What is the Cognitive Project Management for AI (CPMAI) Methodology?

AI Project Management Skills are in High Demand

Companies of all sizes are implementing Artificial Intelligence (AI), machine learning (ML), and cognitive technology projects for a wide range of applications across many industries and customer sectors ranging from insurance to finance, IT to construction, retail to automotive and beyond. In fact, according to Cognilytica research, by 2025, over ​90% of organizations surveyed will implement some form of AI or machine learning project. Already, over 40% of the largest global companies surveyed have implemented at least one AI project and plan to continue their investment in AI and ML. As such, there is significant and growing demand for skilled project managers and implementers across the whole range of AI capabilities, especially those with certifications such as CPMAI.

Some of these AI efforts are focused on natural language processing (NLP) and conversation systems, while others are focused on recognition or autonomous systems. Other efforts are internally-focused advanced predictive analytics, pattern and anomaly detection management, or highly personalized “hyperpersonalization” systems that aim to optimize an offering for each individual. 

Despite all these different ways in which AI is being applied to today’s organizational needs, the goals of these efforts are the same: the application of cognitive technologies that leverage the emerging capabilities of machine learning and associated approaches to meet a range of important needs. Yet, organizations struggle to make AI systems work by using dated processes, methodologies, and approaches that are application development-centric or aren’t rooted in the data-centric nature of AI. These traditional approaches face significant challenges when organizations attempt to apply them to the unique lifecycle requirements of AI projects. This is because what drives AI and ML projects is not programmatic code, but rather the data from which learning must be derived. Therefore, what is needed is a project management methodology that takes into account the various data-centric needs of AI while also keeping in mind the application-focused uses of the models and other artifacts produced during an AI lifecycle, and also leverages best-practices experience around agile and iterative forms of project management. 

Dealing with the High Failure Rate of AI Projects

Despite decades of experience running major technology projects and with billions of dollars invested in advanced technology, organizations are experiencing a high rate of failure for their AI projects. According to many estimates, up to 80% of AI projects fail due to a wide range of reasons, such as a lack of availability and accessibility of data to a misalignment of the specific business problem to the attempted AI solution. With all the great technology we have and with so many highly trained developers and data professionals, why do we have such a high rate of failure?

With the experience of thousands of AI projects, Cognilytica identifies 10 major reasons for why AI Projects fail:

  1. Applying application development approaches to data-centric AI
  2. Lack of sufficient quantity of data
  3. Lack of sufficient quality of data
  4. ROI Misalignment of AI solution to problem
  5. Lack of planning for continued AI, model, data iteration and lifecycle
  6. Misalignment of real world data and interaction against training data and models
  7. Applying proof of concept thinking to real-world pilots
  8. Underestimating time and cost of the data component of AI projects
  9. Vendor misalignment on promise vs. reality
  10. Overpromising AI capabilities and underdelivering projects

What we realized is that great technology doesn’t solve any of the above problems. Neither does simply throwing more people at the problem. The solution to the problems listed above is a robust, iterative method by which to reliably run AI projects with a high degree of success. That approach is the Cognitive Project Management for AI (CPMAI) methodology, which has subsequently been successfully implemented and adopted by many of the leading large private and public sector organizations.

Fortunately, certified AI project managers who apply best-practice AI methodologies can not only rescue poorly performing AI projects, but set up processes and practices to ensure their continued success. This is why project managers with certified AI project management skills are in such high demand.

Why can’t we use existing Project Management Approaches for AI Projects?

There are millions of people certified on leading project management methodologies and approaches such as PMP, PRINCE2, Agile, ScaledAgile (SAFe), and CRISP-DM, so how can there be so many AI project failures? Clearly existing approaches to project management are failing to meet the need for AI and advanced data projects. Many of these highly proven, but decades-old project management methodologies, such as the Project Management Institute (PMI) Project Management Professional (PMP) certification or the PRINCE2 methodology are more generally-focused approaches for managing a wide range of technology and non-technology projects. While they work for general project management in industries such as  construction, legal, IT, healthcare, and other industries, and have been successfully applied for both strategic and tactical project management,  they don’t provide specific guidance on how to successfully run and manage an AI project. 

Other approaches such as Agile Methodologies or ScaledAgile or SCRUM approaches are more focused on the aspects of managing complex application development projects or running technology teams, but aren’t focused on the specific aspects of data-driven projects. While the Cross Industry Standard Process for Data Mining (CRISP-DM) has been around for over 20 years, it is no longer maintained and hasn’t been updated to deal with AI projects nor takes an Agile perspective on data projects. 

Why can’t we use Agile for AI?

Agile methodologies are extremely popular for a wide range of application development purposes, and for good reason. Prior to the widespread adoption of Agile, many organizations found themselves bogged down by traditional “waterfall” methodologies that borrowed too much from assembly line methods of production. Rather than wait months or years for a software project to wind its way through design, development, testing, and deployment, the Agile approach focuses on tight, short iterations with a goal of rapidly producing a deliverable to meet immediate needs of the business owner, and then continuously iterating as requirements and needs become more refined. To this end, the Agile approach first popularized by the Agile Manifesto emphasizes focusing on individuals and interactions over strict processes and tools, delivery of working products over a focus on planning and documentation, continuous customer collaboration versus a drawn out contract negotiation process, and a focus on responding to change rather than strict adherence to a plan. There is no doubt that Agile methodologies have forever changed the way organizations develop and release functionality in a world where the pace of change continues to accelerate.

However, with decades of Agile experience under our belts, we’re finding the limitations for this general approach to project management. Agile methodologies are challenged by the requirements of AI systems. For one, what exactly is being “delivered” in an AI project? You can say that the machine learning model is a deliverable, but it’s actually just an enabler of a deliverable, not providing any functionality in and of itself. In addition, if you dig deeper into machine learning models, what exactly is in the model? The model consists of algorithmic code plus training model data, the parameters trained by that training data, hyperparameter configuration data, and additional support logic and code that together comprise the model. 

But even that doesn’t really define what the AI system is doing. Indeed, you can use the same algorithm with different training data and that would generate a different model, and you can use a different algorithm with the same training data and that would also generate a different model. So is the deliverable the algorithm, the training data, the model that aggregates them, the code that uses the model for a particular application, all of the above, none of the above? The answer is yes. As such, we need to consider additional approaches to augment Agile in ways that make them more AI-relevant.

Rather than reinvent the wheel, we can stand on the shoulders of giants. Agile forms a basis for an AI methodology but can’t be used solely on its own. We need more guidance to help us figure out how to run projects where the data tells us how the system will perform, and where the resulting system will behave in different ways depending on the environment.

Why can’t we use CRISP-DM for AI Projects?

Before this most recent  wave of AI and machine learning interest and hype, organizations that had data-centric project needs also looked for methodologies that suited their goals. Emerging from roots in data mining and data analytics, some of these methodologies had at its core an iterative cycle focused on data discovery, preparation, modeling, evaluation, and delivery. One of the earliest of these developed is simply known as Knowledge Discovery in Databases (KDD). However, just like waterfall methodologies, KDD is in some ways too rigid or abstract to deal with continuously evolving models.

Responding to the needs for a more iterative approach to data mining and analytics, a consortium of five vendors developed the Cross-industry Standard Process for Data Mining (CRISP-DM) focused on a continuous iteration approach to the various data intensive steps in a data mining project. Specifically, the methodology starts with an iterative loop between business understanding and data understanding, and then a handoff to an iterative loop between data preparation and data modeling, which then gets passed to an evaluation phase, which splits its results to deployment and back to the business understanding. The whole approach is developed in a cyclic iterative loop, which leads to continuous data modeling, preparation, and evaluation.

CRISP-DM 1.0 Methodology: Source: CRISP-DM

However, further development of CRISP-DM has stalled, with only a 1.0 version fully produced almost two decades ago, and rumors of a second version under way almost fifteen years ago but further development and versions were never published. IBM and Microsoft have both iterated on the methodology to produce their own variants that add more detail with respect to more iterative loops between data processing and modeling and more specifics around artifacts and deliverables produced during the process. However, both companies are primarily leveraging their modifications in the context of delivering their own premium service engagements or as part of product-centric implementation processes. Clearly vendor-centric, proprietary methodologies can’t be adopted by organizations that have diverse technology needs or desire to utilize vendor-agnostic approaches to technology implementation.

The primary challenge to making CRISP-DM work is in the context of existing Agile methodologies. From the perspective of Agile, the entire CRISP-DM loop is contained within the development and deployment spheres, but it also touches upon the business requirements and testing portions of the Agile loop as well. Indeed, if we bring Agile into the picture, these two independent cycles of application-focused agile development and data-focused data methodologies are intertwined in complex ways.

What makes things even more complex is the fact that the roles in the organization between the application-focused Agile groups and the data-focused methodologies groups are not the same. While frequently the project manager is the center of the Agile universe, connecting the sides of business and technology development, the data organization with the roles of data scientist, data engineer, business analyst, data analyst, and the line of business is the center of the data methodology universe. Frequently the language of communication between Agile-centric approaches and data-centric approaches is not the same, with Agile “sprints” focused on functions and features, and data “experiments” focused on data sources, data cleansing, and data models. Clearly the two parts of the organization serve the same overall master so we need to combine these two approaches into a cohesive whole that provides organizations the power they need to deliver AI projects reliably. CRISP-DM needs more detail to be used successfully for managing AI projects.

The CPMAI Methodology: An Iterative, Agile, Data-Centric AI Project Management Methodology

So if Agile doesn’t work on its own, and CRISP-DM hasn’t evolved and isn’t sufficient, what do we need to address the 80% failure rate of AI projects and make them successful? The answer, of course, is a blended methodology that starts from the same root of business requirements and splits into two simultaneous iterative loops of Agile project development and Agile-enabled data methodologies. We can think of this as an Agile Data-Centric project management approach, but to avoid loaded terminology, a better way to think of it is a Cognitive Project Management for AI methodology, or CPMAI. 

The Cognitive Project Management for AI (CPMAI) methodology is a vendor-neutral, data-centric, AI-specific, Agile methodology for running and managing AI, ML, and cognitive technology projects. The CPMAI methodology for AI project management borrows and extends upon previous methodologies and approaches for project management, such as Agile Methodology and CRISP-DM, which have both pioneered methods for running large, complex, and constantly changing projects that can respond to continuous needs while also focusing on the data-centric aspects of those projects. 

CPMAI makes specific enhancements to Agile and CRISP-DM methodologies to meet AI-specific requirements, especially as they pertain to the above requirements. By extending these methodologies rather than creating a new approach, CPMAI can be implemented in organizations with already-running Agile teams and already-running data organizations. Introducing something new and foreign is a sure way to get resistance. So the key is to provide a blended approach that simultaneously delivers the expected results to the organization and provides a framework for continued iterative development at the lowest risk possible. At the end of the day, successfully running and managing an AI project should be everyone’s goal.

How CPMAI applies Agile for AI Projects

CPMAI projects are specifically projects that involve the development and iteration of AI, machine learning, and/or cognitive technologies. Since the primary emphasis of machine learning is the use of data to create models, we can’t simply focus on functionality as the output of a CPMAI project. Rather, CPMAI projects iterate on models, so that each iteration should be the development of successive models and/or the use of those models for specific user needs. This means that the majority of User Stories in CPMAI projects are focused on model iteration. However, it is not possible to have each User Story correspond only to a single iteration of a model. It might be necessary to have preliminary work done to get data into a ready state to be used in a model, and there must also be User Stories that correspond to operationalizing developed models. 

As such, Agile Backlogs developed for CPMAI projects will be a combination of data preparation and management activities, ML model development and iteration activities, and model consumption / operationalization activities. These should be prioritized in a way that makes the developed models the center of attention for the overall project, rather than being simply a single User Story or task.

How CPMAI extends CRISP-DM for AI Projects

In a similar fashion to the CRISP-DM Methodology, CPMAI utilizes the concept of six primary phases of a data project. What makes CPMAI different though is that we consider each trip through all the phases to be one iteration as part of multiple iterations in an AI project. Each CPMAI iteration can be  applied fully for one User Story or across multiple User Stories as appropriate. Each phase focuses the efforts of the project team on accomplishing objectives for preparing, developing and operationalizing models that meet specified user objectives and value outcomes.

Unlike CRISP-DM, these phases are all meant to be mutually iterative, which means that if a project team is engaged in activities at the Data Modeling phase and realizes that there are Data Understanding issues, they can shift back to the Data Understanding phase before iterating again on Data Preparation and Data Modeling activities and prior to embarking on Model Evaluation activities. Just because you have started a phase does not mean you can’t go back to a previous phase for the same project iteration, as might be required by the business objectives. And while there’s no such thing as a CRISP-DM certification, CPMAI certification provides all the capabilities previously covered by CRISP-DM, while making it relevant to today’s advanced data and AI projects.

What are the steps involved in an AI project?

The main steps for each iteration of an AI Project are embodied in the the highest level of the CPMAI hierarchy, which are the six major Phases which collect the activities that aim to achieve one primary objective within that part of the project. You can see a high level overview of the six primary CPMAI Phases and their objectives below:

CPMAI Methodology image
CPMAI Methodoogy: Source: © Cognilytica

There are six primary CPMAI phases, all of which are iterative and data-centric:

  • CPMAI Phase I: Business Understanding – “Mapping the business problem to the AI solution.”
  • CPMAI Phase II: Data Understanding – “Getting a hold of the right data to address the problem.”
  • CPMAI Phase III: Data Preparation – “Getting the data ready for use in a data-centric AI Project.”
  • CPMAI Phase IV: Model Development – “Producing an AI solution that addresses the business problem.”
  • CPMAI Phase V: Model Evaluation – “Determining whether the AI solution meets the real-world and business needs.”
  • CPMAI Phase VI: Model Operationalization – “Putting the AI solution to use in the real-world, and iterating to continue its delivery of value:”

Each of these CPMAI phases are iterative with each other and allows for progression backwards or forwards during AI project development depending on the need and challenges met in the real world. Below we provide greater detail of each phase, the reason the phase is needed, and key questions to address during that phase.

Phase I: Business Understanding

The first phase in any CPMAI project is gathering an understanding of the business or organizational requirements. Borrowing from CRISP-DM, but customizing for AI purposes, the Business Understanding phase focuses on understanding the project objectives and requirements from a business perspective, then converting this knowledge into an AI and cognitive project problem definition and a preliminary plan designed to achieve the objectives.

CPMAI Phase I Business Understanding aims to address three key needs for AI projects: Business Requirements, determining which (if any) pattern or patterns of AI meet those business requirements, and identification of what the most important deliverable would be for that particular iteration of the AI Project. 

The key questions and needs that Phase I of CPMAI aims to address are:

  • What data-centric problem are we attempting to solve?
  • Should we solve this problem with AI / Cognitive Technology?
  • What portions of the project require / do not require AI?
  • What AI pattern(s) are we implementing?
  • What are the criteria for project success?
  • What requirements are needed to complete the project iteration?
  • What are the specific performance or KPI requirements for success for this project iteration?
  • What other important security, governance, cultural, ethical, and environmental considerations are there for the AI project?
  • Which of those considerations would be deal-stoppers if they were to have a negative impact?
  • What skills are necessary for successful project completion?

CPMAI uses Cognilytica’s Seven Patterns of AI as a way to shortcut and speed up cognitive projects. Each pattern in the Seven patterns represents projects that share similar objectives, technology basis, and other aspects that once acknowledged will help to fill in the missing blanks as to how any other project in that pattern should run. Identifying the pattern and even other projects that have been developed in the pattern will prove to be convenient for project teams during all stages of AI development.

The Seven Patterns of AI (source: Cognilytica)

Without a firm grounding in business understanding, any AI project will fail right out of the gate. Properly scoping an AI project to meet immediate business needs and establishing the immediate business requirements and constraints will make subsequent iterations have a much higher chance of success. Why wait until later in the project to realize that a deal-stopper will cause your project to fail?

Phase II: Data Understanding

The second phase in a CPMAI project is the Data Understanding phase, which is focused on data needs identification, initial data collection, data requirements, data quality identification, insights into data, and potential interesting aspects of the data worth further investigation. The most important part of this phase is understanding what data is required to address the business problem, whether or not that data is available and what format it is in.

CPMAI Phase II Data Understanding aims to address three key data requirements for AI projects: the availability and sources of data to meet business needs, the quality of that data and need for enhancement or augmentation, and the environments in which data is needed for training and real-world inference. The key questions and needs that Phase II of CPMAI aims to address are: 

  • What data are necessary to achieve the objectives?
  • What is the quantity and quality of our data?
  • What internal and external data is necessary?
  • What are the requirements to augment existing data?
  • What are the requirements for ongoing data gathering and preparation?
  • What are the requirements for technology for data manipulation and transformation?
  • What other important data-relevant considerations are there for the AI project, including aspects of data governance, data security, data privacy, data access, data sharing, data storage and other key considerations?

Since AI projects are at their core data projects, a firm understanding of the data environment and availability is absolutely essential to AI project success. The number of AI projects that fail due to a lack of understanding of data availability, quality, or other related factors is significant. Why be one of those failure statistics? Run your AI projects grounded in data understanding that is in turn based on business understanding and you have already surmounted many of the hurdles that plague unsuccessful projects.

Phase III: Data Preparation

The third phase in a CPMAI project is the Data Preparation phase, which focuses on activities needed to construct the dataset that will be used for modeling operations. Data preparation includes data cleansing, data aggregation, data augmentation, data labeling, data normalization, data transformation and any other activities for data of structured, unstructured, and semi-structured nature. 

Garbage in is garbage out is the fundamental rule with data-driven projects. While application development or other projects aim to eliminate defects by way of fixing “bugs” or addressing functionality or user interface mismatches, data driven project flaws are driven by problems with the data. Once we know what data we need for a project, the next logical step is making sure that the data is in a form and quality ready to be used for the AI project.

CPMAI Phase III Data Preparation aims to address three key data preparation requirements for AI projects: wrangling data from the sources and transforming it to its required state, data cleansing to eliminate critical data flaws, and data augmentation and enhancement including data labeling to add necessary meaning and context to the data so that AI systems can properly learn from the data. 

The specific needs and questions addressed in CPMAI Phase III Data Preparation are:

  • How must data be transformed to meet requirements?
  • Implementation of data cleansing, transformation, and manipulation
  • Iterations of the data engineering pipeline
  • Means by which data quality can continuously be monitored and evaluated
  • Use, extension, and modification of third-party data
  • Human-involved data annotation and manipulation (“labeling”)
  • Performance of additional data augmentation steps
  • Creation of data engineering pipelines

It’s astounding to realize how many AI projects fail because the data is not of sufficient quality to meet the business needs. Would you develop software that is full of bugs and lacks established coding standards? If not, why would you develop AI systems that are based on poor quality data? By focusing on high quality data from the previously identified sources that meet business needs, you will have greatly increased your odds of AI project success. 

Phase IV: Model Development

In the fourth phase of a CPMAI project, the AI project team embarks on the creation and development of machine learning models and other cognitive technology modeling artifacts as part of the AI development process. This activity includes model technique selection and application, model training, model hyperparameter setting and adjustment, model validation, ensemble model development and testing, algorithm selection, and model optimization. By the time we are ready to build our very first model in the CPMAI methodology, we’ve already determined the business needs, the data requirements, and we’ve set the data up to be in the right format and quality. This greatly improves the odds of success for the models we train and develop.

In CPMAI Phase IV Model Development, which is also known as Data Modeling, we are primarily focused on selecting the right approaches and algorithms for the model, based on the business requirements, data availability, and performance needs, and we also perform the actions of tuning and configuring the model for optimal performance with hyperparameter tuning, as well as perform necessary model training activities.

The key questions and needs that are addressed in CPMAI Phase IV Model Development are: 

  • Appropriate algorithm selection
  • Performance of model training activities
  • Performance of model optimization activities
  • Determination of appropriate algorithm settings, and hyperparameters
  • Creation of ensemble models
  • Use of third-party models or extensions of models
  • Model development as appropriate for selected machine learning technique
  • Matching model performance against business requirements
  • The selection of appropriate infrastructure for model training

The first iterations of models should be quick and short enough so that a model is produced within the first week or two of the project iteration. To build such a quick model requires selecting only the smallest amount of data that needs to be prepared and aggregated and labeled so that training time is efficient. The biggest mistake AI project owners make when doing machine learning (ML) project management is using too much data and selecting algorithms that are particularly data hungry, require expensive GPUs, and take a long time to train. They burn significant time and resources before even determining if the model is a good fit for the business problem. This is why the motto for CPMAI is “think big, start small, and iterate / succeed often”.

Phase V: Model Evaluation

Once a model has been created to meet the business needs, it needs to be evaluated to make sure it performs according to the business requirements and other factors set in the previous phases of a CPMAI iteration. Model evaluation from an AI perspective includes model metric evaluation, model precision and accuracy, determination of false positive and negative rates, key performance indicator metrics, model performance metrics, model quality measurements, and a determination as to whether or not the model is suitable for meeting the goals of the that iteration or whether earlier phases should be iterated upon to reach those goals.

The key considerations for CPMAI Phase V Model Evaluation are model evaluation and testing, model performance measurement and improvement, and determining needs for ongoing model iteration, a necessary part of any machine learning project plan. The key questions and needs for this phase include:

  • Does the model meet requirements for accuracy, precision, and other metrics?
  • Determining and evaluating concerns on overfit and underfit of models
  • Evaluation of training, validation, and test curves for overall acceptability
  • Evaluation of models against business Key Performance Indicators (KPIs)
  • Determination of model suitability with regards to operationalization approach
  • Determination of means for model monitoring
  • Determination of means for model iteration and versioning

Just like quality assurance and testing in the non-AI world, model evaluation is the heart of making sure that the AI solution meets the business needs. Far too many organizations are short-changing their model evaluation steps and as a result are failing needlessly. Those who are armed with CPMAI skills and are CPMAI certified know to pay specific attention at this phase so that the team and project can be successful!

Phase VI: Model Operationalization

The final phase of each iteration of the CPMAI methodology is putting the developed model into operation, namely operationalizing it in a manner consistent with delivering the functionality required for the sprint iteration. Model operationalization might mean deploying the model for use in a cloud environment, edge device, for use within an on-premise or closed environment, or within a closed, controlled group. Model operationalization considerations include model versioning and iteration, model deployment, model monitoring, model staging in development and production environments, and other aspects of getting the model in a position to provide value to meet the stated purpose. 

The key needs to address during CPMAI Phase VI Model Operationalization include model deployment, model management, and model governance. The specific questions to address during this phase include:

  • How will this model be used in production / operational environments?
  • What are the requirements for data flow for a model to be useful?
  • What are the requirements for performance?
  • Operationalization of model in different environments
  • Implementation of model monitoring
  • Implementation of model versioning and governance
  • Evaluation of business performance
  • Determination of ongoing iteration requirements

In the world of data-driven projects, especially AI projects, there’s no such thing as  “set it and forget it”. Models continue to change in their performance over time as data and the real world continues to change. Organizations that haven’t budgeted time or resources for ongoing AI project maintenance realize quickly that their expectations of how the model will perform in the real world don’t meet the real world realities. As we mentioned earlier, overpromising and underdelivering is one of the top reasons for AI project failure and a major reason for mismatch between real world expectations and realities. CPMAI certified project managers know to plan for, clearly communicate, and manage this mismatch so that each iteration can bring them continued success.

Putting CPMAI Methodology to Work: Artifacts, Templates, Workbooks

While the above provides a good overview of the CPMAI methodology, the real work is in the details. How exactly do you provide specific enough business understanding? What are the steps needed to perform an adequate data understanding that matches to the business needs? How do you go about selecting the right data for preparation and achieving the desired state of data quality? What approaches do you do for model development that allow for quick iteration that satisfies business objectives? How do you evaluate models to make sure there’s a fit between the AI solution and business problem? How do you iterate and manage models so that they continue to provide value? 

The answers to these questions help drive success for each individual project, and the answers are specific for each individual artificial intelligence project plan. As part of the Cognilytica CPMAI Training and Certification, we provide workbooks, templates, and the artifacts needed to put CPMAI methodology to use. There’s no need to reinvent the wheel and create these artifacts yourselves. 

The below chart outlines each of the CPMAI Phases mapped to Generic Tasks per phase of each AI project plan. Items in bold are the main generic tasks and items in italics are outputs / artifacts created during the Generic Task process. The specifics of these Generic Tasks are outlined in the CPMAI Workbook and supporting templates.

CPMAI Methodology Artifacts
Source: Cognilytica – CPMAI Methodology Phase Artifacts

Success in applying CPMAI is all about making sure that the necessary questions have been asked and answered, and building iterations of your agile project so that as new questions emerge or previous answers change, so too can your AI projects iterate to success.

CPMAI Methodology Certification: A Must Have for the Modern Project Manager

Knowing the basics of the CPMAI methodology is a good first step, but actually being able to put it into use and getting the credential needed to prove to employers and managers that you know and are certified on CPMAI is a more effective step. This is where the Cognilytica CPMAI certification and training provides real value to the modern project manager.

CPMAI is vendor-neutral. Unlike AI project management approaches from technology or other third party vendors, CPMAI provides a scalable, proven approach that supports technology solutions across the diverse ecosystem.Evolving from hundreds of real-world implementations CPMAI methodology is optimized for the delivery of in-production, high value, succesful AI projects. 

CPMAI is data-centric. AI and advanced analytics are not about systems or functionality – they’re about extracting value from data. CPMAI is also iterative and agile leveraging established data methodologies updated with today’s modern agile approaches. The CPMAI methodology is also best practices based,  proven in real-world adoption and based on well respected CRISP-DM and Agile. Learn from others’ successes and failures. 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.

CPMAI is not theory. Get success on your very first project iteration with tangible results by following a proven method. Get certified in CPMAI today and add the fastest growing, most valuable AI project management certification to your resume and skillset today. With several thousand CPMAI certified and hundreds of organizations looking to hire skilled AI Project managers, the CPMAI certification provides a tangible increase in skills, pay, and contract value to those who have the latest CPMAI certification. 

Make Yourself More Competitive. Learn Established Best Practices for AI & Data Project Management that helps anyone involved in project and product management for AI and Data Science. Get a foundation in AI & Data Understanding. Leverage & extend your existing Certifications and skills. Become part of a community and get access to detailed, relevant, continuously updated, and in-depth knowledge and tools that will make you more successful. Get access to Cognilytica’s AI project management training and courses. The CPMAI certification is trusted, well respected, high value, and provides a high ROI, providing a way to become certified project management in AI. To join the quickly growing  community and build this sought after skillset, get CPMAI Certified today!

Getting CPMAI certified

Do you have the desire and interest to run AI and data projects the successful way? Do you want to dig deeper into this methodology to actually be able to put it into use? Then, earning a CPMAI certification is the right choice for you and your career. CPMAI certification does not require any prerequisites, however it is helpful to be in a project or program management role. The training for this certification is offered a variety of different ways  to provide you the flexibility to learn the material around your busy schedule. CPMAI is the fastest growing certification for running and managing AI projects, with 220% annual growth rate. Join this quickly growing community of AI and data project management professionals and be able to put the CPMAI Methodology into practice.

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