A situation to explore
A customer may be able to assemble a rough version of a report, workflow or analysis that previously required your product. The result does not have to match every feature to change the purchase decision; it has to solve enough of that customer’s immediate problem.
The assumption worth revisiting.
This is a hypothesis to test, not a prediction that customers will abandon products. It asks which value comes from the interface and which comes from dependable data, integration, judgement, service or a result they cannot readily own themselves.
If our customer could build the visible part, what would they still need from us?
Give the capacity a purpose.
Use some of your own insourcing capacity to explore the customer’s alternative. Involve Revenue and Operations in what that means for the offer.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: If our customer could build the visible part, what would they still need from us? Identify the assumptions in our current approach. Offer alternative explanations, including one that challenges our preferred answer. Separate hypotheses from evidence and suggest what we should ask the people affected.
Treat the answers as possibilities to test with people and evidence.
Go deeper into the evidence.
UK Government Commercial Agency / Government Commercial Function · Official decision framework
The framework compares internal, purchased and mixed delivery using whole-life costs, capability, risks and outcomes.
Public-sector guidance, not evidence that insourcing saves money or a general legal requirement for private companies. Applying the method here is TIP’s interpretation.
Klarna Group plc · Company annual filing; company-reported attribution
Klarna reports a $7m Q1 2024 reduction in external marketing-supplier spending versus Q1 2023 and attributes 37% of that decrease to AI. Named services include translation, production, CRM and social agencies. The filing also describes workforce reduction alongside AI efficiency, cost optimisation and normal-course employee attrition.
Management attribution, not an experiment. Supplier reduction is not proof every service moved in-house. The filing does not establish that every leaver went unreplaced, that there were no layoffs, or that all workforce changes were caused by AI.
podcast
Agentic AI and the future of global business services
McKinsey · McKinsey Talks Operations
A useful prompt to reconsider the boundary of shared services: which decisions and activities could move closer to internal operating knowledge?
Listen or watch for: Would moving more work inside improve the complete process, or simply move a bottleneck?
About this resource
Consultancy examples and forecasts. The episode does not establish that insourcing is always cheaper or that predicted headcount reductions will occur.
Turn the question around.
Your customer’s insourcing opportunity could change your own business model. Explore which part of your offer they could own, and the value they would still want from you.
Explore the commercial risk →How TIP applies this to itself →