Bespoke practical modules that teach candidates how to connect Lean Six Sigma, AI and Agile delivery. Build a better process, test an appropriate AI intervention and show whether it creates value.
Each belt programme includes EF Lean-AI™ modules that fuse Lean, AI and Agile, at no additional module charge. Depth and exercises are tailored to the belt level. Standalone team workshops remain available by quotation.
For contributors learning to see waste, map processes and use AI responsibly. Practise customer value, simple evidence checks and a small improvement backlog.
Complete belt package: £695 + VAT. EF Lean-AI™ modules included in this price.
Explore Yellow BeltFor people leading improvement projects. Connect DMAIC evidence to AI opportunity selection, future-state design, pilot acceptance criteria and sprint delivery.
Complete belt package: £1,495 + VAT. EF Lean-AI™ modules included in this price.
Explore Green BeltFor complex cross-functional transformation. Challenge AI business cases, experimental design, evaluation quality, risk, scaling and sustainment.
Complete belt package: £2,495 + VAT. EF Lean-AI™ modules included in this price.
Explore Black BeltStandalone Lean AI + Agile workshops and application sprints can be tailored to your role, maturity and business challenge.
ILSSI accreditation, examination and certification apply to the existing Lean Six Sigma belt pathway. EF Lean-AI™ AI and Agile modules are included in each belt package. They are Executive Fusion modules, distinct from ILSSI-accredited belt learning, and do not confer Scrum Alliance certification.
All six EF Lean-AI™ module themes are included in the belt training price, with depth and practical exercises adapted to Yellow, Green or Black Belt level. Exercises use approved tools and suitably anonymised or synthetic information where required.
VOC, CTQs, SIPOC, Gemba and value-stream mapping. Distinguish customer value from waste and choose a process suitable for investigation.
Create a process map and waste-removal plan.
Build a baseline, assess measurement quality and use Pareto, fishbone and 5 Whys. Challenge AI-generated hypotheses and distinguish correlation from causation.
Create an evidence-led problem statement and analysis plan.
Compare elimination, simplification, conventional automation and AI. Apply FMEA, decision rules, human review and mistake proofing.
Create a future-state process and prioritised opportunity matrix.
Translate opportunities into backlog items and user stories. Define a Sprint Goal, acceptance criteria and Definition of Done; practise reviews and retrospectives.
Create a pilot backlog and sprint plan.
Work with approved AI tools on drafting, retrieval, classification or analysis. Test representative tasks, exceptions, accuracy and review effort.
Create a test set, pilot evaluation and go/no-go recommendation.
Calculate realised benefit without double counting. Apply control plans, suitable SPC or run charts, standard work, ownership and monitoring.
Create a benefits review and operational control plan.
Order opportunities by value, evidence, feasibility and risk. Waste-removal tasks sit alongside process redesign and AI experiments.
Choose a contained outcome and define what must be tested. Agree quality, human review, exception handling and the Definition of Done.
Build and evaluate a small process change or AI use case. Use representative examples, including failures and exceptions, rather than a polished demo alone.
Inspect the result with stakeholders. Review the way the team worked, adjust the backlog and decide whether to continue, change direction or stop.
Sprint length and delivery support are agreed by scope. Scrum Sprints are one month or less; a full Scrum engagement includes its defined accountabilities, events and artifacts. A short workshop alone is not Scrum.
A process map, opportunity matrix, pilot backlog, evaluation plan and control plan—selected to match your programme.
