Ship What Works

What is the delivery phase of an AI strategy engagement?

The delivery phase is where an AI strategy becomes working software. In the TurnerNet method, it is called Ship What Works: validate feasibility first, build in two-week sprints with a working demo at each checkpoint, and put monitoring and rollback in place before the system scales.

Ship What Works takes the ranked roadmap from Step 02, Prioritize Ruthlessly, and builds the initiatives at the top of it. Each initiative arrives with an owner and a milestone, so the team knows what done means before the first sprint starts. It is the hands-on half of the TurnerNet AI strategy service, run with your engineers rather than for them.

What Happens in This Phase

• Feasibility assessments validating technical viability before committing engineering resources or budget
• Two-week sprint cadence with working demos at each checkpoint, so stakeholders see progress, not presentations
• MLOps pipeline setup: model evaluation, deployment automation, monitoring, and rollback procedures
• Weekly accountability checkpoints with measurable progress against the roadmap milestones set in Step 02
• Rapid iteration based on real user feedback from production environments, not theoretical requirements documents

How do two-week AI delivery sprints work?

Each two-week AI delivery sprint ends with a working demo, so stakeholders judge real progress instead of status slides. Before the first sprint, a feasibility assessment confirms the initiative is technically viable, which keeps engineering time and budget on work that can ship. Weekly accountability checkpoints then track progress against the roadmap milestones. Business stakeholders attend the demos and give feedback while changes are still cheap.

Engineering and product teams build side by side with TurnerNet through paired implementation, and the skills stay in house. Feedback comes from real users in production rather than requirements documents, and each sprint adjusts to it. If an approach is not working, the sprint rhythm exposes it within the first cycle, while there is still time to change course.

What must be in place before an AI system scales?

An AI system should not scale until it has automated deployment, model evaluation, live monitoring with alerts, and a tested rollback procedure. These controls turn a bad model release into a routine fix instead of an outage. Every model is wrong sometimes, so the real question is how far a bad answer travels before someone catches it.

Scaling also needs go/no-go criteria agreed in advance, such as measurable checkpoints at 30, 60, and 90 days, and an internal owner with the authority and skill to run the system after launch. A pilot that misses its criteria stops or changes direction instead of drifting into production by default. Ownership is the focus of Step 04, Transfer Ownership, which hands governance, runbooks, and monitoring playbooks to your team.