AI strategy
The first AI workflow should be boring, valuable and measurable
A practical scorecard for choosing an AI use case with enough value, feasibility, control and evidence to justify investment.
Read guideSorsana insights
Practical guidance for production AI
Evidence-led guides for choosing the right workflows, building evaluations, governing risk and turning AI into measurable operational results. Browse the guidesTen guides for making better AI decisions
Each guide includes a practical framework, decision points and links to primary guidance for further reading.
AI strategy
How to measure AI ROI: from task improvements to operational outcomes
A practical method for baselining AI value, measuring workflow impact and avoiding inflated business cases based on theoretical time savings.
Read guide Production AI
AI pilot to production: a go, refine or stop checklist
Twelve evidence gates for deciding whether an AI pilot is ready for production, needs a narrower scope or should stop.
Read guide AI evaluation
How to build an evaluation framework for production AI
A step-by-step framework for testing AI quality, safety and workflow value before release and throughout production.
Read guide Insurance AI
Using AI in complaints handling: workflow design and human oversight
Where AI can assist complaints teams, which decisions should remain controlled and how to measure quality, fairness and customer outcomes.
Read guide AI strategy
Build, buy or configure? A decision framework for enterprise AI
Decide whether to build bespoke AI, buy a product or configure a platform by comparing differentiation, control, evidence and lifetime cost.
Read guide AI strategy
How to build an AI strategy and 12-month delivery roadmap
Turn an AI ambition into a focused portfolio, operating model and quarterly roadmap with measurable delivery gates.
Read guide Insurance AI
AI for claims document processing: where automation should stop
A practical claims automation model covering document intake, extraction, summarisation, decision support and human review boundaries.
Read guide Agentic AI
How much autonomy should an AI agent have?
Use a risk and approval matrix to set safe AI agent boundaries for recommendations, tool use and operational actions.
Read guide Production AI
What happens after launch? How to monitor and improve production AI
Build a practical AI operating loop for monitoring quality, risk, adoption, cost and change after production launch.
Read guide Need a practical next step?
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