Practical thinking on operating-model transformation

A dashboard shows you the numbers. It can't show you why.

Every executive in an asset-intensive organisation has a dashboard. Cost to serve is up, backlog is growing, cycle times are drifting — the numbers are all there, refreshed daily, argued over monthly. What the dashboard cannot tell you is why. Why does an approval that should take two days take eleven? Why does the same work order bounce between three teams before anyone owns it? Why did the business case promise a saving the operation never felt?

The answer is almost never that people are working badly. It is that the process you actually run is not the process in the manual. Between the official version and the real one sit the workarounds, exceptions, hand-offs and waiting loops that quietly consume the investment — and none of them appear on a dashboard, because dashboards report outcomes, not behaviour.

Process intelligence closes that gap. It reconstructs how work actually flows through the systems you already run — orders, approvals, hand-offs, exceptions — from the event data those systems record every day. Not workshops. Not interviews. Not what people believe happens. The evidence of what does happen: the real process versus the official one, the true cost of delay, and where the effort actually goes.

Seeing it is only half the job. The other half is knowing what is worth fixing. A reconstructed process surfaces more issues than any organisation can sensibly act on, so the findings have to be ranked — by value at risk, by asset criticality, by regulatory exposure. That ranking is what turns an interesting picture into a board-ready decision: fix these three things first, for these reasons, and expect this order of value.

This is why we start engagements with a deliberately narrow lens. One high-value process, X-rayed on your own operation, findings in three weeks. Not because the rest of the operation doesn't matter — but because the fastest way to build confidence in the evidence is to show leadership something true about their own business that no dashboard has ever shown them.

How the Operations X-ray works →

Where the value leaks — the five questions every asset-intensive organisation is asking

Sit in enough executive and board conversations across defence, water, energy, transport and government, and you hear the same question asked five different ways. We're investing more than ever — where is the value going?

"Our assets are ageing faster than our funding is growing." The renewal backlog lengthens, the funding envelope doesn't, and every intervention competes with every other. The real question isn't whether to spend — it's which interventions actually matter, and which can wait without moving risk somewhere unacceptable.

"Every function has its own systems, data and version of the truth." Fragmentation is rarely a technology problem alone. When data and operating model are fragmented, no one is accountable for the end-to-end result — and decisions that should take a meeting take a quarter.

"The business cases promised value. The systems went live. Where are the benefits?" This is the most common leak of all: benefits assumed at approval, never baselined, never tracked, never owned. The system delivered; the value was presumed.

"Everyone says adopt AI. Are we actually ready?" Boards are being told to move. The honest question underneath is whether the operation — its processes, its data, its decision rights — is fit for AI to make a measurable difference, or whether AI would simply automate the confusion.

"We've transformed before and drifted back." Transformation that isn't governed and sustained decays. Without a cadence that keeps benefits owned and measured, the old ways of working reassert themselves — and the next business case starts from scratch.

Five questions, one pattern: value leaks in the gaps — between functions, between the official process and the real one, between the investment decision and the operating reality. The work is making those gaps visible with evidence, then closing them by design.

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Modelled is not managed

Most organisations have modelled their business at some point. Process maps in a repository, asset registers in a system, architecture diagrams on a wiki. And most of those models share the same fate: they were true the day they were published, and they have been quietly wrong ever since. Modelled is not managed.

A model only earns its keep when it is living — fed by data, current enough to trust, detailed enough to test decisions against. That is all a twin is: a living, data-fed model of something real. The process twin shows how your work actually flows — orders, approvals, hand-offs, exceptions — rebuilt continuously from the systems you already run. The digital twin of the asset shows the physical thing — its condition, configuration and history — current enough to trust for capital, maintenance and risk decisions before money is committed.

What has changed recently is the cost of keeping models alive. Building and maintaining them used to be a documentation programme — expensive, slow, and stale on arrival. Agentic AI and process intelligence now do that work: they build the models, keep them current, and turn them from static diagrams into a living decision-support system. From zero to a full process twin in a six-to-eight-week AI-enabled sprint — not a multi-year modelling programme.

The payoff isn't the model itself. It's what a trusted, current model lets you do: test an intervention before committing capital, see the real cost of a delay before it compounds, give an AI use case the context it needs to be worth governing. The number-one reason enterprise AI fails is missing context — and the twins are that context.

So the question worth asking isn't "have we modelled the business?" Almost everyone has. It's "is the model current enough that you would make a capital decision against it?" If the answer is no, the model is a record, not a tool — modelled, but not managed.

The twin foundation →

Activity Based Costing — knowing what things really cost

Whether you are trying to reduce operating expenses, improve efficiency or work out what to charge, understanding your cost structure is the first step — and most organisations don't actually have it. Activity Based Costing (ABC) measures the real cost and performance of a business from the resources consumed by the activities that produce its products and services. It differs from conventional costing, which apportions costs by revenue contribution or production volume — a convention that quietly hides cross-subsidisation and non-value-adding work.

The distinction matters most in the public sector, where many agencies and government-related businesses operate on a cost-recovery basis. The funding environment demands consistency, transparency and accountability in cost-recovery arrangements: agencies need to understand the real, full cost of output delivery, seek funding commensurate with those costs, and continuously improve the processes and work practices underneath them. Pro-rata cost allocation cannot carry that weight — all costs and overheads have to be allocated meaningfully to outputs, and ABC has long been recognised in Commonwealth cost-recovery guidance as the way to do it.

Done well, ABC delivers four things: the full and true cost of activities and outputs based on resources consumed; a cost structure that is justified and transparent enough to manage processes against; visibility of non-value-adding work, wastage and unused capacity; and a robust platform for negotiating fees and charges.

Our approach builds costing models that are effective without being over-engineered — efficient to maintain, integrated with existing corporate and management reporting, and handed over with the skills to own them. Where models already exist, we redevelop and extend them: converting purpose-built models to organisation-wide ones, integrating them with corporate systems, and improving the data capture underneath. And because the activity data now often comes straight from your systems, ABC pairs naturally with process intelligence — the same evidence that shows how work flows also shows what it costs.

Operating Model & Process Redesign →

What is Business Process Management — and when is work a process?

Business process management (BPM) is the discipline of discovering, modelling, analysing, measuring, improving, optimising and automating how work runs. Any combination of methods used to manage an organisation's processes is BPM — the processes themselves can be structured and repeatable or unstructured and variable, and enabling technology is common but not required.

The useful test is predictability. If the structure and sequence of work is unique, it's a project. In a process, the sequence can vary from instance to instance — gateways, conditions, business rules — but every fork in the road is known in advance, along with the conditions for taking one route or another. That predictability is what makes a process manageable, measurable and improvable in a way a one-off never is.

BPM treats processes as strategic assets: understood, managed and developed to deliver value to the people the organisation serves. It sits in the same family as total quality management and continual improvement — ISO 9000 itself promotes the process approach to managing an organisation.

In practice the work spans five moves: documenting and analysing the current state, honestly; designing the future state against coming technology, regulatory and behavioural change; re-engineering where the process needs to work differently, not just better; modelling and testing the redesign before it goes anywhere near production; and automating the sequential work so people carry the judgement, not the paperwork. What has changed since BPM was coined is where the evidence comes from — the current state no longer needs to be workshopped, because process intelligence reconstructs it from the event data your systems already record.

Process Intelligence — the evidence layer → · See process mining live →

BIM and public-infrastructure policy compliance

Building Information Modelling (BIM) is a digital process that brings every aspect, discipline and system of a built asset into a single virtual model — so the people planning, building and operating it collaborate against one version of the truth rather than a folder of drawings. It is also, increasingly, a policy obligation.

Following the UK Government's lead in using BIM to improve outcomes from public infrastructure, the Commonwealth and state governments in Australia have moved to transform how public-sector infrastructure is delivered. Cost blowouts, design flaws, safety risks and unclear returns across major projects have produced policies and mandates for BIM and the ISO 19650 standards — governing how digital information about infrastructure projects and assets is created, collected, used, reported and maintained. We have worked with industry and with the Commonwealth, NSW, Queensland and Victorian governments on the strategies and information architecture that make those mandates real.

Capability comes before compliance. Developing BIM capability — a digital way of managing assets — only works when the organisation's capabilities align with how digital information is actually collected, managed and used to realise value. Our rapid capability assessment benchmarks current maturity across the digital-information landscape and prioritises investment into the low-maturity capabilities first, so organisations reach their target state in a phased, risk-controlled and value-driven way. A companion digital-maturity self-assessment looks across five pillars — governance and leadership, people and culture, capacity and capability, innovation, and technology — and locates the organisation on a five-level scale from minimal to transformed.

From there, adoption is deliberately incremental: a minimal viable BIM product retrofitted into the existing technology landscape in close consultation with stakeholders, a common data environment to hold the information, and benchmarking that ties the adoption back to realised value. BIM is where the digital twin of a built asset usually begins — the model is the starting point; keeping it fed and current is what turns it into a twin.

Asset Intelligence & Digital Twin — the instrument →

Emerging technologies for human-centric change

Digital engineering is the art of creating, capturing and integrating data using a digital skillset. From drawings to simulations and 3D models, engineers increasingly capture data and craft design in a digitised environment — exploring possibilities and developing solutions virtually before anything is built.

The technologies people notice are the visible ones: gaming engines, augmented and virtual reality, 3D visualisation. They are transforming how the vision for major infrastructure and built-environment projects is communicated to the people who have to believe in it — government, investors, operators and the public. A stakeholder who has walked through the asset in VR asks better questions than one who has read the drawings.

But the model is not the point. While 3D models are the most comprehensible face of digital engineering, it is the computable data behind the model that opens the real possibilities. Once design is finished, that digital information passes to construction and operations teams, who unlock its value by working the data — for their own decisions, and for the community the asset serves.

That is why we treat immersive technology as a change instrument, not a rendering exercise: design thinking to find the human decision the technology has to support, then the technology chosen to fit it. It is the same principle that runs through everything we do — the tool earns its place by moving a defined outcome.

The twin foundation →