Course Objective & Scope
Bolting an AI tool onto a single step of a legacy workflow usually just shifts the bottleneck downstream without delivering real productivity gains. True transformation comes from rethinking the end-to-end workflow around what AI enables—automated triage, first-draft generation, instant knowledge retrieval and agentic execution.
This course covers principles for moving from as-is to to-be workflows, human-AI task allocation (copilot, straight-through processing, exception review), streamlining legacy hand-offs, redefining job roles and managing adoption. In a hands-on workshop using one of the organisation's own processes, teams produce a to-be workflow blueprint and transition plan.
Who Should Attend?
- Process owners across operations, customer service, finance, procurement and HR
- Process excellence, organisational design and business analysis teams
- Digital transformation leads and target operating model (TOM) architects
- Product managers embedding AI capabilities into enterprise workflows
Key Takeaways
- Distinguish incremental tool bolting from genuine AI-first process redesign
- Allocate tasks across full automation, AI-assisted copilot work and human-only judgement
- Design confidence-based straight-through processing alongside exception handling paths
- Anticipate downstream bottlenecks, revised SLAs and evolving role profiles
- Deliver a transformation pack containing a to-be process map, role cards and adoption plan
Curriculum
From Bolted-On Automation to AI-First Process Design
- Eliminating legacy hand-offs and checks built around human information limits
- Design patterns enabled by AI: pre-validation, instant synthesis and parallel triage
- Optimising the end-to-end value stream rather than isolated subtasks
Human-AI Task Allocation Archetypes
- Straight-through processing, copilot (AI draft + human sign-off) and specialist routing
- Confidence thresholds and risk-based triage rules
- Exception handling and routing complex cases to senior specialists
Mapping the To-Be Workflow and Interaction Touchpoints
- Designing where and how employees interact with AI outputs inside existing tools
- Capturing corrections and feedback seamlessly within the workflow
- Updating SLAs, capacity models and quality assurance sampling
Evolving Roles, Skills and the Target Operating Model
- Shifting staff from manual drafting to quality review and exception resolution
- Revising team structures, incentives and performance metrics
- Upskilling plans and safeguards against automation complacency
Transition Roadmap and Change Management
- Phased rollout, parallel run and criteria for retiring the legacy workflow
- Addressing employee resistance and scaling through early adopters
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