01
Client context
The company had a long list of possible AI features and pressure to show progress, but limited evidence of what customers would use or pay for.
02
Business challenge
Customers expressed enthusiasm for AI in general but concerns about accuracy, data security and cost.
03
Research objective & questions
Identify which AI use cases deliver enough value and trust to drive adoption, and how to package and price them.
- Which AI use cases solve meaningful problems in customers' workflows?
- What concerns block adoption, and what would address them?
- Should AI features be bundled, tiered or sold as add-ons?
- What are customers willing to pay?
04
Methodology & approach
- Use-case interviews
- Users and decision-makers reacting to concept descriptions.
- MaxDiff prioritisation
- Ranking AI use cases by value.
- Willingness-to-pay survey
- Packaging and price sensitivity for AI features.
- Trust and governance review
- Requirements from IT and security stakeholders.
05
Sample & geography
- ~25 use-case IDIs
- ~350 customer survey responses
- Geography — Global (North America, Europe, India)
06
Key findings
- Assistive features that saved routine effort ranked well above fully autonomous ones.
- Data-handling transparency was a precondition for adoption in larger accounts.
- Customers preferred AI included in higher tiers rather than metered add-ons.
- A small number of use cases accounted for most perceived value.
07
Business implications
- Prioritise three assistive use cases for the next release.
- Publish clear data-handling and model-governance documentation.
- Include AI in upper tiers and review usage before metering.
08
Outcome
The product team gained an evidence-based AI roadmap, packaging approach and trust requirements to address before launch.
Example engagement — Shows how Hoog approaches this type of question. Not a specific client project. Findings are directional.