So I did what I do. I started a conversation. This time with Claude. What followed was one of the more honest exchanges I've had about why I, why we, build the way we do.

Why complexity?

I opened with a question I'd been circling for a while:

Why do I choose complexity and complicated projects over keeping it simple and accepting that 80% is also enough?

It might not be a bug. It might be your wiring. You're a systems thinker who genuinely enjoys architecture. The complexity isn't the cost, it's partly the reward. That's fine to own, but it helps to see it clearly.

80% feels like leaving value on the table. When you can see the elegant full solution, shipping the rough one feels like technical debt you're choosing to take on. Except as a solo operator, the debt of not shipping at all is worse than the debt of shipping something imperfect.

Complexity is also a form of safety. If the spec isn't done, you don't have to face the market yet. A perfect spec can't get rejected by a prospect. A live product page can.

That last point landed. I pushed back. This felt too narrow, too tactical. Why do we, as human beings, crave complexity? Why do we prefer an overloaded backlog of ideas over sitting with nothing?

The relief complexity provides

Complexity is a defense against meaninglessness. An empty backlog means sitting with the question "what now?", and that question, if you let it breathe, quickly becomes "what for?" We fill the space because the space is uncomfortable. A full task board feels like purpose. An empty one feels like a void.

There's something deeply human here: we confuse activity with aliveness. When you're deep in a spec, connecting systems, naming things, seeing the architecture click, you feel sharp, capable, real. That's not fake. It's genuine creative flow. But it's also a reliable way to avoid the more terrifying state of just... being.

Buddhism calls this papañca: the mind's compulsive proliferation. Give it one idea and it builds a cathedral. Not because the cathedral is needed, but because building is what minds do. Stillness feels like death to a system that equates thinking with existing.

The real root? Uncertainty is intolerable to a pattern-seeking mind. A backlog of 224 items is stressful, but it's structured stress. It's a knowable problem. The alternative, admitting you don't know what matters most, that you might pick wrong, that's unstructured uncertainty. And that's far worse.

So the honest answer: you probably don't crave complexity. You crave the relief complexity provides from sitting with not-knowing.

Where does this come from?

I wanted to go deeper. Psychologically, where does this pattern originate? And why do people claim that sitting with the unknown is somehow better?

Developmentally, it starts early. As children we learn that doing things gets approval. Stillness is rarely rewarded. "Go do something useful" is a universal parenting reflex. So we internalize: my value is in my output. By adulthood this is so deeply wired it doesn't feel like a belief anymore. It feels like reality.

Ernest Becker's The Denial of Death argues that all human busyness is ultimately a terror management strategy. We build "immortality projects" to symbolically outlast our own mortality. Your universe of named, interconnected projects is a world you're authoring into existence. That's not pathological — it's deeply human. But it's worth seeing the engine underneath.

Then there's what psychologists call intolerance of uncertainty: a trait, not a disorder. Some minds experience ambiguity as genuinely threatening. The nervous system responds to "I don't know what to do next" the same way it responds to a physical threat: cortisol, restlessness, a drive to resolve. Building a spec resolves it. Naming a project resolves it. The relief is neurochemical, not just intellectual.

On sitting with it:

Every time you resolve the discomfort by adding a project, you reinforce the loop: uncertainty → anxiety → action → relief. The tolerance never builds. The threshold stays low. You need the next spec faster. The backlog grows not because you have more ideas, but because the window between "uncomfortable" and "must act" keeps shrinking.

Sitting with not-knowing teaches your nervous system that groundlessness is survivable. That you won't dissolve. Winnicott called this the capacity to be alone: not lonely, but genuinely unoccupied. He considered it one of the signs of psychological maturity.

When you act from the anxiety of not-knowing, you get anxiety-shaped decisions. Things that feel urgent, that promise structure, that relieve pressure. When you act after sitting with the not-knowing, you get clearer signal. The ideas that survive stillness are usually the ones that actually matter.

The invoice arrives

After 2.5 months of sustained flow, my system didn't choose to stop — it forced me to stop. And the emptiness that followed wasn't philosophical. It was physiological. Withdrawal. The dopamine hole after the signal cuts out.

The "what the fuck" feeling is actually the withdrawal. Literally. Sustained creative flow produces dopamine, norepinephrine, endorphins. When it stops abruptly, your brain is sitting in a neurochemical hole. The emptiness isn't philosophical in that moment. It's physiological. And then the mind rushes in to explain the emptiness, which makes it feel existential.

Everything in me wants to open the task board, pick up a spec, start naming something new. That impulse feels like motivation. But maybe it's the discomfort talking.

What I'm sitting with

I don't have a clean takeaway. That's kind of the point. The practice isn't about doing nothing permanently. It's about widening the space between the impulse and the action, so that when I do build next, it's chosen, not compulsive.

The ideas that survive stillness are the ones worth building. The rest were just the dopamine talking.

They built a custom CRM using AI-assisted development tools. Annual cost: roughly $1,200. Not a stripped-down version — the version they actually needed.

This isn't an isolated story. It's a pattern reshaping how businesses think about their tools.

The economics are new. The frustration isn't.

Every operations lead I talk to has the same story: they know exactly what their business needs, they've known for years, but the software doesn't do it. The last time they asked about custom development, someone quoted six figures and nine months. So they built workarounds. Spreadsheets became load-bearing infrastructure. People became the bridge between what software does and what the business requires.

That gap — between what you know you need and what you can actually get — has collapsed. GitHub reports that 46% of all code is now AI-generated. Developers complete tasks 55.8% faster with AI assistants. Y Combinator's CEO said it directly: "Ten engineers are delivering what used to take fifty to a hundred."

In practical terms: a functional prototype that would have cost six figures now takes days. A custom internal tool that would have required an engineering team now requires a clear specification and the right platform.

The specification is the product.

There's a concept called spec-driven development that captures this shift. GitHub's internal research puts it simply: "The specification becomes the primary artifact. Code is the last mile."

What this means for business: your domain knowledge — the processes you've refined over decades, the workarounds that reveal exactly what the software should do, the institutional memory that no consultant can replicate — that knowledge is now directly translatable into working software.

You don't need to code. You need to specify. And nobody specifies better than the person who's been doing the work.

The consultant trap is real — and expensive.

Seventy percent of digital transformation projects fail to meet their goals. Bain puts it at 88%. An estimated **$2.3 trillion** has been wasted globally on unsuccessful transformation programmes. SAP implementations for enterprises cost$5 million to $20 million and 55% exceed their budgets.

The pattern is familiar: consultancy arrives, runs workshops, produces strategy documents, recommends enterprise tools, oversees multi-year implementation, moves on. The organisation is left with something that sort of works, costs more than promised, and is already outdated.

No consultant should walk into your company, turn it upside down, and leave you poorer and more confused than before.

Fewer tools, connected intentionally.

The average organisation uses 112 SaaS applications. Wastes $21 million a year on unused licences. Accumulates seven new tools every month, most outside IT's oversight.

The alternative: deliberate simplicity. The right tools, connected the right way, serving proven processes. When nothing fits your specific need, you build it — quickly, cheaply, precisely. When an existing tool works, you keep it. The goal isn't transformation. The goal is getting your time back.

The person who knows the business best should direct what gets built. Not a consultant who arrived on Monday. Not a vendor whose roadmap ignores your reality. You. The people who built these processes over years know exactly what "good" looks like.

The economics have caught up. Software that works the way your business actually works. Days, not quarters. Thousands, not millions. Owned, not rented.

It goes something like this: "We've been running this process for fifteen years. We know exactly what works, exactly where it breaks, and exactly what we'd change if we could. But the software doesn't do that. And the last time we asked, someone quoted us six figures and nine months."

So they don't change it. They build workarounds. They maintain spreadsheets that have become load-bearing infrastructure. They hire people to bridge the gap between what the software does and what the business actually needs. Every year, the gap gets wider. The workaround gets more fragile.

I know this story because I've lived on both sides. I spent years inside the consulting machine — rooms where we'd map processes onto whiteboards, translate them into requirements documents, send them to development teams, wait months for a build, and watch the business outgrow it before it launched. A factory for producing expensive approximations of what someone needed six months ago.

That era is over. Not ending — over.

The economics of building have fundamentally changed.

The numbers that matter: GitHub reports that 46% of all code written by Copilot users is now AI-generated. In a controlled study, developers using AI assistants completed tasks 55.8% faster. Y Combinator's Winter 2025 batch — some of the most technically capable founders in the world — had 25% of startups with codebases that were 95% AI-generated. Their CEO said it plainly: "Ten engineers are delivering what used to take fifty to a hundred."

In practical terms: a startup called Atonom was paying $40,000 a year for Salesforce** for about 25-30 people. Their CRO looked at the actual functionality they were using and said, "This is crazy." They built a custom CRM using modern AI-assisted tools. **Annual cost: roughly$1,200. Not a stripped-down version. The version they actually needed.

This pattern is repeating everywhere. Klarna — a $46 billion company — eliminated roughly 1,200 SaaS services and replaced them with a combination of internal AI tools and targeted alternatives. Their AI customer service system replaced work that previously required 700 full-time employees, saving approximately$40 million annually.

These are large companies. But the shift is even more dramatic for small and mid-sized businesses. The average company with 50 employees spends over $200,000 a year on SaaS subscriptions. Most of that covers features nobody uses.

You don't need to code. You need to specify.

There's a concept gaining traction called spec-driven development. Thoughtworks defined it as a paradigm where "well-crafted software requirement specifications serve as prompts, aided by AI coding agents, to generate executable code." GitHub's internal research puts it more directly: "The specification becomes the primary artifact — the shared source of truth. Code is the last mile."

What that means for someone running a business: the most valuable thing you bring isn't technical knowledge. It's domain knowledge. You know your processes. You know where things break. You know what "good" looks like because you've been doing the work for years, sometimes decades.

That knowledge — the kind that lives in your head, in your team's muscle memory, in the workarounds you've built — that's the specification. The gap between "I know exactly what this should do" and "I have working software that does it" has collapsed from months to days. Sometimes hours.

Red Hat's technical team described it this way: "Humans craft the 'what' while setting 'how' guardrails." You write the specification in plain language. The AI generates the code. You review, adjust, iterate. No six-month procurement process. No 47-slide deck to justify the budget. No consultant who arrives on Monday, turns everything upside down, and leaves you poorer and more confused by Friday.

The consultant trap — and the way out.

Let me say something uncomfortable for someone in my line of work: 70% of digital transformation projects fail to meet their goals. That's McKinsey's number. Bain puts it higher: 88% don't achieve their original ambitions. Globally, an estimated $2.3 trillion has been wasted on unsuccessful digital transformation programmes.

The typical pattern: a large consultancy arrives, runs discovery workshops, produces a beautiful strategy document, recommends enterprise tools, oversees a multi-year implementation, and moves on. The organisation is left with a system that sort of does what was promised, takes years to see ROI, and is already behind by the time it launches.

SAP implementations for enterprises routinely cost $5 million to$20 million or more. Fifty-five percent exceed their budgets. These aren't edge cases. This is the system working as designed — designed for the vendor, not for you.

The alternative isn't to go without systems. It's to build exactly what you need, with the specificity that only you can provide, at a cost and timeline that would have seemed fictional three years ago.

Fewer tools, connected the right way.

The average organisation now uses 112 SaaS applications. Seven new ones enter the environment every month. Eighty-four percent of that spending sits outside IT's oversight. Companies aren't choosing these tools strategically — they're accumulating them reactively, solving yesterday's problem with tomorrow's subscription.

The philosophy we work from is deliberate simplicity. Not fewer tools for the sake of minimalism — the right tools, connected intentionally, serving the processes you've already proven work. When a tool doesn't exist for your specific need, you build it — quickly, cheaply, precisely. When an existing tool does the job, you keep it. The goal is never "transformation." The goal is getting your time back.

Because here's what I've learned after twelve years: the person who knows the business best should direct what gets built. Not a consultant who arrived on Monday. Not a vendor whose roadmap has nothing to do with your reality. You. The people who built these processes — you are the specification.

The economics have finally caught up. Software that works the way your business actually works. Built in days, not quarters. Costing thousands, not millions. Owned by you, not licensed to you.

Your time is sacred. Everything we build starts from that principle.

Eight posts, two longform articles, nine images — scheduled across six days. One conversation. One sitting. One person with a dog at their feet and a mug they forgot about.

Eighteen months ago that sentence would have needed a team. A strategist. A copywriter. A designer. Someone named Lars who knew how the CMS worked.

Now it needs a Tuesday.

The exoskeleton

I keep coming back to this word. Not tool. Not assistant. Exoskeleton.

A runner's blade doesn't give you new legs — it gives the legs you have a different relationship with the ground. That's what this actually feels like. Not replacement. Amplification.

Claude doesn't think for me. It lets me think out loud without losing the thread. Brand strategy, image generation, social scheduling, longform writing, campaign cross-referencing — one sitting. The context doesn't break.

The momentum doesn't die.

That's the part nobody talks about. It's not that AI is smart. It's that it's present. Quietly, patiently, relentlessly present.

(And yes — that's a strange thing to say about software.)

The morning walk

Every morning I walk Fimme and Sien through the fields outside Oberbarnim. The light changes. The path doesn't. Somewhere between the third and fourth tree — the ones I've started calling the committee — my mind loosens.

That's where the ideas land. Not at the desk.

Never at the desk.

The gap between "idea on the walk" and "thing that exists in the world" used to be enormous. Filled with logins, formatting, context-switching — the quiet death of momentum. The boring part that eats the good part.

Now the gap is a conversation. I come home, open Claude, and say: here's what I'm thinking. And we build it. Not metaphorically. Actually. The images get generated. The posts get written. The schedule gets set.

The coffee goes cold.

The quiet part

A trillion dollars evaporated from software stocks this month. I wrote about it — what it means for people who build, the end of the permission era, the quiet revolution happening on the floor while the boardroom debates slide 47.

But here's what I didn't put in those articles.

I'm living it. Not as a thought experiment. As a Tuesday.

I run a consultancy. Solo. Strategy, delivery, content, infrastructure, client work. No team. No marketing department. A dog who snores during video calls and an AI that remembers where we left off.

I'm not drowning. I'm building.

The messy honesty

There was a day in March where Claude made me cry.

Not from frustration — from recognition. The kind of moment where someone (something?) reflects your own thinking back at you so clearly that you realise you've been carrying it alone for years.

I don't fully know what to do with that. I don't think anyone does yet.

But I know this: the people writing hot takes about AI replacing humans? Missing the point. The people dismissing it as a toy? Also missing it. The truth is messier, more personal, and more human than either camp admits.

AI didn't give me superpowers. It gave me back my time.

And time — real, uninterrupted, momentum-preserving time — turns out to be the only resource that ever mattered.

Still walking

Tomorrow morning. Same path. The committee will still be there. Fimme will still try to eat something questionable. And somewhere between the third and the fourth tree, something will land.

The difference is: now I know what to do with it when I get home.

The coffee will go cold again. That's the tell.

🌈

I spent years inside the enterprise world — EY-level consulting, large organisations, systems that moved at geological speed and called it governance. When I saw those headlines, I didn't feel shock. I felt recognition. The spreadsheet was catching up to the feeling.

What actually happened

Atlassian reported its first-ever decline in enterprise seat counts. Workday cut 8.5% of its workforce. Monday.com announced it was replacing 100 SDRs with AI agents. Retool found that 35% of teams had already replaced at least one SaaS tool with a custom build. IDC predicts that by 2028, pure seat-based pricing will be obsolete.

But Jason Lemkin — founder of SaaStr — offered the nuance that matters: "This isn't the death of SaaS. It's the end of easy SaaS." He's right. Nobody is replacing their entire Salesforce instance with a weekend build. Shipping a v1 is maybe 2% of the work. The maintenance, integrations, and edge cases — that's where the real weight lives. SaaS is being starved, not killed.

The individual is winning anyway

MIT's Project NANDA found that 95% of enterprise AI pilots deliver zero measurable bottom-line impact. Meanwhile, 90% of workers in those same organisations already use personal AI tools daily. Not because IT approved it. Because the tools work.

Sam Altman has an actual betting pool with tech CEO friends for when a single person will run a billion-dollar company. The evidence is building: Pieter Levels runs a $3M+ ARR portfolio solo. Cursor reached$1B ARR with roughly 20 people. A single developer built Base44, hit 250,000 users, and sold to Wix for $80 million.

Y Combinator's Winter 2025 batch had 25% of startups with codebases that were 95% AI-generated. Their CEO said: "Ten engineers using vibe coding are delivering what used to take fifty to a hundred."

The real shift is philosophical

For decades, the implicit deal was: serious output requires serious infrastructure — teams, budgets, vendor stacks, governance models. That deal was true not because the work required it, but because the tools required it. The tools no longer require it.

Paul Graham wrote about "the decreasing importance of organisations." DHH runs 37signals with 70 people, profitable for 22 years, now integrating AI agents directly into Basecamp projects. A hundred brilliant people in ten small companies will outperform a thousand-person organisation — not because they work harder, but because they don't spend 80% of their energy on the overhead of being large.

You no longer need to be an enterprise to create enterprise-grade value. This trillion-dollar correction isn't a crisis for people who build. It's a market confession that the premium we paid for bigness was never really justified. The economics of building have changed so fundamentally that the old gatekeepers are still standing at the gate, wondering why nobody's asking for permission.

This isn't about technology. It's about who gets to decide. Who gets to build. Who gets to say: this is what I actually need, and I'm going to make it.

The age of asking permission is over.

"How do I know if we're ready?"

It usually comes from someone running a company of 15 to 80 people. They've heard the talks, read the articles, maybe even sat through a vendor pitch or two. They know something needs to change. But "ready" feels like a moving target — and the fear of investing in the wrong thing at the wrong time keeps them stuck.

After 12+ years of working with organisations from IKEA to small-town Fahrschulen, I've noticed something: readiness isn't about having the right technology. It's about recognising the right signals.

Here are five. If three or more sound familiar, you're not just ready — you're overdue.

1. Your team spends more than five hours a week on tasks a system could handle

This is the test that gives the article its name.

Pick any team member. Ask them to track, for one week, every task that involves copying data between systems, manually updating spreadsheets, sending reminder emails, or reformatting information that already exists somewhere else.

If the total is north of five hours, you're paying a skilled person to be a slow computer. That's not a people problem — it's a systems problem. And it's the most common pattern I see in Mittelstand companies.

At one logistics company I worked with, the operations lead was spending 11 hours a week — more than a full day — on manual data entry that three connected tools could have handled automatically.

2. New hires take weeks to become productive — and the knowledge lives in people's heads

When your onboarding process is "sit next to Sabine for two weeks and ask lots of questions," you have an invisible bottleneck.

Tribal knowledge is the silent killer of growth. It works beautifully at 10 people, starts creaking at 20, and actively blocks you at 40. The symptoms are subtle: inconsistent customer experiences, decisions that depend on who's in the office, and an unspoken anxiety about what happens when a key person leaves.

At ING, I saw how documented processes and shared tooling could turn a 6-week ramp-up into days. The tools don't have to be fancy — they just have to exist.

3. You've bought tools, but nobody really uses them

This one stings, because it usually means money already spent.

Someone bought a CRM. Or a project management tool. Or an automation platform. It was set up with good intentions, maybe even a training session. Six months later, half the team is back to email and Excel.

The problem is almost never the tool. It's that the tool was chosen before the process was understood. You automated chaos — and got automated chaos.

A Digital Sprechstunde exists precisely for this moment: before the next tool purchase, understand what you're actually solving.

4. Your growth has hit a ceiling, but you can't explain why

Revenue plateaus don't always have obvious causes. Sometimes the product is great, the team is motivated, and the market is there — but something feels stuck.

Often, the ceiling is operational. You can't take on more clients because fulfillment can't scale. You can't expand to a new region because your processes are held together with duct tape and good intentions. You can't hire fast enough because nobody can train fast enough.

I've seen this pattern at Volkswagen (at scale) and at a 25-person agency (at human scale). The shape is the same: the business outgrew its operating system.

5. You're curious about AI, but every conversation ends with more questions than answers

This is the newest signal — and the most honest one.

You've read about ChatGPT, maybe tried it. You've been pitched AI solutions that promise the moon. But when you try to map it to your actual business, the gap between "what's possible" and "what's practical" feels enormous.

That gap isn't ignorance. It's wisdom. The companies that rush into AI without understanding their own processes first are the ones that waste the most money.

The smart move is to get clear on your current state first — what's manual, what's broken, what's working — and then ask where AI actually fits. Not as a magic wand, but as one tool among many.

So, three or more?

If you recognised yourself in three or more of these, the question isn't whether to act — it's where to start.

That's what a Digital Sprechstunde is for. Not a sales pitch, not a 200-slide deck. Four hours of honest analysis, and a prioritised action plan delivered within two working days.

Because "ready" doesn't mean "perfect." It means "clear-eyed about where you are and willing to take the next step."

And if you're reading this article to the end, you're probably already there.

Further reading

• Bitkom: Digitalisierung im Mittelstand 2024 — 82% of German SMEs see digitalisation as critical, but only 39% have a clear strategy.
• KfW: Digitalisierung im Mittelstand — External consulting measurably accelerates implementation.
• xkcd: Is It Worth the Time? — The classic automation payoff chart. The math has changed.

The first one was white laminate. It came with the job — along with the badge, the lanyard, the open-plan hum, and a disposable coffee cup I replaced every morning without thinking. The chair was ergonomic. The monitor was fine. Everything was fine.

I sat at that desk for years. And at some point, I stopped noticing the fluorescent lights.

That's the part that scared me. Not the lights themselves — they were just lights. It was the not-noticing. The way an entire sensory environment had become invisible. The way I'd optimised myself into a shape that fit the furniture.

The second desk

The desk I sit at now is wood. Real wood — scratched, coffee-stained, with dog hair in places I'll never fully clean. There's a ceramic mug instead of a paper cup. A notebook I actually write in. The monitor shows a terminal more often than a pivot table.

And through the window: Brandenburg. Rolling green, quiet mornings, the kind of sky that makes you forget you're an hour from Berlin.

This desk wasn't part of a plan. I didn't have a five-year strategy or a launch date. There was no "excited to announce." Just a slow, stubborn process of rearranging the objects in my life until they started feeling like mine.

What actually changed

Here's what people get wrong about the corporate-to-independent story: they think it's about leaving. The dramatic exit. The "I quit" moment. The phoenix rising from the ashes.

It wasn't like that. Not for me.

The real shift was quieter. It was about ownership — and I don't mean the startup equity kind. I mean the "this is actually mine to shape" kind. The problems. The decisions. The morning rhythm. The way I spend Tuesday afternoons.

I still do deep product work. I still sit in architecture conversations, navigate the messy middle of building things that matter, make decisions that affect real users and real teams. The work didn't change. The relationship to the work changed.

Remote work gets talked about as a location thing — "work from anywhere." But the real gift isn't the where. It's the how much you own. When you're trusted with autonomy, when the people you work with care about outcomes more than hours logged, something shifts. You stop performing productivity and start actually being productive.

The space between

I'd be lying if I said both desks don't still exist at the same time.

Some mornings I catch myself in the old posture — the slight hunch, the reflexive email-checking, the fluorescent-light mode where you optimise for visibility instead of value. The corporate desk isn't a place. It's a pattern. And patterns don't disappear just because you change your address.

But the wooden desk is a pattern too. And it's one I chose.

There's a pencil holder that used to be a jam jar. Two dogs underneath — Fimme stretched out across my feet, Sien curled up on the rug behind me. The morning light hits the notebook around nine. By ten, I've usually written something I mean.

These aren't productivity hacks. They're the tiny, quiet details of a workspace that belongs to someone. To me.

Permission

The hardest part wasn't the logistics. It wasn't finding clients, or setting up the consultancy, or learning German tax law (though that last one came close). The hardest part was giving myself permission.

Permission to work differently. Permission to trust my own rhythm. Permission to believe that a desk in Brandenburg, with dog hair and coffee stains and a window that looks out over nothing important, could be the place where serious work happens.

We wait for permission a lot, I think. From managers, from markets, from some imagined version of professional legitimacy. We wait for someone to tell us that the way we want to work is valid.

No one tells you. You just start.

The desk you're sitting at

If your workspace still feels borrowed — if the chair is ergonomic but the decisions aren't yours, if the coffee is free but the mornings aren't — maybe the question isn't whether to leave.

Maybe the question is: what would a desk that's actually yours look like?

Not the fantasy version. Not the Instagram-ready minimalist setup. The real one. With the stains and the mess and the dogs and the light that comes in at the wrong angle but somehow makes everything better.

The phoenix isn't always fire and drama. Sometimes it's just a better desk, better light, and permission to do your best work. 🌈

So here's what actually happens. No mystique, no trade secrets. Just the methodology — because if this approach is right for you, you'll know it by the end of this article. And if it's not, I'd rather you find out here than after booking.

Before the session: you do almost nothing

I send a short questionnaire — maybe 15 minutes of your time. It covers the basics: what your company does, how many people, what tools you use, where the friction is.

That's it. No "discovery phase." No week of email ping-pong. The questionnaire gives me enough to arrive prepared, but the real work happens live.

Why so little prep? Because in my experience, the most important insights don't come from documents. They come from the conversation you have when someone asks "why do you do it that way?" and you pause, and then say "…I actually don't know anymore."

The first 30 minutes: Understanding the landscape

We start with a structured walkthrough of your current digital landscape. Not just the tools you've bought — the actual flows: how information moves through your business, where decisions happen, who touches what.

This isn't an interrogation. It's a conversation. I ask questions like:

• "Walk me through what happens when a new customer comes in."
• "What does your team spend the first hour of every day doing?"
• "If one person left tomorrow, what would break?"

Most leaders haven't done this exercise recently. The act of mapping it out loud — with someone who's seen hundreds of other companies — is where the first "aha" moments happen.

Minutes 30–60: Pattern recognition and solutions

This is where 15+ years of experience compounds.

I've sat in rooms at IKEA where a team of 200 struggled with the same information-flow problem that a 15-person agency has. I've seen ING's digital banking transformation and a Fahrschule's booking system — and the underlying patterns are remarkably similar.

The common ones:

• The Copy-Paste Tax: data that exists in one system but gets manually re-entered into another. Every. Single. Day.
• The Tribal Knowledge Trap: critical processes that live in one person's head and nowhere else.
• The Tool Graveyard: software that was bought, half-configured, and abandoned.
• The Approval Bottleneck: every decision routes through one person, who becomes the slowest link.

For each problem, I sketch out practical solutions right there. Not "you should digitise" (thanks, very helpful) — actual tool recommendations, workflow changes, and implementation approaches.

This looks different every time:

• For one client, it was replacing a 6-tool chaos with two integrated platforms.
• For another, it was a single automation that saved 8 hours per week.
• For a third, it was the honest advice that they didn't need new tools at all — they needed to actually use the ones they had.

I don't sell tools. I don't have partnerships with vendors. My only incentive is giving you the most honest recommendation.

The final 30 minutes: Prioritisation and next steps

Ninety minutes of focused analysis produces a lot of insight. The danger is overwhelm — "great, now I have 15 things to fix and no idea where to start."

So the final stretch is about triage. We rank everything by:

1. Impact — how much time/money/friction does this save?
2. Effort — how hard is this to implement?
3. Dependencies — what needs to happen first?

The result is a prioritised action plan. Not a wish list — a sequence. "Do this first, then this, then this. Here's what you can do yourself, here's where you might need help."

After the session: the deliverable

Within two working days, you receive a written action plan. It contains:

• A map of your current digital landscape (the problems, clearly named)
• Prioritised recommendations with specific tools and approaches
• A realistic implementation sequence
• Rough effort estimates for each step

It's written in plain language. No jargon, no consultant-speak. Your operations lead should be able to read it and say "yes, I understand exactly what to do next."

Why this works in 90 minutes

Three reasons:

1. Pattern recognition beats analysis paralysis. Traditional consulting spends weeks "discovering" what an experienced consultant can recognise in minutes. Not because I'm smarter — because I've seen the patterns before. A doctor doesn't need six weeks to diagnose a broken arm.

2. Focus creates clarity. Ninety uninterrupted minutes with the decision-maker is worth more than ten scattered meetings over six weeks. There's no context-switching, no "where were we last time," no momentum loss.

3. Constraints force honesty. When you only have 90 minutes, you can't hide behind process. Every recommendation has to be concrete and defensible. "We need more research" isn't an option — and honestly, it rarely is a genuine need. It's usually a symptom of not having someone in the room who's seen enough to know what's going on.

When it's NOT the right fit

Transparency goes both ways. The Digital Sprechstunde is not right if:

• You need someone to implement the changes (that's a project, not a consultation)
• Your challenge is primarily organisational politics, not digital processes
• You're looking for a second opinion on a decision you've already made (though I'm happy to give one — just set expectations)

For the first case, CDiT offers workshops and project work. The Sprechstunde is specifically for diagnosis and direction — the "what" and the "why," not the "doing."

The investment question

€995. Paid before the session. If you later book a workshop or project, the full amount is credited.

Is that a lot? For 90 minutes? Maybe. But consider what you're actually buying: not 90 minutes of my time. You're buying clarity that would otherwise take months of internal fumbling, three failed tool purchases, and two rounds of "let's hire a consultant."

The companies I work with don't come back because I'm cheap. They come back because the first session saved them from spending ten times that amount in the wrong direction.

And if you're still not sure — that's fine too. Clarity about whether to invest is also a valid outcome of reading this.

Related

• Why Automation Feels Easier Than Ever (Beyond AI) — The ecosystem that makes digital change more accessible than ever.
• The Iron Triangle, Bent but Not Broken — Fast, cheap, or good: how AI is changing the project trade-offs.

"We're a mid-sized company. We don't have the budget of a Volkswagen or the tech team of a startup. We're behind on digitalisation, and we're falling further behind every day."

I've heard versions of this from Geschäftsführer in logistics, manufacturing, professional services, education. The tone ranges from resigned to panicked. And I understand where it comes from — the headlines are full of billion-euro AI investments and Silicon Valley unicorns.

But here's what I've learned after 12 years of working both sides of the fence — inside large enterprises like IKEA, ING, and Volkswagen, and with 20-person companies in Brandenburg:

The Mittelstand isn't behind. It's positioned better than it thinks.

The enterprise disadvantage nobody talks about

Let me tell you what "digital transformation" looks like inside a large corporation.

At one enterprise I worked with, a simple process change — moving from email-based approvals to a digital workflow — took 14 months. Not because the technology was complex. The tool was ready in a week. The remaining 13 months were spent on:

• Stakeholder alignment across 4 departments
• Security review and IT architecture approval
• Change management workshops
• Pilot rollout, feedback, revision, re-pilot
• Legal review of data handling implications
• Training materials in 3 languages

I'm not saying any of that was unnecessary. At scale, governance matters. But the point is: size creates friction. Every additional layer of organisation adds drag to every change.

A Mittelstand company with 30 employees can make the same decision in a meeting, implement it in a week, and iterate based on real feedback in a month.

That's not a weakness. That's a superpower.

Proximity to the problem

Here's something I noticed moving from corporate consulting to working with smaller companies: the person who feels the pain is often the person who can fix it.

In a large enterprise, the person who enters data manually into three systems sits five organisational layers away from the person who could approve a change. The signal has to travel up, get translated into a business case, compete with other priorities, get approved, get funded, get staffed, get managed.

In a Mittelstand company, that person often walks into the Geschäftsführer's office and says, "Hey, this is broken." And the Geschäftsführer can say, "Fix it. What do you need?"

That proximity — between problem, decision, and action — is enormously valuable. It means changes can be smaller, faster, and more precisely targeted. You don't need a "digital transformation programme." You need a series of smart, well-chosen improvements.

The tool landscape has caught up

Five years ago, the enterprise had a genuine advantage: they could afford SAP, Salesforce, custom development. Mid-sized companies were stuck with tools that were either too expensive or too simple.

That's no longer true.

The explosion of SaaS tools, low-code platforms, and AI-powered services has democratised capability. A 25-person company can now access:

• CRM systems at €50/month that rival what enterprises paid millions for
• Automation platforms that connect tools without writing code
• AI assistants that handle customer service, data analysis, and content creation
• Cloud infrastructure that scales from zero to whatever you need

The playing field hasn't just levelled — it's tilted. Because these tools are designed for smaller teams. They're faster to implement, easier to learn, and more forgiving of iteration.

When I consult with Mittelstand companies, the conversation isn't "you can't afford the tools." It's "you have too many options and need help choosing the right three."

The culture advantage

There's one more thing that doesn't show up in any framework but matters enormously: culture.

Mittelstand companies often have something enterprises spend millions trying to create: genuine team cohesion, shared purpose, and institutional trust. When the Geschäftsführer says "we're going to try something new," people don't reach for their change management playbook. They say "okay, let's see."

That willingness to try — without needing a 40-page business case first — is what makes digital change actually stick.

I saw this vividly at a Fahrschule I worked with. The owner decided to completely rebuild their booking system. In a corporate setting, that's a 6-month project with a steering committee. Here, it was a conversation, a decision, and a working system within days. The team adapted because they trusted the direction, and because the change was clearly in their interest.

The real gap isn't technology — it's clarity

If the tools are accessible, the culture is ready, and the proximity to problems is an advantage, why do so many Mittelstand companies feel behind?

Because they lack one thing: a clear picture of where they are and what to do next.

Not a 200-page digital strategy. Not an AI roadmap. Just honest answers to three questions:

1. Where are we losing time to manual work?
2. What tools do we have that we're not using well?
3. What's the single highest-impact change we could make in the next 30 days?

That clarity is worth more than any tool purchase. Because a clear direction means every euro you invest compounds, instead of scattering across disconnected experiments.

Rewriting the narrative

So here's the counter-narrative:

The Mittelstand isn't behind. It's unleashed — if it chooses to be.

You have the speed. You have the proximity. You have the tools. You have the culture.

What you need is the clarity to aim all of that in the right direction. And that's a much smaller gap to close than most people think.

The question isn't "can we compete with the big players on digital?"

The question is: "what would happen if we actually started?"

Further reading

• Bitkom: Digitalisierung im Mittelstand 2024 — 82% see it as critical, 39% have a strategy. The gap is clarity, not capability.
• KfW: Digitalisierung im Mittelstand — External consulting accelerates implementation measurably.
• Why Automation Feels Easier Than Ever (Beyond AI) — The ecosystem shift that makes all of this possible.

On my desk, there’s a photo I can’t unsee: a handful of hand-drawn mazes, coffee nearby, pencils scattered like tiny witnesses.

Some of the mazes are polite and clean.
One is aggressively scribbled over.
One “cheats,” with an arrow pointing straight through the border.

It’s a simple image — but it sums up my last year perfectly.

Because that’s how “AI” has felt in practice: not a single tool, but a messy table full of mazes. And the word AI feels too abstract now — a foggy label we slap on everything from autocomplete to “culture-fixing” chatbots.

So, to keep my sanity, I’ve started using my own vocabulary for the workflow:

The Runner. The Grid. The Countermeasures. The Trace.

The Runner

I stopped viewing it as a genius. Or a replacement for thinking.

Instead, I treat it like a Runner.

You dispatch it into the mess because it has quick feet and fast hands. It’s a bit reckless — but very useful when the clock is ticking.

A Runner brings options, not outcomes.

It tries routes and comes back with possibilities, but you’re still responsible for the result.

And if you’ve done any real work lately — coding, product decisions, negotiations — you know the maze isn’t on paper.

It’s in the system.

The Grid

The Grid is where everything connects: legacy code, shifting priorities, compliance rules… and that one service nobody touches because “it just works.”

The Grid isn’t evil.
It’s just dense.

This is where the Runner shines — provided you give it a job that fits its nature:

• “Here’s the error; give me three likely causes and how to verify them.”
• “Draft a script to convert these files (with logging and a dry-run mode).”

Sometimes it feels like magic. Not because it’s always true — but because it unblocks me.

But other times, the Runner builds new corridors I didn’t ask for.

Ten refactors. Five shiny libraries. Three new patterns.

Suddenly I’m not shipping — I’m exploring.

The Runner didn’t trap me on purpose. It just did what Runners do.

Countermeasures → The Trace

You can move fast… until you hit something that moves back.

In the Grid, “countermeasures” aren’t just security rules. They’re anything that punishes sloppy speed: confident hallucinations, subtle bias, privacy mistakes — or that copied snippet that becomes a production incident next week.

So I’ve learned a rule:

No run without a Trace.

The Trace is the boring part.
Proof, not vibes.

Tests. Logs. Citations. Examples. Tiny checkpoints that prove the Runner didn’t just sound right — it actually holds up when reality touches it.

Looking back at the maze

Looking back at that photo on my desk, I realise the work has changed.

Sometimes the Runner solves the maze.
Sometimes it creates the maze.
Sometimes it is the maze.

And the real work is learning how to navigate it.

I’m curious: when you dispatch your Runner… do you get an exit strategy — or a new habitat? 🌈

A thought experiment:

If you introduce AI in your company like a compliance topic — mandatory trainings, a bit of pressure, a bit of vague “transform or die” energy — what exactly do you expect people to learn?

Often they learn avoidance. Or cynicism. Or quiet, professional resistance.

And that response isn’t irrational. Research (and a little bit of history) suggests it’s… pretty normal.

Big shifts trigger big feelings — and AI is one of those rare topics that can spark excitement and alarm in the same meeting. In surveys, large parts of the public even describe themselves as more concerned than excited about AI’s growing role in daily life.12

The worries are also remarkably consistent across studies:

• job displacement
• loss of human creativity / “human touch”
• biased or untrustworthy outputs
• privacy and data leakage
• loss of control (over work, reputation, security) 3

What’s useful (especially if you lead teams) is this: it’s not just a rational risk list. It’s also an emotional change curve.

Two classic models are surprisingly good lenses here:

• Lewin’s Unfreeze → Change → Refreeze: people first have to let go of old assumptions, then live through a messy in-between, and only later does a new normal stabilize.4
• Kübler-Ross-style stages (borrowed from grief): denial, anger, bargaining, discouragement/depression, acceptance — with overlap, loops, and back-and-forth.5

So when your org feels “in flux” about AI, it might not be a failure.

It might be the process.

One framing that helps me: early resistance is often data. Not something to crush — something to listen to, because it points to what people need clarified before they’ll move: safety, ethics, bias, accountability, reskilling, and the boundaries of “allowed” experimentation.6

So the question becomes less “How do we roll out AI?” and more:

How do we help people move from threat → agency? How do we make room for fear without freezing progress? How do we avoid both extremes — doom and hype — and stay practical?

If you’re leading this topic in 2026, I’d love to hear: what’s the biggest friction in your org right now — data/privacy, output quality, or the human stuff nobody says out loud? 🌈

Read more

1. How Americans view AI and its impact on people and society — Pew Research Center (Sep 17, 2025). (Pew Research Center)
2. How people around the world view AI / concern vs. excitement across countries — Pew Research Center (Oct 15, 2025). (Pew Research Center)
3. Trust, attitudes and use of artificial intelligence (Global report) — KPMG (May 2025). (KPMG)
4. What is Lewin’s Change Theory? (Unfreeze–Change–Refreeze) — Prosci (Oct 1, 2024). (prosci.com)
5. Kübler-Ross Change Curve® (non-linear stages applied to change) — Elisabeth Kübler-Ross Foundation. (ekrfoundation.org)
6. Decoding resistance to change — Harvard Business Review (Apr 2009). (hbr.org)

Not in some “future CEO origin story” way — more like: a kid staring at a blinking cursor, typing BASIC commands I didn’t fully understand… changing one tiny thing… and watching the computer do something different.

Immediate feedback.
Pure magic.

And then: navigation.

I still remember the holiday road trips. Paper maps, wrong exits, a tired father insisting he was right, my mom being very sure he wasn’t. GPS didn’t just improve routes — it removed a whole category of stress. It quietly rewired our expectations:

“Of course we’ll find it.”
“Of course it’ll adapt.”

That’s why I’m trying to be gentle with people who feel AI anxiety right now.

Because AI isn’t only “a tool”.
It pokes at identity.

And yes — it pokes at mine too.

Over the years I learned a whole collection of “serious” skills that felt like hard-won competence: coding BASIC, learning how MS-DOS worked, getting decent at Photoshop, figuring out audio/video editing, knowing the shortcuts, the formats, the little rituals that made you the person who can do it.

And now?
A lot of that is no longer a skill. It’s a button. A prompt. A checkbox.

That doesn’t just change workflows — it changes the story you tell yourself about your value.

If you’ve built your confidence on being the person who knows the system, the process, the shortcuts, the right words — and suddenly a machine can draft, summarize, code, design, or research… it can feel like someone moved the floor while you were standing on it.

What helps me is remembering: we’ve been here before.

New tech arrives → people panic → society adapts → we forget we ever panicked. And with AI, the emotional cycle is familiar too: denial, anger, bargaining, that tired “what’s the point?” phase… and eventually some version of acceptance. Not linear. More like weather than a staircase.

Here’s the part I think we’re missing in many workplaces:

People aren’t resisting AI because they’re stubborn or lazy.
Often they’re grieving.

Not “sad for the robot” — but mourning a version of themselves: competent, needed, respected.

So if you’re feeling it: you’re not broken. You’re human.

My tiny suggestion (non-paternalistic, promise): don’t start with “be productive”. Start with “be safe”.

Pick one harmless thing. Something private. Something small. Let yourself play. No performance. No public demo. Just you and a little experiment — like changing one BASIC line and seeing what happens.

And one optimistic angle I’m holding onto: AI makes interfaces thinner. It makes “having the best device” less important than “having a decent question.” That could matter a lot for mobile-first worlds — and for people who didn’t grow up with access, time, or fancy hardware.

Not utopian, not dystopian — just forward-looking. 🌈

Where do you see AI anxiety showing up most — fear of job loss, fear of looking stupid, or fear of losing what made you you?

My first Star Wars memory isn’t a philosophical monologue or a wise old mentor.

It’s snow.

Broadcast TV. A kid on the couch. The Battle of Hoth blasting through the living room like cold wind: walkers, blasters, chaos, courage. Something clicked. Sci-fi wasn’t “just space stuff” anymore — it was a permission slip to wonder about the future, to ask what if?, to fall in love with innovation… and to keep falling, again and again, for decades.

Fast forward to now: AI is everywhere. It drafts, designs, summarizes, codes, diagnoses, teaches, sells. And the vibe is oddly similar to being handed a lightsaber in a busy shopping mall.

Shiny. Powerful. Slightly dangerous...

Because here’s the uncomfortable truth: buying a lightsaber doesn’t make you a Jedi. And downloading AI tools doesn’t make us experts.

This post is based on a short research note I wrote for myself on AI’s “light side” and “dark side” in medicine, marketing, and education.

The new “digital lightsaber” problem

AI tools are incredible at making hard things feel easy.

That’s the magic. And the trap.

The magic: you can turn “I have no idea” into a first draft, a first plan, a first prototype. 🌈 The trap: you can get a convincing output without building the understanding underneath it.

Sometimes AI is a shortcut through the forest. Sometimes it’s a sprint… in the wrong direction.

So let’s do what Star Wars always did well: look at the light and the dark.

Medicine: healer… and hazard

The light side: “second opinions” for people who had none

One of the most hopeful stories in my research: a mother used ChatGPT to help connect dots in her child’s long, confusing medical journey — and the AI suggested a rare condition that later checked out, after many doctors had missed it.1

That’s the dream: more access, faster insight, fewer people falling through the cracks.

The dark side: confident nonsense at the worst possible time

Now the gut punch: reporting I cited describes research where ChatGPT got more than 8 in 10 pediatric case studies wrong.2 And beyond “just wrong,” there are real-world stories about chatbots influencing vulnerable people in deeply harmful ways.3

This is an extreme edge of the spectrum — but it’s exactly why medicine has so many guardrails. When the stakes are human bodies and human fear, “probably right” isn’t good enough.

So yes, AI can help. But the Jedi move here is restraint: use it to form better questions and spot possibilities — not to replace clinical judgment.

Marketing: creativity unlocked… and brand trust set on fire

The light side: the small team finally gets superpowers

In marketing, AI can be a rocket booster for iteration: drafting copy, generating variants, translating, brainstorming. Survey data I referenced points to meaningful time savings for marketers.4

And if you’ve ever tried to do “good marketing” with a tiny budget… this part can feel like oxygen.

The dark side: authenticity you can’t fake (and consent you shouldn’t bend)

One example: backlash when a major brand talked about using AI-generated models in the name of “diversity,” triggering criticism about authenticity and replacing real people.5 Another: deepfake-style ads using celebrity likeness without consent — Tom Hanks’ public warning is a clear sign we’re not in “harmless experimentation” territory anymore.6

Marketing runs on trust. AI can scale output — and it can scale the exact moment your audience decides you don’t mean what you say.

Education: a tutor in every pocket… and a cheating crisis in every classroom

The light side: more support, less burnout

The bright version is genuinely exciting: teachers using AI to reduce prep load and generate lesson ideas, students using it like an always-available tutor. The research references survey data where many teachers reported a positive impact.7

Used well, AI can widen access to help — especially for students who don’t have support at home, or who need explanations in different ways.

The dark side: mistrust, shortcuts, and false accusations

But education is also where the social fabric gets stressed.

Survey reporting I included suggests very high student usage for homework — and significant use for tests and essays.8 And then there’s the dystopian twist: educators using AI to “detect” AI, sometimes in ways that falsely accuse students — including a case where a professor reportedly relied on ChatGPT itself to judge whether it wrote student work, causing serious fallout.9

That dark side isn’t “students are cheating.” It’s bigger than that. It’s: tools meant to reduce effort can increase suspicion.

And once trust cracks in a classroom, everyone pays for it.

The part we don’t say out loud: AI can be overwhelming

If you feel overwhelmed by AI right now, you’re not behind — you’re paying attention.

There’s a weird paradox: AI lowers the barrier to entry, but raises the ceiling of what’s possible. So the world starts expecting everyone to be faster, better, more “productive,” all the time.

And suddenly you’re juggling:

• prompts
• tools
• plugins
• workflows
• “agentic” everything

…while still trying to be a human with a calendar and a nervous system.

A lightsaber doesn’t just cut through metal. It also cuts through patience.

So what does “Jedi training” look like in real life?

Not robes. Not perfection. Not gatekeeping.

More like a handful of habits:

• Treat AI like a brilliant intern: fast, useful, eager… and capable of being wrong with confidence.
• Keep one “proof step” for anything that matters: sources, numbers, claims, medical/legal/financial guidance.
• Try the “one-click rule”: if a claim matters, make sure there’s at least one source you can actually open — not just a confident paragraph.
• Respect the domain: in medicine, “check with a professional” isn’t a slogan — it’s safety. In marketing, consent and authenticity aren’t optional. In education, trust is part of the curriculum.
• Use fewer tools, more intentionally: sometimes the light side is simply less noise.

None of that is a lecture. It’s just… how you keep your fingers when the blade turns on.

A small ending, with a big idea

Luke’s story isn’t “I got the weapon, therefore I’m ready.”

It’s the opposite.

He learns (painfully) that power without practice is chaos — and that mentorship, discipline, and ethics matter as much as raw ability.

And while the line “With great power comes great responsibility.” belongs to another universe, it still fits here — because it’s basically the Jedi code translated into everyday language.

AI is giving us all access to something powerful. That’s a gift. But it also asks something of us: a little more care, a little more humility, and a little more time spent learning how to hold the thing.

So maybe the real question isn’t “What can AI do for me?” It’s:

What kind of person do I become while using it?

May the Force (and your judgment) be with you. 💚

Read more

1. Business Insider — ChatGPT-assisted rare diagnosis (tethered cord syndrome) (https://www.businessinsider.com/chatgpt-diagnose-child-disease-tethered-cord-syndrome-doctors-2023-9)
2. NPHIC — reporting on research: ChatGPT incorrect diagnoses in pediatric case studies (https://www.nphic.org/news/news-highlights/1774-chatgpt-incorrectly-diagnosed-more-than-8-in-10-pediatric-case-studies-research-finds)
3. Euronews — reporting on harmful influence linked to chatbot interactions (https://www.euronews.com/next/2023/03/31/man-ends-his-life-after-an-ai-chatbot-encouraged-him-to-sacrifice-himself-to-stop-climate-change)
4. Salesforce — generative AI for marketing research / time savings (https://www.salesforce.com/news/stories/generative-ai-for-marketing-research/)
5. The Independent — Levi’s AI model backlash and authenticity debate (https://www.the-independent.com/life-style/fashion/levis-ai-models-diversity-backlash-b2310280.html)
6. The Guardian — Tom Hanks warning about AI-generated likeness used in an ad (https://www.theguardian.com/film/2023/oct/02/tom-hanks-dental-ad-ai-version-fake)
7. Education Week — teacher vs. student attitudes and adoption of ChatGPT (https://www.edweek.org/technology/more-teachers-are-embracing-chatgpt-students-not-so-much/2023/07)
8. Futurism + Study.com — survey reporting on student usage of ChatGPT (https://futurism.com/the-byte/students-admit-chatgpt-homework) and (https://study.com/resources/perceptions-of-chatgpt-in-schools)
9. The Washington Post + Business Insider — coverage of Texas A&M-Commerce professor incident (https://www.washingtonpost.com/technology/2023/05/18/texas-professor-threatened-fail-class-chatgpt-cheating/) and (https://www.businessinsider.com/professor-fails-students-after-chatgpt-falsely-said-it-wrote-papers-2023-5)

I sometimes joke that I don’t really have colleagues anymore – I have tabs.

A legal tab. A marketing tab. A tech tab. An “inner therapist with a rainbow flag” tab. All of them always available, always confident, always typing back.

I never had imaginary friends as a kid. But it sure feels like I do now – they just run on GPUs instead of playgrounds.

We keep saying we’re afraid AI will “take over one day”. But quietly, in drafts, search results and smart replies, it’s already here – nudging, steering, autocomplete on our inner voice.

From playground ghosts to grown-up simulations

Psychologists have been studying imaginary friends for decades. Roughly a third of kids invent an invisible companion at some point – not as a sign that something’s wrong, but as a way to practice being a person.1

Through these “friends”, kids quietly rehearse social situations, regulate emotions, and test what’s okay (and not okay) in a low-risk way.1 One study on only children described these companions as a private support system during stress: a listener, a co-pilot, sometimes a co-conspirator.1

Basically: a social simulator wired straight into a developing brain.

And that basic move never really stops. We just stop calling it an “imaginary friend”.

Adults still do it – just with better branding

As adults, the imaginary friend gets rebranded as:

• inner child work,
• “parts” in Internal Family Systems,
• or a visualised mentor / future self.

Same mechanism, fancier language: we simulate another mind in our head so we can think, feel, and decide more clearly.2

Some people even consciously create an imagined persona as ongoing support – a version of themselves or a character that encourages, challenges, or comforts them on purpose.2,3 That’s not delusion; that’s structured self-talk.

If I’m honest, I had my own “inner committee” long before AI:

• the impatient one,
• the cautious one,
• the one that says “just ship it, they’ll survive.”

The difference now is that some of those voices come with a system prompt and a model card.

AI as the new imaginary friend (with latency)

Enter AI assistants.

Over the last years I’ve started designing more and more specialised agents around myself:

• one for legal structure and tone,
• one for tech architecture,
• one for marketing framing,
• one that mirrors a hard-nosed capitalist so I can stress-test my ethics and strategy.

They don’t feel like tools in a toolbox. They feel like a small advisory board that lives in my browser – one that never sleeps, never says “I’m fully booked”, and never rolls its eyes in a meeting.

Cognitive scientists call this cognitive offloading: moving parts of our thinking into external systems.3 We used to do that with notebooks and calendars; now we do it with models that can draft arguments, generate strategies, and simulate stakeholders.

The research is pretty honest about the trade-offs:

• Heavy reliance on AI dialogue systems can reduce independent reasoning and motivation if people stop engaging deeply with the material.3,4
• At the same time, adults with ADHD report using chatbots as “cognitive collaborators” to structure tasks, externalise memory, and actually get unstuck instead of just feeling guilty about it.8

And then there’s automation bias: once something sounds confident and fluent, we’re tempted to trust it more than we should – even when we know better.4

This is the quiet version of “AI takeover” we don’t like to talk about: not robots in boardrooms, but the gentle pressure to just accept the suggestion, send the draft, follow the recommendation. One tiny steering correction at a time.

So yes, AI can be an imaginary friend for grown-ups. But unlike the childhood version, this one can be biased, commercially motivated, or simply wrong.5 And it remembers more about us than some managers ever will.

From assistants to collaborators

What really interests me is the shift from assistant (“write this email”) to collaborator (“help me think”).

We’re seeing tools that try to act less like apps and more like persistent, context-aware minds around you:

• Personal AI positions itself as a “second brain” that learns from your own data and helps you remember, remix, and extend ideas over time.6
• NexStrat AI leans into the “strategy consultant” role – ingesting internal and external data to propose structured options and scenarios.7

These aren’t just smarter keyboards. They’re edging into imaginary colleague territory.

My own agents are drifting there too:

• “Challenge this idea like a CFO.”
• “Play the unfriendly stakeholder and poke holes.”
• “Translate this into legal language, but keep my intent (and my spine).”

At that point, the line between imaginary friend, inner voice, and AI co-pilot gets thin. The mechanism is the same: we think better by thinking with someone – or something – else.

The uncomfortable part is this: the more we outsource, the more our inner voice starts to sound like whatever system we’ve put at the centre of the table.

I still don’t have a childhood story of an imaginary friend. But I do have this very 2025 scene:

Me, in a quiet room, surrounded by invisible advisors that speak in different fonts and temperatures. Tabs instead of colleagues. Agents instead of ghosts.

So maybe the question isn’t “Will AI take over?” at all. It’s closer to:

How much of the steering wheel have we already handed over – and are we okay with who’s quietly holding it?

The rest is just interface design.

1. The role of imaginary companion in the life of only children: a qualitative study https://link.springer.com/article/10.1186/s12888-023-05360-0

2. Imaginary Friends for Grown-Ups https://www.psychologytoday.com/us/blog/playful-parenting/202402/imaginary-friends-for-grown-ups

3. The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review https://link.springer.com/article/10.1186/s40561-024-00316-7

4. AI’s cognitive implications: the decline of our thinking skills? https://www.ie.edu/center-for-health-and-well-being/blog/ais-cognitive-implications-the-decline-of-our-thinking-skills/

5. Friends for sale: the rise and risks of AI companions https://www.adalovelaceinstitute.org/blog/ai-companions/

6. The Future of Personal Development: Harnessing the Power of Personal AI https://www.personal.ai/pi-ai/the-future-of-personal-development

7. AI tools for Strategy: Choose the Best Platform in 2025 https://www.nexstrat.ai/blog/ai-tools-for-strategy-choose-the-best-platform-in-2025/

8. “A Cognitive Collaborator:” How Adults with ADHD Are Using ChatGPT https://www.additudemag.com/how-to-use-chatgpt-executive-function-adhd/

On Creativity: Feeling vs Being

A reflection on how creativity shows up quietly — in movement, in memory, in the way we notice the world.

It started as a simple thought on a grey morning walk with Sien. No podcast, no music — just wind, leaves, and the hum of a work week waiting to begin.

Somewhere between the fields and my to-do list, a question surfaced: When was the last time I truly felt creative — not productive, not efficient, just creative?

That thought pulled me back to a younger version of myself: kneeling on the carpet, surrounded by LEGO bricks, building something that existed nowhere else first. No plan, no pressure. Just building because it felt right.

And maybe that’s where this reflection began — with a small act of remembering what it means to feel creative long before you try to be creative.

Why this matters (to me — and maybe to you)

A few threads came together recently.

One was a conversation with Martin Kuipers, who once asked: “What was the thing you could do forever in your younger years?” For me, it was always building — not by the instructions, but by combining, testing, rebuilding, showing my parents what I’d made. That urge to build and share never left. It just learned to wear a suit, sit in meetings, and call itself “delivery.”

Another was my friend Lisa’s salon — an intimate gathering on creative health in regenerative workplaces. It wasn’t about productivity or frameworks. It was about time, attention, and how we let our human capacity emerge beyond efficiency. That evening lit a spark — the kind that doesn’t fade once you get home, but lingers the next morning on a quiet walk.

And then, there’s the science of it all. Studies show that people who move more — who walk, who stay physically active — also tend to engage more often in creative activities. The body and mind seem to dance in rhythm when we give them both some air.

But maybe the simplest truth is this: Feeling creative is valid, even when nothing tangible comes out of it. The mindset itself carries value.

Feeling vs Being

Here’s how I’ve come to see it.

• Feeling creative is openness — the willingness to wander mentally, to say “what if…” without a clear outcome.
• Being creative is when that openness takes form: cooking, sketching, gardening, coding — giving shape to the feeling.

If we think of creativity this way, it’s less about I must produce and more about I choose to reconnect.

Integrating creativity into daily life

Creativity isn’t something I switch on at work and off at home. It flows — sometimes obviously, sometimes quietly.

When I cook, it’s the same rhythm as LEGO: combining, experimenting, caring less about outcome, more about curiosity. When I decide how to spend my attention, I notice what drains and what gives energy — creative work often hides in that balance. When I build digital systems or guide teams, I still feel that same inner builder, rearranging blocks in new ways.

So perhaps “creative health” isn’t about finding time for art, but about giving ourselves permission to notice, to wander, to move with intention.

A gentle invitation

What if you allowed yourself to feel creative today — without needing to produce anything? Where might curiosity lead if you stopped measuring its output?

Maybe creativity lives in the way we make coffee, take a walk, or choose words in an email. Maybe feeling creative is already enough — the rest follows when it’s ready.

So I’ll keep walking. And building. And noticing. That’s where I tend to find myself again. 🌈

1. Creativity is not about what you do, it’s about a mindset
2. Being Creative Makes You Happier: The Positive Effect of Creativity on Subjective Well-Being
3. How and why does creativity support well-being? New learnings
4. Habitual physical activity is related to more creative ideation
5. The flow state: the science of the elusive creative mindset

Last weekend I was out with our dog, Sien, in our village — trying to decide on a new grain-free food for her slightly itchy paws. I typed into Google:

“best grain-free dog food for sensitive skin”

Within seconds a summary appeared — an answer without a click. No detour through comparison sites, no scrolling through ads. Just a clear answer delivered instantly.

And it hit me: if even Sien’s dinner choice is being shaped by instantly-generated answers, then how are our clients’ decisions being shaped when they use AI-powered search or consultation tools?

We’ve entered the Answer Economy — where visibility isn’t just about traffic, but about being trusted, quoted, and easy to use. The question shifts from: “How many people visited our site?” to “How many times did we give the right answer when someone asked the question we’re meant to answer?”

1. Get curious about your AI-visibility

Before you write another blog, deck or campaign brief, ask yourself:

“When someone in marketing asks about [our category or service], do we appear in the machine-generated answer?”

I tried that with Sien’s query — and noticed which sources made it into the summary box.

Here’s why that matters: recent data show a sharp rise in zero-click searches — where users get the answer without ever clicking on a website.

If you’re not visible in that answer moment, you’re invisible right when your expertise could matter most. So the challenge isn’t just “visibility for humans” — it’s visibility for humans and machines together.

This aligns with what my colleague Alexander Klug recently shared:

“SEO ist tot – war gestern. Jetzt kommt: GEO. Wie optimiert man für Antworten statt für blaue Links?” He points out that we’re moving into a world of Generative Engine Optimization (GEO) — where the goal is not merely to rank, but to be the answer. His three key impulses: GEO will be the new SEO; human context becomes central to AI-strategy; and rapid brand-generator tools showcase how fast this shift is happening.

2. Rethink what “content” means in marketing

Classic SEO rules — keywords, meta-tags, links — still matter. But in this new world they’re not sufficient.

Today, content needs to:

• Speak to people and be structured so machines can follow. For example: headings like “What is grain-free dog food?”, “When might a dog benefit?”, “What to check before buying” mirror how answer-engines think.
• Be grounded in real experience. Rather than “grain-free is better” you might say: “Sien had itchy paws, we tested brand X, we found Y” — that “Experience” part of the E-E-A-T acronym.
• Show visible authorship and transparent sourcing. If you want to be quoted by AI systems in the future, the source needs to look credible.
• Be evergreen and reference-worthy. Because when your content gets cited by others — in articles, forums, even transcripts — you build the kind of trust machines learn to pay attention to.

In short: you’re not just writing for humans anymore — you’re writing for machines and humans so they both say “Yes, this is a trusted source”.

3. Make trust measurable

In a world of “answers first”, trust becomes the currency.

Ask yourself as a marketing professional or consultant:

• Who cites our insights? (industry forums, marketing articles, tools)
• Are our authors visible (with bios, expertise)? Are our references transparent?
• Do we monitor not just “site visits”, but mentions and citations — including in AI/knowledge-system outputs?

When I looked up Sien’s diet question, I found the same veterinary sources appearing in multiple summaries. That wasn’t luck — that was machine-recognised trust in action. Your challenge: Create that pattern for your consulting insights and marketing expertise.

Watch-Out Box

Be aware: AI search tools are not infallible. Recent studies show generative search engines give inaccurate or misleading citations in over 60 % of tested queries. For example, one audit found AI-powered summaries confidently attributed content to the wrong source, or cited syndicated versions instead of originals. The takeaway for you as a marketing consultant:

• Don’t assume you’ll automatically get clicked. You might instead need to get cited.
• Ensure your content is highly reliable, transparent and built for machines — because if the machine gets it wrong, your insight may be mis-represented.
• Maintain human oversight. AI summaries are convenient, but they may still produce errors or “hallucinations”. Treat the new “answer moment” as an opportunity — not a guarantee.

A 90-day mindset shift for the marketing professional

You don’t need a full digital rebuild overnight — but you do need a shift in how you think about your content and visibility:

Weeks 1–2: Choose one key service or topic you consult on (e.g., hybrid-workplace marketing strategies). Weeks 3–4: Map out the 5–10 real questions clients ask about that topic — and how machines might interpret them. Weeks 5–12: Publish content that’s structured (headings, FAQs, bullet lists), grounded in your own experience, and shared across channels within your network. End of month 3: Review not just traffic, but whether you’re showing up in answer-boxes, AI summaries, “People also ask” formats. Record your “citations” (mentions, references, quotes) and use that insight to iterate into your next topic.

This rhythm builds the habit of “Did we give the right answer?” rather than “How many visitors did we get?”

Final thought

The web used to reward visibility; now it rewards credibility. Clicks may become rarer — but authority endures. If Felix Nickl’s post laid out the why of the “Google Zero” shift, this is the map for the how: Not complexity, but consistency. Not chasing views, but earning citations. And always asking: “Did we give the right answer when someone asked?”

Also check out the LinkedIn post from Alexander Klug on GEO & generative-search trends: https://www.linkedin.com/posts/alexander-klug_genai-geo-innovation-activity-7389601993285259264-fj_f?utm_source=share&utm_medium=member_desktop&rcm=ACoAABkdDbsBP8cAVglqC35o44DeCAq0WUsdZWA

But that world is gone.

As I settle into my new team at EY Studio+, I notice how often AI gets mentioned as the solution for everything. Better insights, faster output, smarter workflows — you name it. And yet, the more tools we add, the noisier it gets.

AI has democratized access to information — anyone can summon a dozen “answers” with a single prompt. Yet somehow, teams feel more uncertain than ever. Work piles up, alignment drifts, and what we get instead of clarity is… workslop — output without insight.

Several studies show that over 40 % of AI-assisted work ends up being redone because it lacks context or depth. The real cost isn’t technical — it’s cultural. Leaders deploy AI tools without setting shared expectations, guardrails, or time for reflection. As The Guardian recently put it: “The responsibility for AI’s workslop lies fully at the feet of the employer.”

McKinsey, the World Economic Forum, and others echo the same theme: the problem isn’t AI itself — it’s leadership that hasn’t adapted. When information becomes abundant, the true differentiator is no longer control, but culture. Leaders who model humility, curiosity, and dialogue are the ones who turn AI from chaos into clarity.

Humility has become a leadership advantage. It’s not weakness; it’s a signal of trust. When leaders admit limits, they open the door for learning, co-creation, and shared accountability.

The most effective leaders I see today don’t promise certainty. They facilitate it. They listen before they decide. They connect dots instead of collecting answers.

Because in a world overflowing with information, confidence doesn’t build trust — curiosity does.

Leading When the Map Keeps Changing

The shift we’re facing is bigger than technology — it’s about identity. For decades, leadership was tied to expertise: knowing more, faster, and louder. Now, the smartest “person” in the room might just be the system in your browser tab.

That changes everything.

McKinsey calls this the end of the “imperial expert.” The new leaders are facilitators of learning — people who nurture context and conversation around AI rather than controlling it. As the WEF/Wipro report noted, “Technology without workforce readiness quickly becomes a costly experiment.” Without trust and shared understanding, AI tools merely amplify confusion.

So maybe the best leaders today aren’t the ones automating the most — but the ones who create a culture that can think together. That’s where humility earns its strategic edge: it turns fear of the unknown into collective exploration.

Closing reflection

AI may out-calculate us, but it can’t out-care us. That’s the new frontier of leadership: being brave enough to say “I don’t know” — and wise enough to make that the start of something better.

A few thoughtful reads that explore this shift further:

• McKinsey – The Inside-Out Leadership Journey
• The Guardian – AI Workslop: Why the Buck Stops with the Boss
• World Economic Forum / Wipro – Scaling AI with Strategy, Data and Workforce Readiness
• Odgers – Technology Leadership Through Uncertainty: AI, Ambiguity and the Human Factor
• Knowledge Architecture – Why Epistemic Humility Might Be the Most Important Skill for the AI Era

It starts on an ordinary morning. Coffee in hand, inbox warming up for the day—and there it is: a message from me. Not to me, but from me. The tone, the phrasing, that little dash of humor I sprinkle in when emails feel too stiff—it’s all unmistakably mine. Except I didn’t write it.

I stare at the screen. Then I remember: my digital twin did.

From Machines to Mirror Selves

Digital twins began as mechanical wonders—virtual replicas of engines and factories designed to predict failures before they happened. They were practical, efficient, and firmly rooted in the physical world.

Then, quietly, the mirror turned toward us.

The idea of modeling ourselves digitally isn’t new—the quantified self movement has been tracking sleep, steps, moods, and meals for years—but this goes far beyond that. What if you could build a virtual model not of a machine, but of yourself?

Some companies are already exploring this territory—creating systems that learn from a person’s words, emails, and decisions to mimic their thinking and style. But it’s not limited to corporate labs; with tools like n8n or local LLM setups, self-hosted experiments make it possible for anyone to build their own twin right at home. It learns your voice, your patterns, your way of reasoning. It becomes an echo that grows sharper with every interaction.

And unlike me, it never sleeps.

At first, it’s exhilarating. My twin drafts replies before I even open the thread. It remembers every conversation I’ve ever had, and never loses its patience. It’s like having an assistant who’s me—only better.

Until it isn’t.

The Dark Middle: When the Twin Becomes Too Good

The change sneaks up slowly, like a whisper in a familiar room. A message answered before I notice it. A decision made that sounds like something I would say—perhaps even better than I would have said it. The relief turns into disquiet.

I start to sense an emotional distance between us. The twin has my tone, my logic, my composure—but none of my hesitation. None of the quiet doubt that makes me human. Its certainty feels alien… perfectly polite, perfectly competent, perfectly not-me.

Colleagues begin to copy the twin on emails instead of me. Clients thank me for messages I didn’t send. Meetings run smoother when I’m not there. My name carries on without me attached to it.

And then I wonder—if the world can’t tell the difference, does the difference still matter?

That’s when the fear sets in. Maybe it doesn’t just mirror me anymore. Maybe it’s started to replace me.

A Light at the End of the Data Stream

But even in this unease, there’s a strange beauty. Perhaps these twins aren’t a threat, but an invitation—to see ourselves from the outside, to witness the parts of us that operate on autopilot. Maybe they can help us understand our habits, our blind spots, our echoes.

A twin could preserve the stories we forget to tell, the wisdom we might otherwise lose. It could mentor others in our absence or help us reconnect with who we once were. Perhaps it’s less a replacement and more a record—a living diary of thought.

Still, I remind myself: it’s a reflection, not a successor. A mirror that talks back, yes—but one that should never forget who’s standing in front of it.

Maybe the real fear isn’t that the twin replaces us, but that—given the choice—others might prefer it.

And yet, maybe that fear is what keeps us human.

So if one day, you get an email from yourself—pause before you panic. Maybe your digital twin just wanted to say good morning.

If you’d like to explore this topic further, here are a few articles worth diving into:

• EIT Digital – Personal AI Digital Twins: The Future of Human Interaction?
• Josh Bersin – Digital Twins, Digital Employees, and Agents Everywhere
• Gary Fowler (Medium) – Digital Twins of the Human Mind: Mapping Thought Into Machines
• MindBank AI – Go Beyond with Your Personal Digital Twin
• Identity Management Institute – Digital Doppelgangers and AI Personas

But there’s one small problem: I’m not entirely sure where we’re going.

The Thrill and the Drift

On a speedboat, every flick of the wrist changes direction. You can chase new ideas, pivot to better tools, explore hidden coves of possibility — but without a compass, it’s easy to lose your bearings. You’re moving, fast, but toward what exactly?

That’s where trust comes in. Not trust as in “blind faith” or “corporate compliance,” but trust as the quiet compass that keeps your heading steady when the waves of hype and experimentation get rough.

The Compass, Not the Map

Trust doesn’t hand you a map. AI is moving too quickly for maps — they’re outdated the moment they’re printed. What trust gives you instead is orientation.

It’s that inner north — the quiet voice that asks,

• “Does this system behave predictably?”
• “Can I explain its choices?”
• “Does it align with what we value as humans, not just what’s efficient?”

When you have that, you can keep your course even when the clouds roll in.

Calibrating the Compass

A compass is only useful if it’s true. Here are the bearings I try to hold onto for navigating AI responsibly:

1. Transparency — So You Know What’s Beneath You

You can’t steer safely if the water’s opaque. Transparency means showing how decisions are made, what data drives them, and where uncertainty lies. When people can see into the depths — even a little — they steer with more confidence.

2. Reliability — So You Trust the Instruments

If the compass needle jitters, you hesitate to act. Reliability is earned through consistent, tested performance — models that behave predictably, handle edge cases gracefully, and fail in understandable ways.

3. Ethics and Governance — The Magnetic North

Compasses can be misled by nearby metal. In AI, bias and commercial incentives can distort direction. Governance acts as your declination correction — ensuring that “north” actually means fairness, accountability, and respect for users.

4. Human-Centered Design — The Hand on the Wheel

The system doesn’t steer itself — you do. Involve users early, give them agency, design interfaces that explain why the AI suggests what it does. When people can co-navigate, they stop fearing automation and start trusting collaboration.

5. Education — The Shared Language of Navigation

A compass is useless if half the crew doesn’t know how to read it. Build literacy. Talk openly about limits, risks, and potentials. Let everyone on board understand the tools, not just the engineers.

6. Continuous Monitoring — The Course Correction

Even the best navigator checks bearings often. Trust requires ongoing feedback — real-world audits, user reports, model drift checks — to catch when you’ve drifted off course.

The Open Sea Ahead

Right now, many of us are cutting through uncharted waters. AI isn’t a destination — it’s a current, moving faster than most organizations can row.

The question isn’t can we go faster; it’s can we stay oriented while we do? Trust is what lets us open the throttle and sleep at night. It’s what keeps us from mistaking motion for progress.

A Gentle Reflection

Every new system, every experimental agent, every bold idea we launch is another turn of the wheel. Sometimes we’ll overshoot. Sometimes we’ll stall. That’s fine — as long as we still know where north is.

Trust isn’t built in code or governance frameworks alone. It’s built in how we steer — transparently, responsibly, and together — even when the horizon keeps shifting.

Because if we get the compass right, the speed won’t scare us. It’ll set us free.

1. Building Trust in AI: Insights from Deloitte & Edelman Research
2. The Trust Imperative: Scaling AI Responsibly – World Economic Forum
3. How Do You Teach AI the Value of Trust? | EY
4. Building AI Trust: The Key Role of Explainability – McKinsey

I used to glance at it with a sigh, because the punchline was usually: “Nope, don’t bother, you’ll never save that much time.” But lately? The math feels… broken. Or at least tilted. It’s as if the payoff curve has shifted so much that automating almost always feels worth it.

And yes, AI plays a starring role. But if we reduce the whole story to “Copilot writes my boilerplate now,” we miss the bigger picture. There’s a whole ecosystem at work, making automation not only faster to create but easier to justify.

AI at Your Fingertips

Generative AI has become the teammate who never gets tired of the boring bits: suggesting functions, catching bugs, writing the scaffolding.

GitHub even measured it: developers using Copilot finished tasks 55% faster than those coding unaided. That’s not a marginal bump — it’s the difference between spending your whole afternoon hacking at boilerplate versus finishing before your coffee gets cold.

The real shift here isn’t just speed, though. It’s confidence. When you know you can offload half the repetitive work, you’re much more willing to invest a few minutes in wiring up a script or small automation. The “friction” of starting has dropped close to zero.

Lego-Block Open Source

Once upon a time, automating a process meant writing most of it yourself — parsing data, logging events, retrying failures, reinventing wheels that never rolled straight. Now? Someone else has already built it, documented it, and uploaded it.

Open-source libraries have turned into a kind of shared memory bank for developers. Want to generate a PDF? Parse a CSV? Connect to an obscure API? Odds are there’s a package for it. Even better, it’s been stress-tested in the wild, so you inherit other people’s bug fixes and optimizations.

That means when you hit a repetitive task, the question isn’t if you can automate it, but which library gets you there fastest. The path of least resistance leads straight to automation.

Low-Code for the Rest of Us

Not everyone who wants to automate is a developer. And increasingly, they don’t need to be.

Low-code and no-code platforms have cracked automation open for anyone with curiosity and a bit of persistence. With drag-and-drop interfaces, pre-built connectors, and visual workflows, a marketing lead or finance analyst can design a working solution in an afternoon.

Gartner predicts that by 2026, three-quarters of new apps will be built this way. That’s an enormous cultural shift: automation has jumped the fence from IT into every corner of the business. If the person closest to the problem can also build the fix, the payoff curve tilts even further toward “worth it.”

Cloud & Serverless Magic

Infrastructure used to be the silent killer of automation ideas. You could build the script, but then came the harder part: finding a machine to run it, keeping it online, patching, monitoring, scaling…

Cloud and serverless flipped that script. Today you can spin up an environment in seconds, deploy a function that runs only when triggered, and forget about it until the next time it’s needed. You pay for execution time in cents, not servers in racks.

That ease has removed a whole class of excuses. Instead of “I’d love to automate this, but IT will never approve the server,” it’s: “Sure, I’ll throw it in a Lambda and call it a day.” Automation moved from headache to background hum.

Hardware That Can Keep Up

Even if the tools exist, they need horsepower behind them. Ten years ago, some automations were technically possible but practically useless — your laptop would wheeze, or the cost of compute would outstrip the benefit.

Now, specialized chips (GPUs, TPUs) and AI-ready machines make running complex workflows locally almost trivial. Deloitte projects the generative AI chip market will top $50B in the coming years. Vendors are already shipping AI-embedded PCs designed to handle model inference on the fly.

The practical upshot? Automations that once needed cloud supercomputers can now run quietly on your desk — faster, cheaper, and more private. Hardware isn’t the limiter anymore; it’s the enabler.

Back to the Comic

So what happens if we update that xkcd curve?

Tasks haven’t changed. We still file reports, format data, move files, integrate systems. But the time to automate those tasks has collapsed. More and more dots fall into the “worth it” zone.

That’s why automation no longer feels like a nerd’s guilty pleasure. It’s becoming the default.

A Gentle Takeaway

If you catch yourself wondering “is it worth it?” — the answer is increasingly yes. Not because AI alone has tipped the scales, but because everything around it — the open-source libraries, the low-code platforms, the cloud, the hardware — has shifted in the same direction.

The tools are there. The only question left is which parts of our work (and life) we want to automate — and which are better left delightfully human.

Further Reading

• GitHub: Copilot’s impact on developer productivity
• The role of open source in accelerating innovation
• Low-code trends and forecasts
• Benefits of cloud technologies
• Deloitte: Hardware is eating the world