Copilot, not Autopilot: Three weeks of learning to trust (and argue with) my AI assistant
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Artificial Intelligence
Categories: Artificial Intelligence
| A few weeks ago, I started a new job managing a portfolio of pharmaceutical projects , orchestrating the tech transfer process of products from CMO to CMO, and market launch. Anyone who has worked in pharma knows this is a world of handovers, documentation, and enough acronyms to make your head spin. But that's not what this article is about. During onboarding, I sat down with the IT guy for the standard "here's how things work here" chat, and he said something that caught me off guard: "You can use Copilot as much as you want. It's internal, nothing you type is used to train anything. You can upload confidential data, ask whatever you want, be as explicit as you need to be." I paused.. In previous companies I worked for, AI tools came with a leash. You could use them, sure, but with a healthy dose of suspicion. So hearing "go ahead, be fully transparent with it" felt like a genuine game changer. And it made me realize something: reading about AI capabilities is one thing, but actually using them without restrictions, first person, is a completely different experience. It's the difference between reading a restaurant review and actually eating the meal. So here's what I learned. Consider Copilot a glorified assistant — and I mean that as a compliment. The Inbox archaeologist Anyone starting a new role knows this ritual: you inherit an inbox (or get looped into one) full of email threads that started weeks or months before you arrived. You scroll. And scroll. Trying to piece together who's who, what's being asked of you, what decisions were already made, and what's still hanging in the air like an unanswered question at a dinner party. Copilot turned out to be excellent at digesting these threads and giving me a summary along with a suggested course of action. To be clear: this doesn't mean I skip reading the emails myself. I still do. But now I walk in with a head start, already knowing roughly what I'm dealing with instead of reverse-engineering a six-week-old conversation from scratch. It's a time saver, plain and simple. The end of "Can you make this bold?" If you've built slide decks for a manager, you know the drill. You present a draft, and the feedback sounds something like: "Can you make this bold?" "Can you enlarge this?" "Can you add a bit more detail there?" This is where being fully transparent with the tool changes everything. Instead of explaining formatting preferences one bullet point at a time, I can just tell Copilot exactly what I need: the structure, the key messages, the story I want the deck to tell. And that part is important: the thinking still has to come from me. Copilot won't magically know what matters to my stakeholders or what the strategic message should be, that's the actual PM job. What it does is take that thinking and turn it into a crisp, polished deck in minutes instead of hours. So the real time saver isn't that AI writes my presentations for me. It's that I no longer spend half my day fighting with alignment, fonts, and "make this pop." My job becomes making sure the content is right and tailored to the audience, which frankly, is the part I should be spending my energy on anyway. Suddenly, everyone's an Excel wizard Here's a scenario every PM knows: you join a new project and inherit a spreadsheet with formulas so complex they look like they were written by someone auditioning for a codebreaking role in a spy movie. My first reaction is usually a mix of admiration and mild panic. With Copilot, I stopped trying to become that person and started working with the tool instead. I don't ask it to do the analysis for me, I tell it what I want to see, how I want the data structured. It's a genuine back-and-forth: I define the destination, and we iterate together until we get there. The result is a spreadsheet that's clean, well-presented, and running formulas that would normally take an advanced user real time to build. An Assistant today, maybe a Colleague tomorrow For all of this, I want to be clear about one thing: AI, as I'm experiencing it right now, is still in its infancy. Today it's an assistant. A very capable one, but still someone (something) that waits for me to ask. What's exciting to think about is where this goes next. I can picture a future where AI moves from assistant to agent, where it's analyzing data while I sleep, and when I log in the next morning, it tells me: "This is running late, you should follow up." Or it doesn't even wait to tell me, it sends the email itself, or makes the call! And maybe, one day, that call doesn't even reach a person. It reaches their agent instead, and the two negotiate the update between themselves while we drink our coffee. I honestly don't know if I'll still be working by the time that future arrives, or if I'll be watching it unfold from retirement. Either way, I'll be genuinely curious to see how it reshapes the way the next generation manages projects. The one thing still missing: a backbone If I'm being fully honest there's one thing I've found consistently lacking: critical thinking. Too often, I give Copilot a prompt, and it tells me my idea is great. I try it, it doesn't quite work, so I go back with a different approach, and it tells me that one is great too. Even when the two ideas contradict each other. It rarely pushes back and says, "Actually, no, that doesn't hold up." It's a bit like having a colleague who agrees with every version of your plan, including the ones that cancel each other out. I get that this is likely a training and design choice rather than a flaw exactly, but if AI is going to become a real thinking partner rather than just an eager one, this is the part that needs to grow up. As Peter Drucker once put it, "The most important thing in communication is hearing what isn't said." Right now, Copilot is very good at hearing what I *do* say, it just isn't very good yet at telling me when I'm wrong. That, I suspect, is the next frontier. |
Posted on: August 15, 2026 02:45 PM
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What history reveals about AI and the Project Manager profession
| Every major technological revolution has triggered the same anxiety. Steam engines would destroy artisanal work. Tractors would eliminate farm labor. Computers would make offices obsolete. Each time, the warning sounded familiar: “This time is different.” Today, artificial intelligence has taken that role. For months, if not years, the impact of AI on the Project Manager profession has been debated. Will AI replace Project Managers? Will project management as a discipline disappear? Or will it be fundamentally transformed? I want to elevate this debate by stepping away from prediction and alarmism and instead looking backward. History, as economist Xavier Sala‑i‑Martín argues in De la sabana a Mart (literally From the Savannah to Mars), is not a forecast but a powerful teacher. In his book (unfortunately still untranslated into English), Sala‑i‑Martín traces how Homo sapiens evolved from its emergence roughly 200,000 years ago in the Serengeti savannah to a species capable of landing spacecraft on Mars. In spirit, it sits close to the work of authors like Yuval Noah Harari: a long‑arc view of human progress, and adaptation. One of its most relevant messages for today’s AI debate is simple but profound: while technology repeatedly destroys specific jobs and tasks, it has never eliminated human work as a whole. What changes is where humans add value. Below, I map five historical lessons from technological revolutions to concrete project management competencies; not to argue that Project Managers are “safe,” but to explain why the role is likely to become more human, not less. 1. We are bad at imagining future jobs and future project workOne of Sala-i-Martín’s central arguments is that humans systematically fail to imagine the jobs that will be created by innovation. In 1895, no expert could have predicted digital marketers, YouTubers, or UX designers. MIT economist David Autor estimates that roughly 60% of today’s occupations did not exist in 1940. The problem is not that experts were careless. Future work often emerges indirectly, as a second or third order effect of technology. What this means for Project Managers Much of today’s AI anxiety focuses on current PM tasks: scheduling, reporting, risk tracking, documentation... Yes, many of these will be automated or heavily augmented. But history suggests the more important question is: what new coordination problems will AI create? Early signals are already visible:
PM competencies amplified: systems thinking, strategic framing, ambiguity navigation. 2. Automation replaces tasks, not professionsWhen calculators entered offices, many believed accounting roles would vanish. When computers arrived, clerical work was expected to disappear. Neither happened. Instead, productivity rose and roles evolved. Technology consistently eliminates tasks, not entire professions. What this means for Project Managers AI will outperform us at:
PM competencies amplified: judgment, prioritization, decision‑making under uncertainty. 3. Technological transitions are painful and increase the need for PMsSala-i-Martín is explicit: the fact that innovation ultimately creates work does not mean transitions are easy. Workers displaced by mechanization did not automatically reskill. Societies had to invest in education, coordination, and institutional change. What this means for Project Managers AI adoption is not a technical rollout. It is a transformation. And transformations fail most often because of:
PM competencies amplified: change leadership, stakeholder management, organizational navigation. 4. Innovation creates new needs and new project portfoliosThe automobile didn’t just replace horses. It created tourism, hotels, road infrastructure, logistics networks and entirely new urban designs. Innovation doesn’t merely solve problems, it also creates new needs that later become essential. What this means for Project Managers AI is already creating new categories of work:
PM competencies amplified: portfolio management, value realization, cross‑functional integration. 5. “This time Is different” has always been wrong, including nowFrom tractors to computers to AI, the recurring claim has been: this time, humans will not adapt. History shows the opposite. Not because progress is guaranteed, but because societies reorganize around new constraints. What this means for Project Managers As automation increases, complexity does not disappear, it rather intensifies. And complexity elevates the value of deeply human capabilities:
PM competencies amplified: human leadership in complex systems. Conclusion: from controllers of work to designers of progressHistory does not tell us that Project Managers are immune to technological change. It tells us something more useful. Roles that sit at the intersection of technology, people, and decision making do not disappear. They evolve. AI will not end project management. But it will act as a filter. It will steadily automate coordination and execution mechanics, and leave behind the parts of the role that require judgment, ethical reasoning and leadership across uncertainty. For Project Managers, the real question is not whether AI will change our profession. It already is. The real question is whether we choose to remain controllers of tasks or step fully into our role as designers of progress, stewards of change, and leaders of complex human systems. For those willing to adapt, that shift is not a threat. It is an invitation. |
Posted on: February 10, 2026 10:25 AM
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Artificial Intelligence & Machine Learning: Data is King
| I am far from being an expert in artificial intelligence (AI) and machine learning (ML). Actually, I spent some time googling these two concepts which - the truth to be said - are commonly used interchangeably. The ultimate goal of AI is to create intelligent machines that simulate the human thinking capability and behavior. Deep Blue, the famous supercomputer that defeated chess world champion Garry Kasparov falls into this category. On the other hand, ML is the art of these machines to learn in real time from all gathered data without being programmed explicitly. Most subject matter experts state that Deep Blue cannot be considered an example of ML because it was programmed to beat humans but learned little along the way. ML has evolved tremendously since the Deep Blue times in late 1990s. Its skyrocketing advancement poses novel challenges in our lives, especially when it comes to trust. An example of this - perhaps not the most relevant, yet illustrative - can be observed in Nascar races. In them, AI and ML play a vital helping hand in understanding a massive data set, such as identifying anomalies and contributing causes in real-time. The algorithms analyze the real time data and yield the best course of action to win the race: optimum timings to tank or change tires, best time to overtake a rival, etc. In one of the races, the machine advised to do A, yet the team went with their gut feeling (they knew better!) and picked B. They lost and realized that option A, indeed, would have been a far better choice.
At the end of the day, AI and ML require above all just one thing, data. And a project generates a massive amount of it. Having in mind the DIKW pyramid, data is treated to obtain information, which is then further processed into knowledge and finally wisdom. How this translates to project management? One can think of a situation that project managers often come across during a project: making scenarios. The PM is responsible for gathering and process all relevant inputs from SMEs or any other suitable sources and present the various options with their cons and pros to the sponsor, steering committee... It is easy to envision a machine (or software) that is able to not only analyze the data and define possible scenarios but also to provide timely alerts to avoid certain less favorable scenarios, thereby increasing the odds of delivering a successful project. This specific case example wants to reflect on the adapting role that the PM must face as the AI/ML technology becomes more mature. Cab drivers will become obsolete when self-driving cars become available at mass scale level. The threats that AI/ML will exert in project management is yet to be seen. Can they live in perfect harmony? Have your saying in the comments section below. |
Posted on: October 27, 2020 12:00 PM
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