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SOLUTION · 02 · 90 DAYS

Corporate AI Starter

The most common cause of AI failure is not technology but selection. In 90 days we choose one right use case and make it measurably work.

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01 · PROBLEM

What are
we solving?

Several teams are trying AI, nobody is measuring the outcome. There is pressure not to fall behind on one side and data security anxiety on the other. Management wants to allocate budget but does not know to what. The result: scattered pilots, shadow usage and unmeasured spend.

In the ninety-day starter the scope is deliberately narrow: a single use case is chosen, piloted in one team and turned into one measurable win. That win is more persuasive internally than ten presentations and becomes the argument that opens the next budget.

Governance is written alongside the pilot, not deferred. Deferred governance means legal, audit and information security all arrive at once during scaling and the pilot is shelved. A three-to-five page framework becomes ready ground for every subsequent use case.

02 · Who it is for

  • Organisations that want to start with AI but do not know where
  • Companies with several disconnected pilots and no measurement
  • Management teams worried about employees using unapproved AI tools
  • Boards looking for concrete evidence of benefit before an investment decision
03HOW IT WORKS

The logic of
the approach.

One discipline sits at the centre of the programme: decision before technology. In the first 30 days we build the organisation's decision inventory and list repetitive, high-cost decisions. In the second 30 we take one of them into a pilot together with data, workflow and oversight rules. In the last 30 we measure adoption, write the governance framework and produce the scaling roadmap. The output is not a demo but a working business workflow.

04 · Deliverables

  • AI maturity assessment
  • Decision and use case inventory
  • Value–effort matrix and prioritisation
  • Data readiness report for the selected use case
  • A working pilot workflow
  • AI usage policy and risk classification
  • Human oversight and escalation rules
  • Role-based training package
  • 12–24 month scaling roadmap and budget frame

08 · Success measures

  • Employee hours saved in the selected process
  • Output quality consistency (human assessed)
  • Active user share and weekly usage frequency
  • Reduction in off-policy (shadow) usage
  • Achievement against the benefit projected in the business case
05PROCESS

How the six steps
map to this solution.

Diagnosis

Maturity assessment, decision inventory, data access check. 3 weeks.

Strategy

Use case prioritisation and business case. 2 weeks.

System design

Workflow design, governance rules, measurement framework. 2 weeks.

Delivery

Pilot build, integration and testing. 3–4 weeks.

Adoption

Role-based training, support and internal communication. 2 weeks.

Measurement

Usage, quality and time-saving measurement; scaling decision.

06 · TEAM

Who is
on the project?

Chief strategistAI solution architectData specialistProcess designerGovernance and compliance advisorLearning designer

The team is formed after diagnosis and written into the proposal by role.

Team
07 · Example project

At a services company, proposal preparation took an average of two days and quality varied by author. The decision inventory put this process first. A draft-generation workflow was built on past proposals and pricing rules, with every proposal still approved by a person. By the end of the pilot draft preparation had fallen to hours and structural consistency had clearly improved.

All projects
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10COMMONLY SEARCHED

Topics this page
covers.

corporate AI starterhow to start with AIAI pilot programme
11FAQ

Frequently asked questions

Is 90 days realistic?
For a single use case, yes. That is exactly the discipline of the programme: keep scope deliberately narrow, produce one measurable win, and open the next budget with that evidence.
Which tools or models do you use?
Tool selection follows the use case and data sensitivity, and we look at your existing technology stack. We are not tied to a vendor and we present the rationale in writing.
Will our data leave the organisation?
That is a design decision. Where sensitive data is involved we evaluate local processing, anonymisation or in-house hosting. The decision and its rationale go into the governance document.
Can we continue alone after the programme?
That is the goal. The governance framework, training package and scaling roadmap are prepared to be handed over. We also continue with organisations that want support during scaling.
→Let's begin

Let's take the first step
on Corporate AI Starter Programme.

In the diagnosis session we clarify scope, timing and success measures together.

Free discovery call +90 540 548 26 97