CPMAI Methodology

Achieve Success with Best Practices

Cognitive Project Management for AI

Vendor-Neutral Best-Practice Methodology for Data Analytics & Cognitive Projects of Any Size


Methodology for Implementing AI & Advanced Analytics Projects Successfully.

There’s no excuse for failing AI projects. If you’re not successful, you’re not doing it right.



Methodology that supports technology solutions across the diverse ecosystem


AI and advanced analytics are not about systems or functionality - they're about extracting value from data.

Iterative and Agile

Established data methodologies updated with today's modern agile approaches

Best Practices Based

Proven over and over in real-world adoption. Learn from other's successes and failures.

Cognitive Technology & Advanced Analytics

CPMAI Methodology is focused on extracting big value from big data


CPMAI is not theory. Get success on your very first project iteration with tangible results by following a proven method.

The Core Elements of CPMAI Methodology

Best Practices Based

CPMAI extends the well-known CRISP-DM methodology with AI and ML specific documents, processes, and tasks. The CPMAI methodology also incorporates the latest practices in Agile Methodologies and adds additional DataOps activities that aim to make CPMAI data-first, AI-relevant, highly iterative, and focused on the right tasks for operational success. The CPMAI methodology is composed of six primary phases listed below:

PhAse I: Business Understanding

Meeting business objectives

  • Assessment of Business Situation
  • Cognitive Requirements
  • AI Go / No Go
  • AI Pattern Identification
  • Agile Release Planning
  • AI Project Success Criteria
  • Transparency Requirements & Responsible AI
  • And other criteria for business success
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    Phase II: Data Understanding

    Identifying critical data needs

  • What are the core data requirements and objectives?
  • Where are the AI-relevant data sources?
  • What type of data is available for AI projects to meet business objectives?
  • What is the quality of the available data?
  • How can we iterate data needs for success?
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    PhAse III: Data Preparation

    Data Quality is kEy

  • Methods to clean, prune, shape, and optimize data to make modeling more accurate and effective
  • Provide transparency for methods of data selection, pruning, feature enhancement
  • Creation of data pipelines
  • Assuring data quality throughout the model lifecycle
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    PhAse IV: Model Development

    extracting value from data

  • Modeling Techniques
  • Algorithm selection
  • Modeling Assumptions
  • Generate Test Design
  • Model development and iteration
  • Model optimization techniques
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    Phase V: Model Evaluation

    Making Sure models actually work

  • Does the model really work against real-world data?
  • Is the model right/wrong in acceptable ways?
  • Model evaluation considerations
  • Real-world model testing and validation
  • Model iteration and improvement
  • Business KPI Evaluation
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    Phase VI: Model Operationalization

    Putting Models & Analytics to Work

  • Putting AI models into operation
  • The inference phase of AI
  • Model deployment configuration
  • Real-world model monitoring and management
  • AI operationalization considerations
  • Continuous model management and evaluation
  • register for cpmai training & certification

    Real People and Real Organizations getting Real Value

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    Improvement in iteration time

    International CPG company adopted CPMAI methodology, cutting model iteration time from 3 months to 4 weeks per iteration cycle

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    Project costs saved

    Large retailer implemented CPMAI utilizing actionable AI project success criteria to save over $6M in AI project costs

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    Time reduction

    International telecommunications company put into practice CPMAI, standardizing data project efforts, and reducing data identification and selection time by 60%

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    Project Speed improvement

    Large healthcare provider adopted CPMAI for AI and data analytics project and it provided 8x project speed improvement

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    Single project ROI

    International construction firm realized over $2.5M in saved data preparation and engineering costs through CPMAI adoption

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    Improvement in compliance

    Large financial institution adopted CPMAI and increased compliance and reduced risk by 500%

    Why are you Running your most important DATA projects without a standard methodology in place?

    Cognilytica has worked with dozens of organizations and through hundreds of real-world implementations to develop a methodology optimized for the deliver of in-production, high value, low risk AI projects. The Cognitive Project Management for AI Methodology leverages decades of real-world methodology experience for running big data projects combined with the latest best practices expertise learned from running real-world AI projects. AI and big data project failure is rooted in a failure of planning, expectation setting, and establishment of practices.