Research
AI Strategy & Adoption
Most AI programs stall between pilot and production. This area covers how to pick the right use cases, justify the investment, organize teams, decide what to build or buy, and prepare the workforce.
Topics in AI Strategy & Adoption
Use Case Prioritization
Selecting the AI use cases most likely to deliver measurable value.
Overview
AI tools are easy to try and hard to scale. Organizations often run many pilots, see promising demos and then struggle to show results in the budget. The gap is rarely the technology. It is usually unclear priorities, weak business cases, unclear ownership and people who have not been prepared to work differently.
A sound AI strategy connects a short list of use cases to measurable business outcomes, assigns accountable owners, and invests in the data, governance and skills those use cases need.
What leaders need to get right
- Focus. A small number of use cases with measurable value, rather than many scattered pilots.
- Economics. Business cases that count the full cost, including data work, change management and ongoing operation.
- Ownership. Clear decision rights between business units, IT, data and risk teams.
- People. Training and role redesign so employees can use AI safely and productively.
Questions leaders should ask
- Which three AI use cases would we fund if we could fund only three?
- How will we know, in dollars or hours, that an AI use case worked?
- Who decides which AI projects move from pilot to production?
- What will we need our people to do differently?
Other research areas
Related research reports
In-depth reports with ready-to-use templates.
- AI-Ready Data$59.99 PDF report
- Third-Party AI Risk$59.99 PDF report
Related research reports
Reports connected to this topic.
Latest insights on AI Strategy & Adoption
How AI Is Changing Enterprise IT: The Technology, the Work, and the People
New AI tools can arrive as a subscription, but useful change in enterprise IT depends on decisions about delivery, operations, and accountability.
Beyond the Model: What Enterprise AI Implementation Actually Requires
A model is one component. Six readiness questions turn a promising demo into a service that keeps working in production.
The Economics of AI in IT: Productivity, Cost, and Business Value
Time saved, capacity released, and money saved are different outcomes. A credible AI investment case explains the connection.