AI is reshaping every industry, but building the tech is only half the battle. The bigger question? How do you price it. 

A top law firm launched a pioneering AI claims product for insurance providers, but stepping into SaaS meant rethinking how to price and package their innovation. With no benchmarks, low visibility on client value drivers, and the risk of over-delivering for free, they needed clarity and confidence AND fast. 

Here’s how Pearson Ham Group helped them go from uncertainty to commercial success. 

What happens when a professional services firm enters the AI product world? 

A top-tier law firm was breaking new ground: developing an AI-powered claims handling product for insurers. But with innovation came a fundamental shift in business model. While their core business remained rooted in time-and-materials professional services, launching a SaaS product meant rethinking how to monetise entirely. 

They needed a partner who could navigate unchartered territory, adequately monetise their customer base, design pricing that would resonate with clients, and ensure scalability. That’s where we came in. 

Why is pricing AI products so difficult? 

Everyone’s building AI, but few know how to price it. And that was the heart of the challenge. 

There were no pricing benchmarks to draw from. This was a first-of-its-kind product in the legal-insurance space. Without clear insights into what clients truly valued or how much they’d be willing to pay, pricing decisions felt like guesswork. The client also didn’t want to give away too much value. They were wary of bundling high-impact features without charging appropriately, but also needed a simple and scalable model.   

Finally, the product needed to feel like more than “just ChatGPT repackaged.” Differentiating it and communicating its unique value through pricing and offer design was key for conversion. 

How do you design pricing when there’s no precedent? 

Our approach focused on removing guesswork and building strategy from the ground up. 

We began by grounding the work in insight. Through multiple in-depth client interviews, internal workshops, and competitive analysis, we uncovered the value drivers that mattered most. 

We then applied our proven product packaging framework to evaluate each feature against perceived value and cost to the customer, ensuring the pricing model reflected actual usage patterns and client needs. 

To identify the right pricing structure, we explored several denomination strategies: should clients be charged per user, per claim, per feature, or something else entirely? We tested each to find the best alignment with customer expectations. 

Pricing strategy is incomplete without considering ease of execution. Hence, we also considered operational feasibility, ensuring the pricing model worked with billing systems and contract structures. Finally, we built the model with tomorrow in mind: designed for easy future up-sell, feature expansion, and increasing client maturity. 

What changed for the client? 

The result was commercial clarity, delivered fast. 

• A clean, usage-based pricing model, with clear upsell opportunities 

• Stakeholder buy-in, uniting product, commercial, and sales teams 

• A confident, strategic product launch that resonated with early adopters 

• A scalable foundation built to grow with the business 

Conclusion 

Great AI tools deserve equally great monetisation strategies. 

For companies moving into SaaS or launching new AI products, pricing should never be an afterthought. It’s not just about ARR, it’s about communicating value, building trust with customers, and ensuring long-term growth. 

At Pearson Ham Group, we help SaaS and professional services firms design and implement monetisation strategies from day one. If you’re building an AI product and want to make sure the monetisation model is as innovative as the tech itself, we would love to help. 

 Reach out to explore how we can help unlock the full commercial potential of your AI product.