LinkedIn Content Strategy for Enterprise AI Strategists | Netwarm
LinkedIn playbook · Case study
LinkedIn content strategy for enterprise AI strategists: how a New York AI author reached 83 executives and founders in 4 weeks
We read every reaction on 24 posts from a New York enterprise AI strategist and author. 788 people, 83 executives and founders. Here is what brings buyers, what brings peers, and the 5 posts we’d publish next.
Max Sawyer · Netwarm··12 min read
The creator in this case studyA New York enterprise AI strategist and author
Followers32,149
Posts~6 a week
SellsAI strategy, AI agent builds, executive engagements, keynotes and leadership offsites, plus brand partnerships
These numbers are from 4 November. Your audience has already moved.
New executives and founders turn Hot every week and others go quiet. Run your free scan to see today’s version, with the names.
Ideal client: operators, founders and senior leaders who are done with AI conversations that go nowhere and want AI in production.The challenge: The strategist writes for the decision-maker. But a profile about AI attracts other AI people: consultants, builders, marketers and creators react the most. Which posts actually reach the executives who sign the engagement?
Key takeaways
About 1 in 10 readers is a buyer.83 executives and founders reacted in 4 weeks, among 788 people. 573 were AI consultants, builders, marketers and creators.
Posts on AI pilots, workflows and data bring 2x more of them per post.9.4 executives and founders per post, against 4.4 for brand partner posts and 3.7 for privacy guides.
7 of them are Hot right now.They reacted to 5 posts or more. One reacted to 22 of the 24.
01
Who actually reads you, and what the LinkedIn algorithm changed
The strategist posts about 6 times a week: AI pilots that stall, data that isn’t ready, boards that say they’re comfortable with AI, agents in real workflows, privacy guides and brand partner posts. 24 posts between 7 October and 4 November.
The LinkedIn algorithm shows a post to the people who reacted to the last ones, then to people like them. On a profile about AI, that means other AI people: consultants, automation builders, ghostwriters and creators. They react a lot, and they rarely hire an AI strategist. Reach can’t tell them apart from a COO. Who reacted can. We sorted every person who reacted by role.
24posts in 4 weeks
788people reacted
83executives and founders
11came back 3+ times
Audience by roleThe 788 people who reacted, by roleFree
AI consultants, builders, marketers, ghostwriters & creators573 · 72.7%
Everyone else132 · 16.8%
Almost 3 in 4 people who reacted work in the same market as the strategist: AI consultants and trainers, automation and agent builders, LinkedIn ghostwriters and brand strategists. They push reach, and they compete for the same clients.
The 83 buyers are mostly founders and C-level executives of tech and software companies, with chiefs of staff, VPs and directors running operations, finance and real estate businesses. They are the people the profile is written for, and they react less often than peers do.
SeniorityAll people who reactedFree
Founder, owner, C-level33%
Director, VP4.7%
Manager7.1%
Independent8%
Other47.2%
IndustriesExecutives and founders onlyNetwarm
AI, software and tech48.2%
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See all industries and company sizes in your free scan.
Core audience11 executives and founders engaged 3+ times · 7 engaged 5+ timesNetwarm
11 executives and founders keep coming back. One of them reacted to 22 of the 24 posts.See your regulars
Engager company sizeEveryone who reacted, by company sizeNetwarm
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See which company sizes your executives and founders work at.See company sizes
02
What to post on LinkedIn to reach executives and founders
Most AI creators judge a post by its reactions. A strategist who sells to executives should judge it by who reacted.
We tagged each of the 24 posts by topic, then measured the share of executives and founders among the people who reacted, and how many each post brought.
Your topicsWhat your 24 posts were aboutFree
24posts
AI rollout: pilots, workflows and data8
Leaders, teams and AI adoption5
Brand partner posts5
Privacy guides3
Newsletter teasers3
Topics that attract executives and foundersShare of each topic’s reactions from executives and foundersNetwarm
AI rollout: pilots, workflows and data10.5%
Leaders, teams and AI adoption8.9%
Privacy guides8.5%
Brand partner posts8.1%
Newsletter teasers7.7%
One topic brings about 2x more executives per post than another. See which.See which topics
Posts on AI pilots, workflows and data bring about 2x more executives and founders per post than brand partner posts: 9.4 against 4.4, and 2.5x more than privacy guides (3.7).
Posts that brought the most executives and foundersShare of executives and founders in each post’s reactions1 of 5 open
#1Your AI pilot worked. So why hasn’t the business changed?18.8%
#2Your AI has access to company data. So why does it still get things wrong?14.4%
#3In 2021 I had weeks until my son was born and zero maternity leave12%
#4A CEO asks me for an AI win11.6%
#5A company can have dozens of AI tools and still see very little change11.2%
For comparison, the post on employees’ fears when leaders introduce AI reached 2.6% executives and founders, the lowest share of the period, and the AI forecasting post 4.4%.
6 rules for LinkedIn engagement from executives and founders
Each rule comes from this profile’s own numbers or from the posts working best right now for enterprise AI strategists, advisors and authors over the last 90 days.
1
Open on the gap between an AI win and the business. “Your AI pilot worked. So why hasn’t the business changed?” is the question an executive is already asking. A post that starts there speaks to the person who owns the result, not to people who build the tools.On this profile: the AI pilot post brought 18.8% executives and founders, the highest share of the 24 posts.
Put a real room in the first three lines. A board, a CEO, an SVP after an audit. Executives recognise the room before they read the advice. Name the role, never the company.Across the niche: an advisor’s story of an SVP at a $1B company drew 667 comments.
Name the operational problem, not the model. Data nobody trusts, an owner nobody named, a workflow with exceptions. These are problems executives have. Model news is a problem for builders.On this profile: the data readiness post brought 14.4% executives and founders, against 4.4% for the AI forecasting post.
Give leaders the questions to ask. End the post with 4 or 5 questions a leader can take into the next meeting. It turns a read into something they use, and something they save.On this profile: the pilot and data posts, both built around a list of questions for leaders, brought 18.8% and 14.4%, the two highest shares among posts with 50+ reactions read.
Show one workflow end to end, with a number. One company, one process, one result. A concrete before and after reads as proof; a list of AI tools reads as a newsletter.Across the niche: the IKEA post (8,500 roles moved, $1.4 billion a year) drew 742 likes.
In partner posts, lead with the buyer’s problem. A partner post that opens on a real story or a real pain keeps the executives. One that opens on the product loses half of them.On this profile: the Zoom partner post that opens on the weeks before the birth of a son brought 12.0%; the prospecting partner post 6.0%.
When executives and founders read: the best time to post on LinkedIn for enterprise AI strategists
We compared the share of executives and founders by the day each post was published (New York time). 19 of the 24 posts went out between 6 and 8 a.m., so there is no hour to compare. There were no Sunday posts.
Share of executives and founders by posting dayAmong the people who reacted to each postFree
5.9%
Mon
10.5%
Tue
9.5%
Wed
9.1%
Thu
11.5%
Fri
7.7%
Sat
Friday (11.5%, 9.3 per post) and Tuesday (10.5%) brought the highest shares of executives and founders. Monday was the lowest (5.9%, 3.5 per post), and Saturday 7.7%.
Keep the 6 a.m. slot, put AI rollout posts and board stories on Tuesday and Friday, and move partner posts and newsletter teasers to Monday or Saturday, where buyer share was already lower.
05
5 posts ready to publish to target executives and founders
Each post follows one of the rules above and is modelled on a post that worked, from this profile or from the niche. They are written in the strategist’s voice: short lines, a real room, and a question to close.
1Ready to postLeadership story
We hit $115M. Not because of a tool.Rule 1 and Rule 2: open on the gap between a result and the business, with a real room. Modelled on the AI pilot post, the one that brought the highest share of executives and founders.
Hook
The target was $90M in annual sales pipeline. We hit $115M in three months.Everyone wanted to talk about the AI.But the AI was the easy part.The hard part happened before anyone opened a tool:We sat with the sales team and mapped the process by hand.
We asked which tasks needed a human, and which ones only needed to get done.
We made hard choices about what work was worth a person’s time.Then we built.
Then we got it through Infosec and Privacy.
Then we gave it back to the people who actually do the work.That order matters.Most AI plans I see start with the tool and work backwards to the problem.The ones that move the number start with the work and decide where AI fits.Same technology.
Very different result.What’s the first process you would map by hand before adding AI?
Visual · Single imageTwo numbers on a clean background: “Target: $90M” crossed out, “Result: $115M in 3 months” underneath, with the three steps (map, decide, build) in small type.Check before postingUse only the figures you can share publicly about this deployment; keep the client unnamed.
The 5 questions before you scale an AI pilotRule 4: give leaders the questions to ask, then offer them as a one-page scorecard. Modelled on the AI pilot post and on the decision workbooks that get the most requests in the niche.
Hook
Your AI pilot worked. Would you bet your next budget on it?Most leaders hesitate. And they’re right to.A demo runs on clean data, a friendly team and no deadline.
Real operations don’t.Before I help anyone scale a pilot, we answer five questions:1. Can it handle real business data, not the test set?
2. Does it improve the whole workflow, or just one step?
3. Who owns performance after launch?
4. How will we measure business impact, not usage?
5. What happens when something goes wrong?If a team can’t answer one of them, that’s not a reason to stop.
It’s the next thing to fix.I turned these into a one-page AI Pilot Scorecard: each question, what a good answer looks like, and the red flags I watch for.👉 Comment SCALE and I’ll send it to you.Which of the five is hardest for your team right now?
Visual · Document previewThe first page of the scorecard: 5 rows, each with a question, a green “good answer” column and a red “red flag” column.Check before postingBuild the scorecard before posting so you can send it within the day; reply to each comment personally.
The system nobody wants to touchRule 3: name the operational problem, not the model. Modelled on the data readiness post (14.4% executives and founders) and on Stephanie Hills’ NASA story.
Hook
Every AI roadmap I review has a slide with a happy agent on it.None of them have a slide for the spreadsheet from 2014.You know the one.
Three teams update it.
Two of them use different definitions of “active customer.”
Nobody wants to touch it.And that’s exactly what the agent will read first.When AI gets an answer wrong, leaders blame the model.
Usually, the model did its job.
It just worked from data nobody would sign their name to.Before you connect AI to one more system, ask:Which source is the real one?
Who owns it?
Do our teams agree on what the numbers mean?
Would I make a decision on this data without AI?If the answer to the last one is no, the agent won’t fix it.
It will just get you to the wrong answer faster.What’s the one system in your business nobody wants to touch?
Visual · Single imageA sleek “AI agent” box with an arrow pointing into an old, cluttered spreadsheet labelled “customers_FINAL_v7.xlsx”.Check before postingSwap in a real (anonymised) example from your own projects if you have one.
Share your screenRule 2: a real room in the first lines. A follow-up to the board post, the most commented post of the period (249 comments), modelled on Gabriel Millien’s “who owns your AI results” question.
Hook
A lot of you asked what happens after the crickets.Quick recap: I ask a board how comfortable they are with AI. Everyone says pretty comfortable. Then I ask someone to share their screen and build a project in Claude.Silence.Here’s what I do next.I don’t run a training.
I don’t show slides.I pick one decision that board makes every quarter.
We build it together, live, on someone’s screen.
The prompt. The data. The review. The output.Twenty minutes later, two things have changed:They know what AI can actually do for them.
And they know what they don’t know yet.That second one is the real win.
Because an honest gap is something you can plan around.
A comfortable answer isn’t.What’s one decision your leadership team makes every quarter that AI could help with?
Visual · PhotoA real photo from a board or leadership session (faces blurred or from behind), a laptop screen shared on a big display.Check before postingDescribe the session the way you actually run it; change the details if your format is different.
Your team already uses AI. Just not yours.Rule 5 and Rule 6: bring the privacy guides into the executive’s world with one concrete workflow. Modelled on Stephanie Hills’ SVP story (667 comments) and the social apps guide.
Hook
A sales manager pastes a client’s call notes into a free AI tool at 11 PM.She isn’t trying to create a risk.
She’s trying to send the proposal before the morning.That’s how most data leaks with AI happen.
Not through hackers.
Through people doing their jobs faster.I’ve written two privacy guides this fall, for AI tools and for social apps. The question I got most from leaders was the same:“How do I stop this without banning AI?”Here’s where I’d start:→ Ask each team which AI tools they really use. Not which ones are approved.
→ Give them a secure tool that is just as fast.
→ Write down what never goes into a prompt: client data, contracts, payroll.
→ Turn off training on your company accounts. Most tools leave it on by default.
→ Check again in 30 days.Banning AI pushes it into personal accounts.
Giving people a better option brings it back into the open.What’s the one rule about AI and data your team actually follows?
Visual · Carousel5 slides, one per step, with a cover slide: “Your team already uses AI. Just not yours.”Check before postingThe sales manager scene is illustrative: replace it with a real (anonymised) case you have seen if you have one.
Our pick for the strategist: the AI Pilot Scorecard
Our pick for you: “Our pick for the strategist: the AI Pilot Scorecard”The pilot post already lists the five questions to answer before scaling AI, and it brought the highest share of executives. Turn them into a one-page scorecard (question, good answer, red flag), offered for a one-word comment, the way the privacy guides were. It is the second post above, and it starts a conversation with exactly the people who own a stalled pilot.With a Reveal link, every person who downloads it is shown by name, with their role and how warm they are. Your first Reveal link is free.Create your free Reveal link
07
From reactions to clients
Content brings executives to the profile. It doesn’t say when to reach out. Most never comment, so they stay invisible, and a COO who read every post about stalled pilots may have hired someone else by spring.
On LinkedIn, the right moment to start a conversation is when someone keeps coming back. We call that Hot: 5 or more points over 90 days, where a reaction counts 1, a repost 2, a download or a reply 4.
Hot right now7 executives and founders · not contacted yetNetwarm
7 executives and founders are Hot. See who to message first.See who’s Hot
First messageAn opener for each Hot person, based on the posts they readNetwarm
Hi Sam, you’ve been reading the posts on AI pilots that stall. Is one of yours stuck between demo and production?Hi Alex, I saw you reacted to the post on data readiness. Is your team connecting AI to systems this quarter?
A first line for each of them, ready to send.See the openers
Netwarm sends nothing for you. It tells you who and when, and you write the message. It also works inside Claude and ChatGPT: ask “Who’s Hot today?” and get the list.
08
FAQ
For this strategist, posts on AI pilots, workflows and data brought about 2x more executives and founders per post than brand partner posts (9.4 against 4.4). Start from a problem the executive owns, put a real room in the first lines, and end with the questions to ask.
For this strategist, Friday (11.5%) and Tuesday (10.5%) brought the highest share of executives and founders, and Monday the lowest (5.9%). Most posts went out between 6 and 8 a.m. New York time.
This strategist posts about 6 times a week, and 11 executives and founders came back 3 times or more in 4 weeks. Frequency works when most posts carry the buyer’s problem and partner posts stay the minority.
Look at who reacts, not how many. On this profile, 73% of the people who reacted were AI consultants, builders and creators. Group reactions by role, measure the share per post, and follow the people who keep coming back. A free Netwarm scan does this for you.
Yes. Netwarm only reads public reactions to your own posts. It never logs in as you, never asks for your password, and never messages anyone for you.
How we made this playbook
We read the public reactions on 24 posts published between 7 October and 4 November 2026, up to the first 100 people per post, and sorted the 788 people by role. Reposts, duplicates and posts written by other people were left out. Comments were not counted, so engagement figures are a floor.
Niche rules come from top posts by enterprise AI strategists, advisors and authors over the last 3 months. Role groups are approximate; a Netwarm scan matches people to your own ideal client.
