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PM 2030: Business Model Gut Check

Updated: Jun 20

In the first installment of this series, I described a manufactured scenario of a company considering the development of an AI-based mental health companion and coach (”Stella”).

Isaac pulls up Dagama, a specialized product discovery tool. It's a software platform with an adaptable discovery practice that provides guidance and valuable services to product teams.

Isaac’s organization’s first step is to perform a “business model gut check”, creating a Product Decision Canvas to capture what they know (or think they know) about what they’d like to build to identify any early showstoppers and begin qualifying the idea, identifying key areas of uncertainty. After all, the overarching goal of discovery should be to eliminate ideas before the organization wastes time, money, and effort on it.

Isaac enters a half-page description of the “mental health coach and companion”. It’s high level and describes many aspects like the target audience and key hypotheses as simple bullets. Dagama generates a “report” containing a:

  • brief introduction describing the idea

  • draft Product Decision Canvas

  • score for each of the canvas’s building blocks and an overall score

  • blurb on well-known gaps like the competitive environment

  • list of the elements of the business model with the highest level of uncertainty

  • summary of its assessment of the idea indicating if it believes discovery is worthwhile

The AI-based tool has done days of work in a few seconds. It’s leveraged its knowledge of the company’s portfolio and key competencies, current customer base, and publicly available information on the market including alternatives like competitive offerings and alternatives. Isaac asks it to refine the business risk building block as it seems lean and obvious (little insight). He then asks it to compare the business model score to other discovery efforts over the last year.

Stella Product Decision Canvas

Business Model Score

To help Isaac and the team understand the strength of the business model, Dagama has generated a scorecard based on critieria it suggests and a few added by Mindease. It uses its own heuristics and previous canvas scores to rate these key dimensions.




Understanding of Problem


The PDC clearly identifies key market problems like stress and habit formation, demonstrating a deep understanding of user challenges.



The product’s focus on personalization and user engagement is likely to drive high desirability among target users.

Go-to-Market Reach


The approach is robust with partnerships and community building, but the effectiveness of these strategies is not yet proven.

Moat Effectiveness


The identified moats are valid, but their long-term sustainability and impact against competitors need further substantiation.

Revenue Trends


Multiple revenue streams are promising, but the feasibility and execution of monetized data and corporate wellness programs need validation.

Sustainable Competitiveness


Personalization and scalable AI models contribute to competitiveness, but continuous innovation and adaptation are crucial for sustainability.

Addressable Market Size


While the target market is broad, there is a lack of detailed segmentation and market research to validate the size and potential.

Win Confidence


Although personalization and AI models are strong points, there is insufficient evidence of how these differentiate Stella significantly from competitors.

Cost Trends


Significant costs are identified, but there is a need for a clearer strategy on managing and optimizing these costs.

Delivery Velocity


Focus on software development is positive, but a clear timeline and milestones for delivery are lacking.

Technical Risk


Quality of AI insights is a potential risk; detailed plans for ensuring robustness and reliability are necessary.

Overall Business Risk


Risks are well identified, but the lack of detailed mitigation strategies lowers confidence in handling these risks effectively.



Funding details are not mentioned, creating uncertainty about financial backing and resource allocation.


Isaac clicks a button and shares the report with Vada for her feedback and guidance. She'll mark up the report and add comments in Dagama. Next step will be a checkpoint with leadership, including representatives from engineering, UX, Marketing, and Sales.


These activities can be performed today using generalized platforms like ChatGPT. The canvas and score above were generated by a draft GPT I created. The other elements of the report can easily be drafted with a few additional prompts.

You can learn how to use the business model canvas here. Coach PM members can access the editable scorecard here.

At Coach PMs, we build customized professional development programs for people and organizations. We can help you and your organization revolutionize your impact (using GenAI for example!). Contact us today for more information.

Join our next GenAI for Product Managers session to turbocharge your productivity!

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