that the majority AI pilot projects fail — not due to technical shortcomings, but attributable to challenges in aligning recent technology with existing organizational structures. While implementing AI models could seem straightforward, the true obstacles often lie in integrating these solutions with the organization’s people, processes, and products. This idea, commonly known as the “3P” pillars of project management, provides a practical lens for assessing AI readiness.
In this text, I introduce a framework to assist teams evaluate and prioritize AI initiatives by asking targeted, context-specific custom questions across these three pillars, ensuring risks are identified and managed before implementation begins.
Whether you’re involved within the technical or business side of decision-making within the AI process, the concepts I outline in this text are designed to cover each points.
The challenge of implementing AI use cases
Imagine being presented with an inventory of over 100 potential AI use cases from across a worldwide enterprise. Moreover, consider that the list of use cases breaks down into quite a lot of specific departmental requests that the event team must deliver.
The marketing department wants a customer-facing chatbot. Finance desires to automate invoice processing. HR is asking for a tool to summarize 1000’s of resumes. Each request comes with a unique sponsor, a unique level of technical detail, and a unique sense of urgency, often driven by pressure to deliver visible AI wins as soon as possible.
On this scenario, imagine that the delivery team decides to start implementing what appears to be the quickest wins, they usually greenlight the marketing chatbot. But, after initial momentum, the issues start.
First are the people problems. For instance, the marketing chatbot stalls as two teams within the department can’t agree on who’s answerable for it, freezing development.
After this issue is solved, process issues arise. For instance, the chatbot needs live customer data, but getting approval from the legal and compliance teams takes months, and nobody is accessible for extra “admin” work.
Even when this gets resolved, the product itself hits a wall. For instance, the team discovers the “quick win” chatbot can’t easily integrate with the corporate’s essential backend systems, leaving it unable to deliver real value to customers until this issue is sorted.
Finally, after greater than six months, budgets are exhausted, stakeholders are upset, and the initial excitement around AI has worn off. Fortunately, this consequence is precisely what the AI-3P framework is designed to forestall.
Before diving into the framework concept, let’s first take a look at what recent research reveals about why AI endeavors go off target.
Why do AI initiatives derail?
Enthusiasm around AI — or more precisely, generative AI — continues to peak day after day, and so we read quite a few stories about these project initiatives. But not all end with a positive consequence. Reflecting this reality, a recent MIT study from July 2025 prompted a headline in Fortune magazine that “95% of generative AI pilots at corporations are failing”
A part of the report relevant to our purpose involves the explanations these initiatives fail. To cite a Fortune post:
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With these reasons in mind, I would like to emphasise the importance of higher understanding risks implementing AI use cases.
In other words, if most AI endeavors don’t fail due to models themselves, but due to issues around ownership, workflows, or change management, then we now have pre-work to do in evaluating recent initiatives. To attain that, we are able to adapt the classic business pillars for technology adoption — and with a deal with the top .
This considering has led me to develop a practical scorecard around these three pillars for AI pre-development decisions: AI-3P with BYOQ (Bring Your Own Questions).
The general idea of the framework is to prioritize AI use cases by providing your personal context-specific questions that aim to qualify your AI opportunities and make risks visible the hands-on implementation starts.
Let’s start by explaining the core of the framework.
Scoring BYOQ per 3P
As indicated earlier, the framework concept relies on reviewing each potential AI use case against the three pillars that determine success: people, process, and product.
For every pillar, we offer examples of BYOQ questions grouped by categories that will be used to evaluate a selected AI request for implementation.
Questions are formulated in order that the possible answer-score combos are “No/Unknown” (= 0), “Partial” (= 1), and “Yes/Not applicable” (= 2).
After assigning scores to every query, we sum the entire rating for every pillar, and this number is used later within the weighted AI-3P readiness equation.
With this premise in mind, let’s break down the right way to take into consideration each pillar.
Before we start to contemplate models and code, we should always be certain that the “human element” is prepared for an AI initiative.
This implies confirming there’s business buy-in (sponsorship)and an accountable owner who can champion the project through its inevitable hurdles. Success also is determined by an honest assessment of the delivery team’s skills in areas resembling Machine Learning operations. But beyond these technical skills, AI initiatives can easily fail with out a thoughtful plan for end-user adoption, making change management a non-negotiable a part of the equation.
That’s why the target of this pillar’s BYOQ is to prove that ownership, capability, and adoption exist before the construct phase starts.
We are able to then group and rating questions within the Peoplepillar as follows:
Once we’re confident that we now have asked the best questions and assigned the rating on a scale from 0 to 2 to every, with No/Unknown = 0, Partial = 1, and Yes/Not Applicable = 2, the following step is to envision how this concept aligns with the organization’s day by day operations, which brings us to the second pillar.
The Processespillar is about ensuring the AI use case solution suits into the operational fabric of our organization.
Common project stoppers, resembling regulations and the interior qualification process for brand new technologies, are included here. As well as, questions related to Day 2 operations that support product resiliency are also evaluated.
In this manner, the list of BYOQ on this pillar is conceptualized to know risks in governance, compliance, and provisioning paths.
By finalizing the scores for this pillar and gaining a transparent understanding of the status of operational guardrails, we are able to then discuss the product itself.
Here is where we challenge our technical assumptions, ensuring they’re grounded within the realities of our Peopleand Processespillars.
This begins with the elemental “problem-to-tech” fit, where we want to find out the form of AI use case and whether to construct a custom solutionorbuyan existing one. As well as, here we evaluate the soundness, maturity and scalability of the underlying platform, too. Other than that, we also weigh the questions that concern the top‑user experience and the general economic fit of the Productpillar.
Because of this, the questions for this pillar are designed to check the technical selections, the end-user experience, and the answer’s financial viability.
Now that we’ve examined the , the , and the , it’s time to bring all of it together and switch these concepts into an actionable decision.
Bringing 3P together
After consolidating scores from 3P, the “ready/partially ready/not-ready” decision is made, and the ultimate table looks like this for a selected AI request:
As we are able to see from Table 4, the core logic of the framework lies in transforming qualitative answers right into a quantitative AI readiness rating.
To recap, here’s how the step-by-step approach works:
Step 1: We calculate a raw rating, i.e., Actual rating per pillar, by answering an inventory of custom questions (BYOQs). Each answer gets a price:
- No/Unknown = 0 points. This can be a red flag or a major unknown.
- Partial = 1 point. There’s some progress, however it’s not fully resolved.
- Yes/Not applicable = 2 points. The requirement is met, or it isn’t relevant to this use case.
Step 2: We assign a selected weight to every pillar’s total rating. In the instance above, based on the findings from the MIT study, the weighting is deliberately biased toward the Peoplepillar, and the assigned weights are: 40 percent people, 35 percent processes, and 25 percent product.
After assigning weights, we calculate Weighted rating per pillar in the next way:
Step 3: We sum the weighted scores to get the AI-3P Readiness rating, a number from 0 to 100. This rating places each AI initiative into one in every of three actionable tiers:
- 80–100: Construct now. That’s a green light. This means the important thing elements are in place, the risks are understood, and implementation can proceed following standard project guardrails.
- 60–79: Pilot with guardrails. Proceed with caution. In other words, the concept has merit, but some gaps could derail the project. The advice here could be to repair the highest three to 5 risks after which launch a time-boxed pilot to learn more about use case feasibility before committing fully.
- 0–59: De-risk first. Stop and fix the identified gaps, which indicate high failure risk for the evaluated AI initiative.
In summary, the choice is the product of the AI-3P Readiness formula:
That’s the method for scoring an individualAI request, with a deal with custom-built questions around people, processes, and products.
But what if we now have a portfolioof AI requests? A simple adoption of the framework to prioritize them on the organizational level proceeds as follows:
- Create a list of AI use cases. Start by gathering all of the proposed AI initiatives from across the business. Cluster them by department (marketing, finance, and so forth), user journey, or business impact to identify overlaps and dependencies.
- Rating individual AI requests with the team on a set of pre-provided questions. Bring the product owners, tech leads, data owners, champions, and risk/compliance owners (and other responsible individuals) into the identical room. Rating each AI request together as a team using the BYOQ.
- Sort all evaluated use cases by AI‑3P rating. Once the cumulative rating per pillar and the weighted
AI-3P Readinessmeasure is calculated for each AI use case, rank all of the AI initiatives. This leads to an objective, risk-adjusted priority list. Lastly, take the highest use cases which have cleared the edge for full construct and conduct an extra risk-benefit check-up before investing resources in them.
Now let’s take a look at some essential details about the right way to use this framework effectively.
Customizing the framework
On this section, I share some notes on what to contemplate when personalizing the AI-3P framework.
First, although the “Bring Your Own Questions” logic is built for flexibility, it still requires standardization. It’s essential to create a hard and fast list of questions before beginning to use the framework in order that every AI use case has a “fair shot” in evaluation over different time periods.
Second, inside the framework, a “Not applicable” (NA) answer scores 2 points (the identical as a “Yes” answer) per query, treating it as a non-issue for that use case. While this simplifies the calculation, it’s essential to trace the entire variety of NA answers for a given project. Although in theory a high variety of NAs can indicate a project with lower complexity, in point of fact this will sidestep many implementation hurdles. It might be prudent to report an NA‑ratio per pillar and cap NA contribution at perhaps 25 percent of a pillar’s maximum to forestall “green” scores built on non‑applicables.
The identical is valid for “Unknown” answers with rating 0, which present a full “blind spot,” and possibly ought to be flagged for the “de-risk first” tier if the knowledge is missing in specific categories as “Ownership,” “Compliance,” or “Budget.”
Third, the pillar weights (in the instance above: 40 percent people, 35 percent processes, 25 percent product) ought to be viewed as an adjustable metric that will be industry or organization specific. As an illustration, in heavily regulated industries like finance, the processes pillar might carry more weight attributable to stringent compliance. On this case, one might consider adjusting the weighting to 35 percent people / 45 percent processes / 20 percent product.
The identical flexibility applies to the choice tiers (80–100, 60–79, 0–59). A corporation with a high-risk tolerance might lower the “construct now” threshold to 75, whereas a more conservative one might raise it to 85. Because of this, it’s relevant to agree on the scoring logic evaluating the AI use cases.
Once these elements are in place, you’ve gotten all the pieces needed to start assessing your AI use case(s).
Thanks for reading. I hope this text helps you navigate the pressure for “quick AI wins” by providing a practical tool to discover the initiatives which can be ready for fulfillment.
I’m keen to learn out of your experiences with the framework, so be at liberty to attach and share your feedback on my Medium or LinkedIn profiles.
The resources (tables with formulas) included in this text are within the GitHub repo situated here:
CassandraOfTroy/ai-3p-framework-template: An Excel template to implement the AI-3P Framework for assessing and de-risking AI projects before deployment.
Acknowledgments
This text was originally published on the Data Science at Microsoft Medium publication.
The BYOQ concept was inspired by my discussions with Microsoft colleagues Evgeny Minkevich and Sasa Juratovic. The AI‑3P scorecard idea is influenced by the MEDDIC methodology introduced to me by Microsoft colleague Dmitriy Nekrasov.
Special due to Casey Doyle and Ben Huberman for providing editorial reviews and helping to refine the clarity and structure of this text.
