AI Implementation
The AI strategy is set. The use cases are validated. And now? In many companies, this is where failure begins: the strategy paper ends up in a drawer because nobody builds the bridge between strategic decision and technical execution. IT departments translate business requirements into technology projects — and after 6 months, the result has little to do with the original business value.
AI implementation at Aydoo closes this gap. We manage the implementation process strategically — in a 2-week sprint rhythm with clear results after every cycle. Not as programmers, but as translators between business logic and technical execution.
Format
| Sprint rhythm | 2 weeks |
| Phases | Scoping → Implementation → Validation → Scaling |
| Participants | CEO + project lead + technical team or partner |
| Deliverable | Productive AI system with measurable business value |
| Investment | Mid five-figure range (EUR, depending on number of sprints) |
Does this format fit? → Schedule a conversation
Process
Sprint 0: Scoping (Weeks 1-2)
Before the first implementation sprint begins, we define the scope: Which use case gets implemented? What data is available? Which technical partners or internal resources are involved? Which success metrics measure business value? The result is a sprint plan with clear milestones and a binding offer for the implementation.
Sprints 1-3: Implementation (Weeks 3-8)
Every sprint follows the same rhythm: sprint planning (define goals and tasks), execution by the technical team, daily check-ins as needed, sprint review (assess results). We manage requirements, review quality, escalate blockers, and ensure the technical execution serves the business goal — not the other way around.
Sprints 4-5: Validation (Weeks 9-12)
The implemented system is measured against the defined success metrics: Does the AI solution work in daily operations? Do the results match expectations? Where does it need adjustment? Pilot users test, feedback flows into optimization sprints. At the end: a production-ready system with proof of business value.
Sprints 6+: Scaling (optional)
After successful validation: rollout to additional departments, connect further use cases, train end users, hand over to internal operations. The scaling phase only starts when validation confirms the expected business value.
What AI Implementation Is — and What It Is Not
| AI Implementation (Aydoo) | AI Development (others) | |
|---|---|---|
| Role | Strategic management of the implementation process | Technology project with code focus |
| Starting point | Validated use case with clear business value | ”Let’s build something with AI” |
| Method | Sprint rhythm with business value validation | Waterfall project or endless PoCs |
| Deliverable | Productive system + success measurement | Technical solution without business context |
| Risk | Detected early through 2-week cycles | Detected late after months of development |
| Who decides | CEO based on measurable results | IT department based on technical feasibility |
Results from Practice
Starting point: The AI strategy had identified the claims process as the greatest lever — manual review took an average of 5 business days. During the scoping sprint: data resided in three separate systems, none had an API. Sprints 1-2: data integration and preprocessing. Sprints 3-4: AI model for automatic claims classification. Sprint 5: validation with 500 historical cases. Result: processing time reduced to 1.5 days, classification accuracy 94%. Scaling to all claim types in sprints 6-8.
Starting point: The AI Potential Workshop had identified return prediction as the top use case, AI consulting had validated the data basis. In the implementation project: Sprint 0 defined the success metric (reduce return rate by at least 8%). Sprints 1-3: integration of order history, product data, and customer segmentation into a prediction model. Sprints 4-5: A/B test with 20% of traffic. Result: return rate reduced by 14%, ROI positive in the first quarter after full rollout.
Starting point: Predictive maintenance validated as strategic use case, but internal IT capacity only for basic operations. During scoping sprint: external implementation partners evaluated and selected. Sprints 1-2: sensor data pipeline built. Sprints 3-4: prediction model trained and integrated. Sprint 5: validation on 15 machines. Result: unplanned downtime reduced by 35%. Partner handover after sprint 6 to internal operations.
Frequently Asked Questions
What does an AI implementation project cost?
An AI implementation project starts in the mid five-figure range (EUR) — depending on complexity and number of sprints. You receive a binding offer after the scoping sprint.
How does AI implementation differ from AI consulting?
AI consulting answers the strategic question: Where does AI have the greatest lever? AI implementation executes the answer: From validated use case to productive system. Typically, implementation follows consulting.
Do you program the AI systems?
No. We manage the implementation process strategically: requirements definition, partner management, quality assurance, success measurement. Technical execution is handled by specialized partners or your internal team.
How long does a sprint last?
Each sprint lasts 2 weeks. Typical projects comprise 3-8 sprints (6-16 weeks). The rhythm adapts to the complexity.
Do we need a validated use case before we start?
Yes. AI implementation requires that the use case is strategically validated — otherwise you implement the wrong thing. If no use case has been validated yet, start with AI Consulting or the AI Potential Workshop.
What happens if a sprint fails?
That is the advantage of the sprint rhythm: failure costs 2 weeks, not 6 months. In the sprint review, we analyze the cause, adjust the plan, and start the next sprint with corrected assumptions. Fail early, learn fast.
Related Services
- AI Consulting — Strategic precursor: identify and validate use cases
- AI Potential Workshop — Entry point: identify AI levers in 1-2 days
- Business Design — Extend business model with AI capabilities
- Strategy Sparring — Ongoing support after implementation
Next step: Use case validated but unsure what execution looks like? A no-obligation conversation clarifies the scope.
From use case to productive AI system → Schedule a conversation
