CPMAI™ Phase 1: Business Understanding
- Sep 13, 2025
- 2 min read
Updated: Sep 15, 2025
Before you unleash the algorithms, figure out where you’re actually going — or prepare for a very expensive detour.
Most AI projects fail not because of poor technology, but because they solve the wrong problem—or no clear problem at all. Phase 1 of the CPMAI Framework is all about clarity: identifying the business challenge and defining measurable success.
In this phase, project managers play a critical role:
🎯 Aligning stakeholders on what success looks like — with clear, complete language.
📋 Documenting the problem in business terms—not just technical jargon.
🪜 Linking the AI effort to strategic goals like revenue, efficiency, customer satisfaction, or risk mitigation.
Here are two very real-world examples of Epic AI Project Implementation Fail:
Case Study #1: Zillow once dreamed big with its AI-powered home-buying program, “Zillow Offers.” The company leaned heavily on its Zestimate algorithm to predict home values and make instant purchase decisions. But the algorithm couldn’t keep up with volatile market shifts, leading Zillow to overpay for thousands of homes it couldn’t resell profitably. In 2021, the company shut down the program, laid off 25% of its workforce, and took a $304 million write-down. The AI wasn’t the problem—it was the mismatch between the tech’s capabilities and Zillow’s aggressive business goals.
Case Study #2: In 2018, IBM partnered with M.D. Anderson Cancer Center to deploy Watson for Oncology, aiming to revolutionize cancer treatment recommendations. But instead of training the AI on real patient data, they fed it hypothetical cases, leading Watson to suggest unsafe or incorrect treatments. Clinicians quickly lost trust, and the project was quietly shelved after spending over $62 million without delivering usable results. The mismatch between the AI’s capabilities and the clinical realities was stark. Watson wasn’t broken—it was just never aligned with the hospital’s actual workflows or decision-making needs.
Before you unleash an AI that’s solving the wrong problem, remember: even a rocket headed in the wrong direction just crashes faster and more spectacularly.


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