Across industries, companies are integrating AI to streamline operations, enhance customer service, and remain competitive. Yet, identifying where AI can add real value isn’t always straightforward. The challenge for many organizations lies not only in generating ideas but also in determining which of those ideas are worth pursuing.

This Insight piece offers recommendations — the same ones we explore in our workshops — to help organizations generate AI use cases and ideas effectively. No coding is required; just curiosity and a willingness to rethink the potential of artificial intelligence.

This article explores the process of running an AI ideation workshop, highlighting key steps and frameworks that ensure ideas are both innovative and actionable.

Laying the Foundation: Preparing for AI Ideation

You don’t need to be an AI expert to uncover valuable use cases. The key is to take a step back and reflect on how your organization operates and competes.

Take the supply chain, for example. AI presents significant opportunities to enhance logistics, inventory management, and supplier relationships. In an ideation session, the focus should be on pinpointing inefficiencies like delays, errors, or bottlenecks. Tools such as predictive analytics and real-time tracking can help streamline these processes, making operations more efficient and reliable.

Another area is knowledge creation. AI can process and synthesize vast amounts of data to generate actionable insights and support decision-making. Here, the challenge is identifying where information overload or slow analysis is holding back performance. Solutions like AI-driven summarization and content generation can boost productivity by making information clearer and more accessible.

Another approach for identifying potential AI use cases is to focus on tasks where AI could assist: 

1. Automate a process: Can AI handle tasks that are repetitive or rule-based?
2. Make information always available: Can AI provide information quickly and accurately when needed?
3. Extract insight: Can AI analyze and interpret data to provide meaningful insights?

Every great solution (whether AI-related or not)  starts with a problem. The first task is to identify the pain points—the inefficiencies, bottlenecks, and challenges that AI might address.

The goal is to create a long list of potential use cases. Some ideas will be straightforward to implement, while others may be ambitious and complex. That’s okay—at this stage, creativity is key.

Questions to ask:

1. Where do tasks frequently get bogged down?
2. Which processes are repetitive and could be automated?
3. What challenges frustrate customers or employees the most?

The ideation phase is where the magic happens. Participants brainstorm solutions, building on each other’s ideas to imagine how AI can transform their work.
In these sessions, teams are encouraged to frame their ideas as "How might we..." statements. For example:

a) How might we reduce the time it takes to create customer presentations by half using AI?
b) How might we use AI to predict equipment failures before they happen?
These prompts keep discussions open-ended and focused on solutions.

After an initial burst of brainstorming, groups refine their ideas. Some ideas may need to be combined; others might be split into more manageable projects. The process is iterative — ideas evolve with each discussion.

Mapping the Big Picture: The Digital Value Canvas

The Digital Value Canvas (DVC) helps teams see how their AI ideas fit into the company’s broader strategy. 

The DVC maps potential benefits across three rings:

• Inner Ring: Direct financial benefits, like revenue growth or cost reduction.
• Middle Ring: Indirect benefits, like improved process efficiency or employee productivity.
• Outer Ring: Strategic goals, like brand enhancement or sustainability.

By aligning ideas with the DVC, teams ensure their AI projects contribute to the company’s overall mission.

The image below illustrates how eight potential AI ideas can be mapped within the DVC framework, showing where each delivers value.

Since management teams often have differing expectations for the digital portfolio, the Digital Value Canvas (DVC) facilitates discussions by highlighting areas of agreement and disagreement. While the possibilities for AI use cases are vast, the DVC helps focus and align the ideation process.

Reality Check: Assessing Ideas with the DFV Framework

Not every idea is ready for prime time. Once the brainstorming session yields a list of potential AI use cases, the DFV (Desirability, Feasibility, Viability) Framework provides a structured way to assess which ideas are worth pursuing.

A DVF assessment is conducted by testing hypotheses and collecting data to assign high-confidence scores to each dimension.

Desirability (D). This dimension examines whether the solution addresses a real need. Teams ask questions like:
•    Do users or customers want this solution?
•    Is there strong interest from many stakeholders?
A high score in desirability means the idea solves a meaningful problem and is likely to be embraced by users.

Feasibility (F). Assesses whether the organization can realistically implement the solution with existing resources and technology. Key considerations include:
•    Do we have the data and technical infrastructure needed?
•    What challenges do we anticipate during development or deployment?
Ideas with high feasibility are technically achievable and have a clear path to implementation.

Viability (V). Evaluates whether the solution makes business sense. This involves questions like:
•    Will the solution provide a high return on investment (ROI)?
•    Does it align with the organization’s strategic goals?

High viability means the project is not only profitable but also strategically valuable.
Ideas with the highest scores are prioritized for implementation. This process helps teams focus on solutions that are not only innovative but also actionable and valuable.

Here is how the DFV assessment could look for the eight different AI ideas discussed previously: 

Conclusion

AI ideation discussions or workshops are more than brainstorming sessions; they are a launchpad for innovation. By identifying challenges, generating ideas, and rigorously assessing their feasibility and value, companies can uncover AI opportunities that deliver real results. 

With frameworks like the Digital Value Canvas and the DFV Assessment, internal teams are equipped to turn ideas into actionable strategies.