With all the hype around AI and its impressive advancements lately, many project managers are asking themselves, “Will I still have a job, or is AI taking over?”
AI and Humans
Much of the mental picture we had about AI from sci-fi films feels less far-fetched than it used to, especially when new capabilities continue to emerge from AI tools with impressive speed and accuracy. However, machines are still fundamentally different from people. Their algorithms are trained for specific tasks to emulate human delivery within a certain scope of work or expertise, yet they lack a deep understanding of context, purpose and values.
A human, on the other hand, can think creatively about how to solve a problem and can gain the soft skills needed to be a good leader. In this way, the greatest opportunity doesn’t come from choosing man vs machine; it’s pairing them. Human judgement, leadership, and creative thinking should be complemented by AI’s technical aptitude, while each focus on what they do best.
Leveraging the best of AI
Much of the industry conversation still leans on Gartner’s previous forecast that “80% of project management tasks will be automated by 2030. Going further than just automated tasks today, organisations piloting AI-enabled PMO practices pursue significant cycle-time reductions and operating-cost savings. This means that PMOs and project portfolios must become more efficient all-together. Thus, the more useful conversation has moved from “how much can be automated by AI” to “how much better can projects perform thanks to it”.
Used well, AI presents a clear opportunity to move the profession forward by allowing project teams to focus on the value-adding tasks that are otherwise neglected or not implemented to begin with.
The evolving role of Project Manager
While AI is busy completing data-intensive and procedural tasks, Project Managers can focus on better-informed decision-making, leadership, and cultivating better relationships across their supply network. This shift in work is not only an opportunity for project managers to realign their priorities, but it also presents an obligation for them to focus on the skills needed to complement today’s technology. Those who do not accept and adapt to using AI effectively may very well be displaced, but those who leverage it will achieve more than the traditional project manager ever could.
Becoming well-versed in querying agents, data management, process improvement, automation, and training models will become an expectation for high-performing project managers.
Outcomes for an AI-enhanced project manager
Handing routine work to AI raises the bar for what competencies a good project manager needs. Those who get the most from AI understand how to frame the right problem for the tool, sanity-check what it hands back, and spend their freed-up time on what AI still can’t do: reading a stakeholder’s real concern, negotiating a trade-off, knowing when a plan needs to bend.
To realise greater value within their projects, the AI-enhanced project manager must focus on three key outcomes:
1. Driving efficiency to reduce time-to-market
Project success has always been determined by on-time and on-budget delivery, although many projects still fail to achieve this despite technological advancements. Furthermore, projects are becoming more complex with organisations expecting more valuable outcomes. In fact, PMI reports that “complex” projects are nearly twice as susceptible to failure than traditional projects overall.
This means that companies expect more from project managers. Too much time is lost on work that doesn’t need a human: chasing status updates, reformatting reports, reconciling numbers across trackers. Having AI absorb more of that load lets project managers redirect their time towards driving efficiency and reducing time-to-market.
To succeed, project managers must:
- Clearly define all project outcomes and gain alignment on the expected benefits from project stakeholders
- Establish robust governance that can intervene in projects losing progress
- Review the project team’s processes to ensure they are as lean as possible
- Improve relationships with delivery partners to minimise risk across the project’s entire supply chain
These actions require strong leadership, communication, and the ability to manage change. By using AI to assist with more technical activities, project managers will be able to focus more on improving their team’s processes and building the relationships needed to keep the project moving forward.
2. Increasing the quality of project management outputs
Even with the use of today’s technologies, it becomes increasingly difficult to keep up with all the work that is required of a project team. The Association for Project Management (APM) Competence Management Framework describes 29 competences based around outcomes that project professionals need to achieve.
On smaller projects, you often find few people can span across multiple competence areas. Therefore, it becomes very difficult to keep up with the volume of work, or we sacrifice quality for quantity. On larger programmes where budget is available, many organisations will dedicate teams as subject matter experts to manage each competence area, thereby resourcing a project organisation. This strategy is not only costly, but the organisation also risks developing silos across competencies and losing track of the relationships between them.
AI can help drive automation across the different competency areas, and some capabilities have the potential to be fully replaced by AI. However, the quality of output depends on data orchestration. Real-world project data, such as spreadsheets, report presentations, and meeting minutes, must be brought together in one place and in a form that both people and AI can interpret.
Well-coordinated AI agents can do this only when the integrations between PM capabilities are properly managed. Therefore, project managers must facilitate the relationship between the AI-driven capabilities and those led by people. Only then can project leaders access the right information in time to make strategic decisions that influence the quality of output.
3. Protecting the value of large PM investments
Large projects represent significant investments for an organisation, and with greater scale comes greater exposure to risk. As complexity increases, it becomes harder to respond quickly to unexpected changes or issues that emerge across interconnected areas of delivery. By the time those problems surface through traditional reporting, valuable time and budget may already have been lost.
AI can help project managers protect the value of these investments by identifying risks and opportunities earlier. By connecting information across the different project domains, AI can uncover patterns that are difficult to see and simulate the potential impact of decisions before resources are committed.
Large projects inevitably bring more dependencies, information, stakeholders, and constraints. AI can help manage this complexity, but technology alone will not guarantee a return. Project managers need systems thinking, data literacy, and critical thinking to understand how the project environment connects and to challenge AI-generated insights. They also need strong communication and change management skills to turn those insights into action.
The AI-enhanced project manager therefore becomes an important link between people, processes, and technology. By combining AI's ability to interpret complexity with human judgement, project managers can intervene earlier, adapt more confidently, and protect the intended value of large project investments throughout delivery.
Putting it into action
Using technology to improve the delivery of project teams sounds simple in theory, but in reality, organisations are struggling to implement it effectively enough to generate the value they’ve promised.
Having the right skills to utilise AI in a project setting is but one aspect of improving project outcomes. To move AI beyond isolated tasks and allow it to become meaningfully embedded in delivery, organisations need:
- The right technology platform – The selected AI tool must perform well within the context of the organisation’s project environment and their existing toolbox.
- Well-structured data – AI needs access to real project data, knowledge, and workflows, all of which must be set up in a way that can be understood by AI.
- AI-enabled governance – The entire organisation, from the project delivery team up to executive stakeholders, must be aligned on processes that facilitate AI’s effectiveness, especially being consistent across data inputs.
These are the principles behind MIGSO-PCUBED’s solution, Clayverest. Our AI agent, Clay, is capable of orchestrating real-world project data and operating across the project environment. Rather than relying on the user to prompt AI for individual outputs, Clay can access relevant project information and knowledge bases to deliver more complex workflows autonomously. Interacting with Clay is closer to working with a colleague in the sense that you can discuss the objective, agree on a work plan, adjust it where necessary, and then launch its delivery.
Nevertheless, the first step is getting control of your data. To realise any value from AI, data is critical and must be efficiently structured. For the project manager, this reinforces rather than removes their responsibility. They must be able to not only facilitate data efficiency across the project environment, but also to define the right objectives, provide the context AI needs, challenge its recommendations, and ensure its outputs translate into meaningful action.
With the right technology platform supporting them, project managers can spend less time operating the mechanics of project management and more time applying the judgement, leadership, and decision-making that ultimately determine whether projects deliver value.
