Starting With the Business Problem, Not the AI Solution

Imagine your team just invested 6 months and thousands of dollars into perfecting an AI-Powered ChatBot. But, when you launch it, you find your customers aren’t satisfied with it, support agents bypass it, and leadership questions the AI investment. What went wrong?

This team fell for the “AI-first” trap. With mounting pressures from organizational leadership across industries to find ways to use more AI, there’s a knee-jerk reaction to jump straight to the solution. But, the best AI projects are brutally honest right from the start about the business problem that needs solving and if AI is the right fit.

Why teams jump to AI without validating the problem:

• Competitive pressure ("Everyone else has a chatbot")
• Technology hype and FOMO
• Vendor promises that sound too good to be true
• Executive mandate without ground-level validation

The costly consequences:

• Building solutions that don't address real pain points
• Customer frustration when chatbots can't handle actual needs
• Wasted investment when simpler solutions would have worked
• Team burnout from implementing technology nobody wanted

The Right Starting Point: Business Understanding

AI Thinkers starts from step 1 and asks the question: what problem are we actually trying to solve? We will quantify the problem with real data such as call center metrics and website analytics to narrow in on where the opportunity is. For example, we may calculate the cost per interaction × volume of repetitive queries to estimate your potential savings. But we will also consider: are these inquiries truly repetitive, or do they require human judgment?

Using a feasibility assessment during this first phase takes a data-driven and structured approach to successfully reaching an informed AI “go/no-go” decision for a project.

The go/no-go framework questions to ask:

• Business Feasibility: Do we have executive support, reasonable timeline, budget aligned with ROI?
• Data Feasibility: Do we have quality data to work with?
• Technical Feasibility: Can AI actually solve this, or is it a workflow/integration problem?
• Organizational Feasibility: Will the operational team adopt this? Will customers want it?

Conclusion: Start Honest, Stay Humble

The mindset shift required:

• Technology serves the business problem, not the reverse
• "I don't know if AI is right for this" is a valid and valuable conclusion
• Continuous validation and improvement beats "perfect" launches
• Success is measured in business outcomes, not model performance

Call to action:

• Before your next AI project, spend real time in the Business Understanding phase
• Challenge the assumption that AI is the answer
• Build metrics that matter to your business, not just your data scientists

What Comes Next?

Business Understanding is just Phase 1 of the six-phase CPMAI framework we use to build successful AI chatbots. Once we've validated that AI is the right solution and defined clear success criteria, we would work with you to:

• Understand your data landscape (Phase 2) - What do you actually have to work with?
• Prepare that data properly (Phase 3) - Where most of the project effort will go
• Develop the right model (Phase 4) - Choosing the technical approach that fits your reality
• Evaluate before you launch (Phase 5) - Go beyond accuracy metrics
• Operationalize for continuous improvement (Phase 6) - From lab to living system

In our next post, we'll dive into the data challenge: what to do when you're starting from scratch with no chatbot history, just call center recordings, emails, and FAQs. Because here's the thing—most companies don't have perfect training data sitting around. They have messy, real-world information scattered across systems. And that's exactly where we'll pick up.