The rise of the AI-powered PMO
…] Everything that we have electrified, we are now going to cognify.And I would suggest that the formula for the next 10. 000 startups be very very simple: take X – and add AI.” – Kevin Kelly, WIRED
Introduction
In February 2019, I applied to be a presenter for the PMI Summit organized by the Medellín, Colombia Chapter. I was selected, and that October, I presented my lecture on the future of the Project Management Office (PMO) to dozens of PMPs and project professionals.Alas, time flies! It has been more than five years since that event. The incredible progress of Artificial Intelligence, particularly Large Language Models (LLMs), is paving the way for the fulfillment of many of the “prophecies” I shared that pre-pandemic day in beautiful Medellín. It is time to refresh those ideas and tweak that vision based on current developments.
The lecture
The underlying principle of my 2019 speech was simple: Project Management and its related disciplines—PMOs in particular—were on an irreversible path toward automation through increasingly advanced computerized systems. I predicted four phases:
1. Integration & Automation: Using RPA and “dummy-proof” workflows.
2. Chatbots: Virtual team members acting as user-friendly “wikis.”
3. Machine-Learning Based Project Management: AI analyzing data via neural networks to discover hidden trends and suggest predictive courses of action.
4. Fully Autonomous Project Management: this is the complete automation of the project manager role, something foreseeable at least for simple projects with few stakeholders.
Let´s elaborate on those stages. Phase 1 was not truly AI-powered, but a mix of Robot Process Automation (“RPA”), dummy-proof workflow enablement, “Groupware“ massive adoption and other items. Phase 2 was about the enablement of chatbots within our communication tools, some through text, some using voice, acting as virtual team members and user-friendly equivalents to “wikis and knowledge repositories. Phase 3 is where things are supposed to get interesting. Chatbots become readily available even in wearables. AI steps in and analyzes the data through powerful neural-networks, discovering trends and relationships invisible to the human eye: conclusions are now predictive and the AI suggests courses of action to the PM. I also anticipated something I still linger for: actionable outcomes, updates and tools derived of project logs, history & lessons learned. Then Phase 4 is defined as to be understood as the equivalent of a Level 5 Automation grade as per the standards of the Society for Automotive Engineers (SAE) scale of self-driving. The speech also included mentions to the impact of drones in construction projects, the Volatility-Uncertainty-Complexity-Ambiguity nature of our moder world (VUCA) and concluded with the need of ever improved soft-skills from professionals in general and PMs in particular, and the importance of “defending” our human nature. That was more than five years ago.
Hits & Misses
Let´s begin with the latter. My analysis ran under the assumption that those phases were going to occur basically in a “step-by-step” fashion. That was a mistake. As William Gibson famously said, “The future is already here – It’s just not evenly distributed”. He was right: the phases mentioned are running simultaneously, or at least with strong overlaps. There are also different levels of progress across different geographies, sectors and industries. And then there is back & forth, a tremor in which progress is made but then a crisis, an error or backlash halts or even setbacks the process for a while. For example, at this point I thought that powerful & knowledgeable bots should be ever present across organizations of all sizes & types, and that AI-powered agents should be available within internal project spaces, automating a bunch of the tedious, repetitive work PMOs and PMs execute. This is only partially true: it depends on the industry, country, sector, organization size and type.
Now, even if I mentioned the ever more “VUCA” nature of our reality, I could have never anticipated the degree that this has reached. Wars, international rule order disruption, immigration, energy scarcity… you name it. The “VUCA” term was coined by the Pentagon in the mid-80s and became popular during the first decade of the 2000 century, but the “mess” went off the scale with the pandemic and subsequent major events (the Ukraine war, tariff changes, chips war, Iran war, etc.). These events add chaos layers over each other. The ultimate consequence may be global stagflation, even a new Great Depression. We will see.
I also missed the non-linearity of this process. If we graphed our progress, it wouldn’t be a straight line; it would be a “hockey stick.” We are just reaching the curve where progress becomes exponential. It took time and effort to get to that point, but here we are, empowered all across the place through AI. However, my prediction on an equivalent of “augmented reality” for PMs and PMOs is not yet around us.
Let´s analyze now the hits. I had a big-hit with Groupware. MS Teams, Slack and other similar software is now omnipresent even for small organizations, and meeting through a screen is a daily thing (this was turbo-charged by the pandemic). Hybrid project management has become usual, and LLMs regularly check contracts, emails and documentation in general. Drones are now ubiquitous and regularly used for a variety of needs: civil engineering, topography, agriculture, military, security, etc. But indeed my “home-run” was the evolution of the value added by PMs & staffers in general. AI & software is each day more capable and is taking over tedious, repetitive tasks and in general work that does not requires critical thinking. This means that soft-skills and an elevated level of judgement is ever more important.
What comes ahead for PMs and PMOs
Its 2026. As a whole, my predictions were off by five years, thus an automated PMO should be available around 2030. What do I mean by “automated”? Well, PMOs come in different flavors and sizes. I think that in five years the amount of collected data, the recursive nature of algorithms´ improvement and organizational maturity will enable the first PMOs that run more than fifty percent of their processes using agents and LLMs. Some of the best candidates for automation are:·
- Induction & Training
- Gates´ reviews & approvals workstream (not Bill, but the project progress thresholds :-)·
- Documentation creation & versioning·
- Predictions with probabilities and ranges: Cost, Duration, Risks
- 24×7 AI powered support agents & bots providing answers, tools and help for the staff to better manage projects; programs and changes. Examples: staffing details, task info, RAID analysis, blueprints, timelines & schedules, costs, scope, performance, monitoring & controlling, quality, etc.
However, there are aspects of PMOs that cannot be fully delegated to an AI. Agents are now here and some decisions can be delegated to them: it’s a matter of defining which decisions those should be and calibrating them to the organizational risk appetite, industry & governance model. However, in my perspective, there is a limit to what should be delegated to AI. Decisions that imply medium & high impact should be overseen, if not driven or entirely made by human beings. This does not mean removing the outputs from the AI analysis off the decision process, but to include those as inputs for a more comprehensive evaluation that includes human judgement & instinct: think of it as a “cyborg” that merges the deep trend & relationships analysis of AI with human common-sense, big-picture perspective and sensibility. It’s a winning combination.
I also think that these changes imply challenges to organizations, particularly from the governance perspective. Who would be accountable for AI powered decisions, particularly if they go wrong? What type of decisions should those be? What are the limits, controls, thresholds, derailers and alarms associated to those decisions? Who should audit those decisions, and with what frequency? What AI models & agents are to be used – internal or external? Where should the data be located? What security aspects should be considered when operating under such a model? What are the legal implications? What information should be communicated to shareholders & other stakeholders? What would be the correct approach for the “moments of truth” – the moments to make decisions? Eg, should the AI agent have a single vote such as the persons in the board? None? Several? How and when to vote? Ultimately, we may need to ask ourselves about the very purpose of PMOs, meetings and decision sessions Nothing is off the table
Conclusion
In short, it looks like my crystal-ball was showing me somewhat fuzzy images, but lately the images are becoming reality. PMOs have a brighter future in which lessons-learned are actionable, predictability is built into the system and information is readily available in user friendly interfaces. At the same time, these changes disrupt organizational governance and accountability, thus deep thinking and preparation is required. Ultimately, this evolution is inevitable and required to navigate an ever more volatile, uncertain, complex and ambiguous world. Tell me, what are your thoughts on this?
Best human vibes,
Fernando
The project is (S/M/L/XL/XXL/XXXL). So WHAT?
Every PM suffers now and then a slight attack of anxiety when notified about the assignment of a new project. It´s just natural: he/she will have a close relationship with this “entity” for weeks, months or years, and he/she knows nothing about it. Thus, he/she jumps to the Business Case, Charter, Launch Gate document or any other available source to understand what the effort is about. Again, all good here. The part that puzzles me is how little organizations prepare to deal with the project. Let me cut to the chase: most organizations limit to a generic characterization of the effort, mainly by size; sometimes also by complexity. In a few cases there are further categorizations as per the scope, geo, nature of the effort. But the consequences of this analysis are quite limited, if any.
In my experience, for most organizations, most of the time, the sole actual result to the initial analysis (categorization) of the projects limits to allotting a predefined range of hours to the effort, in rare cases a budget. The best I´ve seen is an actual prioritization, which is not a bad thing at all, but these are scarce cases and the impact is constrained. This limited output makes me wonder if the initial set of parameters with which projects are analyzed is insufficient. Or perhaps the actual process to act upon those results is utterly flawed, if not entirely absent. Candidly, I think it’s a mix of both, but I also think that the biggest proportion of the issue relies on the latter.
I think that we need to take this topic more seriously in our organizations. It doesn’t make sense to waste time on the analysis of our projects to do it incorrectly and then to basically ignore it: this is a Portfolio Management “chronic disease”, if I may be allowed to use the analogy. I am not certain about the cure to this problem, still, I have already a couple prompt points. Let me say that a broader range of parameters to select upon (size, complexity, risk, urgency, stakeholders’ profiles, expected duration, budget) would help a lot. Then perhaps an algorithm, a formula could be used to produce a conclusion, an actual project comprehensive characterization as per the values of each one of the numbers. Finally – and more importantly – there must be a process to act upon it: there must be consequences. For example, if the project is urgent and risky, assign this type of PM, if the project is long and complex, request for a bigger management budgetary reserve. If these stakeholders are engaged, it is mandatory to inform them every two days of the status. You get the idea: the characterization of the project through pre-defined parameters derives into actual actions, guidelines, rules, strategies. I also think that using Lessons Learned and a Focus Group with the most experienced PMs would greatly benefit the creation of the mentioned algorithm (formula). I also foresee interesting opportunities for PMOs to this analytical, semiautomatic approach.
Imagine that: you would be receiving your projects with guidance, structure and “warnings”: now that would be a sight, isn´t it? Of course, these “automated” guidelines would have to be tuned & tweaked as per the project subtleties by the PM and his Team, but nothing like actually receiving insight from the shared pool of experience and knowledge of the organization – as a standard input right from the beginning. Not only that, the organization would be nudging projects toward success: better staffing, resource allocation, wisdom injection right from the launch. COOL, isn’t it?
And now… what do you think? Do you know any examples of this idea? How would you improve it? Let us hear your thoughts.
Best regards,
Fernando
