We start producing value with AI in four weeks.
The Adoption Sprint delivers the first value in companies where AI has not yet entered or is just being explored. Not preparation; a PoC working in the field, measured, handed over to the team.
AI is not used systematically.
4 weeks, 1 scenario, 1 team.
Working, measured, handed over.
- Entry
AI is not used systematically.
- Adoption Sprint
4 weeks, 1 scenario, 1 team.
- Exit
Working, measured, handed over.
- Week 1
Business scenario selection.
The single scenario promising the highest return, put in writing with a stakeholder map.
- Week 2
First working prototype.
An AI workflow standing up on the team's real data.
- Week 3
Going to the production line.
A pilot validated in the field with baseline + T₁ measurements.
- Week 4
Handover.
The playbook, ownership and metric instrument passing to the team.
Who it's for.
AI-Absent
AI is not used systematically inside the company; even individual experiments are invisible. Leadership says AI should be on the agenda, but no concrete step has been taken.
AI-Curious
A few individuals or teams experiment on their own initiative; there is no company policy, measurement or structure yet. Leadership feels 'we should do this too' without knowing where to start.
AI-Integrated
Some workflows show systematic use; measurement and ownership are partly in place. The next job is not running a single scenario but redesigning the operating model.
AI-Native
AI and agent infrastructure sit in the core layers of the operation; decisions are designed together with AI. The next job is moving to autonomous operation and matching governance.
No one inside the company uses AI systematically yet.
There is scattered individual use; no company practice.
Leadership says 'we need to start somewhere' without knowing where.
A pilot was tried but stayed at the slide-deck stage.
You want to keep the first engagement small and show a quick win.
Deliverables.
Priority business scenario
The single scenario promising the highest return in the field, put in writing together with a stakeholder map; it defines the Sprint's focus.
Working PoC
An AI workflow standing up on the team's real data, testable on the production line.
Enablement playbook
A transferable guide that fits into the daily operations of the team running the pilot; it works without Lokomotif AI.
Baseline metric instrument
The measurement setup capturing T₀ + T₁; it makes ROI validation possible in the field.
Structured handover
Ownership of the pilot passes to the team; a written protocol and risk inventory remain for sustaining it.
What we don't do.
Tool comparison
We don't spend Sprint time on which LLM or which platform. Tool selection comes after method.
Operating-model redesign
Redrawing process maps and decision rights happens in Workflow Rewire. The Sprint focuses on a single scenario.
Agentic system setup
Autonomous agent infrastructure is Agentic Scale's job. The PoC built in the Sprint is an AI workflow used within a person's flow of work.
Formats run in this move
Starting the Adoption Sprint with an activation format speeds up momentum; an ideathon surfaces the use case from the field, a prompt-a-thon produces a quick win.
- Ideathon
Surfaces use cases from the field; builds demand from the ground up.
- Prompt-a-thon
Builds a shared prompt library with RTCS-G.
- AI Office Hours
A recurring clinic carrying momentum between major events.
Frequently asked.
Does something genuinely working come out in four weeks?
Yes. We limit the scope to one real business scenario; the result is a working PoC, measured in the field and handed over to the team. Output, not promise.
What is the difference between a PoC and a pilot?
A PoC proves that a scenario works with AI. Scaling and embedding into the workflow belong to Workflow Rewire; the Sprint exists to produce the first proof.
What if we don't know which scenario to pick?
We choose together; if needed, a short Diagnostic or an ideathon first surfaces the scenario from the field. The right scenario is the Sprint's most critical decision.
Does the output stay with us after the Sprint?
Yes. The working PoC, the setup and the learnings are handed over to your team; you are not left dependent. Handover is a defined part of the work.
What happens when the four weeks end?
We evaluate the measured result together. If you want to take the scenario to scale, we plan the next move; if not, the gain still stays with you.
The most expensive step is starting in the wrong place.
In enterprise AI, most budgets evaporate on the wrong first step. The right starting point differs from company to company; a free 30-minute discovery call pins down yours.
