The Go Blog
Practical insights into AI Operating Model Design: redesigning how your operation works with AI, and turning AI deployment into operating impact and enterprise value.
AI Governance After Approval: Who Controls the Authority an Agent Accumulates?
AI governance approves a use case, but agents accumulate authority through everyday use. Why the unit of governance is the operational agent, and who has the right to expand its authority.
22 July 2026 · 7 min read
AI Quality Management: Why AI Fails Differently From People
Quality systems were built around how people fail. AI fails consistently, confidently and by familiarity, not difficulty. Why AI quality is a design question before an inspection one.
15 July 2026 · 4 min read
When AI Performance Metrics Stop Measuring Performance
As AI absorbs routine work, dashboards keep counting human activity, a shrinking slice of the operation. Why activity-based metrics describe an operating model you have left behind.
8 July 2026 · 4 min read
Why The Answer Is Not the Decision
AI now produces the answer before the expert is involved. Why judgement moves to the smaller, harder part of the decision, the context the model cannot see.
1 July 2026 · 5 min read
When Performance Drops, People May Not Be the Problem
In an AI-enabled operation, a fall in performance may sit in the system, not the people. How to tell a system failure from a human one, and why misattribution is costly.
24 June 2026 · 6 min read
Performance Architecture: From Measuring Activity to Evidencing Value
Activity is too thin a proxy for value in an AI-enabled operation. The five fronts a performance architecture has to evidence, from full cost-to-value to the value AI newly makes possible.
17 June 2026 · 5 min read
The Constraint Is Not AI. It's the Operating Model.
AI value is constrained less by the workflow than by the operating model around it: quality, governance, performance and workforce design decide whether AI delivers enterprise value.
10 June 2026 · 4 min read
Cost Is Where AI Measurement Starts. It Should Not Be Where It Stops.
Usage-based pricing makes AI cost a live variable worth tracking closely. But an operation that measures cost in detail and return not at all is deciding with one side of the equation lit.
3 June 2026 · 4 min read
You Cannot Build AI Capability with Training Alone
Training teaches people to use AI. It does not change how the organisation works with it. Why adoption is an individual outcome and capability is a design outcome.
27 May 2026 · 4 min read
Why AI Workflows Feel Inefficient Even When the Technology Works
AI workflows feel slow even when the technology works because the workflow was built for human coordination. Why the constraint is workflow design, not the tool.
20 May 2026 · 5 min read
AI Strategy Without Designing a Future-State Function Is Ineffective
AI strategies collapse when leaders cannot describe what the organisation becomes once AI is embedded. Why use-case activity hides strategic weakness, and why coherence is a design problem.
13 May 2026 · 4 min read
Why AI Transformation Fails When Its Dimensions Are Treated as Steps
AI transformation fails when Value, Design, Capability and Performance are treated as sequential phases. Why the four dimensions have to be designed as one connected logic.
6 May 2026 · 5 min read
Measuring Customer Satisfaction in an AI-Enabled World
NPS, CSAT and CES were built for periodic measurement of human-handled work. As AI reshapes the interactions and the queue, the measurement model itself has to be redesigned.
29 April 2026 · 4 min read
The Coordination Problem Inside AI Transformation
The coordination needed to make AI reliable can consume much of the efficiency it creates. Why that overhead is the cost of an unearned trust, and why design upfront resolves it.
22 April 2026 · 3 min read
Key Signs of Uneven AI Adoption and What to Do About It
Uneven AI adoption is rarely a capability gap. It is a design gap. Why the same workflow produces different outcomes once AI joins it, and what that reveals about the operating model.
15 April 2026 · 4 min read
The Measurement Gap: Why AI Value Fails the Evidentiary Test
Why AI value is hard to evidence. The measurement gap sits in value design, not measurement: define the value type and where in the workflow it forms before you measure it.
3 April 2026 · 5 min read
Why AI Investment Rarely Becomes Enterprise Value
AI investment rarely becomes enterprise value because it is applied to tasks, not the operating model. Why converting intelligence into value is an operating-model redesign.
9 March 2026 · 4 min read
When AI Efforts Multiply Without Alignment: Why Strategic Cohesion Matters
AI is accelerating in every function while alignment slips. Why divergence, not resistance, is the risk, and why cohesion comes from capability, not agreement.
2 March 2026 · 4 min read
Designing AI Readiness Across the Organisation
AI readiness is a structural condition, not a technical milestone. The design signals that reveal whether an organisation can use AI coherently and at scale.
27 February 2026 · 4 min read
Why AI Capability Is a Leadership Issue, Not a Training Problem
AI capability is a leadership issue, not a training problem. Why training without redesigning decisions, workflows and roles leaves capability stalled, and what leadership has to change.
23 February 2026 · 5 min read
AI Governance as Capability, Not Control
AI governance is a strategic capability, not a compliance overlay. Portfolio thinking, decision clarity and governance designed into the operating model, so it enables flow rather than friction.
20 February 2026 · 5 min read
7 Characteristics of an AI-native Operating Model
The characteristics of an AI-native operating model, where AI is built into how the organisation thinks, decides and delivers rather than layered onto legacy systems.
16 February 2026 · 5 min read
Capability First: What Strong AI Foundations Make Possible
AI progress rests on internal capability, not tools. Why capability-first organisations scale coherently while premature scaling fragments, and what strong foundations make possible.
13 February 2026 · 4 min read
Why Most AI Strategies Fail: AI Strategy as Operating Model Design
Most AI strategies fail because they are drafted apart from the operation they must change. Why AI strategy is operating model design, anchored in strategic alignment.
9 February 2026 · 4 min read
AI Leadership Accountability: Executive Decision-Making in the Age of AI
As AI shapes executive decisions, accountability moves upward to the leader who owns the outcome. Why judgement, fluency and composure become the centre of the role.
6 February 2026 · 4 min read
How to Map Behavioural Risk Before It Derails Your AI Programme
AI programmes fail less on tools than on trust, clarity and human alignment. How to see the behavioural frictions that undermine adoption, and design to resolve them before momentum is lost.
22 December 2025 · 4 min read
Where AI Adds Value, and Where It Doesn't
Potential without precision wastes focus. A way to identify high-value AI use cases through automation, augmentation and acceleration, and a clear view of where AI should not be applied at all.
11 November 2025 · 3 min read
Accountability Gaps in AI Governance: The Hidden Risks of Undefined Responsibility
Governance is not the same as accountability. Why undefined responsibility for AI outcomes creates hidden risk, and what real ownership looks like.
22 September 2025 · 5 min read
Scaling AI Adoption: Lessons from Vodafone's Copilot Rollout
A promising AI pilot proves the technology works, then momentum stalls. What Vodafone's Microsoft 365 Copilot rollout shows about scaling AI as a leadership and operating challenge, not a technical one.
20 August 2025 · 4 min read
What Organisations Are Getting Wrong About AI, and How to Fix It
AI initiatives falter for structural reasons, not technical ones. The four common missteps, treating AI as a fix, technology over people, weak data foundations and scaling before value, and how to correct each.
14 May 2025 · 3 min read