Leveraging AI in Pricing
Challenges, Strategies & Organizational Impact - Insights from George Boretos
Artificial Intelligence is reshaping the way companies manage pricing — yet most organizations still struggle to unlock its full potential. During the fifth edition of the Pricing Gym, George Boretos, CEO and founder of Future App, shared three decades of experience in AI-driven price optimization. The session brought to life the real opportunities, misconceptions, and organizational obstacles companies face when implementing AI in pricing.
If you’re a pricing leader, revenue manager, or executive exploring how AI can elevate your pricing maturity, this session distilled what truly matters.
Why AI in Pricing Matters Today
Pricing is one of the fastest, most powerful levers for increasing profitability. Yet pricing teams remain vastly outnumbered — 50 to 70 times fewer pricing professionals compared to sales and marketing. As companies navigate inflation, shifting demand, and margin pressure, AI is emerging as a critical tool to compensate for limited resources and rapidly evolving markets.
Boretos’ experience — spanning 30 years, three AI startups, and thousands of pricing decision-makers — shows one thing clearly:
AI is not just a technical opportunity. It is a strategic moment for pricing to claim its seat at the leadership table.
🔍 What Future App’s AI Pricing System Reveals
Boretos’ AI platform integrates sales data, competitive intelligence, macroeconomic trends, and market research into an econometric model refined over 25 years. Key capabilities include:
Price sensitivity–based segmentation for more accurate forecasts
Dynamic price optimization by country, market, or customer segment
Forecasting uplift in revenue or profit under different pricing scenarios
Rapid identification of quick wins, such as low-elasticity opportunities
Ability to run multi-objective simulations (profit, growth, churn)
The takeaway? AI delivers value only when you know exactly what you want it to optimize.
And that brings us to the biggest misunderstanding companies face.
🚨 Challenge #1: The #1 Reason Why AI in Pricing Fails: Lack of Clear, Stable Business Objectives
Across industries, the most common failure point is not technology, data, or modeling sophistication. It is not knowing what the business actually wants AI to achieve.
Companies often launch AI initiatives with vague intentions:
“Improve margins”
“Grow revenue”
“Reduce churn”
“All of the above — simultaneously”
These objectives frequently conflict. Growth and profitability do not rise together. Churn reduction may contradict margin improvements. Without clear priority setting from leadership, AI becomes directionless and ineffective.
Boretos summarized it perfectly:
“AI can optimize anything — but only if you tell it what matters most.”
Changing business priorities mid-year creates whiplash for pricing teams and disrupts AI project continuity. The solution is to align on stable, strategic objectives before the first model is ever trained.
Why does this matter? Clear objectives enable:
targeted data selection
faster model deployment
measurable outcomes
realistic stakeholder expectations
higher internal trust and adoption
In essence: objective clarity is the foundation of successful AI-driven pricing transformation.
🧩 Challenge #2: Imperfect Data — And Why It Shouldn’t Stop You
Companies often delay AI projects because their data isn’t “ready”. But as Boretos emphasized:
“Business decisions are made every day with imperfect data. AI should be no different.”
Common data challenges include:
scattered ERP systems
inconsistent data quality across divisions
missing competitor pricing
incomplete product attributes
Despite this, companies can — and should — launch AI initiatives with the best available data subset, such as a single product line or business unit.
One surprising insight: In some cases, including competitor data actually reduced model accuracy, especially when competitors’ pricing was irrational.
The message? Start pragmatic. Start small. Start now.
⚡️ Challenge #3: Quick Wins vs. Big Bets — What Actually Drives Value
Most organizations believe AI requires massive, multi-year transformation projects. But research shared during the session revealed:
50–60% of AI’s business value comes from quick wins
20–30% comes from long-term, complex initiatives
Quick wins help teams learn, experiment, reduce risk, and build confidence. Boretos referenced the “1% improvements” philosophy of British Cycling — small, consistent gains compound dramatically over time.
The fastest way to AI success is iterative experimentation, not perfection.
🏢 Challenge #4: Organizational Structure & The Pricing Function’s Voice
Perhaps the most important non-technical insight was the organizational challenge:
Pricing teams are tiny
Pricing leadership is rare
Sales and marketing dominate internal influence
Pricing decisions are often diluted or resisted
CFOs may hold power, but they are not typically the owners of commercial pricing. Meanwhile, Chief Pricing Officers are still an exception, not the norm.
Yet AI provides a unique opportunity:
Pricing can lead digital transformation in commercial strategy — if the organization empowers it.
Historically, marketing evolved from a marginalized role to an executive function over decades. Pricing is on a similar trajectory, estimated to mature into a core leadership pillar within 10–20 years.
💬 Audience Reflections & Practical Takeaways
Participants echoed common struggles:
unclear goals
poor alignment across teams
fragmented data
difficulty securing buy-in
uncertainty about SaaS vs. project-based AI solutions
The consensus was clear: Start with defined objectives. Start with manageable pilots. Build momentum one win at a time.
🏁 Conclusion: AI Is the Catalyst — But Strategy Is the Engine
The Pricing Gym session highlighted one powerful truth:
AI can transform pricing — but only when business leaders define clear, stable objectives and empower the pricing function to lead.
With pragmatic data use, quick-win execution, and the right organizational support, pricing teams can deliver significant impact and elevate their role across the company.
AI is not the destination. It is the accelerator — and pricing leaders who embrace it today will shape the commercial organizations of tomorrow.
🎯Defining clear business objectives is the single most critical success factor in AI pricing.
Without clarity, AI can’t optimize.
Without prioritization, AI can’t deliver.
Without alignment, AI can’t scale.
This is a business challenge — not a data or technology one — and it must be solved first.
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Laurent Dosogne is a partner at Nexo Consulting and host of the Pricing Gym, a peer-to-peer forum for pricing leaders to pressure-test ideas and share candid insights.
George Boretos has over 25 years of professional experience leading commercial functions and pricing, a deep understanding of AI technologies, and a successful journey as an entrepreneur launching three AI startups and raising $9mn in Seed & Series A funding, working with Fortune 500 and other customers worldwide. His most recent endeavor, FutureUP, brings in this experience to help enterprises make data-driven decisions, powered by AI, to optimize pricing, improve profitability, and accelerate growth.
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