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🎓 Certified in AI Project Management: What We’ve Learned About Responsible AI Delivery

  • Writer: Karlien Miles
    Karlien Miles
  • Nov 1
  • 3 min read

Updated: Nov 2

AI Project Management: Why Responsible Delivery Matters More Than Ever


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AI project management is transforming the way organisations plan, deliver, and measure success. But as the excitement around AI grows, so do the challenges. Many projects start with enthusiasm but struggle to deliver real business outcomes. The difference between success and failure often comes down to how the project is managed and whether your PMO is ready to lead AI initiatives responsibly.


In today’s climate, everyone wants to “do AI,” but success starts with solving a real business problem, not using technology for the sake of it.

“The primary focus must be on a well-defined business problem or a clear user need, not a technology-driven idea.” — PMI Blog: 6 Stages to Run a Successful AI Project

What We’ve Learned

  • Automation is not AI. If it can be done with rules, use automation or development, not AI.

  • AI isn’t always the answer. Use it only when necessary and when the data supports it.

  • AI projects are short and iterative. They typically run in weeks, not months — drawn-out waterfall-style timelines are a red flag.

  • According to Gartner, up to 85% of AI projects fail, which is even higher than transformation projects, where McKinsey reports that about 70% fail to achieve their goals.

  • AI projects are data projects. Bad data in = bad data out. Never skip the data readiness phase.


Why PMOs Matter More Than Ever

With every company eager to “get a piece of the AI action,” a strong PMO structure has never been more important. PMOs play a crucial role in ensuring that AI initiatives are strategically sound, achievable, and responsibly delivered.


Before taking on an AI project, your PMO should:

  • Assess organisational buy-in: Does leadership truly support the initiative and understand its impact?

    Evaluate skills and capacity: Remember that 80% of AI projects relate to Data so if you don’t have the right data expertise in place, chances are your project will fail. Ensure you have the right mix of skills, whether internal or outsourced and that team members have the time and capability to do the job effectively.

  • Never underestimate change management. The human side of transformation is just as critical as the technology. AI adoption affects processes, roles, and trust and those shifts need structured, empathetic management.


PMOs should also validate business objectives, prioritise high-return opportunities, and advise project teams to start small, testing, learning, and scaling as confidence grows.


Above all, trustworthy AI principles are non-negotiable. There are no shortcuts worth taking when it comes to transparency, ethics, and responsibility.


Want to Get Your PMO Ready for AI Projects?

If your organisation is looking to structure its PMO to confidently take on AI and automation initiatives, now is the time to prepare. The right framework helps teams make smarter decisions, reduce risk, and deliver real business value, responsibly.


📩 Get in touch with us here to find out how we can help your PMO get AI-ready and future-fit.



This article was created with the assistance of AI tools to support content drafting and editing. All ideas, insights, and perspectives expressed are my own.

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