From ad hoc experiments to a repeatable way of shipping AI.
AI Transformation
Overview
Most companies do not have an AI problem. They have an AI adoption problem: a handful of promising prototypes, no shared opinion on how to evaluate them, and no path from a demo that impressed someone to a feature customers depend on. Shikhar leads engineering for this organisation’s AI-first transformation, covering the strategy, the standards, and the organisation that turns that gap into a repeatable process.
The business context
The business runs several product and platform initiatives at once, across commerce, operations and internal tooling. AI was obviously relevant to all of them, and that was precisely the difficulty. When a capability is relevant everywhere, every team adopts it differently, and the organisation ends up with several incompatible half answers rather than one good one.
The work was never to add AI. It was to make AI something the organisation could build with predictably: a view on when a model is the right tool, a way to tell whether a given system is actually working, and a route to production that does not depend on the enthusiasm of whoever prototyped it.
Key decisions
Standards before scale.
Shared frameworks for building, evaluating and deploying AI powered applications came first, so teams could move quickly without each one inventing its own definition of good enough to ship. Evaluation in particular, because a feature backed by a model cannot be signed off by clicking around it.
Lead the leaders.
The lever at this altitude is engineering managers, not individual contributors. Mentoring EMs, setting expectations around ownership and execution, and building a culture where experimentation is cheap and rollback is cheaper.
Predictable delivery is a feature.
Alongside the AI work, modern development practice as a baseline: CI/CD, observability, automated testing, code quality, and enough measurement to make delivery predictable rather than heroic.
The work
- Engineering strategy and technology roadmap, defined with executive leadership and aligned to business growth.
- Frameworks and standards for building, evaluating and deploying AI applications safely and quickly.
- Architectural direction across cloud infrastructure, backend services, frontend applications and AI enabled systems.
- Engineering process that improves delivery predictability, software quality and developer productivity.
- A hiring, coaching and development track for the organisation’s next layer of technical leadership.