5-Step Framework to Solve Problems with AI
Artificial intelligence (AI) is reshaping industries at an unprecedented pace, offering businesses the opportunity to solve complex problems more efficiently than ever before. However, diving into AI without a structured approach often leads to wasted resources and failed projects. To help decision-makers in mid-to-large-size businesses, startups, and government agencies in Canada, this article outlines a practical and transformative 5-step framework for solving problems with AI.
Developed by an experienced AI practitioner, this strategic guide emphasizes starting with a clear problem rather than focusing on the technology itself. By following these steps, organizations can avoid common pitfalls, ensure a higher success rate, and achieve impactful outcomes.
Why Most AI Projects Fail: The "Hammer Problem"
Before diving into the framework, it’s important to understand why so many AI projects fail. Too often, organizations approach AI with a "hammer-and-nail" mindset. They become enamoured with the technology and attempt to apply it indiscriminately to various challenges, whether or not AI is an appropriate solution.
This approach – the "man-with-a-hammer fallacy", as coined by Charlie Munger – leads to wasted time, money, and effort. Instead of rushing to adopt AI because it’s a shiny new tool, businesses must begin by identifying their most pressing problems. Only then can AI be applied in a targeted, high-impact way.
The 5-Step Framework for Solving Problems with AI
Step 1: Define the Problem
The foundation of a successful AI project lies in identifying the right problem to solve. Instead of starting with ideas for AI solutions, ask:
- What are the most pressing problems my organization faces?
- What problems do my customers encounter?
Choose a problem that has:
- High impact: Does this issue significantly affect operations, revenue, or customer satisfaction?
- Feasibility: Do you have the skills, resources, and data required to address it?
For example, challenges like "writing LinkedIn posts takes too much time" or "free users aren’t converting to paid users" may seem small but can be directly tied to productivity and revenue.
Pro Tip: If you’re solving problems for external clients, focus on issues you’ve already overcome yourself. Past experience translates into deeper insights and faster solutions.
Step 2: Brainstorm Solutions
Once the problem is clearly defined, brainstorm a range of potential solutions. At this stage, don’t limit yourself to AI-based ideas. Consider any approach that could address the issue effectively, such as:
- Optimizing workflows
- Providing training
- Developing software tools
For example, to tackle inefficient LinkedIn post creation, possible solutions could range from creating viral post templates to building a custom AI tool that mimics a user’s writing style.
After generating ideas, evaluate them based on value versus effort. Use a simple matrix:
- High Value, Low Effort: These are your best starting points.
- High Effort, High Value: Promising but resource-intensive – consider these for long-term goals.
- Low Value, Low Effort: Avoid unless they’re quick wins with minimal investment.
- Low Value, High Effort: Eliminate these ideas immediately.
Step 3: Scope the Minimum Viable Product (MVP)
With a solution in mind, define an MVP – a simplified version of your idea that can be built quickly to test its effectiveness. Scoping your MVP requires clear success metrics and constraints:
- Success Metrics: Define measurable, objective goals. For instance, "Reduce LinkedIn post-writing time by 50%" or "Increase landing page conversions by 30%."
- Constraints: Identify budget, time, and technical limitations. Use tools and technologies you already know to accelerate development.
Key Tip: Speed is critical in the AI landscape, which evolves rapidly. Building an MVP quickly allows you to test, iterate, and stay ahead of competitors.
Step 4: Build the MVP
Today’s AI tools make it easier than ever to create an MVP. Options range from no-code platforms like Custom GPTs to basic automations using platforms like Zapier. Even something as simple as a Notion dashboard or a curated newsletter can serve as an MVP.
Consider which type of software best suits your needs:
- Software 1.0 (Rule-Based): Reliable, predictable, and low-cost but limited in complexity.
- Software 2.0 (Machine Learning): Requires training on examples but delivers more sophisticated results.
- Software 3.0 (Large Language Models): Powerful and highly flexible but comes with higher costs and less predictability.
Pro Tip: Start by testing solutions with large language models (LLMs) like GPT-4 for speed and flexibility. Once the concept works, you can refine it using simpler, cheaper methods if needed.
Step 5: Reflect and Iterate
After deploying your MVP, take the time to assess its performance against your success metrics. This reflection phase is critical for understanding what worked, what didn’t, and how to improve.
Ask yourself:
- Did the MVP meet or exceed success metrics? If yes, how can it be scaled or monetized?
- If not, what were the root causes of its failure? Were there issues with the model, the data, or the implementation?
You have two options at this stage:
- Push: Refine the current solution to meet your goals.
- Pivot: If the problem or solution isn’t viable, explore a new idea.
Real-World Examples of AI Problem-Solving
To illustrate how this process works, consider these practical scenarios:
- Challenge: A small business struggles with customer support response times.
- Solution: Deploy an AI chatbot trained on FAQs to handle 80% of inquiries.
- Outcome: Faster responses for customers and reduced workload for human agents.
- Challenge: A logistics company needs to optimize delivery routes.
- Solution: Use machine learning to analyze traffic data and predict delays.
- Outcome: Reduced delivery times and fuel costs.
- Challenge: A healthcare organization wants to improve patient scheduling.
- Solution: Implement an AI-powered scheduling assistant.
- Outcome: Increased efficiency and patient satisfaction.
Key Takeaways
- Start with the problem, not the technology: Identify high-impact challenges before exploring AI solutions.
- Evaluate solutions based on value and effort: Focus on ideas that deliver significant results with minimal resources.
- Define success metrics for your MVP: Ensure your goals are measurable and achievable.
- Leverage today’s AI tools: Platforms like GPT-4 make it easier than ever to build fast, functional prototypes.
- Reflect and refine: Test your solution, learn from failures, and iterate to improve outcomes.
- Speed is your competitive advantage: The AI landscape evolves rapidly – prioritize quick experimentation and adaptability.
Bringing It All Together
This 5-step framework offers a strategic approach for organizations looking to harness the power of AI effectively. By focusing on real-world problems and adopting a disciplined, iterative process, businesses can sidestep the common pitfalls of AI adoption and achieve meaningful results.
For Canadian businesses operating in industries like energy, logistics, and public services, the opportunity for transformation is immense. Whether it’s optimizing internal workflows, enhancing customer experiences, or driving revenue growth, this framework provides a roadmap to success.
By investing the time to define problems clearly, test solutions quickly, and learn from outcomes, organizations can ensure their AI initiatives deliver lasting value.
Source: "How to Solve Problems with AI (not just use it)" – Shaw Talebi, YouTube, Aug 10, 2025 – https://www.youtube.com/watch?v=hugQUr4VwRA
Use: Embedded for reference. Brief quotes used for commentary/review.
