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Making technology useful

We help clients assess emerging technologies, identify worthwhile applications and introduce new capabilities into existing operations. Our work covers strategy, experimentation, integration, adoption and ongoing improvement.

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Aerial view of a data centre campus

How the work runs

The hard part is rarely the technology itself but knowing which application is worth the effort, and then making it work inside an existing organisation. An engagement can stop at any stage, and a decision not to invest is a perfectly useful result.

  1. DiscoverUnderstand the objectives and where time, cost or quality is lost, then decide which technologies matter, which can wait and which are not worth pursuing.
  2. AssessAn honest view of what each tool or method can do today, what it costs to adopt, what it depends on, and its readiness and risk.
  3. PrioritiseRank the opportunities by what they would change and what they would take, and agree which to pursue first.
  4. ExperimentSmall, measured trials on real work with the people who will use them, so a decision to scale rests on evidence.
  5. ImplementIntroduce what works into the systems and processes already in use, with the roles, skills, checks and governance people need to rely on it.
  6. MeasureCheck the result against the measures agreed at the start, and keep adjusting as the technology, the organisation and the evidence move on.

More than 700 mapped AI use cases

A body of research we use early in an engagement to find and compare opportunities. It is there to start a discussion rather than to offer ready-made solutions.

We have mapped more than 700 AI use cases across the disciplines of the built environment. We use it to compare possible applications against an organisation's objectives, readiness and appetite for risk.

Mapping a use case is not the same as proving it. We are careful to say how far each one has got, because the difference matters to anyone deciding whether to invest.

Researched opportunitiesApplications identified from research and practice that look relevant, but have not yet been tested in a given organisation.
Validated applicationsApplications that have been tested on real work, with the results and their limits recorded.
Deployable capabilitiesApplications ready to introduce into operations, with the integration, controls and support they need.

How we keep AI-assisted work accountable

A tool that produces fluent output is not the same as one that can be trusted, so these principles apply to everything we introduce.

A person makes the callAI output is checked against the record, and a named person makes and owns the decision.
Uncertainty stays visibleEach model and tool states its assumptions, its limits and how far it has been validated.
Your data stays under your controlWe usually work with information where it is held, and agree access and ownership at the start.

Two of our articles set out the thinking behind this. Fluency Is Not Competence explains why skill at prompting says little about whether AI-assisted work can be relied on. The Machine Never Holds the Stamp looks at why professional accountability has to stay with a person.

Questions

Can you help us work out where AI fits?

Yes. We start from what the organisation is trying to achieve, then look at where AI is relevant, practical and worth the investment. Sometimes the better answer is to use existing systems more fully.

Do we need clean data before we start?

No. Historical data helps where it adds understanding or validation, and we look at what is available early on. Cleaning every record is not a condition of asking a better question.

Will you recommend Synaform software?

Only where it helps. Our advice is independent of our products, and most technology engagements work with the systems a client already has.

Is the knowledge base a product we can buy?

No. It is research we use within an engagement to find and compare opportunities. It is not sold separately.

Related services

Strategic and operational advisoryTechnology direction as part of wider strategic choices. Systems modelling and simulationTest the effect of a change before it is made. Data, integration and implementationConnect new capabilities to the systems already in use.

Talk to us about where technology might help

Tell us the aim. We can help work out whether technology is part of the answer.

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