Pricing Infrastructure: The Real Roadblock and the Road Ahead

Drawing on joint research from Vendavo and Copperberg, this article explores why spreadsheets still dominate pricing workflows, where automation efforts stall, and how organizations can build the foundations for scalable, dynamic pricing.

Manufacturers and distributors have spent decades modernizing production and logistics, but pricing infrastructure often lags. Spreadsheets and manual steps are still propping up the pricing process, despite good intentions and digital investment. Every delay, every disconnected workflow erodes margin, slows decision-making, and weakens strategy execution.

This is where the transformation must begin. Let’s take a closer look at why.

Spreadsheets: Familiar But Fragile

85% of companies still use spreadsheets in their pricing workflows (see Figure 1). And this isn’t just about comfort. Spreadsheets are a fixture even among companies that have partially or fully automated pricing systems. More than 55% of respondents that report full automation still rely on them.

Why? Because when pricing tools feel disconnected from daily operations, teams default to what they know. Spread sheets are the “digital pacifier” of pricing teams. Comforting, but limiting.

But this fallback comes at a cost. Spread sheets introduce errors, lack auditability, and slow responsiveness in dynamic markets. The problem isn’t tools or preferences. It’s planning the transition. New tools are often deployed without clear workflows, full integration, or end-user trust. The result is a pricing function that looks modern from the outside but still runs on manual work underneath.

The Automation Stall-Out

60% of surveyed companies report some level of automation in pricing, but just 14% say they have fully scalable, modern pricing systems (see Figure 2).

Most live in the middle ground:

• 46% report being partially automated
• 29% report are still evaluating options

This “in-between” state is where transformation often stalls. It’s where a CPQ tool is added, but processes don’t change. Where a workflow engine exists but is not embedded into how pricing decisions are made.

The issue isn’t technical. It’s organizational. Our survey found:

• 37% of respondents cite capability gaps
• 23% point to integration challenges
• 19% flag budget constraints

Modernization is an operational shift, not just a software deployment. The right tools exist. The challenge is in preparing people, aligning workflows, and building trust in the system to add clarity instead of complexity.

The Case for Dynamic Pricing

The good news? The early wins are clear for companies ready to move forward. Among those that have adopted dynamic pricing, 83% report measurable improvements in accuracy, profitability, or both. These aren’t incremental changes, but significant gains in competitive, margin-sensitive environments.

And adoption is growing. 54% of respondents have either implemented or are considering dynamic pricing (see Figure 3).

But barriers remain. Many teams lack the confidence, context, or clarity to operationalize AI. There may also be concerns around transparency. How do you explain an AI-generated price recommendation to a sales leader or a customer? Transparency is non-negotiable, especially in regulated markets.

What once seemed exclusive to high-frequency consumer markets is now delivering value in B2B. It’s accelerating deal velocity, improving segmentation, and driving more resilient margins. It’s a smart first step for those seeking quick, measurable returns from modernization.

AI Adoption: Early Days, Growing Interest

Adoption of AI in pricing is still in its early stages, but the momentum is there. 13% of surveyed companies have deployed AI within pricing workflows.

But barriers remain. Many teams lack the confidence, context, or clarity to operationalize AI. There may also be concerns around transparency. How do you explain an AI-generated price recommendation to a sales leader or a customer? Transparency is non-negotiable, especially in regulated markets.

Part of the hesitation stems from mis-understanding. There’s a big difference between generative AI, which creates content, and explainable AI, which offers interpretable logic behind decisions. Explainable AI is already helping pricing teams identify margin leakage, analyze discounting trends, and model pricing sensitivity. It enhances control, not just automation, and it builds trust when introduced with a clear purpose.

Your Essential Action Points

Create the foundation for scalability. You can’t modernize pricing without first earning trust in your tools. That starts with infrastructure.

• Shift the mindset, not just the software – Spreadsheets feel familiar, but they can’t scale, support pricing governance, or promote speed and agility. Success starts with systems your team believes in.
• Build automation as infrastructure, not an add-on – Partial fixes won’t change anything. True visibility and control require tech that’s fully embedded in workflows. It needs to be engrained, not bolted on as a patch.
• Build automation as infrastructure, not an add-on – Partial fixes won’t change anything. True visibility and control require tech that’s fully embedded in workflows. It needs to be engrained, not bolted on as a patch.
• Use AI with purpose and transparency – AI isn’t magic, but explainable AI can deliver real results. Focus on trust, clarity, and human oversight, especially in regulated or relationship-driven environments.