AI-augmented SDLC
AI embedded across the delivery lifecycle — where it earns its place, where it does not, and what changes in how the work is done.
Services · AI Strategy
Making AI real in how engineering actually works.
Most AI strategies stop at the slide. This is the layer underneath: the roles, the workflow, and the measures that decide whether any of it reaches production.
01 / What it covers
Four things, and each one is buyable.
AI Workflow Engineering
Designing the workflow itself rather than buying tools and hoping: what the machine does, what a person decides, and where the hand-off sits.
Forward Deployed Engineers
Specialists placed inside the business who own the outcome rather than a backlog — defined as a role, with a path to hire and deploy them.
Outcome Engineering
Holding engineering accountable for the result and the artifact, not for closed tickets.
02 / Where it usually starts
If any of this sounds familiar.
- There are pilots, and none of them has reached delivery.
- Tools were adopted, and the way the work happens never changed.
- Nobody can say who owns an AI outcome, or how it would be measured.
- The ambition is funded, and the delivery model was never designed for it.
What you leave with
- A delivery model for AI work, not a list of tools
- The roles the work needs — defined, with how to hire and deploy them
- Measures that show adoption and outcome, readable without a narrator
- A first sequence: what changes this quarter, and what deliberately does not
03 / How it runs
The same ladder as everything else.
Usually starts with a diagnostic
A diagnostic →A few weeks to separate what is real from what is a demo, and to name the first change worth making.
Originated the operating concepts behind this work — Forward Deployed Engineering, AI Workflow Engineering, Outcome Engineering — and put them into practice inside a large engineering organization.
04 / Questions
What people ask first.
We already have pilots. Where would you start?
With why none of them has reached delivery. It is rarely the model — it is that nobody owns the outcome, or the workflow around the tool never changed, so the pilot had nowhere to land.
Do you build the AI systems themselves?
No. I design the delivery model, the roles, and the measures, and stay hands-on until they run. Model work and platform engineering belong with specialists — I will say so, and help you scope it.
Is this about choosing tools?
Tools are the easy part, and the part most organizations have already done. The work is the change in how delivery happens and who answers for the result.
Does our data need to be in good shape first?
Not to make these decisions. Roles, ownership, and sequence can be settled on incomplete data — and the gaps you find while deciding are useful in themselves.
How would we know it is working?
Measures are part of the engagement, not an afterthought: adoption on one side, outcome on the other, each with a baseline taken at the start so the comparison means something later.
Also under Services
Advisory →The engagement ladder itself — a working session, a fixed-scope diagnostic, a hands-on transformation, or a retainer. Start wherever the decision is.Infrastructure →Platform and reliability assessments, cloud migration, SRE and observability — delivered as offerings rather than a team on a timesheet.