// AI_DEPLOYMENT_SPRINT
One painful workflow. One controlled pilot. One measurable decision.
The AI Deployment Sprint is a focused process that starts from one painful workflow and ends in a controlled pilot and a clear decision: scale, change, or stop.
Start with the workflow. Prove the value. Then deploy what works.
Who it is for
Start-ups and SMBs of roughly 50-500 people in service businesses with operationally heavy workflows: work that repeats every day, passes between people, and costs time, errors or customer patience. You do not need an AI strategy to start. You need one workflow that hurts.
Five problems this is built for
Typical examples, not client cases.
How the AI Deployment Sprint runs
Workflow Baseline
WHAT HAPPENS
We map one workflow with your team, agree 3 baseline numbers in writing, and rank where AI or automation could help, scored Automate, Assist or Human.
YOU GET
The baseline, an opportunity map, and a written recommendation on whether a pilot makes sense and with what scope.
Controlled Pilot
WHAT HAPPENS
We write a Pilot Charter with success, quality and stop criteria before anything starts. The pilot begins in shadow mode, then runs on a small slice with a controlled comparison.
YOU GET
The Pilot Charter, the running pilot, and a metrics report against the agreed thresholds.
Measure and Decide
WHAT HAPPENS
We compare the results with the baseline and the thresholds, and separate realized savings from freed capacity.
YOU GET
An evidence-based recommendation - scale, change, or stop - and a handover with documentation and a runbook, so your team owns what works.
After the Sprint: Scale and Optimization, as needed - a wider rollout, adoption and governance. That is a separate decision, made on the evidence.
What we measure
A baseline before any tool
Before anything is built we agree 3 numbers for the workflow, for example time per case, rework rate and waiting time.
A controlled comparison
A controlled comparison - holdout group, phased rollout, matched site, or before/after shadow baseline - chosen according to the workflow and data volume.
A quality guardrail
Speed alone is not success. Each pilot also tracks a quality metric, with a stop threshold.
Realized savings, not freed capacity
Freed hours are reported as freed capacity. They count as savings only when there is an agreed way to realize them.
How a pilot stays safe
Shadow
Read-only start. The AI runs alongside your people and changes nothing. A privacy review comes before any real data.
Pilot
A small, controlled slice. Evals before release, a kill-switch, and human approval on sensitive decisions.
Ramp
Widen only after the agreed thresholds are met.
Handover
Documentation and a runbook, so your team owns it and does not depend on me.
How I protect your investment
- The baseline comes before the tool. Tool choice is vendor-neutral and follows the numbers.
- No savings promises. Freed capacity is reported separately from realized savings.
- No chatbot dressed up as a strategy. Every use case is tied to one workflow and one metric.
- Stop criteria are agreed in writing before the pilot starts.
- Stop is a real answer. If the evidence says stop, the recommendation says stop.
Why I trust measurement over demos
I build and run AI-native products and a multi-model orchestration system, AOa, with independent QA gates: the agent that builds is never the agent that checks. Running it every day taught me how often "it works" means "nobody checked". The same rule shapes every pilot here - separate testing, evidence before "done", and a human on sensitive decisions.
Founding engagements
I am taking on a small number of founding engagements, so the method is tested on real workflows, not polished in isolation. Founding engagements run the full Sprint, with the same controls and the same written recommendation.
FAQ
Do you have case studies?
The practice is at the founding-engagement stage. What you can check today is 12+ years in product, AI products I have built and run, and the method on this page. The first case studies will come from founding engagements, with the client's permission.
Which AI tools do you use?
It depends on the workflow. I am vendor-neutral: the tool is chosen after the baseline, to fit the workflow, your data and your controls.
What if the numbers say no?
Then the recommendation is to change the scope or to stop. A clear stop is a valid result: it saves the budget a larger rollout would have cost.
How long does it take, and what does it cost?
Each stage has a fixed scope and a written end point. Scope, timing and price are set per workflow in a written proposal after our first meeting.
Who will I work with?
With me directly. Inon Baasov Ltd is a solo practice.
Have one workflow that costs more time than it should?
Zoom, in person or by email - whichever suits you. No pitch deck.