A situation to explore
A colleague creates a useful internal tool quickly. It now looks possible to replace a subscription. The next decision is whether the organisation wants to own the service when requirements, users, dependencies and people change.
The assumption worth revisiting.
The strategic issue is the portfolio of responsibilities you accumulate. Owning a distinctive capability can be valuable; owning many incidental tools can absorb the attention needed for it. AI-generated code does not settle that choice.
Which of these tools would we still choose to own if building it took no time at all?
Give the capacity a purpose.
Give teams a route from experiment to explicit ownership. Choose what to build, what to buy and what to stop using in the context of the capabilities the business wants.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: Which of these tools would we still choose to own if building it took no time at all? 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 →