Lost in AI: How to Ensure You're Solving the Right Problem

AI can accelerate anything – both good and bad. If you try to solve the wrong problem while using AI, you’ll just get to the wrong answer faster, not the right solution. As businesses rush to integrate artificial intelligence into their processes, it's crucial to take a step back and ask: Are we using AI to solve the right problems? In my upcoming book, "The AI Process Playbook for Business," I detail best practices for AI integration.

Today, let's explore a fundamental aspect of this journey - problem definition.

Before you start prompting ChatGPT or any other AI tool, it's essential to clearly define your problem. This critical pause can lead to more effective solutions and prevent wasted resources on misguided efforts.

Here are key questions to consider:

1. Do you have the correct problem to solve?
2. Have you considered different versions of the problem?
3. Is the problem well-posed and clearly stated?
4. Have you examined all your assumptions?

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Finding the Right Problem

Two powerful techniques can help in this process: the '5 Whys' and problem statement-restatement.

The '5 Whys', developed by Toyota's founder, involves repeatedly asking "why" to drill down to the root cause. For example, a tech transfer office facing low commercialization rates might start like this:

1. Why are our commercialization rates low? - Because few companies are licensing our technologies.
2. Why aren't companies licensing? - Because they don't see immediate market applications.
3. Why don't they see applications? - Because our disclosures focus on technical details, not market potential.
4. Why is our focus misaligned? - Because researchers aren't trained in market-oriented thinking.
5. Why aren't researchers trained in this? - Because we haven't integrated market analysis into our disclosure process.

This process reveals that the core issue isn't just about marketing, but about how the tech transfer office structures its disclosure and evaluation process.

The statement-restatement method involves rephrasing your problem in various ways. For instance, "How can we increase licensing deals?" could be restated as:

• "How can we make our technologies more attractive to industry?"
• "How can we better align our research with market needs?"
• "How can we improve our communication of technology benefits?"

Each restatement can lead to different solutions and reveal hidden aspects of the problem.

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Where does AI fit into problem solving?

So, where does AI fit in this process? While AI is a powerful tool, it's not a complete solution. Use AI to:

• Generate various problem restatements
• Analyze large datasets for trends
• Summarize extensive documents
• Uncover hidden assumptions in your problem statement

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However, rely on human insight for:

• The '5 Whys' exercise
• Stakeholder interviews
• Critical thinking and reflection
• Final problem definition

Remember, AI should complement, not replace, your expertise and critical thinking.

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Calling our tech transfer colleagues!

For tech transfer professionals, AI can be particularly useful in analyzing patent landscapes, summarizing market reports, or generating potential applications for a new technology. However, the nuanced understanding of how a technology fits into the market and a university's ecosystem still requires human expertise.

As you integrate AI into your TTO processes, always view it as a tool, not a solution. Your knowledge, experience, and critical thinking are irreplaceable in accurately defining and solving business problems.

By striking the right balance between AI capabilities and human insight, you can harness the power of AI while ensuring you're addressing the core issues that truly matter to your business.

Your Future-Proof Self

Generative AI offers unprecedented opportunities for forward-looking problem solving. Its ability to process vast amounts of data, identify patterns, and generate novel ideas can help businesses anticipate future challenges and opportunities. By feeding AI models with current trends, historical data, and potential scenarios, we can generate a range of possible futures and solutions. This can be particularly powerful in areas like market forecasting, product development, and strategic planning.

For example, a tech transfer office could use generative AI to explore potential applications of a new technology across various industries, helping to identify non-obvious commercialization opportunities.

However, the key to leveraging generative AI for future-oriented problem solving lies in the synergy between AI capabilities and human expertise. While AI can generate numerous possibilities, it's up to us to evaluate these ideas critically, considering factors like feasibility, ethical implications, and alignment with organizational goals. As we look to the future, the most successful problem solvers will be those who can effectively direct AI's creative power, ask the right questions, and apply human judgment to AI-generated insights. By mastering this balance, we can use AI not just to solve today's problems faster, but to anticipate and prepare for the challenges of tomorrow.

Stay tuned for more insights in my upcoming book, "The AI Process Playbook for Business," where I'll dive deeper into best practices for AI integration in various business contexts.

What challenges have you faced in problem definition with AI? Share your experiences in the comments!

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TTO Acceleration: AUTM's New AI Course for Tech Transfer Professionals

I'm thrilled to share an exciting opportunity to sharpen your AI skills in the context of tech transfer. I'll be one of the instructors for AUTM's new 4-week virtual course: "The Future of Tech Transfer: Leveraging AI to Find & Secure Licensing Deals, Research & Find Your Best Company Licensee."

This course is tailor-made for tech transfer professionals looking to develop a nuanced understanding of AI in our field. Here's what you can expect:

• Learn how to challenge your assumptions and recognize potential biases in AI-assisted processes
• Gain practical insights into creating an AI workflow that balances analytical power with critical thinking and human expertise
• Develop strategies to question AI outputs and make more informed decisions
• Understand how your own biases might influence your use of AI tools

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Course Details:

• Dates: October 15 to November 7
• Format: Virtual, allowing for flexible participation

This is a timely opportunity to upgrade your skills and stay ahead in the rapidly evolving landscape of AI-assisted tech transfer. By participating, you'll be at the forefront of shaping a more reflective and effective future for our field.

Don't miss out! Spaces are limited, and the price will increase soon. Secure your spot now and invest in your professional growth.

Save my spot now!