LinkedIn Content Strategy for Industrial Software CEOs | Netwarm
LinkedIn playbook · Case study
LinkedIn content strategy for industrial software CEOs: how a London CEO reached 70 executives and directors in 5 weeks
We read the reactions on 8 posts from a London CEO of an industrial software company. 570 people, 70 executives and directors. Here is what brings buyers, what brings peers, and the 5 posts we'd publish next.
Max Sawyer · Netwarm··10 min read
The creator in this case studyA London CEO of an industrial software company
Followers34,715
Posts~2 a week
SellsIndustrial software: digital twins, operations and asset data, and industrial AI for energy, process and manufacturing companies
PeriodSep 29 – Nov 3
Ideal client: executives and directors at energy companies, utilities, manufacturers and data centre operators, plus the partners and analysts who shape their technology choices.The challenge: The CEO writes for the leaders who run plants, grids and AI factories: op-eds, reports, video blogs and deal news. Colleagues, sales teams and engineers react first and in large numbers. Which posts reach the executives who choose the software?
Key takeaways
These numbers are from 3 November. Your audience has already moved.
New executives and directors turn Hot every week and others go quiet. Run your free scan to see today’s version, with the names.
1 in 8 readers is an executive or director.70 C-level leaders, VPs, directors and founders reacted between 29 September and 3 November: 12.3% of the 570 people we read.
Project and deal news brings 2x the share.Announcements (a live AI factory site, an acquisition): 18.5% executives and directors. Industrial AI essays and video blogs: 8.3%.
Reach is big, regulars are few.Only 2 executives came back to 3 posts or more. One of them reacted to 4 of the 8, including both posts of the last 7 days.
01
Who actually reads you, and what the LinkedIn algorithm changed
The CEO posts about twice a week on industrial AI, energy policy and the company's own projects and deals: 8 posts between 29 September and 3 November, from an FT op-ed on grids to a video blog on trust in AI.
The LinkedIn algorithm shows a post first to the people closest to the author, then to people like them. For a CEO, that first circle is the company itself: colleagues, sales teams, partners. Reach can't tell them apart from the executives the company sells to. Who reacted can. We read the first 100 reactors on each post and sorted them by role.
8posts in 5 weeks
570people reacted
70executives and directors
2came back 3+ times
Audience by roleThe 570 people who reacted, by roleFree
Engineers, technicians, students & everyone else260 · 45.6%
The largest group is the company's own circle: 240 people from the group's teams or in sales, marketing and communications roles. They give each post its first push, and they don't buy the software.
The 70 executives and directors are the readers that matter: CTOs, chief digital officers, VPs of industrial and process automation, managing directors at engineering and consulting firms, founders of industrial AI and data centre companies. Some sell to the same customers, as partners or competitors. A further 260 engineers, technicians and students read along: future talent, and the people who use the software every day.
SeniorityAll people who reactedFree
Founder, owner, C-level11.4%
Director, VP8.4%
Manager16.5%
Independent4.9%
Other58.8%
IndustriesExecutives and directors onlyNetwarm
Industrial software, automation & AI40%
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See all industries and company sizes in your free scan.
Core audience2 executives and directors engaged 3+ times · 0 engaged 5+ timesNetwarm
2 executives came back to 3 posts or more. One of them reacted to 4 of the 8.See your regulars
Engager company sizeEveryone who reacted, by company sizeNetwarm
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See which company sizes your executives and directors work at.See company sizes
02
What to post on LinkedIn to reach executives and directors
A CEO's posts get reactions from everyone inside the company. The question is which posts also reach the executives outside it.
We tagged each of the 8 posts by topic, then measured the share of executives and directors among the people who reacted.
Your topicsWhat your 8 posts were aboutFree
8posts
Industrial AI4
Projects & deals2
Energy & grids1
Leadership1
Topics that attract executives and directorsShare of each topic’s reactions from executives and directorsNetwarm
Projects & deals18.5%
Energy & grids15.5%
Leadership8.8%
Industrial AI8.3%
One topic brings 2x more executives than another. See which.See which topics
Posts about real projects and deals bring 2.2x the share of executives and directors that industrial AI essays do: 18.5% against 8.3%.
Posts that brought the most executives and directorsShare of executives and directors in each post’s reactions1 of 5 open
#1A live digital twin at a 1.2 GW AI factory in Portugal20%
#2Living digital twins and the S in SCADA19%
#3Supercharging the industrial intelligence portfolio17%
#4Software is energy infrastructure15.5%
#5Radical collaboration starts with clarity8.8%
For comparison, the “Trust, but verify” video blog, the most-liked of the 8 posts with 1,288 reactions, reached 1% executives and directors among the first 99 people we read.
6 rules for LinkedIn engagement from executives and directors
These rules come from what worked on the CEO's own profile, and from the best-performing posts by energy and industrial technology leaders on LinkedIn over the last 3 months.
1
Open with a claim an executive could argue with. “Software is energy infrastructure.” Four words that a utility CEO either agrees with or wants to debate. Put the history and the context after it, not before.Across the niche: Siemens Energy's CEO opened on “Germany has a serious offshore wind problem!” and drew 4,074 likes and 223 comments.
Show a real site, with its numbers. A named site, its size and what changed there beats a vision statement. Executives react to proof they can picture: gigawatts, partners, a date.On this profile: the 1.2 GW SINES campus post had the highest share of executives and directors, 20%.
Give the takeaways, not just the link. Executives won't open every report. A numbered list of what it found lets them use it straight from the feed, and makes the link worth clicking.On this profile: the MIT Technology Review post listed five checks in the post itself and reached 19% executives and directors. The tokenomics report post, with no takeaway in it, reached 5%.
End on a question industrial leaders can answer. “Check out my short video blog” asks for a click. A question about their own plant or grid asks for an opinion, and comments carry a post further than likes.Across the niche: Siemens' CEO opened and closed on a question about where AI creates impact: 907 likes, 78 comments.
Put the cost in the leader's terms. Token costs, energy bills, time to first output: one number a CFO or COO already tracks makes industrial AI a budget conversation instead of a technology one.Across the niche: Cisco's Jeetu Patel listed token economics as one of three CEO worries about AI: 1,193 likes, 155 comments.
Explain a deal through what customers can now do. Deal news reaches executives when it says which problem it solves for them: design data that reaches operations, one view from the product to the plant.On this profile: the acquisition post reached 17% executives and directors, on 695 reactions.
When executives and directors read: the best time to post on LinkedIn for industrial software CEOs
We compared the share of executives and directors by the day each post was published. With 8 posts, no posting hour has 3 posts or more, so there is no hour to compare yet.
Share of executives and directors by posting dayAmong the people who reacted to each postFree
12.7%
Tue
20.0%
Wed
8.4%
Thu
15.5%
Fri
Wednesday's one post brought the highest share (20%), and Friday's one post 15.5%. Thursday's three posts averaged 8.4%: the two industrial AI posts published that day were the lowest of all.
With 2 posts a week, the topic matters more than the day: give Wednesday and Friday to project news and energy posts, and keep the industrial AI essays for Tuesday with a takeaway list and a question at the end.
05
5 posts ready to publish to target executives and directors
Each post follows the rules above, in the CEO's voice. Check the facts and names with the team before posting: the real site and the real numbers are what make them land.
1Ready to postProject story
What changes at a live AI factoryRules 1 and 2: a claim up top, then a real site and what changed there. Modelled on the SINES post, the highest share of executives on the profile.
Hook
An AI factory is a power plant that happens to make tokens.That is the lesson of the SINES Data Campus in Portugal.At 1.2 GW, it is one of Europe's largest and most sustainable data centers. Together with Schneider Electric, Nscale, Start Campus and NVIDIA, we are putting the NVIDIA Omniverse DSX Blueprint to work in a live environment.What changes when the digital twin is live, not a slide:→ AI workloads and energy management are planned together, not by two teams
→ Engineering decisions are simulated before they reach the site
→ Operations see the same model the designers built
→ Every change has one source of truth across the lifecycleThe hard part was never the GPUs.It was making design, power and operations speak the same language.Infrastructure needs to move at AI speed. That starts with treating the whole campus as one system.If you run a data center or an energy-intensive site: which team owns the decision when compute and power compete?
Visual · Single imageA real photo of the SINES campus or the digital twin view, with the 1.2 GW figure as the only text.Check before postingConfirm the four “what changes” points with the project team before posting, and that every partner is happy to be named.
Software counts as grid capacityRules 1 and 4: a claim executives can argue with, then a question about their own grid. Modelled on “Software is energy infrastructure” and Christian Bruch's offshore wind post.
Hook
Europe is counting grid capacity in kilometres. It should count it in decisions too.Yes, we need more clean generation, millions of kilometres of new transmission lines and upgraded distribution.But capacity is also the ability to see what the grid is doing and act on it in time.Today, most of that ability sits in software that is booked as an operating expense. Not as infrastructure.The result:→ Utilities can fund the cable, but struggle to fund the model that tells them where it is needed
→ Regulators reward assets, not coordination
→ Industrial users stay outside the picture, even when they could shift demandThe EU Grids Package is the moment to change that. Incentives, investment models and standards that treat software as part of the grid, not an overhead on top of it.The network is the computer. The grid is the network.If software counted as capacity in your next grid plan, what would you fund first?
Visual · Text onlyNo image. The question at the end does the work.Check before postingCheck where the EU Grids Package trilogue stands on the day you post, and update the sentence if it has moved on.
Five checks before AI runs a plantRules 3 and 4: the takeaways in the post, a resource at the end. Modelled on the MIT Technology Review post (19% executives) and Jan Rosenow's numbered report summary.
Hook
Before an AI agent touches a plant, it should pass five checks.“Trust, but verify” is easy to say in a keynote. On a refinery, a mine or a power plant, it has to be a list someone can sign.Here is ours:1/ Context
The AI works from the plant's real, current data, not a copy from last quarter.2/ Inspection
A human expert can see each recommendation, and the data behind it, before it is acted on.3/ Simulation
Agentic actions are run in the digital twin first, and validated there.4/ Governance
The rules and constraints of the site are enforced by the system, not by memory.5/ Closed loop
Engineering, operations and maintenance see the same result, and learn from it together.If one check is missing, the AI is not ready to run unsupervised. It is still a pilot.Supervisory control has always put a human expert in charge of the plant floor. Industrial AI should keep it that way.I turned these five checks into a one-page checklist for operations and digital leaders. Want it? Comment “VERIFY” and I'll send it.
Visual · Single imageThe checklist itself: five numbered checks with a tick box each, the company logo small at the bottom.Check before postingPut your Netwarm Reveal link on the checklist so you can see which executives open it. Make sure the five checks match how the product team describes them.
What one digital thread means for a plant managerRule 6: explain the deal through what customers can do. Modelled on the acquisition post (17% executives).
Hook
A pump fails on a Sunday night.The design file is in one system. The maintenance history in another. The energy data in a third. The engineer on call opens all three and starts guessing.That is the problem a digital thread solves.When engineering, operational and service data are connected and put in context, the same question gets one answer:→ How was this asset designed to run?
→ How has it actually been running?
→ What did we change last time, and did it work?That is why the plan to bring PTC's product lifecycle capabilities together with AVEVA's industrial operations software, and Cognite's knowledge graph and AI agents, matters to our customers.Not for the org chart. For the engineer at 2 a.m.AI is only as good as the context it works from. A unified data fabric, from design and build to operate and maintain, is that context.How many systems does your team open to answer one question about one asset?
Visual · Single imageA simple diagram: three separate boxes (design, operations, service) becoming one line from product to plant.Check before postingBoth transactions were pending approval when this was written: check their status and the approved wording with legal and communications before posting.
Price the tokens before you scale the agentRules 5 and 4: the cost in the leader's terms, a question at the end. Modelled on Jeetu Patel's “Three worries every CEO should have about AI”.
Hook
Before you roll out an AI agent to every site, do you know what one decision costs?Most industrial AI business cases measure the return: less downtime, less energy, fewer trips to site.Few measure the investment per decision.A pilot on one site hides it. Scale the same agent across every plant, every shift, every alarm, and the token bill becomes a line in the operating budget.Three questions we ask before scaling:1/ What does one recommendation cost, end to end?
2/ Which decisions are worth that cost, and which are not?
3/ What runs at the edge, and what needs the cloud?This is what I call Pragmatic AI: AI innovation balanced with tangible business value.As AI moves out of the lab and onto plant floors, leaders need a solid grounding in tokenomics.What would you cut first if your AI costs doubled next year?
Visual · Text onlyNo image. If you add one, a single chart of cost per decision against sites deployed, illustrative and labelled as such.Check before postingThe three questions are written for you: check they match the approach in the Tokenomics report before posting.
Our pick for you: “The Trust-but-Verify checklist for industrial AI”Built from the five checks the CEO already listed in the MIT Technology Review post (context, inspection, simulation, governance, closed loop), on one page operations and digital leaders can print. Offer it at the end of post 3, with a Reveal link to see which executives open it.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 VP who reads every post about digital twins in November may have picked another vendor by the 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. In this sample, 2 executives came back to 3 posts or more and none has reached Hot yet: on a profile this large, a full scan of every reaction shows who is closest.
Hot right now0 executives and directors · not contacted yetNetwarm
0 executives and directors 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 Daniel, you've read our posts on the digital thread for a few weeks. Is connecting design and operations data on your plan for next year?Hi Sofia, I saw you reacted to the post on AI factories. Are power and compute planned by the same team on your sites?
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
Real projects and deals, explained through what changes for customers. On this profile they brought 18.5% executives and directors, against 8.3% for industrial AI essays. Keep the essays, but put the takeaways in the post and end on a question.
Here, Wednesday brought the highest share of executives and directors (20%) and Thursday the lowest (8.4%). With 8 posts the sample is small: the topic changed the share more than the day.
About twice a week is enough when each post carries one real project, one claim or one useful list. 8 posts in 5 weeks reached 70 executives and directors.
Look at who reacts, not how many. Group reactions by role, measure the share of your buyers 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 8 posts published between 29 September and 3 November 2026, up to the first 100 people on each post, and sorted the 570 people by role. Comments were not counted, so engagement figures are a floor. The 3 November post was 2 days old.
Niche rules come from top posts by energy and industrial technology leaders over the last 3 months. Role groups are approximate, and some executives work for partners or competitors; a Netwarm scan matches people to your own ideal client.