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AI Phase
Timeline

Phase by phase, with honest numbers: a blueprint in 24 hours, a proof of concept in 4–8 weeks, a production system in 3–6 months – and what actually delays projects in practice.

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An AI project with AI Phase in 2026 takes 24 hours to a project blueprint, 4–8 weeks to a working prototype on real data and 3–6 months to a production system – followed by ongoing operation. A prioritized roadmap for complex landscapes is typically ready in 2–4 weeks. The fastest start: the free 24h Fast-Track.

Last updated: July 2026

What phases does an AI project go through?

Four phases from first plan to running operation. The durations are AI Phase's published typical project durations – every project starts with a blueprint, not a contract.

01 · 24 hours

Blueprint & feasibility

With AI Phase's Fast-Track, a concrete blueprint is ready within 24 hours: a scope sketch, an honest feasibility assessment, a matching solution pattern from real projects and next steps. What makes 24 hours possible: a senior-only team and a pattern library built from reference projects. The honest limit: complex landscapes with many systems need a longer assessment.

  • Scope sketch and feasibility assessment
  • Solution pattern from real reference projects
  • Next steps – free and non-binding

For complex landscapes: opportunity assessment in 2–4 weeks

02 · 4–8 weeks

Prototype & proof of concept

A scoped proof of concept on your real data is typically ready in 4–8 weeks. What fits into that window: one use case, real data, measurable success criteria. What does not: production readiness – a PoC proves feasibility, not operability.

  • Working prototype on real data
  • Measurable success criteria instead of demo effects
  • A go/no-go decision backed by numbers

AI prototyping in detail (4–8 weeks)

03 · 3–6 months

Production build

From pilot to production typically takes another 1–4 months; an implementation project runs 3–6 months in total. The long poles are integration, permissions and evaluation – not the model. That the route works is shown by production systems like the ticket automation for netcos GmbH and the 3D computer-vision quality assurance for the B.B.W. Group.

  • Integration into M365, SAP or ticketing systems
  • Permissions, GDPR and EU AI Act requirements
  • Evaluation and sign-off in the real process

How AI implementation works (3–6 months)

04 · Ongoing

Operation & optimization

AI doesn't stay good on its own: after go-live come monitoring, data freshness, performance tuning and new features. Plan for operation from day one – an AI project isn't finished at go-live, it's in production.

  • Monitoring and data freshness
  • Performance tuning and new features

Maintenance & optimization in detail

How long does each AI project type take?

AI Phase's published typical project durations, as of 2026. First usable output arrives well before the project ends.

Fast-Track blueprintTypical duration:24 hoursFirst usable output:Scope, feasibility & plan in 24 h
AI workshopTypical duration:½–2 daysFirst usable output:Ranked use-case list the same day
Opportunity assessment & roadmapTypical duration:2–4 weeksFirst usable output:Prioritized roadmap with an MVP recommendation
Prototype / proof of conceptTypical duration:4–8 weeksFirst usable output:Working demo on real data
Production system (RAG, agents, CV)Typical duration:3–6 monthsFirst usable output:System in pilot operation before go-live
Student project (TUM/LMU)Typical duration:3–6 monthsFirst usable output:Supervised interim results throughout
Operations & maintenanceTypical duration:OngoingFirst usable output:Monitoring from go-live

For context: traditional consulting routes budget 3–12 months to production in the 2026 market overviews. Day rates and the structural comparison live at AI consulting vs. traditional consulting.

What each project type costs: What does AI cost? · What production systems deliver: AI results in numbers · References with real metrics: Projects

What makes AI projects faster – or slower?

The uncomfortable truth from practice: the biggest schedule risk is rarely models or technology – it's the client's own approval loops. Five factors decide the timeline.

Data access lead time

Getting access to SharePoint, SAP or ticketing systems often takes weeks – time in which the project stands still. Data access is more often the bottleneck than data quality itself.

Fix: sort access and data owners in week 1.

Decision latency

Every open decision that waits two weeks for the next steering meeting pushes go-live back by two weeks. The client's own approval loops are the most common schedule risk we see.

Fix: one fixed weekly decision slot.

No accountable owner

Without a product owner with a mandate, every priority gets renegotiated in every meeting – and the timeline with it. A project without an owner is a project without a date.

Fix: one owner with decision authority, named on day one.

Compliance scope

GDPR, the EU AI Act, works councils and IT security are plannable – if they are at the table early. Reviews brought in late cost months; brought in early, they cost weeks.

Fix: involve IT security and data protection from the blueprint.

Number of integrations

Every additional connected system brings its own interfaces, permissions and test cycles. A prototype with one system is fast – rolling out to five is a project of its own.

Fix: pilot with one system, then connect step by step.

Common questions about AI project timelines

AI Phase's Fast-Track delivers a project blueprint within 24 hours; a working proof of concept on real data typically takes 4–8 weeks.

In 24 hours you'll know how long your project takes

Submit your AI idea – you'll get scope, a feasibility assessment and a project plan with phases. Free and non-binding.

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