Banking exception handling
The Cost of Exceptions: Why High-Volume Banking Workflows Accumulate Rework
Why exception populations in high-volume banking operations behave like a second operating model, and how to rank the causes worth removing.
Insights
Essays and operational analysis on cost-to-serve, rework, AI value and evidence-led improvement.
New essays are published first to the LinkedIn newsletter Simplifying AI Enablement. Follow the newsletter on LinkedIn →
Insurance operations capacity
Where Broker and MGA Capacity Really Goes: Repair, Waiting and Manual Effort
Why broker and MGA capacity disappears into the friction around the business, and how to separate judgement, necessary control and failure demand before choosing an intervention.
Private equity value creation
The First 100 Days: Turning VCP Hypotheses Into Evidenced Operational Value
How to move a value-creation-plan hypothesis through workflow, baseline, root cause and value pool before committing to a transformation or AI backlog.
Accountancy operating model
MTD Is an Operating Model Problem for Accountancy Consolidators — Not Just a Tax Deadline
The first quarterly submissions are done. What practices are reporting is less about software and more about client information, capacity and cost recovery — and what to establish before treating MTD as an automation problem.
Value leakage / AI prioritisation
The First 100 Days Should Start With Value Leakage. Not an AI Use-Case Backlog
Why the first 100 days should begin by locating and quantifying operational value leakage before creating an AI use-case backlog.
AI value / ROI
Why AI ROI Is Still Elusive — Even When Adoption Looks High
Why adoption metrics can look healthy while economic value remains unclear, and how to connect AI initiatives to measurable operational outcomes.
AI prioritisation
How to Prioritise AI and Automation Properly
A practical way to move from visible, politically attractive use cases to opportunities backed by business impact and credible economics.
AI capability / operating model
AI doesn’t fail because of technology. It fails because teams build collections of projects instead of capabilities that transform how business works.
Why isolated AI use cases create fragmented delivery, and how reusable capabilities, orchestration and operating-model design help turn AI investment into scalable business value.
Intervention choice
How to Select the Best AI & RPA Tech for Your Business
How to choose between technology options for a given process, and why the choice follows the process evidence rather than the vendor shortlist.
Automation discovery
Day in the life [overview]: Want to be an automation analyst? (Not techie, or are you a traditional BA?)
What discovery work actually involves day to day, and the analytical skills it draws on rather than the technical ones people expect.
Process redesign
5 process re-design tips BEFORE YOU AUTOMATE. Automation + Lean Thinking
Redesign steps worth completing before any technology is applied, so the workflow being automated is the improved one.
Automation delivery
The 14 common pitfalls awaiting your Automation team …and how to avoid them
Recurring delivery problems encountered by automation teams, and the controls that keep each one from taking hold.