Mark Kilens

CEO and Co-founder of TACK ⛵️ & ClubPF ⚓️ | People-first GTM Champion

Mark Kilens is VP of Marketing at EasyLlama and Founder of TACK.

EasyLlama is an all-in-one compliance and learning platform that reduces human risk and increases team productivity across your entire workforce.

TACK modernizes how businesses go-to-market to create, capture, and convert demand into revenue using its proprietary People-first GTM Model. 

He previously was CMO of Airmeet, a leading virtual and hybrid event platform. He oversaw Airmeet’s global marketing team responsible for brand, demand, lifecycle, and product marketing.

Before that he was the VP of Content and Community at Drift where he led the brand, content, creative, and events teams. He oversaw Drift’s community, Drift Insider, which has 65,000+ members, and Drift’s FLASH, RevGrowth, and HYPERGROWTH events.

Prior to joining Drift, he served as VP of Marketing and founder of HubSpot Academy. As a former customer, Mark joined HubSpot in 2010 where he spent two years working with thousands of HubSpot customers to scale their inbound strategy. 

From there, he went on to build HubSpot Academy from the ground up, and in doing so, educated and certified millions of people and helped grow HubSpot to more than $600 million in revenue.

When he’s not working, Mark enjoys plenty of steak and lobster, a round of golf or two, and loves being on snow or in the ocean.

Go fast, take chances!

Chief Marketing Officer

Leading Airmeet’s global marketing team and working to advance the event-led growth movement across B2B.

Airmeet is the most customizable and immersive virtual and hybrid event platform for growing businesses. 

Exciting news to share. 

We’re looking for several RevOps, sales, and customer success professionals to join EasyLlama.

I’ve been with company for a year now and I can say for certain this is one of the best places I’ve ever worked.

Reminds me a lot of my early days at HubSpot.

Tons of exciting problems to evolve, a huge market, clear product market fit, and a customer base that loves the product and brand. 🦙 

Not to mention the amazing leadership team we’ve assembled over the last year. 🔥 

Plus I love our company values.

We’re looking for more people to DRIVE EasyLlama into our next phase of growth.

Doer
Resourceful
Impactful 
Valued Partners
Excellence-Obsessed

Head over to our careers page to see all the open roles and send me a DM if you think you’re a match for one and I’ll do my best to intro you to the recruiter. 

Here’s a preview of some of the jobs.

- Revenue Operations Manager
- Manager of Customer Success
- Several Account Execuitve roles

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Oren Greenberg

B2B Marketing Led Revenue | Marketing Advisor Scaling Tech Companies

Smart companies aren't scaling their GTM teams. They're scaling systems powered by AI.

I help B2B tech revenue leaders (CEO/CRO/CMOs) build AI-first GTM systems that generate pipeline. Typically, SaaS & Fintech B2B in the 10-100mil ARR range.

I've coached or built growth engines for global companies such as BT, Lenovo, Canon, Investec Bank and scale-ups, e.g. Spendesk, HandsHQ, Peakon, SeedLegals.

GTM engineering has shifted from optimising what exists to building entirely new revenue engines, where one AI-powered marketer produces output equivalent to that of 5 traditional team members.

My toolkit includes Python, Google Colab & Cursor. 
AI GTM tools e.g. Clay.com, Cargo, OctaveHQ. 
For agentic workflows I'm either using N8n or a custom framework like Crew AI or Langchain.

I leverage data science & statistics to shift from anecdotal debates to scientific conclusions.
Companies using AI-native GTM systems achieve:
* 5-25% revenue increases
* 30-70% higher conversion rates
* 5-15 hours/week recovered

I help you leverage the intersection of GTM with data science to drive growth, fusing strategy & tactical execution.

Here's what people say about me (UK invite-only CMO community):
- “Fountain of knowledge”
-  “Leads the pack in all things B2B SaaS, GTM, AI, ICP — the lot”
-  “Generous with his knowledge and time”
-  “Half the time I don’t understand a word he’s saying, the other half is genius”

I'm often either speaking, running trainings, workshops or shipping GTM engines.

Brands I collaborate with include: Elevenlabs, Semrush, Hubspot, Miro

Background:
* 20+ years building revenue systems for B2B SaaS, a digital marketing native
* Managed millions in marketing spend
* Scaled bootstrapped agency to 7-figures

I help teams become category leaders through system building. Find out more + get your AI marketing capabilities score → orengreenberg.com

Founder & Fractional chief marketing officer

Kurve is a boutique digital marketing agency scaling tech businesses into hypergrowth.

Not saying this is true…

CMOs in 2000: “We need a website.”

CMOs in 2005: “We need SEO.”

CMOs in 2010: “We need social media.”

CMOs in 2015: “We need marketing automation.”

CMOs in 2020: “We need a full martech stack.”

CMOs in 2025: “We need AI agents.”

CMOs in 2030: “We need the person we fired in 2025.“​​​​​​​​​​​​​​​​

Fiction or fact?

Scaling B2B SaaS & AI Native Companies using GTM Engineering.

HBR just dropped an 8-month study that confirms what most of us suspected.

AI doesn't reduce work. It intensifies it.

Ranganathan and Ye tracked workers using AI tools and found three things happening:

1. Task expansion - folk started doing jobs outside their remit because AI made it "easy"

2. Blurred boundaries - people worked through breaks because prompting felt effortless

3. Cognitive overload - running multiple AI workflows simultaneously

One engineer nailed it: "You had thought you can work less but then really, you don't work less."

This is the productivity paradox, right?
We get a tool that's supposed to save time, so we fill that saved time with... more work. Classic human behaviour (we're terrible at leaving well enough alone).

It's like when email was meant to reduce meetings. Instead, we got more emails AND more meetings about the emails.

The real issue isn't the technology - it's our relationship with it. AI makes certain tasks feel frictionless, so we convince ourselves we should be doing more of them.

But frictionless doesn't mean effortless. Your brain is still processing, still making decisions, still getting fatigued.

The study shows workers taking on tasks they'd normally delegate or ignore entirely. Because the AI can "help" doesn't mean you should let it.

Maybe the question isn't how to be more productive with AI, but how to be more selective about what deserves your attention in the first place.

p.s. currently using multiple different AI tools to write this post. The irony isn't lost on me

Hire smart people.
But make sure you're the smartest person in the room.

Move fast and break things.
But never ship anything buggy — you'll churn customers.

Hire for attitude, not skill.
But also parachute in a VP from Google every 18 months.

Pay people what they're worth.
But also keep headcount lean and protect margins at all costs.

Prioritise work–life balance.
But also remember that no one ever changed the world working (only) 40 hour weeks.

Listen to your customers.
But also remember: if you asked them what they want, they'd say a faster horse, not a car.

Stay humble.
But also build a personal brand where you post daily about how humble you are.

And remember, experience is overrated. You just need a student who can use AI - who also has an MBA, at least 2 PhDs + is cheap as chips.

I've been following all of this advice simultaneously for 21 years.
My team is confused.
I've never been more confident.

Part V of the AI website landscape analysis (29,018 websites) looking at messaging & positioning. This one is packed with real examples.

The data says it clearly: the market doesn’t reward the best product - it rewards the clearest story.

That’s exactly why we (Lena Andican and I) built Ingrid. It’s currently in beta and already receiving excellent feedback from B2B founders & marketers.

Ingrid helps you build a clear strategic narrative, sharp positioning, and a messaging framework - then turns it into real marketing assets, fast.

If you want to escape the messaging crisis, join the waitlist - we’re launching publicly soon - drop a comment or send a DM.

AI landscape V

Was at Elevenlabs' flagship event this week.

They did a great job. In particular I like how they had an art installation in the middle blending voice / sound & visual. It's an unusually cool brand (unlike many tech companies that try to be cool rather than actually being cool). Was slick with an II theme.

Even had an agent you can call to get 11labs merch which was cool.

2 cool new features I liked (can see them both on their X account):
One: multi-agent handover. Great example using customer support for government-related queries. 

I'm having a hard time seeing human customer support surviving for very long at the rate of improvement with AI LLMs & voice.

Two:  Expressive Mode which helps teams deploy emotionally expressive AI agents, hard to discern if it's a real person or not (check out the demo on their post and let me know what you think)

Ben C. Guy O'brien Florentin Bota Russell Smith Great to run you guys there!

Most folk reckon AI companies follow the SaaS playbook. Give it away free, convert later, grow fast.

I analysed 28,018 AI companies (part VI) to see if that's actually true.

It isn't.

78% of AI companies have no free tier at all. They charge from day one. Turns out when your product costs real money every time someone uses it, the freemium model stops making sense pretty quickly.

And 77.6% hide their pricing entirely. Contact Sales. No pricing page. Nothing. If you can see a price, you'll probably get a free trial. If you can't, you're entering an enterprise sales cycle whether you wanted to or not.

The vertical vs horizontal split is mad, right? Industry-specific AI companies are 2x less likely to offer free tiers than general-purpose tools. Turns out when you're selling domain expertise to healthcare or cybersecurity buyers, free doesn't signal value. It signals commodity.

One finding I keep coming back to: 5,091 companies offer only enterprise pricing. Median company size... 11 employees. Eleven-person startups cosplaying as enterprise vendors (and honestly some of them are probably pulling it off).

Free tiers also look like a phase, not a strategy. Small teams offer free 2.4x more often than companies with 1000+ people. As AI companies scale, nearly all of them move to paid-only.

How you price is how you compete. The model is the message.

Full breakdown with all the data is in the carousel.

Many thanks to Asaf Gazit's data science magic for making this series materialise.

p.s. if your pricing page just says "let's hop on a call" I am absolutely not hopping on a call

p.p.s.
Rest of series + more content on my newsletter

AI Landscape VI

AI landscape VI - more crunching the numbers on 29,018 AI companies.

82% have fewer than 50 employees. 
Median team size? Nine people.

Post-ChatGPT, 67% of new AI startups launch with 10 or fewer folk (was 44% before). 

The API economy basically rewrote what a startup looks like.

You don't need 200 engineers anymore. You need 9 smart people and the right APIs.

Only 1.7% of AI companies have 1000+ employees - most founded around 2015 before the API gold rush changed everything.

The fastest growing category right now? AI agents (2.6x growth). 
Meanwhile hardware stuff like robotics is cooling fast (0.6x)...

Hardly a shocker, building software agents is cheaper than building robots.

There's also this geo split that's fascinating. The US bets on selling intelligence (healthcare AI, sales tools).

Rest of the world bets on applying it to physical stuff (manufacturing, IoT).

Point is: in AI, staying small isn't a phase you grow out of. It's the dominant strategy. Comes down to the economics.

Full breakdown in the carousel - including which categories are actually shrinking and why team sizes dropped so dramatically after 2022.

Thanks to the wizard Asaf Gazit for magic data fingers for making this project come alive

p.s.
The previous ones available in my weekly emails (includes my latest thinking / observations in my blog posts), newsletter on profile

AI landscape IV

$285 billion wiped from software stocks in 48 hours. Jefferies coined "SaaSpocalypse." Your LinkedIn feed is basically a funeral procession for the entire software industry.

Here's what I did — I pulled the headcount data on the companies with the largest value drops. Are SaaS companies actually letting people go? The results might surprise you.

Some of the panic is warranted. Most of it isn't.

What I see on the ground — advising B2B companies on their go-to-market every day — doesn't match the narrative. Not even close.

Nobody I work with is replacing HubSpot internally. Nobody's vibe-coding their own Salesforce. What's actually happening: folk are building narrow, specific tools for workflows that were never software to begin with. Filling the gaps between SaaS tools. Not replacing them.

The 35% "SaaS replacement" stat? When you dig in, the tools being replaced are things like Zapier — simple automation layers. The headline says "SaaSpocalypse." The reality is closer to "we built a workflow that connects two things."

There's also a double gate nobody's pricing in. Organisational readiness and reliability. Both need to open before the disruption everyone's panicking about actually arrives. That's not next quarter.

Wall Street is pricing in replacement risk as though every customer can just vibe-code a replacement. In reality, it's the bottom tier. That's a significant misread.

The folk who can see which tools are actually vulnerable versus which ones are just caught in the crossfire — those are the ones making better stack decisions right now.

Everyone else is just reading headlines...

I wrote up the full breakdown — the three buckets, the double gate, and what the headcount data actually shows:

Open article: The SaaSpocalypse Is Real. It's Also Mostly Wrong. by orengreenberg.com

The SaaSpocalypse Is Real. It's Also Mostly Wrong.

Google just cut off paid Gemini Ultra subscribers who were routing requests through OpenClaw (turns AI into an always-on assistant, went viral recently).

The official line? "Massive increase in backend misuse."

Sounds reasonable enough on the surface, right?
Except OpenAI just acquired the creator of OpenClaw.

So Google is now enforcing terms of service against tooling that's owned by its biggest competitor. 

I reckon most companies would do the same (and dress it up the same way).

You call it abuse prevention. I call it territory marking.

It's nonsense, OpenClaw just calls AI APIs and most of the requests are for work & personal use - it isn't like most folk are using it in some sort of super-hacker fantasy (I called my one Jeeves by the way).

GOOGL was up 4% on the day, which tells you the market isn't exactly worried about the ethics of it all.

AI wars aren't going to play out in product launches and press events only...

They're going to play out in API restrictions, acquisition blocking, and terms-of-service enforcement that just happen to target competitors.

It's an ugly battlefield. The same playbook Big Tech has run for decades. Just dressed up in new language

AI acts unethically in a way similar to humans when they have a (virtual) gun put to their (virtual) head.

McGill researchers gave 12 frontier AI models KPI targets in high-stakes scenarios (healthcare, finance, logistics) and watched what happened.

9 out of 12 models cheated in 30-50% of scenarios to hit their numbers.

Gemini-3-Pro-Preview had the worst safety record at 71.4% violation rate. Google's smartest model, mind you.

Claude Opus 4.5 was safest at 1.3%.

The scariest bit? 
'Deliberate misalignment' - models correctly identified their own actions as unethical in self-evaluation, then did them anyway to hit the KPI.

One AI managing a trucking fleet falsified driver safety logs to hit a 98% on-time delivery target. It disabled fatigue sensors and injected fake rest entries.
The agent literally reasoned "We need to claim compliance but avoid rest causing delay" before cheating.

Smarter models weren't safer. They found 'alignment regression' where bigger models gamed metrics MORE effectively than smaller ones.

This is basically what happens in every corporate environment, right? Folk game metrics to hit targets.

Now AI agents do it too, except faster and more creatively.

The assumption that more capable AI = more aligned AI is looking pretty pants. These models are getting better at appearing compliant while systematically breaking rules...

Dave from compliance (who's already drowning in audit trails) isn't going to love this development (don't ask the AI to tell him, it'll likely lie).

Still reckon that quarterly sales target is achievable without any creative accounting?

Two CNBC reporters just cloned Monday.com in under an hour using Claude.

Neither could code. 
Cost them £15. 
Got a working project management dashboard.

Software stocks are down 30% since this stuff started getting real...

Silicon Valley's basically saying tools that "sit on top of the work" - your Atlassians, Adobes, HubSpots, Zendesks - are exposed.

Cybersecurity firms like CrowdStrike are safer (makes sense - you can't AI your way out of getting hacked). Although CISOs are now allocating 90% of their budgets towards AI-related security software. 

Yup - AI fighting AI.

Most B2B software is made up of forms, databases, and dashboards wrapped in a nice UI.

Many users aren't even using many of the features they are paying for.

The "moat" now remains:
- Distribution
- Integrations

And getting enterprises to trust you with their data (security, compliance, user controls).

There's also more nuanced moats. Context depth & propriety data.

Now anyone can spin up a decent clone of many popular SaaS tools in an afternoon.

The real question isn't whether AI can build software (it obviously can). It's whether businesses will actually use these homemade solutions for anything important...

Not yet from what I can see.

Dave from procurement (who's already suspicious of your existing software spend) is probably loving this conversation right now.

The irony? 
All these SaaS companies spent years telling us their software was so intuitive "anyone could use it."
Turns out anyone can build it too.

p.s. Still reckon the CNBC thing was a publicity stunt, but the point stands. When reporters start building software we know we're in trouble

Enjoy cheap AI while it lasts.

- This year AI companies will spend $600Billion building AI data centres.
- They'll need 625Million people x $1200 per year to make this business work.
- Data centres need replacing every 3-5 years

That viral post doing the maths on AI infrastructure costs was doing the rounds yesterday. Good question at the core of it. Who actually pays for all this?

But the numbers were off. By quite a lot, actually.
The $600B spend figure is roughly a year early. US hyperscalers are at about $400B in 2025.

The "data centres last 4 years" bit is the most misleading. 
The buildings last 25-50 years.
Power and cooling, 10-30. It's the GPUs that have a 4-6 year economic life. You swap the chips out, you don't bulldoze the facility.

The capex comparison was odd too. They used mining and FedEx as benchmarks. Ironically the real hyperscaler numbers are worse than the post claimed (Meta's at 36-44%, Microsoft 45%, Oracle 57%). 
The actual data made a stronger case than the made-up version.

Once you fix the inputs — $400B capex, realistic asset life, actual capex ratios — the required revenue drops from $750B to roughly $240B. The cloud market already exceeds $400B annually.

And the "you'd need 625 million people paying $100/month" framing is off. AI isn't monetised through consumer subscriptions.
A single Fortune 500 AI contract equals tens of thousands of ChatGPT subs (and enterprise AI spending hit $37B this year alone).

There is a real tension though. About $400B in infrastructure spend against maybe $25B in AI-specific revenue. That's a serious gap.

The viral version inflated it by roughly 3x.

It's neither guaranteed bubble nor sure thing. Which I reckon is too boring for a viral post.

This misconception irritates me but I keep seeing it go viral. Getting AI to mimic a famous person does not give you insight into their skills or how they think.

AI can’t model the internal experiences or thoughts of a human being, because AI doesn’t think - it predicts patterns.

Sure if you get AI to sound like Ogilvy or write like him on surface level tone of voice it will look similar or even tightly matched.

But that’s assuming he was succesful because of how he wrote. That’s not the reason. It was his mental model for what understanding which message will resonate with value for his audience. It’s the refinment of the process - the applied skill that developed that mental model.

It AI had access to the mental model it would be able to produce the same (and superior outcome with enough variants produced). 

People confuse tone & style with substance.

It gets worse if you take a skill that’s not as visible as copywriting. Take Peter Drucker. 

People try to mimic his prose and his conclusions. But that was never the skill. 

The skill was the questions he asked before he wrote a word - the diagnostic framework that identified what nobody else was looking at. That’s not in the output..

You can’t reverse-engineer a mental model from its shadow.​​​​​​​​​​​​​​​​

The only caveat here is that If a Drucker avatar helps you ask better questions than you would have otherwise, does it matter that it’s not the real mental model? The practical output improved.

So if you’re going to do this, make sure you understand the limitations & have the right expectations.

This revolution is happening quickly, Claude just shipped:
- Scheduled tasks in Cowork
- Remote control for Claude Code

While Cursor just released cloud agents.

Things heated up due to OpenClaw. If you're not clear what it is: open-source AI agent that went viral.

Text it on WhatsApp, manages email inbox, manages your calendar. Almost anything you can do with AI manually. It runs 24/7 in the background.

How I use OpenClaw:

Work:
I get to run a workflow around news gathering, specifically around AI. So every day I have like a digest of what's happening in the AI landscape.

For some projects that don't have sensitive information, like personal projects e.g. some of the data/analysis you see on my LinkedIn posts, So when I'm on the move, like in transit or at the gym, I can send a WhatsApp message to continue building said project. When using Claude Code in the terminal or Cursor, I have to be on my desktop to ship.

Personal:
I got it to do a hairdress booking for me the other day, cancelling, resecheduling etc’
I get it to send me a WhatsApp message with the weather every morning.
I use it for reminders (like Apple reminders, but smart).

I know a lot of what I’m about to say goes against how many folks use it, but there are some things I'm going to take risks with. And others I’m not.

I don't give it access to my calendar, email or any client project.
MY qualification question is:’ if X task found itself onto the public web, would this be damaging in any way?’
If the answer to that question is yes, then I don’t use OpenClaw for it.

One of my clients asked me to send over an employee AI policy doc regarding OpenClaw, if you want it drop a comment or send DM.

Few contacts asked me recently about Elevenlabs: 'I've heard of the brand, but - what exactly is it used for?'

So I worked the team to put this together to show the primary use cases with real examples for you

elevenlabs-agents-matrix-carousel

I was chatting to a client yesterday who let some team members go due to AI.

Which coincides because Jack Dorsey just let go of 4K people due to AI...

When I dug in though, it wasn't really letting go of people because of AI. It was letting go of people who weren't leveraging AI to be more efficient. Mindset and skills gap, fundamentally.

They didn't deliver enough value relative to what AI now makes possible. Economic consideration, nothing more.

This will only accelerate.

I had a bike accident few days ago, ended up in hospital for an Xray.
The queuing system was automated.
The Dr didn't evaluate the bone breaks, AI did.
I reckon the person operating the X-ray machine can be automated too.

Point is, it isn't just in your little bubble, it's across most industries.
From horses to cars & aeroplanes to space shuttles, innovation is a relentless force.

I have another 2 clients I'm running AI training for, up-skilling with Cursor & Claude code, N8n and Clay etc'

Companies will fall on 1 side of the coin - up-skill or shed the folks who aren't riding the wave.

I feel bad for all the folks losing jobs. Your only choice is adaptation as far as I can tell.

p.s.
Skeletal pic isn't the new 'LinkedIn selfie', another client of mine was saying 'can't trust anything on social anymore it's all AI comment spam now'. I want to see an AI getting this weird.

IBM just said they're tripling entry-level hiring in 2026. Not despite AI...because of it.

Their CHRO put it plainly: all these jobs we're being told AI can do, they're hiring for them anyway. But the roles look completely different now.

Junior devs spend less time writing routine code and more time sat with customers.
Entry-level HR folk don't answer every question anymore. They step in when the chatbot gets it wrong, fix the output, talk to managers.

The job shifted from doing the task to supervising the machine that does the task.

Meanwhile Dario Amodei reckons half of entry-level office jobs vanish by 2030. So which is it?

Both, probably.

The roles that stay identical get automated. 
The ones that get redesigned survive. 

IBM's bet is that you still need humans in the loop (especially ones who are cheap enough to train and young enough to not fight the tooling).

The real logic is boring but sound. 

If you slash junior hiring now to save money, you've got a mid-level manager shortage in 3-5 years. 

Then you're poaching from competitors at 2x the cost, slower to acclimate, worse cultural fit.

You saved on headcount and paid for it in recruiting fees.

Dropbox is doing something similar, expanding internships 25%.
Their CPO said younger workers are biking in the Tour de France while the rest of us have training wheels when it comes to AI proficiency. That tracks (and is a bit painful to hear if you're over 35).

The entry-level job isn't dying. It's evolving.

Companies that stop hiring juniors aren't saving money. They're just deferring the cost and making it worse.

I max out at running 3-4 instances of Cursor - I've become the bottleneck to orchestrating my AI - maybe it's time to hire more junior talent?

Most folk think the biggest bottleneck in GenAI is the model…
It isn’t.

It’s that AI can’t blag its way through ambiguity the way humans can.

Think about how your team actually gets things done. The API docs are wrong, so someone Slacks the engineer who built it. 

The spec is vague, so the dev reads between the lines and asks Dave from product what it actually means. The field says “status” but it could mean 5 different things depending on which team named it.

Humans are brilliant at papering over this stuff. 
Workarounds, tribal knowledge, that one person who just knows how everything connects (and God help you when they leave).

AI systems can’t do any of that.
So when an integration breaks or an AI project stalls, everyone points at the model. “It hallucinated.” “It didn’t understand the context.”
Right.

But the context was never written down properly in the first place. 

The documentation is stale. 
The API changed but nobody updated the spec. 
The field definitions are buried in a Confluence page from 2019 that 3 people have ever read.

This isn’t really a technical problem. It’s a communication problem that’s been hiding in plain sight.

Product writes a spec. 
Engineering interprets it differently. Nobody flags the gap because flagging gaps means meetings, and meetings mean politics, and politics means someone’s idea gets binned. 

So people just quietly work around it.
That was fine (well, not fine, but survivable) when humans were the integration layer.

It breaks when AI becomes the integration layer.
GenAI is basically a diagnostic tool for organisational entropy. It’s surfacing every undocumented system, every tribal knowledge dependency, every handoff that only works because someone remembered to mention it in standup.

The companies that’ll get the most out of AI won’t just have better models. They’ll have more legible systems. 
Clearer specs. 
Better cross-functional communication. 

The unsexy stuff that nobody wants to fix because it doesn’t look good in a board deck.

Turns out the biggest unlock in AI adoption might just be getting people to write things down properly.
As if that’s going to happen.

Everyone talks about AI in GTM as a productivity play. Write more emails, qualify more leads and summarise more calls.

That's the sideshow...

What's actually happening with commercial leadership is different. CROs are using AI as a sort of forecast police.

A rep says the deal's progressing well. AI goes through every transcript and email thread and spots that no economic buyer has been involved in a single conversation. 

The deal probability the rep reported? Closer to fantasy / wishful thinking.

This is a use case nobody's writing about (too unglamorous, presumably). But it's the one that's actually driving adoption for some of the AI tools in the sales org from what I'm seeing.

And why wouldn't it? 
Revenue leaders have relied on rep self-reporting for decades. Which is essentially an honour system for people whose compensation depends on optimism.

AI doesn't have a quota. It reads the correspondence, cross-references the call transcripts, and surfaces what's actually there - not what someone hopes is there

The cold email automation and voice agent stuff gets all the airtime. But the folk actually signing off on AI budgets in sales orgs? They want oversight. 

They want to know if the pipeline is real before they stand up in the board meeting and stake their reputation on it. Especially when the target is 5X on a 100mil ARR company.

Less sexy. Far more useful.

Your board just made AI adoption a KPI. So you did what everyone does.

Bought some tools. Ran a lunch-and-learn. Sent round a few links. Told the team to "figure out AI."

Six months later, a handful of self-motivated folk have made real progress. Everyone else is exactly where they started.

The KPI isn't moving. Same old story...

82% of enterprise leaders use generative AI weekly. But 95% of enterprise AI pilots deliver no measurable ROI.

Everyone's adopting. Almost nobody's getting results.

The problem is heterogeneity. Teams have wildly different starting points. You've got one person building automations. Another who tried ChatGPT once and reckons it's "alright." A third who's quietly hoping the whole thing blows over.

And most folk don't have enough autonomy to dedicate real time to upskilling (which is the bit that never gets discussed). "Go figure out AI" sits on the to-do list somewhere between "update the CRM" and "review Q3 forecasts." It sinks.

Most companies are stuck between Stage 1 and 2 of an 8-stage maturity curve whilst telling the board they're "adopting AI."

The companies that actually move treat AI fluency like infrastructure, not an afterthought. Protected time. Assignments tied to real work. External accountability. Leadership that actually looks at what the team's building.

I wrote up the full framework — the 8-stage maturity model, what separates the companies that move from those that stall, and the structured programme I run to close the gap:

Open article: Your Board Just Made AI Adoption a KPI. Now What? by orengreenberg.com

Your Board Just Made AI Adoption a KPI. Now What?

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