In sectors like insurance, financial services and health, technology sits behind every service you offer. Much of it is built by suppliers, bought as platforms and funded across programmes, so no one sees the whole system. We give you one independent, evidence-based view of how technology delivers value, where it stalls, and what to change.
Leaders in enterprise technology organisations typically come to us when one of four things happens. Each raises different questions, but the underlying need is the same: an independent view of where delivery stalls and why.
The trigger
“Milestones keep slipping and nobody can tell me whether it's engineering, the programme, or the demand coming in.”
Where capacity actually goes versus what the plan assumes
How work is funded, prioritised and handed over
Dependencies across teams, platforms and suppliers
Delivery performance against the commitments made
Whether the constraint is capacity, capability, demand or programme design
Early warning on the workstreams most at risk
One set of evidence that all parties can work from
Board-ready output for the programme sponsor
The trigger
“The board wants AI results. Teams are using the tools. I can't show what it has changed.”
Where AI is used and where value was expected
Whether delivery has improved, or only activity
Readiness of platforms, data and ways of working
Adoption barriers across employees, contractors and suppliers
An honest read on AI return against spend
Where the constraint sits: tooling, workflow or something upstream
Where AI will and won't pay off
A sequenced view of what to do first
The trigger
“Maintenance, compliance and legacy take most of our capacity. The strategic roadmap keeps losing.”
Split of capacity between run, regulatory and change work
Where technical debt blocks delivery
Dependence on a few people for critical systems
How change is funded and prioritised against run
The true capacity available for change
Which debt matters and which does not
A case for resourcing decisions the board can act on
Delivery continuity risks surfaced early
The trigger
“A large share of delivery is outsourced. The reports say green. I can't verify it.”
Supplier delivery against contract and outcomes
Quality and flow across employees, contractors and outsourced teams
Cost against value across the delivery model
How supplier performance is reported and verified
An independent view of value for money
Evidence for contract and sourcing decisions
A baseline for audit and spending reviews
A clear view of what to keep, change or renegotiate
The process
Four to six weeks from scoping to roadmap. The same process works whether you're fixing delivery, finding where AI will create value, or both. Each stage builds on the evidence from the last, so by the time we recommend anything, it has been tested against the data, against your people and against reality.
Week 1
We start with what you need to know: what's holding delivery back, where AI could create more value, or both. Your hypotheses, strategic context and the decisions in front of you set the focus: what we measure, who we speak to and what answers we need to get.
Weeks 1–2
We take read-only access to the tools your teams already use and build an objective picture of delivery performance and AI adoption: speed, quality, where capacity actually goes and how AI is really being used today.
Weeks 2–3
We investigate through three lenses. Structured interviews across product, engineering, commercial and leadership. A survey of the wider team. And desk research: your artefacts (strategy decks, roadmaps, backlogs, requirements and process docs), how your tools are configured and actually used, and the market you compete in. Alongside the constraints, we look for where AI could create value: in internal workflows, customer-facing products and the operations that connect them.
Weeks 3–4
We bring everything together: delivery data, interviews, survey results and desk research, mapped across the whole system. Issues surfaced in one area are tested against others, so we can separate causes from symptoms, see where the real constraints sit and shortlist the AI opportunities worth pursuing. The result is a clear set of findings and opportunities, ready to test.
Week 4
Before anything becomes a recommendation, we test it. We validate findings and AI opportunities with the people closest to the work and check each potential move for viability, feasibility and readiness, including whether your organisation can absorb and sustain it. Sometimes the honest answer is “not yet”. What survives is grounded in how you really operate, so nothing reaches the read-out untested.
Week 4
A working session with your leadership team, not a document drop. We present what is happening, what is causing it, what it is costing you and where AI will pay off, with every finding evidenced and already pressure-tested with your people. You leave clear on what matters most and what comes next.
Weeks 5–6
We wrap up with a 90-day mobilisation plan and a 12–18-month target roadmap covering delivery fixes and prioritised bets, sequenced into no-regret, tactical and strategic moves. Each move has owners, measures and explicit scale-or-stop gates.
Optional add-on
We help you deliver the changes and stand up your first AI initiatives alongside your team. Then we re-run the baseline to show what moved, in money terms.
We typically work with the CIO, the Head of Transformation, or the Head of IT. When the question is about cost or customer outcomes, the CFO and Head of Digital are often in the room too.
The diagnostic gives you an independent, evidence-based view of how technology delivers value across teams, platforms and suppliers.
Delivery is spread across internal teams, programmes and suppliers, and reporting arrives in different shapes. The CIO is accountable for outcomes without a single trusted view of what is being delivered and why it stalls.
One independent view across the delivery system, including the decisions that feed it and the operational load that crowds it out. Evidence for the board, and a clear order of priorities.
"We give you the ground truth. Not another supplier report or self-assessment, but evidence you can take to the board and act on."
We give you objective visibility of delivery performance and the levers to pull, and when.
Typically co-sponsored with the CIO. Transformation outcomes depend on teams and suppliers the programme does not control, and status reporting does not show whether delivery can absorb the change.
A clear read on delivery capacity and dependencies, and the evidence to re-plan, re-sequence or intervene before the programme slips further.
"We show where the programme is really constrained, whether that is delivery, decisions, dependencies or suppliers, so the plan reflects reality."
We surface the problems you already see, and go wider to give them the external credibility to move the organisation.
You live the friction daily: handoffs between teams and suppliers, change queues, unclear priorities. You have raised it internally but cannot get traction without independent data.
Evidence. An independent diagnostic that shows leadership what you see, with benchmarks and the credibility of an external view.
"We give you the evidence base for changes you already know are needed, so leadership sees what you see."
A representative engagement: a global technology company where the constraint sat upstream of engineering.
"The constraint wasn't where we thought it was. Engineering was not the problem."
Leadership believed engineering was the bottleneck. The diagnostic showed the constraint sat upstream, in how product was managed and prioritised. The independent evidence moved the conversation from opinion to diagnosis, and from diagnosis to action, and leadership changes followed.
A 30-minute call is usually enough to know whether a Delivery 360 fits a live question you are working on. No commitment.
