Disclaimer
This scenario breakdown is a fictionalized, illustrative case study created for educational and strategic thinking purposes. While inspired by real-world patterns and organizational challenges, all details—company context, team structure, and suggested approaches—are generalized and do not represent any specific employer, client, or confidential situation.
The content is designed to demonstrate strategic problem-solving, not to prescribe one-size-fits-all solutions. Readers are encouraged to adapt ideas and frameworks to suit their unique organizational needs, capabilities, and compliance contexts.
You're supporting a global enterprise where various departments have independently started experimenting with AI — from chatbot pilots in customer support to small recommendation engines in product. However, there's no unifying framework. Each team uses different tools, models, and vendors. Some rely on consultants, others have built internal scripts, and many don’t share what they’re learning.
The org is now planning a broader AI rollout. Leadership realizes there’s potential but also risk: lack of reuse, inconsistent standards, and ethical oversights. They want to move from scattered initiatives to coordinated capability — without stifling team ownership.
Root Problems
- Siloed Experimentation: No visibility across teams, leading to redundant or conflicting efforts.
- Tool and Stack Fragmentation: Different clouds, libraries, and infra in use.
- No Capability Model: Hard to assess maturity or plan org-wide uplift.
- Inconsistent Guardrails: Data handling, explainability, and privacy policies vary.
- Knowledge Drain: Experiments live in notebooks, not shared or documented.
Apply the AI Capability Uplift Canvas: Awareness ➝ Foundation ➝ Application ➝ Productization
Start with the problem and ask “why” up to five times to identify the root cause.