Service
Digital & AI
Technology and AI investments chosen for their return, built to run in production, not in a demo.
- Median return on AI use cases we have taken to production
- 3.1x
- Typical time from use case selection to live operation
- 14 weeks
- Average reduction in run cost after a technology review
- -30%
Overview
AI and analytics use cases, technology strategy and data foundations, selected on business value and delivered into the workflows where people make decisions every day.
Few companies lack AI ambition. Many lack AI results. The pattern we see most often is a long list of pilots, each promising on its own, none of them changing how the business runs. The models work; the work around them does not change.
Our Digital & AI practice starts with the decision a model is meant to improve: which asset to repair, how much stock to hold, which customer to call. We size the value of a better decision, check whether the data can support it, and build only where both add up. Then we change the workflow, the incentives and the systems so the model is used every day.
We apply the same discipline to technology spend in general. An outside review of architecture, contracts and the project portfolio routinely finds a quarter or more of run cost that can be removed, and that money is often what funds the next wave of AI work.
Questions we answer
What clients bring to this practice
- 01We have twenty AI pilots. Which ones should we scale, and which should we stop?
- 02Our technology budget keeps growing. What are we actually getting for it?
- 03Is our data good enough to support AI, and what would it cost to fix?
- 04How do we get frontline teams to trust and use the models we build?
- 05Should we build our own capabilities, buy them, or work with a partner?
What we do
How the practice can help
- 01
AI value assessment
A ranked list of use cases scored on value, feasibility and data readiness, with a business case for each one worth pursuing.
- 02
AI in operations
Forecasting, scheduling, maintenance and quality models built into the systems and routines that frontline teams already use.
- 03
Technology strategy and spend review
An outside view of the architecture, vendor contracts and project portfolio, with a plan to cut run cost and fund what matters.
- 04
Data foundations
The minimum data platform, ownership and quality controls needed for the priority use cases, rather than an enterprise program with no end date.
- 05
Digital customer journeys
Redesign of the journeys that drive the most cost or churn, measured on completion, conversion and cost to serve.
- 06
AI governance and risk
Practical policies for model approval, monitoring and accountability that satisfy regulators without stalling delivery.
Our work
Results from this practice
Industries
Where this practice works most often
Are you independent of technology vendors?
Yes. We take no referral fees or reseller margins, so our recommendations on platforms and partners are not shaped by who pays us.
Do you build the solutions yourselves?
Our data scientists and engineers build the first production version alongside your team, then hand it over with documentation and training.
How do you measure the return on AI?
Against a baseline agreed with finance before work starts, using the same operating metrics the business already reports on.
What if our data is not ready?
It rarely is. We scope the smallest fix that supports the first use cases and build the rest of the foundation as value comes in.
Have a decision on the table?
Tell us what you are weighing. A partner will reply within two working days, and the first conversation is on us.