5 Actionable frameworks to help you (and your team) understand, adopt, and scale AI

1. A framework to brainstorm AI use cases

Just remember “RADIC”: It’ll help you identify common tasks that are well-suited for generative AI assistance:

Research: Finding, surveying, curating, and sourcing data on a topic.
Analyze: Interpreting, summarizing, classifying, and simplifying data to find patterns.
Decide: Evaluating, judging, and selecting to make choices or recommendations based on data.
Innovate: Brainstorming, ideating, and remixing to combine or reimagine ideas in new ways.
Create: Writing, coding, designing, generating, and personalizing to produce something new, whether it's text, audio, video, image, or code.

This framework provides a structured way to think about potential AI applications across various business functions and can help teams identify opportunities for AI integration in their workflows.

Learn more: Finding AI use cases: One helpful framework to try

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2. A framework to uncover AI use cases in your job

The 5x5 AI Framework is a structured approach to identify AI use cases in everyday work tasks. It consists of three main steps:

Identify 5 recurring tasks or projects you do regularly (weekly or monthly).
Break down each task into at least 5 steps, starting each with a verb.
Identify 5 ways to insert AI into these steps.

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It’ll help you uncover your common tasks that are recurring, structured, and narrow. This approach helps professionals systematically analyze their workflows and find opportunities to integrate AI, potentially improving efficiency, creativity, or performance in their daily work.

Learn more:

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3. A framework to evaluate AI risks and rewards

The AI Alignment Matrix is a 2x2 framework designed to evaluate and prioritize AI use cases based on their value and risk. It helps companies make informed decisions about which AI initiatives to pursue.

Align AI initiatives with business goals, consider factors such as productivity potential, efficiency gains, and innovation impact, and most importantly, drive important AI conversations in your company.

Learn more: Simplify AI decisions with this quick framework

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4. A framework to avoid and mitigate AI risk

The 3S framework is an approach to mitigate risks associated with AI adoption, with three strategies to reduce AI-related risks:

Make it Smaller: Reduce the scope of the AI task to something that's more manageable and less risky.
Make it Smarter: Increase human involvement in the process, using AI as a productivity booster rather than a complete replacement.
Make it Safer: Add more guardrails to the process, such as masking sensitive data or using on-premise Large Language Models for increased security and control.

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This framework helps organizations to thoughtfully implement AI by considering ways to minimize risks while still capturing the benefits of AI automation. It encourages a balanced approach where AI augments human work rather than fully replacing it, especially in high-risk scenarios.

Use the 3S approach to tailor your AI implementation strategy based on your specific risk tolerance and operational needs.

Learn more: When AI goes wrong: 3 ways to mitigate your risk

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5. Bonus: A framework for naming and organizing your AI prompts

Choose an organizational system for your prompt (e.g., by function, discipline, project, modality, or task).
Separate prompts from context blocks (reusable elements like brand descriptions or audience characteristics) to make prompts more versatile.
Adopt a consistent naming convention like "Discipline: Task" (“Content: Write a blog post”).

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Don’t forget to include keywords, tags, and usage instructions to create comprehensive "Prompt Playbooks." This approach aims to make prompts more easily discoverable, reusable across different contexts, and aligned with organizational standards, facilitating better integration of AI into day-to-day work processes.

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Taylor Radey

TAYLOR IS THE FOUNDER OF RANDALL PINE. OVER THE LAST 15 YEARS, SHE’S CONSULTED FOR DOZENS OF BRANDS, FROM STARTUPS TO ENTERPRISES, GUIDING THEM THROUGH THE COMPLEXITIES OF DIGITAL TRANSFORMATION.