AI advisory

AI adoption consulting for engineering teams

For engineering leaders who need AI coding to become a useful team capability. The work connects tool adoption with the way teams plan, implement, review, and maintain software.

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Who this is for

CTOs, VPs of Engineering, engineering directors, platform leaders, and technical leads responsible for adoption across teams.

When this is useful

  • You have individual success stories but no repeatable approach across teams.
  • Tool adoption is growing, while review load, quality expectations, or ownership remain unresolved.
  • You need an engineering adoption plan that can be evaluated before a wider rollout.

What we work through

Understand the current workflow

Map how teams develop and review software, where agents are already used, and the obstacles that limit useful adoption.

Design a focused pilot

Choose suitable tasks and participating teams. Agree approved tools, repository access, baseline measures, and the conditions for continuing or stopping.

Support the people doing the work

Define practical training, shared task conventions, review expectations, and feedback channels for engineers and their managers.

Review evidence before expanding

Assess delivery, rework, reliability, and team feedback. Use the findings to adjust workflows and decide how to extend adoption.

Working outputs

These outputs are a starting point for scoping. We agree the priorities together before the engagement.

  • An adoption assessment identifying workflow gaps and near-term priorities.
  • A pilot plan covering ownership, selected tasks, measures, and review checkpoints.
  • A draft rollout and enablement plan informed by the pilot’s findings.

Format and planning

An engineering leadership working session, an adoption strategy sprint, or ongoing advisory. Scope and working rhythm are agreed around the teams and decisions involved. English or Turkish, remote or onsite.

Before we start

Start with team structure, the development workflow, approved tools, current AI use, and the problem you want to solve. Detailed repository access is not required for the first conversation.

The experience behind the work

I am VP of Product, AI at Jotform, where I lead the AI Product division. Alongside product and engineering leadership, I contribute to company-wide AI transformation, hackweeks, practical training, and adoption programs. My advisory and training work draws on that operating experience.

My leadership experience

Practical questions

How is this different from a Codex workshop?

A workshop develops individual and team skills through exercises. Adoption consulting addresses the wider operating decisions: where to pilot, what to measure, who owns the rollout, how to train teams, and when to expand.

How do you measure AI adoption in engineering?

Start with a baseline and the outcome you want to improve. Useful measures can include delivery cycle time, review effort, rework, escaped defects, and developer feedback. Tool usage alone does not establish business value.

Can this include hackweeks?

Yes. Hackweeks can help teams explore workflows and build capability. The important follow-through is selecting what to continue, assigning owners, and reviewing whether the experiment improves everyday work.

Does every engineer need to use the same AI tool?

That depends on the company’s requirements and workflows. We examine consistency, access, cost, support, and review practices before recommending a rollout approach. The engagement does not depend on selling a particular vendor’s licenses.

Discuss the scope for your company

Share your company, the people involved, and the problem you want to address. We can work out the appropriate approach and scope together.

Discuss an engagement