Managerial · Program, outcomes and prerequisites

AI Business Case & ROI Sprint

Practical corporate course for applying AI to AI business cases and ROI, with exercises on realistic work, reusable materials and clear governance criteria.

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The problem it solves

Companies often approach AI business cases and ROI through scattered experiments: a few prompts, a few enthusiastic users, many doubts about data, quality and responsibility. This course turns that uncertainty into an operating method. Participants work on realistic scenarios, learn where AI helps, where human review remains essential and how to make the practice repeatable inside the company.

Duration: 4 hours or a half-day sprint
Mode: In-person or online lab, with guided exercises and materials adapted to the client.
Profile: For managers and non-technical teams; no programming required.
Final output: Operating canvas for AI business cases and ROI.

Audience

For managers and non-technical teams; no programming required.

When to choose it

Choose this course when the company wants concrete progress on AI business cases and ROI and needs training that produces usable workflows, not abstract theory.

Concrete outcomes

Map the work and the decisions where AI can reduce friction.
Build practical instructions, checklists and review criteria.
Identify data, privacy and responsibility boundaries.
Create reusable examples for the team.
Define next steps for adoption and governance.

Program

1
Shared understanding
Capabilities, limits, responsibilities and business implications.
2
Use-case evaluation
Value, feasibility, risk, data and ownership.
3
Governance and quality
Rules, review, escalation and accountable decisions.
4
Adoption roadmap
Priorities, skills, metrics and next steps.

Practical exercises

Map a realistic process connected to AI business cases and ROI.
Create AI-assisted outputs and review them critically.
Define escalation and human review points.
Build a reusable checklist for daily work.

Materials delivered

Operating canvas for AI business cases and ROI.
Prompt and instruction templates.
Quality and privacy checklist.
Risk/control matrix.
Adoption notes for the team.

Data, privacy and limits

The course uses synthetic, public, anonymised or client-approved materials. It explains how to minimise data exposure, protect confidential information, verify outputs and keep human responsibility explicit.

Prerequisites

No programming required. Familiarity with the business process is useful.

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Program, outcomes and prerequisites

FAQ

Is the course tool-specific?

No. Patterns and workflows are adapted to the tools and policies chosen with the client.

Can company data be used?

Only when accounts, contracts and internal policies allow it. Otherwise synthetic or anonymised data is used.

What remains after the course?

Reusable materials, examples, checklists and a clear set of next steps.

Is it theoretical?

No. The course is built around practical exercises and decisions close to real work.

Contact

A short conversation is enough to understand where to start.

The first call lasts 30 minutes, is free and clarifies the process, goal, available data, constraints and next useful step: training, consulting, operational start, data analysis or a controlled technical prototype.

Write to dtr@ar-tik.com