Webinar Recap
On 19 September 2025, the Society of Women Engineers hosted an insightful session on “7 Essential Skills for AI Project Management”. The talk was delivered by Anitha from Otis Elevators, who shared practical knowledge and real-world strategies for managing AI projects successfully. This webinar was part of SWE’s Monthly Webinar Series, aimed at providing continuous learning opportunities for engineers, leaders, and project managers across diverse fields.
Artificial Intelligence (AI) projects differ significantly from traditional software initiatives. Unlike conventional projects, AI solutions require not only coding and deployment but also robust data pipelines, ethical considerations, and continuous monitoring. In this webinar, Anitha broke down the seven most critical skills every AI project manager should cultivate, drawing on her professional experience leading AI-driven initiatives at Otis Elevators.
For those who missed the live session, you can watch the recorded webinar here:
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7 Essential Skills Highlighted
1. Technical Fluency
AI project managers do not need to be expert data scientists, but they must have sufficient technical understanding to bridge communication gaps between business stakeholders and technical teams. Anitha emphasized knowing the basics of model architectures, data requirements, training pipelines, and evaluation metrics such as accuracy, recall, and precision. Having this fluency helps project managers challenge assumptions, estimate timelines realistically, and ensure that the technical direction aligns with business goals.
2. Data Literacy & Governance
AI projects thrive on quality data. Poorly structured, biased, or incomplete datasets often lead to project failures. Anitha highlighted that project managers must ensure strong data governance practices. This includes defining data ownership, securing appropriate access rights, monitoring lineage, and mitigating bias risks. A project manager’s role is not to analyze data directly, but to ensure the processes and accountability around data are well-established. Building checkpoints for data readiness before moving into model development can save time and resources later.
3. Problem Framing & Value Alignment
One of the most common reasons AI projects fail is misalignment between the problem being solved and the business value delivered. Anitha explained that project managers should begin with problem framing—articulating the challenge clearly, identifying measurable success metrics, and validating whether AI is the most suitable solution. Without this clarity, teams risk spending months building models that do not address the core business need. A simple “problem brief” document that lays out the objectives, success criteria, and constraints can serve as a guiding light throughout the project lifecycle.
4. Stakeholder Management & Collaboration
AI projects often involve cross-functional teams: data scientists, domain experts, IT teams, compliance officers, and end users. Coordinating across such diverse groups requires strong stakeholder management skills. Anitha stressed the importance of building a RACI (Responsible, Accountable, Consulted, and Informed) matrix early on, as well as scheduling regular demos to showcase progress. Unlike traditional software projects where deliverables may be more tangible, AI projects benefit from showing intermediate artifacts like baseline models, sample outputs, or error analyses. This keeps stakeholders engaged and reduces misalignment.
5. Ethics, Safety & Regulatory Awareness
Ethics is not optional in AI. From data privacy concerns to ensuring fairness and avoiding unintended harm, ethical awareness is a must-have skill for AI project managers. Anitha noted that many industries, including healthcare, transportation, and finance, are under increasing scrutiny from regulators regarding AI systems. Project managers must therefore integrate fairness testing, explainability, and fallback mechanisms into project plans. A responsible AI checklist—covering consent, bias detection, explainability, and incident response—should be part of every AI project.
6. Risk Management & Continuous Monitoring
AI systems degrade over time as data distributions shift and environments change. Anitha explained that this makes risk management and continuous monitoring critical. Project managers should ensure that monitoring is not an afterthought but a core deliverable. This includes setting thresholds for performance decay, defining retraining schedules, and building rollback procedures. By planning for ongoing monitoring, project managers safeguard the long-term value of AI investments.
7. Communication & Change Management
Even the most technically sound AI models can fail if they are not adopted by end users. Project managers must therefore excel at communication and change management. This involves translating complex model outputs into simple, actionable business insights, preparing end users with training and support materials, and addressing resistance to change. Anitha recommended creating operational runbooks and adoption guides to help teams transition smoothly when new AI systems are rolled out.
Key Takeaways
- AI project management requires hybrid skills. It is not enough to be a technical expert or a business strategist alone. Successful project managers balance both domains, ensuring alignment between technical execution and business outcomes.
- Data governance and monitoring are non-negotiable. Building governance frameworks early prevents downstream issues such as bias, compliance failures, or model drift.
- Communication drives adoption. AI is only valuable when end users trust it and integrate it into their workflows. Project managers play a central role in building that trust through effective change management.
Why These Skills Matter
As organizations increasingly adopt AI, the demand for skilled AI project managers is growing. These managers are expected to not only deliver projects on time but also ensure that AI solutions are ethical, reliable, and aligned with business value. The seven skills shared by Anitha form a strong foundation for anyone looking to enter or grow in this field.
Moreover, the conversation around AI is evolving rapidly. With regulations tightening and AI use cases expanding, project managers must continuously upskill themselves. Sessions like this SWE webinar play a vital role in equipping professionals with practical frameworks to stay ahead of the curve.
Watch the Full Webinar
Click here to watch the recording:
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We extend our sincere gratitude to Anitha for taking the time to share her expertise and experiences with our community. Her thoughtful presentation and actionable advice have inspired many of us to rethink how we approach AI project management.
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