Almost everyone knows that AI-powered pricing is the future, but very few have taken the leap. Companies know that AI will transform how they set, manage, and optimise prices.

Yet, most are still stuck in manual workflows, complex data setups, and fragmented systems. The result? Ambitious pricing goals without the infrastructure to make them happen.

Why? Most are still untangling manual processes, complex product data, and migration challenges.

As we look toward 2026, it’s worth reflecting on what we’ve learned from this year: what pricing teams are struggling with, what’s evolving, and where AI is already making an impact.

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Common Pricing Challenges We Saw in all 3 Quarters of 2025

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A few common pricing roadblocks stood out to us as we conversed with many businesses this year. We’re sharing the average percentage of companies across all three quarters of 2025 that struggled with each of these pricing challenges.

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1. Manual Pricing Workflows Are Still the Norm (100%)

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Every single company we spoke to still relies on manual spreadsheets, ERP exports, or disconnected tools. Despite growing urgency, digital adoption remains slow due to ERP migration projects or limited internal bandwidth. The desire to modernise pricing processes is stronger than ever, but the reality is that most teams are still doing it the hard way.

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2. SKU Complexity Keeps Growing (≈65%)

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Roughly two-thirds of businesses reported significant SKU complexity, whether due to vast product assortments, seasonal variations, or custom configurations.
This complexity makes it hard to maintain consistent pricing logic, and many still rely on manual overrides to fix exceptions. Across the board, teams highlighted the need for structured price logic, automation by rule or segment, and better override management to keep pace with growing portfolios.

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3. Competitor Visibility Has Become a Deal-Breaker (≈82%)

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On average, more than eight in ten companies admitted they lack real-time competitor visibility or scraping capabilities. What used to be a “nice-to-have” is now essential. Businesses increasingly demand access to competitive intelligence, including scraping from portals, login-protected catalogues, and non-EAN items.

Competitor tracking has clearly evolved into one of the most decisive features in modern pricing solutions.

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4. Interest in AI-Driven Margin Optimisation Remains High (≈72%)

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Around seven in ten companies expressed a strong interest in AI-driven margin optimisation or elasticity-based pricing tools. However, most treat it as a phase-two initiative due to challenges like data readiness or confidence in automation. The intention is high, but many are still laying the groundwork: cleaning data, setting rules, and defining governance before activating AI modules.

Download Whitepaper: Estimated margin losses due to 8 common pricing challenges that UK businesses face

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The New Priorities Shaping Pricing in 2025

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This year, pricing conversations have clearly matured. What started as a push to fix manual workflows and clean data has evolved into more strategic themes: governance, elasticity, integration, and performance.

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Here’s what stood out most in 2025:

1. Pricing Structure and support take the centre stage (≈68%)
   Governance has moved from a back-office concern to a key priority. Nearly 70% of companies now focus on override tracking, guardrails, and approval flows once reserved only for large enterprises, which are now essential for mid-market teams too.

2. Elasticity Becomes Actionable
   Elasticity is no longer just a buzzword. Businesses are testing sensitivity, running simulations, and gradually moving toward predictive, AI-driven elasticity as confidence grows.

3. ERP Migration Creates Both Friction and Flexibility (≈55%)
   With over half of companies migrating to new ERP systems, pricing tools that stay functional during transitions, such as ERP-neutral or CSV-based setups, are becoming must-haves.

4. Private Equity Drives Pricing Discipline (≈20%)
   For PE-backed companies, pricing has become a strategic lever for EBITDA growth. This ownership model is driving a greater focus on margin improvement and governance.

As businesses strengthen governance, modernise systems, and prepare cleaner data, 2026 is set to be the year AI pricing turns from ambition into execution.

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Riding the AI Wave: Where the Hype Ends and Symson Stands

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The attention on AI pricing has grown fast this year, not only in tech, but across many industries. Searches for “AI pricing” have spiked as more leaders want to understand what it is and how it can drive value.

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Like many new technologies, it’s now in the early stage of the Gartner Hype Cycle, full of excitement and exploration. Companies are testing and learning, trying to see real results. But many soon realise that without well-organised data or clear pricing goals, AI alone can’t deliver value. That’s when the hype slows down, and the real progress begins, turning AI from an idea into a working part of the pricing process.

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At Symson, we see this as a positive shift, a sign that the market is maturing. The hype shows that AI pricing is here to stay, and now it’s about doing it right. The most successful companies will mix human expertise with AI insights. Instead of chasing trends, they’ll focus on getting the basics right: good data, strong pricing logic, and transparent governance. AI should empower pricing teams, not replace them, and that’s exactly what Symson stands for: turning AI into real, measurable business value.

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Common Misconceptions about AI Pricing

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AI is everywhere right now, from marketing to customer service, but when it comes to pricing, many companies still hesitate. There’s a common fear that AI might replace jobs or overhaul entire pricing strategies overnight.

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In reality, AI doesn’t replace people; it supports them. It takes over repetitive, manual work so pricing teams can focus on strategy and insights. AI isn’t a magic switch; it needs clear goals, clean data, and human direction. When used right, it helps businesses make smarter decisions faster, not losing control. That’s the real power of AI in pricing enablement, not replacement.

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Also Read: AI pricing misconceptions in 2026: What AI Pricing can't do

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Building Your AI Pricing Roadmap for 2026

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As AI pricing moves from hype to real adoption, 2026 is the time to turn ideas into action. To get there, every company needs a clear roadmap that connects data, technology, and pricing strategy. The journey starts with understanding your data and ends with continuous improvement driven by AI insights. Here are the five steps to get started.

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1. Organise your data
   Start by getting your data in order. Collect it from all relevant sources, clean it, and make sure it’s consistent. Without reliable data, even the best AI tools can’t deliver accurate insights.

2. Identify and segment your data
   Next, look at how your data can be grouped. Identify meaningful customer and product segments so you can understand which products or customers behave similarly and how pricing affects each group.

3. Analyse customer and product segments
   With clear segments, you can start exploring how price sensitivity differs across them. Some products or customers may respond strongly to price changes, while others are less affected. This helps define where to focus margin improvements and where to stay competitive.

4. Define your pricing strategy
   Once you know your segments, develop a tailored pricing strategy. Work with pricing specialists or AI-driven models to test approaches like elasticity analysis or margin optimisation. Each segment may need a different pricing logic to achieve the best results.

5. Deploy and optimise continuously
   Finally, implement your strategy in your pricing tools and monitor the outcomes. Review your data regularly, measure results, and adjust as your market and business evolve. AI can help you track performance and suggest updates for ongoing improvement.

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Laying the Foundation: Your AI Pricing Business Case

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At Symson, we always begin by creating a clear business case for every client. Each company is different, but the goal is to show the real financial and operational impact of AI pricing. We start by understanding your current revenue and calculating the potential return on investment. Most companies can expect a two to five per cent increase in margins, which can make a significant difference to overall profit.

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We also assess the operational benefits. AI pricing helps improve accuracy and consistency across teams by using data to adjust prices based on demand, competition, and customer behaviour. It automates time-consuming tasks, reducing human error and saving valuable time. It also improves communication with customers by ensuring transparent and consistent pricing, which builds trust. By bringing all data together in one intelligent pricing system, businesses gain a single source of truth. This makes it easier to track performance, identify missed opportunities, and ensure compliance with regulations like the Omnibus Directive.

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The Key Drivers Behind AI Pricing Models

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To understand how AI can truly guide pricing decisions, we analysed the main drivers that businesses use in their models today. These factors help pricing teams position themselves effectively and make informed choices in changing markets.

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1. Economic Indicators
   Companies often start with the big picture, the overall economy. Factors such as GDP growth, inflation, and interest rates reveal how strong the market is and how much customers are willing to spend. For example, when inflation rises, products become more expensive to produce and buy, which affects pricing strategies. Similarly, changes in interest rates reflect how tight or loose monetary policy is, influencing investment, borrowing, and the overall size of the economic “pie.”

2. Currency Volatility
   For global businesses, exchange rate movements have a major impact. A strong dollar or a fluctuating euro can affect costs, margins, and competitiveness across regions. AI pricing models now factor in currency trends to protect profitability and support stable pricing across markets.

3. Trade Agreements and Tariffs
   Government policies also shape pricing. For example, tariffs on imported goods, such as Chinese electric cars entering Europe, can shift market dynamics and create an uneven playing field. Businesses use AI to simulate different tariff scenarios and adapt prices accordingly, ensuring competitiveness while maintaining margins.

4. Market Outlooks and Expert Reports
   Finally, economic forecasts and industry reports help organisations anticipate change. While these outlooks vary in accuracy, they provide valuable context for AI models to analyse trends and predict possible outcomes. By combining these external insights with company data, AI can support better, more forward-looking pricing decisions.

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