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Business Understanding in CPMAI

Published: 09/06/2026
Last Updated: 09/06/2026

Phase 1: Business Understanding in CPMAI

The first phase of the CPMAI methodology is Business Understanding. To start, teams must state the business problem that an AI solution will address. Teams must defend AI and set targets. When these activities are completed, teams can commence work on the data and models. This phase develops the case and scope statement; without these elements, the subsequent phases cannot continue; in addition, the AI model is often developed to solve the wrong problem, a common trap of many AI projects.

Why Is Business Understanding Important?

AI projects are prone to addressing solutions even if problems do not exist. This is known as the “solution seeking the problem” paradox. One of the failure causes of a corporate AI project is the lack of clarity in framing and identifying the business objective). According to RAND Corporation, about 80% of corporate AI projects failed to deliver the promised value. The primary reason is the failure to set, or to set unclear, and/or invalid business objectives (or no objectives at all) in this initial phase.

What Occurs in the Business Understanding Phase?

This phase contains five activities: problem identification and assessment, AI assessment, business case analysis, and measurement of success criteria and definition of success. Each of these activities brings value to the next Data Understanding phase by providing inputs that define the data required for the project.

.	How It Works	How It Helps
.	Business Problem Identification	Locating business problems, challenges, or opportunities.
.	Assessment of AI Feasibility	Examining whether AI is a better and more appropriate choice than simpler alternatives.
.	Stakeholder Analysis	Identifying relevant stakeholders and understanding their pain points.
.	Building a Business Case	Determining the anticipated ROI and providing justification for the project.
.	Success Criteria Definition	Defining concrete and measurable goals that the model or solution must achieve.

How Do You Locate the Business Problem?

●Generally, the teams reach the Business Understanding phase of a project with a promise to “build an AI model.” Normally, they are tackling a known issue. At this problem-solution disconnect phase, situation statements assist the teams in addressing the real issue.

●Describe the present and the desired state and the obvious gap that is between them in a situation statement.

●Run an analysis on whether the gap is indeed a problem and not a symptom of an underlying issue.

●Conduct a gap analysis on the state and the desired end state.

●Focus on the problem and not on the sponsor.

How Do You Know If AI Is Appropriate?

How do you know if a Give-or-Take or Rule-based system will not work?

Determine if a Problem’s Outcomes Can be Expressed as a Range.

Evaluate if there is a Simple Business Rule that Solves the Problem.

Determine if Past Occurrences of the Problem Are Present to Train the Model.

Evaluate if AI-based Systems Have a Lower Total Cost and Lower Overall Risk.

Evaluate Feasibility for Each Step to Help Justify AI.

How Are AI Projects Justified?

●Determining the business value, expected return on investment, and sponsor support before applying data science and engineering resources to AI projects is necessary to justify an AI project. Most AI projects fail at the implementation phase because sponsors approved an exploratory tech project and not a well-defined business project with a measurable return.

●Estimate the business value the solution can provide.

●Estimate the total cost of the project, including data preparation, model building, and incorporating the model into the project.

●Return on Investment (ROI) estimation and the approximate duration of the Return on Investment.

●Request for sponsor approval and business case for proceeding with Data Understanding.

How Do You Define Success Criteria in Business Understanding?

●Success criteria specify the Model metrics and KPIs it has to meet for the Model solution to have resolved the existing business problem. Success criteria cannot be defined after the construction of the Model. They must be articulated and defined when the counteracting problem is framed. Poor framing of success criteria leads to the problem of over-reliance on Model performance metrics and under-appreciation of the unmet goals for which the Model was constructed.

●Agree to specific, quantifiable, success thresholds that pertain to the business problem addressed.

●Understand the distinction between technical metrics of Model performance (precision) and the business outcome metrics of Model performance (business outcomes like revenue and cost). Decide on these metrics before the evaluation of the Model to avoid evaluating Model performance retroactively. Develop the success criteria of the project and include business case and scope as part of the project documentation.

What Deliverables Are Associated With Business Understanding?

Two important deliverables characterize Business Understanding: the business case and the project scope statement.Each succeeding phase of the CPMAI process — starting with Data Understanding — relies on these deliverables. They articulate the work to be done on the project, and if the work executed in a phase is not aligned with the project scope, then the project scope is adjusted.

Deliverable	Includes
Project Scope Statement	Definition of the problem, project boundaries, project success criteria, and the project timeframe.
Business Case	Estimated return on investment (ROI), project cost, sponsor approval, and risk assessment.

What Happens When Business Understanding is Avoided or Hurried?

In AI projects, if Business Understanding is avoided or hurried, the models that are technically built may be determined to successfully fulfill a business need, only to discover that the model was built to fulfill a different business need. As a result, teams have to perform the work to define the project scope, refine the business need, and prepare the data, as well as build and evaluate the model. This is the most expensive area in the CPMAI methodology because subsequent phases (Phases 2 – 5) are the most time-consuming and resource-intensive, while Phase 1 is the least time-consuming and resource-intensive.

Data are collected and understood without a clear definition of the problem, and hence time and resources are wasted.

The project is currently moving forward without a business case that should have come before the project. The business case then becomes a justification for a project that was done without a business case.

What is the business understanding connection to the PMI-CPMAI exam?

Business Understanding relates most to Domain II of the PMI-CPMAI Exam called Identify Business Needs and Solutions, which makes up 26% of the examined responses, the joint highest weighting of any domain of the framework. Candidates must be prepared for exam items that involve not just identifying issues but also determining the feasibility of the solution and drafting the business case since the activities carried out in this phase of the processes are the closest to what the PMI’s Examination Content describes in this domain.

Where does Business Understanding fit in the full CPMAI methodology?

The CPMAI methodology is made up of six phases, and business understanding is the first of these. For more information on how Business Understanding relates to other phases, see The CPMAI Methodology: 6 Phases Explained.

Where Can You Learn to Apply Business Understanding in Practice?

PMTI offers hands on training for the CPMAI certification. The workshops help build a scope statement and ROI case for the Business Understanding phase.

Phase 1: Business Understanding FAQs

What is the main output of the Business Understanding phase?

Prior to the other phases of the CPMAI process, Business Understanding is able to construct a Project Scope Statement and a business case. This includes the problem statement and the criteria and expected Return on Investment (ROI) of the project.

There are a few ways to know when a problem qualifies for the application of AI.

Is the outcome inherently random?

Could a model with fewer rules do the job? Would the cost and risk to the business decrease if we employed a cheaper, more straight-forward model as a substitute to the suggested AI model?

Which of the following are the most important mistakes Associated with Business Understanding?

Not validating AI’s presence in the first place, not understanding the issue before we build an AI model, and putting AI into the problem where it doesn’t belong.