Marcel van Oosterwijk

Author: Marcel

  • Practice Note: Congratulations. You’re Now a Quality Manager.

    In a previous Practice Note, I suggested that AI agents might turn everyone into a team lead.

    You no longer need people reporting to you to have a team. You might have twenty AI agents performing work on your behalf.

    But there’s a problem.

    Having a team doesn’t mean you can trust its work.

    And when your team consists of AI agents, you may discover that you’ve acquired another responsibility.

    Congratulations. You’re now a Quality Manager.

    Delegating Work Is Easy. Trusting the Results Is Harder.

    Imagine you’re working with several AI agents.

    One investigates a problem. Another develops a solution. A third implements it. A fourth evaluates the result.

    Everything happens remarkably quickly.

    But how do you know whether the work is any good?

    The agents may produce convincing results. They may explain their reasoning, provide evidence and confidently report that their tasks are complete.

    They may also misunderstand the objective, overlook important constraints or make incorrect assumptions.

    You could review everything yourself.

    But if you have to repeat all the work to verify it, much of the benefit of delegation disappears.

    You need another approach.

    Welcome to Quality Management

    Before delegating work, you need to establish what you expect.

    What should be achieved? What constraints apply? What would constitute an acceptable result?

    You also need to decide how the work should be performed.

    Which activities can be delegated independently? Which require coordination? Where are the risks? What should be checked, and by whom?

    And once the work is complete, you need evidence that the results are acceptable.

    You might review deliverables, introduce automated checks, have agents evaluate each other’s work or require human approval for particularly important decisions.

    None of these mechanisms guarantees quality.

    But together, they can provide a more systematic basis for confidence.

    You’ve established expectations, organized work, managed risks and introduced mechanisms for evaluating results.

    Whether you intended to or not, you’re practicing Quality Management.

    Assessing Products and Capabilities

    There’s another distinction worth making.

    Suppose an agent produces an excellent result.

    Does that mean you can trust it to produce excellent results next time?

    Not necessarily.

    Evaluating an individual result tells you something about that result.

    It doesn’t necessarily tell you whether the agent can reliably produce similar results under different circumstances.

    For that, you need to understand the capability behind the result.

    Does the agent have access to the necessary information? Are its instructions adequate? Does it recognize situations it cannot handle? Are appropriate controls in place?

    You may discover that an agent produces good results only when given very specific instructions, or that it performs reliably for routine tasks but struggles with exceptions.

    This information helps you improve how work is delegated.

    And now you’re doing something beyond checking outputs.

    You’re assessing and developing the capability of your little AI organization.

    Quality Becomes Everyone’s Responsibility

    For decades, quality professionals have argued that quality cannot simply be delegated to a testing or quality assurance department.

    Quality depends on decisions and activities throughout the organization.

    Yet in practice, many professionals have been able to treat quality management as someone else’s responsibility.

    AI agents may change that.

    When you personally perform an activity, your expertise and experience provide some confidence in the result.

    When you delegate the activity to an agent, you need other ways to establish that confidence.

    You must become more explicit about expectations, evidence, risks and acceptance criteria.

    And as you delegate more work, you may need increasingly systematic mechanisms for managing its quality.

    Not because someone has introduced a new corporate quality initiative.

    But because you need to know whether you can trust the work being performed on your behalf.

    The Unexpected Quality Revolution

    We often discuss AI as a productivity revolution.

    People will produce more code, documents, analyses and decisions with less effort.

    Perhaps.

    But the more interesting development may be what happens to our responsibilities.

    As execution becomes easier to delegate, defining objectives, evaluating evidence and exercising judgment become increasingly important.

    And those are fundamental concerns of Quality Management.

    Perhaps AI will accomplish something that decades of quality professionals have struggled to achieve.

    It may make Quality Management everyone’s business.

    Not because we’ve finally convinced everyone of its importance.

    But because everyone suddenly needs it.

  • 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.