Gen AI is changing how quickly analytical work can be done. On pricing projects, it can help with everything from structuring an approach and developing hypotheses to drafting analyses and accelerating repetitive tasks.
But pricing work also comes with a responsibility that cannot be overlooked.
When a client shares transaction data, customer information, deal structures, margins or contract terms, they are trusting us with some of their most commercially sensitive information. That makes one question increasingly important: how do we use Gen AI to improve the speed of our work without putting client data at risk?
The Right Approach
The answer is not to avoid Gen AI. It is to be deliberate about where and how it is used.
A lot of the work Gen AI is useful for does not require client data. Developing a pricing framework, structuring an analysis, brainstorming hypotheses, drafting a methodology or refining a presentation narrative can all be done without exposing sensitive information.
This is where Gen AI can deliver significant value while keeping data risk low.ph here
Keep Client Data Out
Our starting point is simple: if a task can be done without putting client data into a Gen AI tool, it should be.
For example, a pricing analysis can be structured using a generic data model before any client information is introduced. Hypotheses can be developed independently, while analysis frameworks, calculation logic and reporting structures can be built in advance.
This allows teams to benefit from the speed of Gen AI while keeping sensitive information outside the tool.
When Data Is Needed
Some tasks require working with actual data. In those situations, the data should be anonymized first.
That can mean replacing company and customer names, removing identifiable contract details, indexing or scaling financial figures, and generalizing information that could point to a specific customer or deal.
The objective is simple: give the tool enough context to work with the problem, without giving it information that identifies the client or exposes commercially sensitive details.
The value of Gen AI comes from helping us work through a problem faster. It does not need to know who the client is or what their actual commercial terms are.
How We Approach This at Commercial IQ
At Commercial IQ, data security is part of how we deliver analytical work. We are ISO 27001 certified, and that discipline extends to how we use Gen AI on client engagements.
For pricing projects, our approach starts with avoidance. If Gen AI can add value without client data being shared, we use it that way.
Where data-level work is required, we apply strict anonymization before using Gen AI. The aim is to preserve the analytical context while removing information that could identify the client, customer or specific commercial relationship.
In simple terms: use Gen AI for what it does well, without giving it information it does not need.
Speed Without Compromising Trust
Gen AI can make analytical teams faster. But speed should not come at the cost of the trust placed in us by our clients.
For consulting firms working on pricing engagements, this is particularly important. Client data is often central to the work, and the way that data is handled is part of the service being delivered.
As Gen AI becomes a larger part of analytical delivery, firms need clear boundaries around how and where it is used.
For us, the principle is straightforward: move fast where Gen AI can help, but keep client data protected throughout the process.
