What is an AI Program Manager?

The potential of AI to transform industries and societies is undeniable. However, bridging the gap between cutting-edge technology and effective project execution is crucial for AI to achieve this potential. To bridge this gap, organizations are turning to the AI Program Manager to serve as the crucial link between AI technologies, business objectives, and project execution.

But what does an AI program manager do, and what qualifications are required for this role? To understand this, we’ll first explore the general program manager role. Then, we’ll examine the specific AI program manager role, responsibilities, and qualifications. And finally, we’ll look at similar roles.

The Program Manager Role

What is a Program Manager?

The Project Management Institute defines a program as:

“a group of related projects managed in a coordinated manner to obtain benefits not available from managing them individually”

PMI

To successfully deliver value from a program, organizations employ program managers. Each program manager oversees and coordinates multiple related projects within an organization. They ensure that each project in the program aligns with the company’s objectives and that they are completed efficiently and effectively.

What is the Difference between Program and Project Managers?

Effectively, program managers create a higher-level wrapper layer on top of individual projects to coordinate resources, dependencies, and objectives shared collectively among the projects. To understand this difference better, explore the table below:

 Project ManagersProgram Managers
Relative scopeNarrow and specificBroader
Tactical vs strategicTacticalVaries. Usually blends both
Key activitiesManage dependencies within a project. Manage scope, schedule, quality, and costManage dependencies among projects. Collectively supports multiple projects.
Functional unitsA single project teamOne or more project teams
Coordination complexityLowerHigher (more stakeholders, deliverables, things to track)
Function endsWhen project completesCan continue indefinitely

Note that some organizations ask program managers to also directly manage individual projects. While other teams have individual project managers or project leads oversee the more granular project-layer activities.

The AI Program Manager Role

With the increasing demand for AI-based projects, companies are hiring AI program managers to manage the successful planning, execution, and delivery of related AI projects. Essentially, the AI program manager is a subset of the broader program manager role but specifically for AI initiatives. They collaborate with cross-functional teams, including data scientists, engineers, business analysts, and stakeholders to define project goals, scope, and timelines.

AI Program Manager Responsibilities

The AI program manager role and responsibilities will vary by company and department. However, below are some common responsibilities.

Program Management: These are the core set of responsibilities that all AI program managers will at least partially manage.

  • Lead cross-functional teams to deliver AI/ML program objectives on time and within budget
  • Develop and manage program plans, budgets, and timelines, ensuring alignment with individual projects
  • Ensure that related data assets and models are discoverable and re-usable for multiple projects
  • Track program progress and performance metrics, identifying and addressing potential roadblocks. For more detail, see the 10 Data Science Project Metrics post.
  • Manage resource allocation and utilization across program projects
  • Ensure projects meet quality standards and contribute to overall program goals
  • Support risk management activities for the program

Agile AI Process Facilitation: Some organizations might employ Agile Coaches, Scrum Masters, or Process Masters who are responsible for evangelizing, designing, and facilitating Agile processes. Regardless, AI program managers at least share in this set of responsibilities.

  • Support the continuous improvement of the AI/ML development process
  • Ensure Agile ceremonies such as standup meetings, sprint planning, sprint reviews, and retrospectives are executed effectively
  • Remove impediments and obstacles that hinder the team
  • Shield the AI and data science team members (data scientists, AI engineers, data analysts, etc) from external distractions and disruptions
  • Coach the team on Agile principles and frameworks such as Scrum, Kanban, and Data Driven Scrum
  • Serve as a servant-leader to support the well-being and development of individual team members.

Project Management: Often AI program managers are also responsible for delivering individual projects.

  • Lead the AI life cycle, from ideation to deployment. By definition of a project, a project manager would not oversee the ML operations phase. However, this does often occur in practice.
  • Develop and manage project plans, budgets, and timelines
  • Secure resources and manage cross-functional teams consisting of data scientists, engineers, analysts, and business stakeholders
  • Monitor project progress and identify and address potential roadblocks
  • Ensure projects meet quality standards and deliver expected business value
  • Identify and source the data sets required for the project

Strategic Leadership: While some AI program managers remain focused on tactical execution, some organizations also have program managers serve in a strategic capacity.

  • Define and implement the AI/ML roadmap, aligning it with overall business goals and objectives
  • Identify and prioritize key AI/ML initiatives based on market trends, potential impact, and feasibility
  • Proactively identify and mitigate risks associated with AI/ML projects
  • Develop and maintain strong relationships with key stakeholders across various departments (e.g., engineering, data science, product, business)
  • Champion ethical and Responsible AI practices within the organization

Communication & Collaboration: All AI program managers are responsible for clearly communicating and coordinating across numerous teams.

  • Clearly communicate technical concepts to non-technical stakeholders
  • Effectively present project updates and results to leadership and team members
  • Foster a collaborative and inclusive environment within the AI/ML team
  • Actively participate in knowledge-sharing and cross-team collaboration initiatives
  • Develop and maintain program documentation for data science and AI and governance processes

Additional Responsibilities: Below are additional AI program manager responsibilities that you might encounter.

  • Manage and optimize AI infrastructure and resources (although the technical development team generally leads this)
  • Recruit and hire new talent for the AI/ML team (although a functional manager tends to be responsible for this)
  • Manage external vendors and partners involved in program execution (or coordinate with the purchasing department)
  • Conduct program audits and assessments (or coordinate with an internal governance department)
  • Participate in industry events and conferences to stay up-to-date on best practices (common)

AI Program Manager Qualifications

To become and grow as an AI Program Manager, individuals need a combination of AI expertise, project management skills, and business acumen.

Experience: Program managers generally need to demonstrate multiple years of experience in related roles such as project management, product management, or operations management. Organizations also often consider data professionals who demonstrate strong communication skills.

AI Expertise: Some organizations might expect AI program managers to have direct data analyst/science working experience. However, this is generally not required. But the candidate should demonstrate a solid grasp on concepts such as AI technologies, data processing, the AI life cycle, SQL, and cloud-based systems.

Education: Most organizations ask for at least a bachelor’s degree. Master’s degrees in business or a technical field can be a differentiating factor. However, with enough professional experience, these requirements can be waived.

AI Program Management Certifications: There are a lot of relevant certifications that you can earn to signal to employers that you have a solid understanding of relevant skills. Notable certifications include:

  • Project Management Professional (PMP) is the most widely recognized project certification in the USA. European nations tend to weigh more toward the PRINCE2 certification.
  • Scrum Master and Scrum Product Owner certifications signal your knowledge of Scrum. Since the certification also covers general Agile concepts, it is often a positive factor, even if the team does not directly use Scrum. There are several certifying organizations including the Scrum Alliance, Scrum.org, and Scrum Inc.
  • The Data Science Team Lead course specifically empowers individuals and teams to deliver AI and data science projects.  

Related Positions

You’ll find a lot of positions like the AI program manager. Sometimes, the same essential role is just titled differently. However, generally each position title suggests different areas of focus.

  • Data Science Program Manager: Relative to an AI program manager, the data science program manager tends to oversee programs that have a stronger focus on discovering insights from data.
  • AI or Data Science Project Manager: Their scope may be more limited to individual projects rather than broader AI programs spanning multiple initiatives. These professionals might report to a program manager.
  • AI Product Manager: The product manager is the more strategic. They define the vision and roadmap for AI-powered products and services. Product managers often lean on the project and program managers to coordinate and execute many aspects of the development of a new product.
  • AI Product Owner: Closely related to the Product Manager, the Product Owner is one of three defined roles in Scrum and Data Driven Scrum teams. They prioritize and verify deliverables for the upcoming planning cycles.
  • AI or Data Science Managers: The functional manager leads teams of AI engineers, data scientists, and data analysts who develop AI systems and produce data science insights. They manage the growth and development of the individual team members and might also directly serve in a senior development role.

Conclusion

In conclusion, an AI Program Manager is a key player in driving the successful adoption of AI within organizations, bridging the gap between technology and business objectives. With the right combination of technical expertise, project management skills, and business acumen, AI Program Managers can help unlock the full potential of AI to drive innovation, efficiency, and competitive advantage.