Marcel van Oosterwijk

Category: Practice Notes

  • Practice Note: Everyone Becomes a Team Lead

    Until recently, becoming a team lead usually meant being given responsibility for other people.

    Now you can become a team lead without having a single person reporting to you.

    You might have twenty AI agents instead.

    The question is no longer just whether you can do the work.

    It’s whether you can organize, direct and assure the work of others.

    From Doing Work to Directing Work

    Consider a software developer.

    Traditionally, the developer writes code, investigates problems, implements changes and tests the results.

    With AI agents, the developer might delegate these activities.

    One agent investigates a defect. Another proposes a solution. A third implements it. A fourth develops tests.

    Meanwhile, the developer coordinates their activities, evaluates their results and decides what happens next.

    The work hasn’t disappeared.

    But the developer’s role has changed.

    Instead of performing every activity personally, the developer is increasingly responsible for organizing work performed by others.

    And that requires a somewhat different set of skills.

    The New Team

    Managing a team involves more than assigning tasks.

    Someone must establish objectives, explain expectations, distribute responsibilities, coordinate dependencies and evaluate results.

    These responsibilities don’t disappear because the team members happen to be AI agents.

    In some respects, they become more important.

    An AI agent may produce a convincing result without having understood the objective.

    It may make assumptions that were never agreed upon, overlook relevant constraints or optimize something that wasn’t particularly important.

    And unlike a human colleague, it may not recognize when it should challenge the assignment.

    Giving an agent a task is therefore not necessarily the same as ensuring that the task is understood.

    The person directing the work must provide sufficient context, establish boundaries and determine how the result will be evaluated.

    That sounds remarkably like management.

    Delegating Work, Not Accountability

    There is another interesting consequence.

    When you perform work yourself, you develop an understanding of it through the process of doing it.

    You encounter difficulties, make decisions, discover assumptions and gradually build confidence in the result.

    When an agent performs the work, much of that experience is no longer yours.

    You receive the result without necessarily having experienced the reasoning and decisions that produced it.

    This creates a challenge.

    How do you know whether the work is good?

    You might review the result yourself. You might ask another agent to evaluate it. You might establish automated checks or require evidence supporting particular conclusions.

    But none of these mechanisms removes the need for judgment.

    An agent reviewing another agent’s work may simply reproduce the same misunderstanding.

    And a result that satisfies every predefined check may still fail to achieve its intended purpose.

    The responsibility for deciding whether the work is acceptable remains with the person directing it.

    We can delegate execution much more easily than we can delegate accountability.

    The Importance of Knowing What Good Looks Like

    This raises a question about professional competence.

    If an AI agent can perform an activity better or faster than you can, do you still need to understand that activity?

    Perhaps not to the same depth.

    But you do need sufficient understanding to establish meaningful expectations and evaluate the outcome.

    A team lead doesn’t necessarily need to be the best programmer, architect or tester on the team.

    But they need to understand enough about the work to organize it, recognize problems and make informed decisions.

    The same applies when the team consists of AI agents.

    In fact, the ability to define what good looks like may become more important than the ability to produce it personally.

    This doesn’t eliminate professional expertise.

    It changes where that expertise creates value.

    Everyone Becomes a Team Lead

    We often discuss AI in terms of productivity.

    How much faster can someone write code, produce documents, analyze information or prepare presentations?

    But perhaps productivity is only part of the story.

    AI agents allow individuals to coordinate work that previously required several people.

    A consultant might direct agents conducting research, analyzing evidence, developing recommendations and preparing deliverables.

    A tester might coordinate agents generating test ideas, executing checks, investigating failures and documenting findings.

    A manager might use agents to collect information, monitor developments and prepare decisions.

    In each case, the individual becomes responsible for a small system of delegated work.

    And the effectiveness of that system depends increasingly on their ability to direct and assure it.

    Perhaps we should therefore think differently about AI competence.

    Learning to use an AI tool is useful.

    Learning to manage work performed by AI agents may be considerably more important.

    Because when everyone can have a team, everyone needs to learn something about leading one.

    The future professional may not be the person who can do the most work, but the person who can make the most work happen — without losing control of its quality.

  • Practice Note: Let’s Put the Secretary Back in Our Meetings

    Once upon a time, important meetings had secretaries.

    They prepared agendas, recorded decisions, documented commitments and helped ensure that one meeting could build on another.

    Of course, not everyone was important enough to have a secretary.

    The boss’s meetings had a secretary. The rest of us had to manage our own meetings.

    And somehow, we ended up with calendars full of meetings without clear purposes, agendas, decisions or minutes.

    We saved money on secretaries.

    I’m not entirely convinced we saved money.

    What Did the Secretary Actually Do?

    Meeting minutes weren’t simply records of what was said.

    They preserved decisions, established accountability and provided continuity.

    What did we decide last time? Why? Who was supposed to do what? What remained unresolved?

    Secretaries also helped organize meetings, prepare material and follow up on commitments.

    When dedicated secretarial support became less common, those responsibilities didn’t disappear. They were redistributed to professionals who weren’t necessarily trained to perform them.

    The meeting organizer became the implicit chairperson. Someone was expected to take minutes. Sometimes nobody did.

    Meanwhile, eight highly paid professionals could spend an hour in a meeting without a clear purpose, only to schedule another meeting because nothing was decided.

    We eliminated an identifiable cost while accepting a distributed one.

    Meet Ada, Our New AI Secretary

    Now imagine everyone could have a secretary.

    An AI secretary.

    Let’s call her Ada.

    Ada could help prepare agendas, record decisions, identify actions and keep track of unresolved questions.

    She could remind us what happened last time and help us prepare for the next meeting.

    Afterward, she could produce meeting minutes for participants to review and confirm.

    This isn’t science fiction. Tools such as Microsoft Teams, Zoom and AI-powered transcription services already provide much of the necessary functionality.

    What’s changed is the economics.

    We can now make secretarial support available to meetings that could never justify dedicating another person to them.

    And Ada isn’t a particular product. She represents a role that different AI services can fulfill.

    Why Stop at Important Meetings?

    Consider one-to-one conversations, sales meetings, requirements refinement sessions or incident investigations.

    These activities produce knowledge: expectations, assumptions, observations, decisions and commitments.

    Yet much of it disappears when the conversation ends.

    Ada could help preserve it.

    And why stop at conversations?

    Consider exploratory software testing.

    A tester investigates a system, observes its behaviour, develops hypotheses and follows interesting leads.

    Documenting the investigation interrupts it.

    Now imagine Ada observing the screen while listening to the tester explain what they’re investigating.

    “That’s strange. I expected the customer status to change after saving, but it didn’t. Let’s try changing the account type first.”

    Ada could associate that observation with the relevant screen state, actions and subsequent results.

    Instead of merely recording a conversation, she helps preserve the investigation: what was attempted, what was observed, why it mattered and what happened next.

    The tester remains responsible for interpreting the results.

    Ada takes care of much of the documentation.

    From Meeting Minutes to Organizational Memory

    Now imagine that Ada’s records don’t simply become documents stored somewhere.

    They contribute to an accessible body of organizational knowledge.

    Decisions remain connected to their rationale. Investigations remain connected to their evidence. Observations remain available for future work.

    People could ask:

    Why did we choose this solution?

    Have we encountered this problem before?

    What did we learn last time?

    Of course, Ada needs some guidance.

    Like any secretary, she needs to understand what matters, what to document and what to leave out. We can coach her by reviewing her work and providing feedback.

    She also needs to respect privacy and confidentiality. Participants should know when she’s present, and unnecessary recordings or personal details shouldn’t be retained. Her minutes should capture the substance of discussions rather than reproduce every word.

    The goal isn’t to record everything.

    It’s to preserve what matters.

    Let’s Give Everyone a Secretary

    Ada won’t automatically make meetings productive.

    A meeting without a purpose doesn’t become useful simply because someone writes excellent minutes.

    But she could make good meeting practices easier to follow, reduce the administrative burden on participants and preserve information that would otherwise be lost.

    So here’s a suggestion.

    At your next meeting, bring Ada.

    Use your phone, Teams, Zoom or whichever AI transcription service your organization supports.

    Make sure participants are informed and the service is appropriate for the conversation.

    Let everyone concentrate on the discussion while Ada takes the notes.

    Review the minutes, confirm the decisions and agree on the actions.

    Give Ada feedback. Tell her what to do differently next time.

    And wherever appropriate, make this your default way of working.

    Let’s put the secretary back in our meetings.

    This time, let’s give everyone one.

  • Practice Note: When Market Friction Becomes a Business Model

    I have always been somewhat uncomfortable with intermediaries whose business models depend on friction in a market.

    Not because intermediaries cannot create value. They certainly can. They help buyers and sellers find each other, facilitate transactions, and sometimes assume risks.

    But what happens when the friction they help overcome could be eliminated altogether?

    And what happens when maintaining that friction becomes more profitable than removing it?

    The Bread Analogy

    Imagine buying a loaf of bread for €5.

    The exact same bread is available elsewhere for €3.50, but you don’t know where.

    An intermediary knows and sells it to you for €5.

    There is nothing inherently wrong with this. You are willing to pay the price, and the intermediary earns a margin.

    But what happens when everyone can instantly discover where the bread is available and compare prices?

    The intermediary’s information advantage disappears.

    Unless the intermediary provides additional value, there is little reason to continue paying the premium.

    The interesting question is not whether the intermediary deserves to make money. It is why the information problem should continue to exist.

    The Consulting Market

    Consider the market for independent consultants.

    A customer needs an experienced consultant and has a budget that reflects that expectation.

    The assignment passes through one or more intermediaries, each taking a percentage of the hourly rate.

    By the time the offer reaches the consultant, the compensation may be considerably lower.

    This is usually discussed as a question of margins.

    But there is a more important consequence.

    An experienced consultant might decline the assignment because the compensation is too low. A less experienced consultant might accept.

    The customer could therefore end up with a less experienced consultant, despite having a budget sufficient to attract someone more experienced.

    Higher compensation does not automatically mean higher competence. But intermediary margins can exclude qualified candidates who would otherwise have accepted the assignment.

    Market friction does not merely consume money. It can influence the quality of what the market delivers.

    What If AI Removed the Bottleneck?

    Today, customers often rely on consulting brokers to reduce the number of candidates they need to evaluate.

    Each broker presents a small selection of consultants. The customer compares a handful of CVs and selects a few candidates for interviews.

    This makes the process manageable.

    But it also means that potentially excellent candidates never reach the customer.

    We have solved the problem of limited human information-processing capacity by restricting access to the market.

    What if that limitation disappeared?

    Imagine an open digital marketplace where customers publish assignments and consultants make their capabilities and availability discoverable.

    AI agents help consultants identify relevant opportunities and prepare applications.

    The customer’s AI evaluates all applications, identifies the strongest matches, and explains its reasoning.

    It can even prepare individual interviews designed to verify each candidate’s claimed experience rather than simply repeat what is written in the CV.

    The customer makes the final selection.

    AI agents then help both parties compare contractual terms and prepare the agreement.

    No traditional consulting broker required.

    Not because we have automated the broker’s existing process, but because we have removed much of the friction that made the broker necessary.

    And the money previously consumed by intermediation becomes available to reduce costs, improve compensation, or attract more experienced consultants.

    Reducing market friction can improve the value delivered, not merely reduce transaction costs.

    But Who Owns the Marketplace?

    There is an obvious danger.

    What if we replace numerous consulting brokers with one dominant AI marketplace?

    We might eliminate some friction while creating a new gatekeeper.

    That is why I believe three principles are essential:

    Openness. Interoperability. Transparency.

    Customers and consultants should be able to use different systems and AI agents without losing access to the market.

    They should understand how candidates are evaluated, what commercial terms apply, and what they are paying for.

    The marketplace still needs infrastructure, security, identity verification, and governance. But these services need not depend on collecting a percentage of every hour a consultant works.

    AI does not automatically guarantee an open and transparent market. These are choices we need to make when designing the systems.

    From Managing Friction to Eliminating It

    There is a broader pattern here.

    When a system contains friction, we often introduce a function to manage it.

    The function becomes specialized. It develops processes, tools, expertise, and eventually a business model.

    Over time, we become so accustomed to the function that we stop questioning why it is needed.

    We optimize the handling of friction rather than asking whether the friction itself could be removed.

    AI creates an opportunity to reconsider this.

    Not simply by automating existing activities, but by making entirely different ways of organizing work and markets possible.

    Some intermediaries will continue to create genuine value through expertise, assurance, coordination, or risk management.

    But those whose primary advantage is that buyers and sellers cannot efficiently find, evaluate, or transact with each other may face a more fundamental challenge.

    Their traditional function may no longer be necessary.

    A Final Thought

    We often discuss AI in terms of productivity.

    How can AI help someone perform an existing activity faster, cheaper, or better?

    But perhaps we should also ask a different question:

    What activities would no longer be necessary if AI removed the problems that made them necessary in the first place?

    In the consulting market, reducing friction could give customers access to better consultants for the same money.

    And the principle extends far beyond consulting.

    The real opportunity is not just to make intermediaries more efficient. It is to make markets more efficient by removing unnecessary intermediation.

  • Practice Note: The Death of PowerPoint

    We learned to eliminate the bullets. Perhaps it’s time to eliminate PowerPoint altogether.

    For years, we’ve been told that PowerPoint presentations are killing our meetings.

    Too many slides. Too much text. Too many bullet points.

    Death by PowerPoint.

    So we learned to make better presentations. We reduced the number of bullets, introduced visual storytelling, and encouraged presenters to engage their audiences.

    But perhaps we’ve been solving the wrong problem.

    What if the problem isn’t how we use PowerPoint, but that we’re using PowerPoint in the first place?

    The Meeting That Could Have Been a Document

    Consider a typical management meeting.

    A group of experienced professionals gathers around a table. Someone opens a PowerPoint presentation and starts explaining what everyone needs to know.

    For the next 30 minutes, the participants listen. Or at least pretend to.

    Some already know the material. Others struggle to follow. A few are checking their emails.

    Then, with ten minutes remaining, the presenter reaches the final slide:

    Questions?

    And that’s when the meeting could finally become interesting.

    Unfortunately, we’re almost out of time.

    We’ve spent most of our collective time transferring information and left very little for actually doing something with it.

    What If We Started by Reading?

    Jeff Bezos once described an alternative approach used at Amazon.

    Instead of PowerPoint presentations, meeting organizers prepare written narratives, often six pages long.

    At the beginning of the meeting, everyone reads the document silently.

    Yes, silently. Like in a classroom.

    The reason is refreshingly pragmatic: if you send material beforehand, some people will read it and others won’t.

    So instead of pretending everyone has prepared, the meeting begins by ensuring that everyone actually has.

    Then the discussion starts.

    And here’s the important difference:

    The meeting isn’t used to present the information. It’s used to think about it.

    PowerPoint Makes It Too Easy

    There is another problem with PowerPoint.

    It makes it remarkably easy to create the appearance of structured thinking.

    Put five statements on a slide. Add a heading. Perhaps an arrow or two.

    Suddenly, it looks like an argument.

    But are the statements actually related? Does one support another? Are there assumptions between them? Is there evidence?

    A PowerPoint slide doesn’t necessarily require us to answer those questions.

    Writing a coherent narrative does.

    You cannot simply place five unrelated statements next to each other and assume that the reader will construct the reasoning for you.

    Well, you can. But it becomes much more obvious that something is missing.

    PowerPoint helps us organize information. Writing forces us to organize our thinking.

    That’s an important distinction.

    Stop Fixing the Audience

    When people lose attention during presentations, we often blame the audience.

    People have short attention spans. They check their phones. They multitask. They don’t prepare.

    So we introduce meeting rules.

    Close your laptops. Put away your phones. Pay attention.

    But what if the audience isn’t the problem?

    What if we’ve designed a meeting that requires people to remain passive for 45 minutes, and then complain when they behave like passive participants?

    The Amazon approach doesn’t solve this by demanding better behaviour.

    It changes the mechanism.

    Everyone reads. Everyone starts with the same information. Everyone can participate in the discussion.

    Instead of trying to make people pay attention to a poorly designed process, redesign the process to make their attention useful.

    Kill the Presentation, Not the Discussion

    Of course, there are situations where presentations are valuable.

    A visual demonstration. A compelling story. An explanation that genuinely benefits from images, diagrams, or animation.

    But how often is that what we’re actually doing?

    How often are we simply displaying information on a screen while someone reads or paraphrases it?

    And why do we need ten people sitting in a room to watch someone do that?

    If the purpose is to distribute information, distribute it.

    If the purpose is to make a decision, provide the evidence and create the conditions for discussion.

    If the purpose is to solve a problem, bring people together to solve it.

    Stop confusing presenting information with working together.

    The Death of PowerPoint?

    Perhaps PowerPoint doesn’t need to die.

    But its position as the default mechanism for meetings probably should.

    We’ve spent decades improving our presentations.

    Perhaps it’s time to improve what happens instead of presenting.

    And that starts with a rather uncomfortable question:

    If we removed PowerPoint from tomorrow’s management meeting, what would we actually do with the time?

    If the answer is think, discuss, challenge assumptions, and make decisions, perhaps we should have killed it years ago.

  • Practice Note: How Testing Became Quality Management Without Leaving Testing Behind

    Testing has changed considerably over the years.

    What once focused primarily on evaluating software products has expanded into test automation, quality assurance, quality engineering, process improvement, and increasingly organizational capability development.

    We have moved upstream, becoming involved in requirements, architecture, development practices, and organizational ways of working.

    Yet we still tend to describe much of this work as testing.

    Perhaps we have expanded the discipline without reconsidering what the discipline has become.

    Following Quality Upstream

    Testing provides information about products.

    When something doesn’t work as expected, testing helps us discover it, investigate it, and understand its consequences.

    But many problems discovered through testing originate elsewhere.

    Ambiguous requirements, architectural decisions, inadequate development practices, or insufficient organizational capabilities may all become visible through testing.

    Improving testing alone does not necessarily address these problems.

    So we started moving upstream.

    We became involved in requirements reviews, development practices, risk management, process improvement, and eventually organizational assessments.

    The underlying motivation remained the same: improving our ability to achieve quality.

    But the subject of our attention gradually changed.

    We were no longer concerned only with evaluating products. We were increasingly concerned with understanding and improving the organizational mechanisms that produce them.

    From Testing to Quality Management

    This development introduced a collection of overlapping concepts.

    Quality Assurance, Quality Control, Quality Engineering, Test Management, and Quality Management are frequently used in ways that make their responsibilities difficult to distinguish.

    Perhaps the problem is not primarily terminology.

    Perhaps we have been trying to accommodate an increasingly broad set of responsibilities within a discipline originally organized around testing.

    A useful distinction might be:

    • Testing investigates products and provides evidence about their behaviour and characteristics.
    • Quality Control evaluates whether products satisfy relevant quality expectations.
    • Quality Assurance provides confidence that the ways of working used to achieve quality are adequate.
    • Quality Engineering develops and applies engineering practices that help achieve quality.
    • Quality Management provides the organizational mechanisms for directing, evaluating, and improving the ability to achieve quality.

    These are not necessarily competing disciplines.

    They address different aspects of the same challenge.

    And testing remains an essential part of that challenge.

    Assessing More Than Products

    Organizations routinely evaluate their products.

    They test software, inspect deliverables, monitor performance, and investigate defects.

    But how systematically do they evaluate the organizational capabilities responsible for producing those products?

    When testing repeatedly reveals poor requirements, do we assess the organization’s requirements engineering capability?

    When defects originate in architectural decisions, do we examine the architecture capability?

    When problems recur across projects, do we investigate the organizational mechanisms that should enable learning and improvement?

    Often, we address the immediate problem without systematically evaluating the capability behind it.

    This suggests an interesting extension of Quality Management.

    Just as product assessment provides evidence about products, capability assessment could provide evidence about the organization’s ability to achieve intended outcomes.

    And just as product development responds to findings about products, capability development could respond to findings about organizational capabilities.

    Quality Management would then concern itself not only with whether quality is achieved, but also with whether the organization has the capabilities required to achieve it repeatedly.

    Without Leaving Testing Behind

    None of this makes testing less important.

    Testing remains one of our most valuable sources of evidence about whether products behave as intended.

    It also provides an important perspective on the consequences of decisions made elsewhere.

    But testing cannot compensate indefinitely for weaknesses throughout the organization.

    Nor should it be expected to.

    Moving upstream does not mean abandoning testing. It means recognizing that the information testing provides can lead us toward questions that testing alone cannot answer.

    And perhaps that is how testing gradually led us toward Quality Management.

    Not because testing became something else, but because understanding quality required us to look beyond testing.

    Quality is created throughout the organization. Quality Management makes that creation intentional, coordinated, and improvable.