A situation to explore
A team buys periodic customer analysis, then struggles to turn it into a change. AI may make some analysis easier to prepare internally. The larger opportunity is a shorter loop between hearing the customer, understanding the cause and changing the service.
The assumption worth revisiting.
Insourcing only the document can leave the decision process untouched. It can also lose useful outside challenge. The choice is which parts of understanding, interpretation and action should be close to the people responsible for the experience.
What would we do differently if we could investigate this customer problem every week?
Give the capacity a purpose.
Build a repeatable learning loop with Product and Operations, retaining specialist research support where it improves the questions or interpretation.
How this fits the capacity allocation →
Use AI to challenge your first interpretation
Help me explore this question: What would we do differently if we could investigate this customer problem every week? 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
Beyond the bot: Building empathetic customer experiences with agentic AI
McKinsey · McKinsey Talks Operations
Encourages customer leaders to decide where human attention adds value as routine interactions change, rather than treating capacity as a headcount calculation alone.
Listen or watch for: Which moments should receive more human attention if routine contact becomes easier?
About this resource
Consultancy and vendor discussion with forecasts and selected examples; automation percentages are not transferable targets.
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 →