Beyond the Buzz: How to Frame AI/ML Problems for Real Business Impact
A 12-Question Framework for Connecting AI to Measurable Outcomes
AI isn’t magic. It’s not a silver bullet that will instantly transform your business. The companies that succeed with AI and machine learning aren’t the ones that rush to deploy the latest shiny model, they’re the ones that start with a disciplined approach to framing the problem. As you know from my previous writings – they’re the ones that generally start small.
Too often, AI projects fail not because the technology doesn’t work, but because the problem was never defined in a way that connects business goals with technical design. If you want your AI initiative to drive measurable value, here are twelve questions that I have expanded upon from Aurélien Géron worth asking before you write a single line of code.
1. Define the objective in business terms
Don’t start with “we need an AI model.” Start with: what business problem are we trying to solve? Maybe it’s reducing customer churn, speeding up claims processing, or improving fraud detection. The key is to translate technical curiosity into a concrete business outcome like revenue growth, cost savings, or risk reduction.
2. How will your solution be used?
A model that produces insights no one acts on is useless. Will the output guide decision-making, trigger automation, or enhance customer experience? Knowing how it will be embedded into workflows ensures adoption.
3. What are the current solutions or workarounds?
If teams are already manually reviewing transactions, or using rule-based systems, that’s your baseline. Understanding the status quo helps you quantify improvement and manage expectations.
4. How should you frame this problem?
Is it supervised (predicting churn), unsupervised (detecting anomalies), reinforcement (optimizing recommendations), or something else? Is real-time performance needed, or can it be batch/offline? Framing the problem correctly sets boundaries around what’s technically feasible.
5. How should performance be measured?
Accuracy, precision, recall, F1 scores are useful, but don’t stop there. A fraud model that reduces false positives by 5% may sound small, but if it saves thousands of wasted investigations, that’s massive business value.
6. Is the performance measure aligned with the business objective?
Your technical metric must be tied back to business KPIs. For example, “model accuracy” is fine, but if your goal is reducing churn by 10%, make sure the performance metric reflects that impact.
7. What’s the minimum performance needed?
Perfect accuracy isn’t realistic. Sometimes a model that’s only 70% effective is still a win if it beats the existing approach. Define what “good enough” looks like to justify deployment.
8. What are comparable problems?
Chances are that your problem isn’t entirely unique. Look for industry case studies, open-source solutions, or vendor tools you can adapt. Reinventing the wheel is often the slowest (and costliest) path.
9. Is human expertise available?
AI isn’t about replacing people; it’s about augmenting them. Domain experts are critical for feature engineering, data validation, and interpreting results. If you don’t have them, your project is at risk before it starts.
10. How would you solve the problem manually?
If you can’t describe a manual solution, you may not understand the problem well enough. Manual steps often reveal decision criteria that can guide model design.
11. List the assumptions you’ve made so far
Assumptions creep in everywhere: “We assume the data is clean.” “We assume customers behave consistently.” Write them down.
12. Verify assumptions if possible
Before investing heavily, test your assumptions. If your customer data is messy or incomplete, no model will save you. Catch these issues early to save time and budget.
Closing Thought
AI success isn’t about bigger models or more GPUs. It’s about disciplined problem framing. By walking through these twelve questions, you create a bridge between business need and the technical solution. That’s how you avoid hype-driven dead ends and deliver real, measurable outcomes.
The Launch of Gradient Descent LLC
With all that said, I am happy to announce that I have launched an AI and technology consulting firm – Gradient Descent LLC. You can find it at gradientdescent.biz. Gradient descent is a common mathematical method that is used to optimize AI algorithms. That is the intent of the firm – to optimize daily operations of an organization through the application of artificial intelligence and technical solutions. The firm is available for advisory services, fractional CTO work, and speaking engagements such as corporate seminars and training sessions. If you are interested in engaging Gradient Descent or know of someone that might be, please feel free to forward this information.
Cheers!