Faster, better service
Target waiting, rework and inconsistent decisions. Measure lead time and quality, including exceptions and human review.
Executive Fusion helps leaders turn AI possibilities into an operational decision: what to change, where AI is appropriate, what the business case supports and how the benefit will be measured.
See the six-stage framework, the DMAIC overlap and the Lean, Six Sigma and Quality tools that support each decision.
Explore the Lean-AI™ FrameworkAI can release capacity, shorten response times and support better decisions. Lean makes sure the work is worth doing. Six Sigma tests whether the result is reliable. Agile helps teams learn before they scale.
Target waiting, rework and inconsistent decisions. Measure lead time and quality, including exceptions and human review.
Remove duplicate work first. Use AI on appropriate remaining tasks. Plan how released capacity will improve throughput, service or growth.
Compare AI with simpler alternatives. Include implementation, integration, review, maintenance and operating costs in the business case.
A process takes 1,000 hours a month. Lean removes 300 hours of unnecessary work. A pilot then reduces human effort on the remaining 700 hours by 40%, releasing another 280 hours.
At an illustrative £30/hour, 580 released hours represent £17,400/month of gross capacity value, before costs. This is not automatically cash savings. Automation alone at 40% would release 400 hours; simplifying first avoids investing in work that should disappear.
Net realised value = cash savings + realised capacity or revenue benefit + quality benefit − implementation and ongoing costs. Avoid double counting the same gain.
The Lean AI Opportunity Assessment examines a defined set of processes and gives leadership a prioritised route forward.
Agree the scope first. Review the work, challenge the waste and assess business value, data readiness, AI suitability, delivery feasibility and risk.
Defined scope. Clear outputs. Quoted before work begins.
Scope and timing are agreed around Friday advisory availability. Technical build work can involve specialist partners; the process design, business case and benefits remain explicit.
Review selected processes, identify waste and prioritise improvements and AI use cases. Receive an opportunity matrix, business case assumptions and a roadmap.
Take one priority process from current-state evidence to a future-state design. Agree what to eliminate, simplify, augment or automate.
Define the pilot, support implementation and compare results with the baseline. Test quality, exceptions, adoption and total cost before deciding whether to scale.
Embed ownership, standard work, training, monitoring and review. Mentor the people responsible for sustaining performance.
Fees are quoted by scope. Released hours are capacity, not automatically a reduction in payroll cost.
No. The best answer may be removing a step, clarifying a decision rule or using conventional automation. The assessment makes that choice explicit.
Discover draws on Define, Measure and Analyse. Eliminate and Redesign develop the improvement. Augment and Automate test suitable interventions. Control validates and sustains the gain. Evidence and measurement run through every stage.
Alan leads process diagnosis, future-state design, opportunity selection, the business case and benefits validation. The technical delivery scope is agreed for each engagement, with specialist implementation partners where needed.
Agree baseline measures and pilot acceptance criteria before implementation. Evaluate quality, cycle time, human effort, adoption and total cost. Distinguish capacity released from cash savings and include implementation, review and operating costs.
Start with the problem, the current performance and the outcome you need.