Organisational Transformation & AI Operating Models
Scope of our engagement
- Organisational assessment and AS-IS mapping of activities and cross-team handovers
- TO-BE process design and target operating model
- Augmented management: decision preparation, variance analysis and decision support
- Allocation of work between people, workflows and AI agents
- Assessment of workforce, skills and review workload impacts
- Manager support, training, team-based experimentation and change management
- Governance of automated decisions, escalation and accountability
Deliverables and decision criteria
AS-IS/TO-BE process maps, RACI matrix, target operating model, impact assessment, change plan and dashboard. Each activity documents its trigger, input data, proposed action, approver, exception handling and retained evidence.
From current to target process
Target-process example: a customer request arrives in the CRM. An agent classifies it, retrieves permitted information and prepares a draft with sources. An adviser checks the context, edits and approves sending. The manager monitors corrections, end-to-end time and escalations. Missing data or an out-of-scope request sends the case back to a human workflow. This illustrates the method, not a documented customer deployment.
Prerequisites, controls and limitations
A business owner remains accountable for the process and decisions. Human reviewers need time, information and the authority to reject a recommendation. We assess review overhead, workarounds, deskilling and effects on collaboration. Sensitive decisions are not automated by default.
Our method: from business need to measured value
Understand
Analyse the organisation, activities, people, data and IT landscape. Observe real work and pain points before selecting a solution.
Prioritise
Compare business value, feasibility and risk. Establish a baseline, a business owner and success criteria for each use case.
Design
Define the target process, architecture, interfaces, responsibilities, human approvals and performance indicators.
Experiment
Test a pilot on representative situations, including failure cases. Compare quality, cost and usefulness with agreed criteria.
Deploy
Integrate with applications, run acceptance and rollback checks, train users and support managers.
Measure
Track benefits, errors, costs, adoption and incidents. Regularly reassess models, data and controls.
FAQ
How should an AI programme be measured?
Compare outcomes with a baseline: quality, end-to-end time, total cost, adoption and incidents. Measure review effort and errors, not just the number of generated answers.