopted in the field. That combination is where Revenue Management Labs intends to focus its next phase.
