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Autonomous vehicles: when 90% done means nowhere near ready

Categories: Complexity, Giga Projects, Strategy

1. The Promise: reimagining mobility

For decades, the idea of autonomous vehicles has captured the imagination of engineers, policymakers, and society at large. At its core, the vision is compelling: a world where mobility is seamless, efficient, and largely invisible.

In such a future, commuting time is reclaimed. Cars become extensions of our living or working spaces rather than tools requiring constant attention. Ownership models shift dramatically: vehicles no longer sit idle 95% of the time but operate continuously, transporting passengers, then repositioning themselves autonomously.

Urban landscapes evolve: fewer parking spaces, less congestion, and a more efficient use of public space.
The promise extends beyond convenience. Autonomous systems hold the potential to drastically reduce human error (the leading cause of road accidents!) thereby improving safety outcomes at scale.
It is, in every sense, a transformative vision. And for a time, it felt imminent.

2. The Reality: understanding levels of autonomy

To understand where we stand, it is essential to ground the discussion in the standardized classification of driving automation, ranging from Level 0 (no automation) to Level 5 (full autonomy).

L0–L1: Minimal assistance (e.g., cruise control, lane keeping).
L2: Partial automation: systems can control steering and acceleration simultaneously, but human supervision is mandatory.
L3: Conditional automation: vehicles can handle most driving tasks under specific conditions, but human takeover is required when prompted.
L4: High automation: vehicles can operate autonomously in defined environments without human intervention.
L5: Full autonomy: no human input required under any conditions.

For many of us, the concept of fully autonomous driving once felt like pure science fiction. I still recall watching Total Recall and seeing robot-driven taxis navigating the city, an idea that seemed completely far-fetched at the time. Interestingly, those fictional vehicles would likely be classified today as Level 4, or possibly even Level 5, depending on whether they were constrained to specific environments or capable of operating universally.
In reality, most commercially available systems, including those from Tesla, operate at Level 2, despite frequent public perception suggesting otherwise.

Meanwhile, companies like Waymo have achieved Level 4 capabilities in tightly controlled environments. However, the leap to Level 5 remains elusive. Notably, for over a decade, industry leaders have suggested that full autonomy was “just around the corner.” Yet, year after year, that milestone has remained out of reach.

3. The 90–10 Problem: when progress stalls at the finish line

This gap between expectation and reality can be understood through the lens of the “90–10 problem,” a well-known engineering principle often cited in discussions of complex systems.

The principle is deceptively simple: The first 90% of a project often takes 10% of the time, while the final 10% consumes the remaining 90%.

In the context of autonomous vehicles, the industry has largely solved the “easy” part: structured environments, predictable conditions, controlled variables. This is the world of highways, clear weather, and well-marked roads.
The remaining 10% is where complexity explodes:

Unpredictable human behavior: a pedestrian suddenly crossing outside a designated crosswalk while looking at their phone
Edge cases (rare but critical scenarios): a mattress falling off a truck on a highway or an unexpected object partially blocking a lane
Ethical decision-making: choosing between two harmful outcomes such as swerving and risking passengers versus maintaining course and harming pedestrians
Adverse weather and ambiguous environments: heavy rain or snow obscuring lane markings and confusing sensor inputs

This is not unique to autonomous driving. Many large-scale engineering and innovation projects face similar asymptotic challenges. Early progress creates momentum and optimism, but the final stretch reveals hidden layers of complexity that demand disproportionate effort, time, and resources.

From a project management perspective, this is where traditional planning often breaks down. Linear assumptions fail. Marginal gains become exponentially expensive. And the definition of “done” becomes increasingly ambiguous.

4. The Downfall: when optimism meets reality

Between roughly 2015 and 2020, the autonomous vehicle space experienced a surge of optimism. Capital flowed freely, timelines were aggressive, and the narrative was clear: full autonomy was imminent. Reality, however, had other plans.

From 2021 onwards, a noticeable shift occurred. Several major players scaled back or exited the race altogether:

Ford and Volkswagen shut down their joint autonomous driving initiative (Argo AI).
Lyft divested its autonomous unit.
Apple discontinued its long-running “Project Titan” in 2024.
Even Uber had previously stepped back after safety incidents.

In total, the industry has seen tens of billions of dollars invested with limited commercial return. Several factors contributed to this downturn:

Technical challenges proving harder than anticipated.
High burn rates with uncertain timelines.
Safety incidents eroding public trust.
Regulatory uncertainty.

The result is a classic pattern seen in innovation cycles: inflated expectations followed by a trough of disillusionment.

5. The Deeper Insight: when knowledge outpaces technology

At a conceptual level, the autonomous vehicle journey highlights a broader strategic misalignment, one that can be framed through a simple but powerful lens: the relationship between knowledge and technological readiness.

This dynamic is often captured in frameworks that map what we know versus what we can reliably build, similar in spirit to technology maturity models such as Technology Readiness Levels (TRLs), where theoretical understanding can significantly outpace real-world deployment capability.

In this case, the industry possessed:

Strong theoretical understanding.
Advanced algorithms.
Significant computational progress.

However, the supporting technology stack, from sensor reliability to real-world generalization, has not matured at the same pace, thereby creating an imbalance. We know what to build, but we cannot yet build it reliably at scale. The consequences are predictable:

Overpromising based on theoretical feasibility.
Underestimating integration complexity.
Erosion of stakeholder trust over time.

For project leaders, this serves as a powerful reminder: feasibility is multidimensional. Technical possibility does not equal operational readiness.

Closing Reflection

The story of autonomous vehicles is not one of failure but of premature certainty. It is a case study in how ambitious projects evolve:

They begin with vision and momentum.
They accelerate through early wins.
They stall at the edges of complexity.
And ultimately, they either adapt or pause until the ecosystem catches up.

Autonomous driving may yet become a reality. But its timeline will be dictated not by ambition, nor by investment, but by the resolution of that final, stubborn 10%.

And in project management, as in engineering, that last 10% is where the real work begins.

Posted on: April 08, 2026 10:12 AM | Permalink | Comments (2)

The accidental path to Project Management

Categories: Career Development

Project management has quietly become one of the most widespread professions in the modern economy. According to the PMI, there are more than 1.4 million active PMP-certified professionals worldwide, and tens of millions of people work in project-related roles across industries. Demand continues to grow, with estimates suggesting that around 89 million people will be needed in project management roles by 2027.

And yet, despite the size and maturity of the profession, many project managers share a similar story: they did not plan to become one. They simply ended up there. A project needed coordination, someone stepped up, and the role slowly evolved into a career.

Looking back, I sometimes ask myself: why did I accept that first role that resembled project management? Applying the Five Whys technique (traditionally used to find root causes) can be surprisingly revealing.

1. Why did I accept the role?
Because I wanted to be close to where decisions are made.

2. Why did I want that?
Because I wanted to see the impact of my work.

3. Why did I want to see that impact?
Because it gives a sense of accomplishment.

Interestingly, I did not need five whys to reach the root cause. After three, the answer already felt clear.

In a way, this resembles the idea behind ikigai, represening the intersection between what you are good at, what you enjoy, and what creates value for others. For me, project management sits precisely there: close to the “engine room” of an organization, where decisions translate into action and outcomes.

Looking at it from this perspective also raises another question: what comes next?
One of the challenges of our profession is that job titles rarely tell the whole story. Two roles called project manager can have very different scopes and responsibilities depending on the organization. The same ambiguity applies when project managers start evolving in their careers.

For many, the natural progression is upward within the discipline itself: becoming a program manager, portfolio manager, or leading a PMO. These roles expand the same core capabilities (coordination, prioritization and strategic alignment) while increasing the level of influence on how initiatives are selected and executed.

Others take a slightly different path but remain close to that same “engine room” where decisions take shape. Roles such as transformation manager, change manager, or even chief of staff often rely on the same skills that project managers develop over time: connecting strategy with execution, aligning stakeholders, and ensuring that ideas translate into outcomes.

Seen this way, project management is not only a profession in itself but also a platform. It places you at the intersection of strategy, operations and people, an excellent vantage point from which several career paths can emerge.

And that leads to another reflection. How has your own path evolved since you first stepped into project management? Have you stayed within the discipline, or has it opened the door to other roles close to the decision-making core of your organization?

Posted on: March 08, 2026 03:59 PM | Permalink | Comments (0)

What history reveals about AI and the Project Manager profession

Categories: Career Development, Artificial Intelligence

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 work

One 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:

Orchestrating work between humans and AI agents
Translating AI capabilities into business outcomes
Managing uncertainty when systems behave probabilistically, not deterministically

These are not execution problems. They are sense making problems.

PM competencies amplified: systems thinking, strategic framing, ambiguity navigation.

2. Automation replaces tasks, not professions

When 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:

Updating plans and timelines
Generating reports and documentation
Analyzing historical performance data

But project management has never been about mechanical execution alone. What remains distinctly human includes:

Judging trade offs when data conflicts
Deciding what not to do
Balancing speed, risk, ethics, and value

AI can propose options. Project Managers choose paths.

PM competencies amplified: judgment, prioritization, decision‑making under uncertainty.

3. Technological transitions are painful and increase the need for PMs

Sala-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:

Weak change management
Misaligned incentives
Cultural resistance
Lack of shared narratives

These are not engineering problems. They are project and program problems. Project Managers are not casualties of disruption; they are the people organizations rely on to survive it.

PM competencies amplified: change leadership, stakeholder management, organizational navigation.

4. Innovation creates new needs and new project portfolios

The 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:

AI governance and compliance programs
Model validation and lifecycle management
Human in the loop operating models
Ethical risk and bias mitigation initiatives

Each new need generates portfolios of projects that must be prioritized and aligned to strategy.

PM competencies amplified: portfolio management, value realization, cross‑functional integration.

**5. “This time Is different” has always been wrong, including now**

From 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:

Trust‑building across disciplines
Ethical judgment in ambiguous situations
Storytelling and alignment
Leadership without formal authority

These have always been core to effective project management. AI simply removes the noise and exposes the essence of the role.

PM competencies amplified: human leadership in complex systems.

Conclusion: from controllers of work to designers of progress

History 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 | Permalink | Comments (5)

When results aren’t enough: Rethinking Leadership

Categories: Stakeholder management, Leadership

We often label bad experiences at work as toxic leadership. It’s a convenient shortcut to explain disengagement or quiet resentment. But the more I reflect on it, the more the term itself starts to feel misleading.

Because if leadership is toxic, if if it consistently erodes trust and destroys long‑term value, should we still call it leadership?

To explore this question, I find it useful to strip leadership down to two fundamental dimensions.

A simple leadership matrix

Imagine leadership mapped across two axes:

Competence ➜ the ability to consistently turn intent into outcomes through sound judgment, clear decision-making and effective execution.
Human awareness ➜ An umbrella term that includes empathy, emotional intelligence, self‑awareness and the ability to understand how one’s actions affect others.

Human awareness and emotional intelligence are closely related, but not identical. Emotional intelligence is the capability to perceive and manage emotions: your own and others’. Human awareness is how that capability shows up in daily behavior: listening, recognizing limits, understanding context and treating people as thinking adults rather than interchangeable units.

Cross these two dimensions, and four leadership archetypes emerge.

1. HIGH COMPETENCE, HIGH HUMAN AWARENESS — THE TRUE LEADER

This is the quadrant most leadership books describe, and also the one people encounter least often.

These leaders are strong decision‑makers who understand the work deeply but they are grounded enough to know they don’t have all the answers. They hire people better than themselves, avoid micromanagement, and create clarity without crushing autonomy. They understand that suistainable success is a collective effort. Results matter, but so do the people producing them.

A commonly cited example is Richard Branson. He has repeatedly emphasized trust and putting people first, not as soft values, but as strategic ones. His leadership style reflects a belief that if you take care of people, performance follows.

This is leadership that compounds over time.

2. High competence, low human awareness — The Extractor

This quadrant is often confused with strong leadership.

These individuals are frequently visionaries: resilient, ambitious, intolerant of excuses, and unafraid of failure. When told something isn’t possible, they respond with “then figure it out.” They push boundaries and redefine industries.

But they do so by extracting relentlessly from the people around them.

In The Everything Store (Brad Stone’s book on Amazon), employees describe environments marked by extreme hours, constant pressure, public criticism and little recognition. Performance is demanded at all costs. People are interchangeable. Burnout is collateral damage.

Jeff Bezos is often cited in this category, as is Steve Jobs during certain periods of his career. Both delivered extraordinary results. Both also left behind well‑documented trails of exhausted, expendable talent.
These leaders don’t lack intelligence or drive. They lack restraint.

3. Low competence, high human awareness — The Beer Buddy

These leaders are easy to like.

They are approachable and genuinely attentive to how people feel. One-on-ones are friendly. Conversations often drift toward weekend plans, personal stories and shared frustrations.

The problem is not intent, it’s direction.

This archetype often shows up as managers who arrive at one-on-ones unprepared, asking questions like “What do you want to do?” without offering structure or a clear development perspective. People feel safe, but stagnant. Without competence, empathy alone becomes passive. Teams don't grow. Standards blur. Potential remains untapped.

Comfort replaces progress.

4. Low competence, low human awareness — The Detractor

This is the most damaging quadrant, and, unfortunately, not a rare one.

These leaders lack the skills to do the job and the awareness to recognize the impact they’re having. They create confusion, drain energy, and slow everything they touch.

Instead of extracting value, they subtract it.

Decisions are inconsistent. Feedback is absent or arbitrary. Accountability flows downward but never upward. Over time, capable people disengage or leave, not loudly, but deliberately.

What remains is inertia.

Why this distinction matters

Most people in organizations don’t choose their leaders, but they live with the consequences.

Understanding these patterns helps explain why some environments feel energizing while others feel depleting, even when the work itself is similar. It clarifies why results alone are a poor proxy for leadership quality, and why good intentions without capability still cause harm.

When we label all of this as leadership, we lose precision. And without precision, we normalize behaviors that should be questioned.

So… is toxic leadership really leadership?

Maybe the real issue isn’t toxic leadership at all.

Maybe it’s that we’ve expanded the definition of leadership so much that we’ve stopped interrogating it. We tolerate behaviors we would never accept from a peer, simply because they come wrapped in authority or justified by results. Competence without human awareness creates output, but not progress. Human awareness without competence creates comfort, but not growth.

Leadership only exists when both are present.

Everything else deserves a different name.

And once you see these patterns, the harder question becomes: which quadrant have you been operating in, and which one are you enabling?

Posted on: February 02, 2026 10:54 AM | Permalink | Comments (6)

The Sagrada Família: A living Project Management case study

Categories: Budget Management, Scope Management, Cost Management

In project management we obsess about three constraints: scope, schedule and budget. Rarely does a single project illustrate the tension between them more dramatically than Barcelona’s Sagrada Família.

1) A Schedule Without a Deadline (…for a Very Long Time)

Construction of the Sagrada Família began in 1882 under architect Francisco de Paula del Villar; Antoni Gaudí took over in 1883 and transformed it into his life’s work. When Gaudí died in 1926, less than a quarter of the basilica was complete. For decades there was no realistic finish date. Interruptions - most notably the Spanish Civil War - and the loss of Gaudí’s plans only compounded uncertainty.

In the early 21st century, project planners set 2026 (the 100th anniversary of Gaudí’s death) as a symbolic completion date for the main structures, especially the tallest central tower. However, decorative elements, interior work and ancillary features (like the controversial grand entrance stairway) are now expected to extend into the mid-2030s.

This makes the Sagrada Família one of the longest-running construction projects in history, approaching 150 years and counting, yet still deeply relevant and alive.

A useful comparison is the Sydney Opera House, which began construction in 1959 with an expected delivery of 1963 and ultimately opened in 1973, 10 years behind schedule and dramatically over budget, yet today is celebrated not as a failure but as a monumental success.

2) Scope: From Cross to Cathedral to Cultural Icon

The project’s scope has not been static. Early plans envisaged a monumental Christian cross configuration that would have required demolishing entire city blocks in Barcelona’s Eixample district, a plan that would be socially and politically untenable today. Over time, the focus shifted to building the basilica itself, and the symbolic cross of the Christian faith is now expressed primarily through the central tower dedicated to Jesus Christ, not as an urban-scale structure.

This evolution reflects a unique interpretation of scope, less as “scope creep” and more as scope negotiation across generations, adjusting to cultural values, urban constraints and stakeholder expectations.

3) Budget: Finance Through Visitors, Not Governments

Unlike most large-scale heritage or civic projects, the Sagrada Família is not financed by state or church funds. From the beginning, it has relied on private donations, and in the modern era its primary funding source is ticket sales, which bring in millions of euros annually. Tourism revenue now directly supports ongoing construction, turning the budget constraint into a living mechanism rather than a fixed baseline.

In 2024 the basilica attracted around 4.9 million visitors, making it one of Europe’s most visited monuments... despite being unfinished!

4) A Project That Breaks the Rules — and Still Succeeds

By traditional PMI standards, the Sagrada Família would seem to fail:

- Schedule: No fixed deadline for most of its existence, regularly revised and extended.
- Scope: Evolved radically from its original concept.
- Budget: Dependent on market-driven revenue, not fixed capital allocations.

And yet the project has become a global icon, a UNESCO World Heritage Site, and a thriving cultural and religious destination that draws millions of visitors each year.

Projects such as the Sydney Opera House remind us that late and over budget does not inherently mean failure. What matters more is impact, enduring value, adaptability, and stakeholder engagement over time.

The Sagrada Família challenges many of the assumptions we make about what defines project success.

- Can a project still be considered successful if scope, schedule, and budget are never fully stabilized?

- At what point does long-term value outweigh delivery efficiency?

- Are there projects today (e.g. digital, infrastructure, or product-based) that should be managed more like living systems than finite initiatives?

And perhaps the most uncomfortable question of all: how many potentially great projects do we cancel too early because they don’t fit our traditional success criteria?

Posted on: January 11, 2026 04:39 PM | Permalink | Comments (0)