What the work looks like

Scenarios built from patterns that recur across enterprise change estates — published so you can recognise your own situation before the first conversation, not to claim someone else's.

Illustrative scenarios — not named client engagements.

FINANCIAL SERVICES Financial Services delivery scenario

Scenario 01

Portfolio visibility across a fragmented change estate

The challenge
Fourteen programme reporting formats, a six-week lag to a consolidated portfolio view, and investment decisions being made on data that was already stale.
The intervention
A single portfolio data model, automated consolidation from existing tooling, and a scenario layer that let the investment committee test trade-offs live in the room.
The outcome
Reporting lag reduced to a weekly refresh; committee time shifted from reconciling numbers to deciding on them.
  • AI Enterprise Portfolio Management
  • Predictive Project Analytics

Illustrative scenario

PUBLIC SECTOR Public Sector delivery scenario

Scenario 02

Governing AI adoption inside a regulated delivery lifecycle

The challenge
AI tooling entering delivery teams ahead of any policy, with no documented decision rights, model assurance or audit trail.
The intervention
Responsible-AI controls embedded into existing stage gates, a decision-rights matrix, and a documentation standard mapped to emerging regulatory expectations.
The outcome
Adoption continued under a control environment internal audit could evidence, rather than being paused entirely.
  • AI Governance for Project Delivery
  • AI Risk Management & Governance

Illustrative scenario

MANUFACTURING Manufacturing delivery scenario

Scenario 03

Early-warning risk signals across a hybrid programme

The challenge
Schedule variance was surfacing at stage gates, weeks after the underlying causes had already compounded across dependent workstreams.
The intervention
A leading-indicator model built on existing delivery data, tuned thresholds, and an escalation path that put named owners against each signal.
The outcome
Risk conversations moved earlier in the cycle, with escalation triggered by evidence rather than by sentiment.
  • AI Risk Management & Governance
  • AI-Enabled Agile & Hybrid Project Management

Illustrative scenario

Measurement

How an outcome gets evidenced

We are wary of consultancy numbers, including our own. So every claim we make about an engagement resolves back to a baseline captured before we changed anything.

01

Baseline first

Delivery performance, data quality and decision latency are measured during the diagnostic, before any intervention. That measurement is the reference point for everything after it.

02

Instrument the change

Pilots are instrumented from day one. If a use case cannot be measured against the baseline, it does not go into the pilot set.

03

Report the misses

Use cases that fail to move the baseline are reported as such and retired. A framework that only ever produces wins is not measuring anything.

Your estate

Recognise any of these?

If one of these scenarios reads uncomfortably like your own portfolio, that is the conversation worth having.

Book a consultation