Weekly insights about GTM effectiveness, building brand reputation, and AI adoption.
Mark Stouse went to a baseball game. The Diamondbacks were hosting the Dodgers.
Five minutes after the final out, a guy walked back to his seat, looked up at the scoreboard, and said, “Wow! We won! How did that happen?”
That’s what a dashboard gives you: the final score.
Useful, sure. But not enough.
It won’t tell you how the team got there, who carried the play, who blew it, or almost did.
This is what Mark and I got into during our latest Causal GTM Leader session, the one that picked up right where AI needs human logic left off.
Here’s the recap.
Mark’s read on this concept stuck with me: data is the past by definition. Something has to happen before it can be measured, and by the time it’s measured, it’s already history. A dashboard is just a polished way of looking at history.
That wouldn’t matter much if markets held still long enough for last quarter’s numbers to still apply. They don’t, and never have.
Mark and I have made a related case before about why a forecast that starts with the past is already behind. Most of us spent the better part of the last 30 years in something close to a steady state, and we built our instincts, and our dashboards, around that. The steady state is gone. The dashboard hasn’t caught up.
The truth is nobody’s actually being held accountable for what their volumetric numbers mean to the business downstream. They’re being paid for hitting a KPI, not for understanding its ripple effect. So the dashboard becomes a kind of moat — the one story you can tell about yourself that you fully control, especially when the board or CFO starts asking harder questions.
I made a version of this case already, calling it process theatre. Dashboards do the exact same job, just with pretty charts instead of boring meetings.
This is where the conversation with a CEO or CFO actually gets hard. And in that context, the KPI itself is rarely the issue. What matters more is whether it still explains the market you’re selling into.
A lot of teams miss the headwinds, the tailwinds, and the crosswinds in the marketplace because they’re so focused on getting the deal across the line that they never look up. Buyer fear, budget pressure, trust, timing — most dashboards don’t show those forces unless you deliberately factor them in.
Mark put it about as plainly as it can be put:
There is a capital R reality out there that doesn’t negotiate with any of us, that doesn’t care how we feel. It just is.
You can disagree with gravity. You can resent it. But it doesn’t care if you step off a 30 storey building. The market runs the same way. It doesn’t lower the bar because your forecast says it should hold steady, and it doesn’t wait for your dashboard to catch up before it moves.
Most teams already sense the gap between the dashboard and the market. They just don’t want to be the one in the room who calls it out. Because doing that changes the conversation from reporting to accountability.
There’s a single question that separates a useful dashboard conversation from a wasted one:
Am I trying to defend past performance, or am I trying to learn what the best performance going forward would look like? That’s the fault line.
Try it on your own numbers before your next leadership update. Pull up whatever you’re about to present and ask, line by line:
Is this here to defend what already happened, or to help someone decide what to do next?
If every line is in the first column, you don’t have a dashboard. You have an alibi.
This is also where most GTM frameworks fall apart, including ones I’ve pitched myself. We’ve gotten very good at showing the symptom and very bad at showing the cause.
Dashboards show the symptoms of what’s happened in the past. The market is creating the causes — causes we haven’t even planned for. The internal metrics arrive way too late to matter.
By the time the dashboard flags the problem, the window to do anything about it has already closed.
So no, dashboards aren’t useless.
Dashboards matter. But they’re just instruments. They’re not reality.
That’s the distinction I see GTM leaders forget every week. The mistake isn’t building a dashboard. It’s mistaking it for the market.
Before your next leadership update, sort every metric on the page into two piles: defending what happened, or deciding what’s next. If one pile is empty, you’ve found your actual problem.
In your next weekly GTM review, ask the fault-line question:
Are we defending past performance, or learning what good performance looks like tomorrow?
Then watch who flinches.
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I don’t use AI because I want it to think or write for me. I use it because it helps me stay with my own thinking long enough to turn it into something useful.
I have ADHD — mostly the inattentive kind — so my brain doesn’t always move in a straight line. Ideas arrive out of order. Connections show up before the structure does. I’ll know what I mean before I can explain it clearly.
I also deal with what I call delayed intelligence: I often understand what I actually meant five minutes, five hours, or five days after the conversation where I needed the thought.
That gap — between knowing something and being able to articulate it — used to cost me a lot of time and frustration.
AI narrowed that gap.
The blank page was never really the issue. The harder part was catching a half-formed thought, keeping it in view long enough to work with it, and turning it into something another person could follow.
For me, that’s got more to do with working memory than productivity. The scattered thoughts, the threads that disappear mid-thought, the idea that felt clear thirty seconds ago and is now somewhere behind three other ideas — that’s ADHD doing what it does.
AI helps with that. Not by generating content, but by reducing cognitive drag. It gives scattered thinking a place to land so I can actually do something with it.
The real value isn’t prompts. It’s conversation.
I bring a messy idea to ChatGPT or Claude and ask: what am I actually saying here? What’s missing? Where’s the stronger argument? The first answer is rarely the answer. But it gives me something to react to, and that reaction is where my actual thinking shows up. I push back, correct it, reject parts of it, sharpen others. Sometimes the AI finds something I hadn’t said clearly yet, something I’d forgotten, or an angle I hadn’t considered.
That back-and-forth forces structure. Not because the AI imposes it, but because explaining a half-formed idea to anything — even a language model — requires you to give it a shape.
They’re not interchangeable, and I don’t use them that way.
ChatGPT handles research, fact-checking, structure, and stress-testing. If I want a second opinion on an argument or need to pressure-test a claim before I commit to it, that’s where I go. It’s good at examining the idea from the outside.
Claude handles long-form drafting, article flow, tone, and tightening language once the idea has a shape — the iterative editorial work, multiple passes, voice calibration.
For visual mockups and video, pulling YouTube transcripts or summarising long-form content, I use Google’s AI tools.
I still decide what’s true, what sounds like me, what deserves to stay, and what needs to be properly designed.
AI can make unfinished thinking sound finished. That’s the trap.
A clean draft doesn’t mean the idea works. A better sentence doesn’t mean a better argument. AI will organise weak thinking just as readily as strong thinking, and the result looks credible enough to fool you into thinking you’re done.
So I still ask: is this true? Is this mine? Would I actually say this? Did we find the point, or just polish the fog?
That last question is the one that matters. If I can’t answer it, the draft isn’t ready — regardless of how good it reads.
For me, that’s the real value of AI. It gives my thinking somewhere to land, then helps me stay with it long enough to turn it into work I can stand behind.
PS: The em dashes are grammatically correct in case you’re wondering.
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Most GTM teams are using AI now.
That part is no longer interesting.
The real question is whether AI is helping teams make better decisions, or just helping them produce more stuff faster with weaker thinking behind it.
That was the core of the discussion Mark Stouse and I had during our Causal GTM Leader chat on LinkedIn last week (formerly The Causal CMO).
We renamed this series because GTM is a system. Marketing alone was too narrow a lens. Sales, CS, Product, Finance, and the C-suite all belong in the conversation.
Here’s the recap.
When pressure rises, cognitive load rises. When cognitive load rises, people look for the easier path.
That’s where AI adoption gets dangerous.
We are starting to see a lot of evidence... about people surrendering cognition. They’re basically saying, “That’s what AI said, and I’m just going to accept it, believe it, and hope that it’s true.”
Microsoft Research surveyed 319 knowledge workers and found that higher confidence in Gen AI is directly associated with less critical thinking. The more people trust the output, the less they question it.
There is a second problem underneath that one.
People also resist AI that challenges their assumptions. Teams complain about sycophantic AI, but every time developers try to stiffen the spine of these tools, users push back.
We say we want to be challenged. We usually don’t.
The old leadership rule applies: delegation is not abdication. You still own the output. You’re still on the hook for what you’re accepting.
You can’t say the software made me do it.
Gen AI is commonly used for asking things like, What should we do? What’s the best path? That is exactly what it cannot answer reliably. The algorithms inside Gen AI tools fundamentally can’t do that.
Why? Because success is not patterned.
Our outside environment controls 70 to 80% of whether it’s successful or not, and that is constantly changing. Full stop.
Failure, however, is heavily patterned. That is where Gen AI earns its place. Feed it a strategy document and you can punch holes in the logic, surface weak assumptions, and de-risk the plan before you execute.
In a recent example, Mark stress-tested a new strategy for a CMO and CRO. The findings made the boardroom uncomfortable and created pushback. But because the logic was legit, the CEO and CFO were okay with it.
De-risk first. Then execute.
Where Gen AI pattern-matches the past, Causal AI tracks what is actually driving outcomes now.
Mark uses a compass rose as an analogy:
Gen AI operates roughly 100 degrees off true north because it cannot model the external environment. Causal AI gets to around 5 degrees off, and keeps updating. It’s perpetual. Like a GPS. You still have to stay on top of it. You can't use either one as a static snapshot.
We went into the decision logic behind this previously in The Decision Layer.
A lot of AI adoption still rides on a cost-cutting premise: replace people, produce more, spend less.
The narrative around Gen AI has gone way off track.
This whole idea of efficiency and low cost and replacement of people... is just crap.
Why? Because the price end users pay today is heavily subsidized.
The fact that we can do whatever we do with ChatGPT for 20 bucks or even 200 bucks a month is astounding, but it is not real.
Reuters reported that OpenAI expects to spend $50 billion on computing power this year and is targeting roughly $600 billion through 2030. When that cost reaches users, the question changes. Not “how much can we automate?” but “which AI use cases are worth paying for?”
The answer is anything that is provably an improvement in effectiveness. Not output volume. Effective outcomes.
There is another cost in the headcount math.
When you cut the people who carry customer context and market judgment, and that knowledge was never captured, you do not get a leaner team. You get a team running AI on stale inputs with nobody left to question the output.
When the board says “do more,” GTM teams often hear: more activity, more channels, more volume.
That is not what the board had in mind.
The board means: I want more sales opportunities, a higher average selling price, a faster sales motion. I want those things for less money. They’re not talking about activity. And that difference is huge.
Activity metrics do not connect to the three things a CFO tracks: more deals, bigger deals, faster close. If your reporting cannot make that connection, you are speaking a different language.
More on this in GTM Reality Gap and The Causal Bridge.
If there was ever one thing every go-to-market leader needs to think about and take to next week, this is it:
Stop trying to make it all about channel optimization or delivery of the message. Start making it about THE message.
Markets are moving faster than GTM teams can adjust tactics. If you try to follow every shift, volatility turn times will defeat you. You end up fighting the last war.
The answer is evergreen customer strategy. Deep knowledge of what keeps your buyer up at night, what earns their confidence and trust over time.
FUD (Fear, Uncertainty, Doubt) has never been more prominent than now. Buyers want a reason to believe in you before they commit.
And THAT is a brand problem, not a demand problem.
The brand is demand.
They are converging fast. If you are cutting brand investment to fund more lead generation, that trade is working against you.
Write down and confirm what the board actually means by “do more”. More opportunities, higher ACV, faster close, lower CAC. Ask them.
Then check your current reporting against those outcomes.
If the connection is not clear, that is the conversation to have before the next planning cycle.
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Co-authored by Gerard Pietrykiewicz and Achim Klor
A lot of AI adoption conversations start with the same question:
How much time does this save?
Not a bad question. But incomplete.
It assumes the main problem is speed. The work was already happening. The person was effective and just needed to go faster.
What it misses? The work that doesn’t happen at all.
For a GTM team, that looks like the competitive battlecard that’s been on the backlog for the last quarter. The customer story nobody has time to write. The post-mortem that doesn’t happen after a lost deal. The onboarding content that’s always months out of date.
The work is valuable. It just never gets started.
Kristina Khutsishvili, writing in LSE Business Review last February, argues that mainstream AI productivity frameworks, including OECD and IMF models, measure value almost entirely through time savings or quantity increases in output. She calls that a serious limitation. It says nothing about quality or novelty. And nothing at all about work that was blocked, avoided, or too painful to start.
Section’s 2026 AI Proficiency Report found that 85% of enterprise employees aren’t using AI to drive real business value. If time savings is the whole story, most of the investment is measuring the wrong thing.
The better question: what work is now possible that wasn’t before?
There’s a word we use for the weak parts of a draft: slop.
And slop is not exclusive to bad AI output. Humans are just as guilty.
The parts that are unclear, unfinished, or not yet honest. The structure that jumps too fast. The argument that sounds right but isn’t specific enough. The wording that’s polished but doesn’t sound like anyone actually said it.
The first AI draft surfaces all of that, even if you give it context and guardrails. It’s nowhere near the answer. It’s a diagnostic. Something to react to, disagree with, push back against. Once there’s a draft on the page, you can see exactly where the thinking is still soft. And the more critically you think, the more you can see.
That’s much harder when nothing exists yet.
In More Output, Less Thinking, we uncovered how AI removes the friction of producing something; how the danger shifts to producing the wrong thing confidently. The diagnostic only works if someone is still doing the critical thinking.
A Writer’s POV: Gerard wrote six articles in ten years. Not because he lacked ideas, but because writing in a second language from a blank page rarely happened. After building a workflow with AI, Notion, Manus, and n8n, he wrote twelve articles in twelve months. The simple process was faster. It just didn’t work.
AI makes it easier to start. It does not make judgment optional.
The teams getting into trouble aren’t the ones moving too slowly. They’re the ones who removed the human layer and called it efficiency. It’s worth repeating: delegation is not abdication.
HubSpot’s 2026 State of Marketing report notes that more content is now generated by AI than by humans, but most of it is average. Frequency without quality control is how you produce a lot of noise fast.
Frequency is not the win. Better work, done more consistently, is the win.
Our workflow, for example, doesn’t end with AI. Gerard and I read each draft and flag what doesn’t sound like us. We meet and debate about the actual point. The final version gets rewritten until it actually says something.
That’s where the work becomes worth reading. The judgment, the cuts, the rewrite: that’s the point. AI made it possible to get there. It didn’t do it on its own.
Note: We are transparent about how we use AI by adopting the Authenticity Commons Framework. You will always find the relevant marker at the end of every article.
Add these to your AI scorecard:
Where is valuable work not happening? Not because people are lazy. Because the first step is too hard. Think about which assets your go-to-market team keeps deprioritizing: the battlecard, the case study, the lost-deal debrief. That’s where AI’s leverage is highest, and it’s one of the core barriers leaders miss when adoption efforts focus on tools instead of friction.
Is frequency increasing? Are your reps publishing more relevant outreach? Is your team producing fresh proof points more than once a quarter? If the answer is no, ask why the first step is still too hard.
Is the thinking getting sharper, or just faster? AI surfaces the slop. What you do with it is still on you.
An efficient process that never happens is not productive.
An effective process that creates useful work is.
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Achim is a fractional CMO who helps B2B GTM teams with brand-building and AI adoption. Gerard is a seasoned project manager and executive coach helping teams deliver software that actually works.
This article is AC-A and published on LinkedIn. Join the conversation!
“Can we just automate all of Marketing?”
That’s what a CMO was recently asked by the C-suite.
It’s a rhetorical question dressed up as a strategic one. And it reveals exactly the problem Mark Stouse and I unpacked in our recent Causal CMO chat.
The problem is the question sounds like it’s just about Marketing.
It isn’t.
Because if AI can replace the logic of Marketing, it can replace the logic of any function. Including the C-suite. AI does not respect org charts.
The only real moat is the quality of your thinking. That’s the decision layer.
Here’s the recap.
The research Mark and his team have done over the past two years shows that improving your prompt architecture produces a 5x to 10x improvement in output quality.
But this isn’t about writing better prompts. Prompts are just an expression of how you think. If the thinking is shallow, the output will be too. Faster, but shallow.
“The greatest test that AI poses to all of us is our ability, and our willingness, and the time that we devote to improving our own thinking. Our own logic. [Our] ability to think it through, to tell the machines what they need to be doing.”
Classes in logic and rhetoric were quietly dropped from business education roughly 70 years ago. Deemed unnecessary for middle management. That decision is coming due now.
AI only knows what you give it.
“An AI agent is a pale shadow of you.”
That’s the difference between AI and HI.
AI can return answers. It can challenge your wording. It can even ask follow-up questions.
But in a Socratic exchange, AI’s questions are derivative. Human questions can be original.
That matters in GTM. The team’s value is not just using AI to answer faster. It’s asking the questions the tool would not know to ask.
Which means the decision layer (the logic that sits above your tools, dashboards, data, and automation) is the only thing AI can’t supply on your behalf.
This is why starting with decisions, not data, is so important. It’s all about the quality of the thinking before getting into the tactics.
When Mark’s firm takes on a new client, the first models they build have nothing to do with the client.
“The very first set of models that we do for them has nothing to do with them. It has everything to do with really understanding the marketplace in which they are operating.”
That output gets shared across Sales, Marketing, Product, Customer Success, and the C-suite: quantified headwinds, tailwinds, crosswinds. One shared version of “today’s reality” (that’s key) before anyone opens a dashboard.
Without it, Sales has its version of the market, Marketing has another, and Product lives in a third. Calling that a coordination problem misses the point. Each function thinks it’s right. A causal model puts the actual marketplace in the room, and the results are usually a jaw-dropper.
In a headwind environment, effectiveness craters even when the work is solid. Just like a plane has to burn more fuel, headwinds drain the energy out of everything.
“In most cases, the go-to-market teams have peanutbuttered their efforts across so many different things that even with additional spend, there’s not enough juice to get over the top of what’s opposing you.”
That’s why CAC keeps spiking even when teams spend more. Seventy to eighty percent of what drives any GTM outcome sits outside what your team controls. If you’re not modelling it, you can’t explain the results to yourselves, let alone to the board.
Left to its own devices, AI confirms your assumptions and sends you away feeling validated about a plan that hasn’t been stressed. It’s the ultimate Yes Man.
The fix is simple: make it argue.
“Gen AI is hardwired to please you. But it can’t define that. Only you can define that.”
Mark’s “5 AI Peer Review System” loads the same question through multiple AI tools. It sets them against each other. They recognise each other’s digital syntax. What follows is, in his words, “ferocious one-upmanship.” That’s where the useful friction starts.
“AI is not really replacing human intelligence. It is showing us the holes in our intelligence.”
Gen AI’s strengths are asymmetrical. It can’t consciously give you the right answer. But it’s very good at poking holes in what you assume.
The questions to drive toward:
Feed those takeaways into a causal model to understand what’s actually driving the outcome.
And by the way, correlation still plays a role in causal AI. But it’s used for discovery, not as the final readout.
When Mark’s team ran a cross-assumption analysis for a client, loading proposals from Sales, Marketing, Product, Customer Success, and Professional Services into a causal model, something became obvious fast.
Every function had positioned itself as the MVP. I mean, why not? Marketing implied it was most important. Sales said the same. Product lived in its lane.
“In fact, the only reason why a Marketing strategy or a Sales strategy even exists is that there’s not a strong Business strategy. So in the absence of that, people make up other strategies.”
A Marketing strategy without a Business strategy above it is a resource allocation argument dressed up as a plan. Same for Sales. Same for Product. Everyone’s protecting budget and turf because nobody’s drawn the map above them.
When the causal model mapped the network of causes and effects, that became hard to argue with. Sales can’t hit its number without Marketing because Marketing is a non-linear multiplier of Sales productivity. CS protects what Sales closes. And all of it sits inside a marketplace none of them control.
The aha moment? Nobody had a dinghy. They were all in the same boat.
Putting efficiency ahead of effectiveness is the core failure mode of AI adoption right now. Teams are using AI to do more of what they were already doing. But if the underlying motion wasn’t working, speed makes it worse.
“You’ve got to make sure that what you’re doing is effective. That is the first thing before you try to scale it, make it more efficient, all that kind of stuff. Otherwise, you’re just driving faster and faster in the wrong direction.”
The adoption data points in the same direction. McKinsey’s 2025 State of AI report shows 88% of companies now use AI in at least one business function, but only 39% report enterprise-level profit impact. Gartner predicts more than 40% of agentic AI projects will be cancelled by the end of 2027 due to rising costs, unclear business value, or weak risk controls.
Usage is outrunning decision quality. By a lot.
The billions wasted in GTM over the last 20 years weren’t wasted because teams were stupid. They kept doing the same things faster.
“Whatever you’re doing, you’re fighting the last war. And you’re losing the war that’s actually happening in real life as a result of that.”
AI doesn’t fix that. Better thinking does.
Three things worth doing before greenlighting the next AI project:
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Co-authored by Gerard Pietrykiewicz and Achim Klor
Most of us have heard the pitch by now:
“We can replace this team with agentic AI.”
The spreadsheet says it saves money. Leadership pulls the trigger. Headcount goes down. The announcement goes out.
Then the work slows down. Quality drops. Customers feel it. The team that remains spends its days cleaning up mistakes instead of moving the business forward.
Efficient? Maybe. Effective? Not at all.
What you’ve actually done is delete the operating context your AI needs to work.
Most leadership teams miss the human reaction.
When employees see colleagues lose jobs under the banner of “AI efficiency,” they do the rational thing: they protect themselves.
They stop sharing how the work actually gets done. Edge cases stop getting flagged. Nobody says “this breaks every quarter because of X” anymore. Experimenting in public feels like a career risk.
That is exactly the knowledge AI needs to be useful. And it disappears quietly, before anyone notices.
“Context is everything.”
The cut happens in the boardroom. The damage shows up in support tickets, missed handoffs, bad data, and customer calls nobody prepared for. By the time leaders see it, the people who knew the work are already gone.
The tool may be capable. But the operating context has disappeared, and without it, AI gives you generic answers to specific problems with zero context.
That’s a leadership problem (not technology) and why most AI adoption stalls at exactly this point.
According to a February 2026 Careerminds survey of 600 HR professionals, 32.7% of organizations that conducted AI-led layoffs had already rehired between 25% and 50% of the roles they cut. HR Executive, citing Forrester’s 2026 Future of Work research, reported that 55% of employers regret laying off workers because of AI.
A U.S. Express Employment Professionals-Harris Poll survey, reported by HR Dive, found the average cost of turnover has risen to $45,236 per worker.
The rehiring cost is just the invoice. The reset is what kills momentum.
New employees don’t just learn the job. They learn your version of the job: the edge cases, the customer history, the “this looks clean in the system but it’s actually wrong” reality that never made it into documentation. Someone has to teach the AI system what good looks like in your specific workflows.
“Tribal knowledge doesn’t come with the technology.”
In B2B, context lives inside customer relationships.
What was promised. What went sideways last renewal. Who actually makes the buying decision. Which account needs careful handling. Which “small” issue could quietly put a six-figure contract at risk.
None of that lives in your CRM. It lives in the people who manage those relationships.
When they leave, it goes with them. Your AI doesn’t know the history. Your new hire doesn’t either. The customer on the other end notices before you do. A staffing decision in a spreadsheet becomes a revenue problem in the field.
Klarna found this out publicly. The company said its AI chatbot could handle the work of 700 customer service agents. Later, the CEO acknowledged the customer-service cost-cutting push had gone too far and said the company needed to invest more in human support.
McDonald’s learned it in a simpler workflow. It ended its AI drive-through pilot with IBM after mixed results, complaints, and examples of misunderstood orders. The demo looked clean. The drive-through did not.
Building an AI-capable team is harder than buying AI tools. You need people who understand the business and know how to use AI to move faster, analyze better, and handle the parts that don’t need human judgment.
When you lose someone with both company-specific knowledge and AI fluency, you take a double hit. You’ve lost the context holder and the person who could have pulled the rest of the team forward on AI.
And you’ve donated that investment to a competitor. You paid for the learning curve, the mistakes, the AI up-skilling. Someone else collects the benefit.
Demand for skills that explicitly reference AI grew 109% year-over-year, according to Upwork’s 2026 In-Demand Skills report. That talent is scarce. Once it walks, you’re behind on headcount and capability at the same time.
Turnover is the polite word for it. You built their capability and handed it to a competitor.
Counterintuitive as it sounds, if you’re serious about agentic AI, you may need to hold onto more people in the near term. The case for keeping people isn’t soft. It’s that your AI is only as good as the context you can feed it, and right now that context lives in people. The ones who know where the bodies are buried in your workflows are the same ones who can make AI useful.
The same leadership gap shows up in how companies handle AI adoption barriers: unclear expectations, no safe space to experiment, and people who go quiet rather than risk being wrong.
Before you automate or cut anything, ask three questions about every role you’re considering:
Pick one workflow where you think an agent could genuinely help. Have the people who do that work map the real process, including the exceptions. In plain language. In examples. In failure modes.
Start there. And when you do go back to hire, make sure you're hiring for the right reasons.
Agentic AI will change org charts. The companies that get ahead won’t be the ones that cut fastest. They’ll be the ones that kept the context, trained the people, and built the agents around them.
The ones that didn’t will spend the next two years rehiring and wondering why the tools aren’t working.
Announcing efficiency is the easy part. The hard part is showing up six months later when the work still needs to get done.
If you like this co-authored content, here are some more ways we can help:
Cheers!
Achim is a fractional CMO who helps B2B GTM teams with brand-building and AI adoption. Gerard is a seasoned project manager and executive coach helping teams deliver software that actually works.
This article is AC-A and published on LinkedIn. Join the conversation!
