Paul Storm

They recommend starting with an existing task, a defined output, and clear ownership, making AI part of a process rather than leaving it in an unused chat window.

In a post about advisory work, Storm describes teams that try several assistants and run pilots, then stop using the tools because nobody gives them a job with a clear start and finish. They offer an AI employee handling outbound and lead generation as an example, with a person approving its proposal before it goes out.

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They present memory files, reusable skills, connectors, planning, checkpoints, and parallel workflows as ways to make a tool useful across repeated tasks.

One post contrasts opening Claude for a single question with building a system around it, then lists eight features, including a business memory file and connectors to other tools. Another argues that a business may need a small set of connected tools rather than twenty separate apps.

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The guests offer the practical advice, while Storm uses it to argue that adoption depends on how organizations work, not only on which model they choose.

In a summary of an interview with Heineken Asia Pacific CIO Ralph Ostertag, Storm quotes Ostertag’s advice, “Think big.

• Start small.
• Scale fast,” and lists business sponsorship, use-case evaluation, data, and early governance as factors in scaling.
• In a separate post, they call the problem an “ownership gap.”

Storm’s posts diagnose unclear ownership; interview summaries spell out organizational conditions for scaling.

• A summary of ServiceNow transformation leader Dirk Ramhorst’s views also stresses governance, data quality, and preparing people.
• These are Ramhorst’s views, not Storm’s.

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In their examples, AI can speed up analysis or carry out steps, but a person still decides whether a recommendation fits the business. They also question whether software adoption figures show that employees actually prefer a product.

In a post about Omnisend’s connection to ChatGPT or Claude, Storm describes asking AI to recommend customer segments from purchase and engagement data, while leaving the marketer to choose the audience and message. In another post, they argue that Microsoft’s reported 30 million paid Copilot seats show procurement, not necessarily employee preference.

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They point readers toward routine, rules-based work, using labor-market data and a company example to make the consequences concrete.

In one post, Storm distinguishes growing physical and AI-related roles from declining clerical roles, citing the World Economic Forum’s jobs report. In another, they point to accounts-payable automation as a case of software taking on invoice work, rather than the coding work many people expected to go first.

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Anyone deciding whether Storm can hold a serious enterprise conversation should start here, before the tooling posts. It shows the interview series in action.

Storm opens with a claim from Dr.

• Ralph Ostertag, CIO at Heineken Asia Pacific and leader of its global Gen AI Lab: 95% of GenAI projects die in the pilot stage.
• Ostertag’s reasons follow: AI initiatives need business sponsorship and ownership from day one; use cases should be judged on value, feasibility, risk, sponsorship and scalability; data foundations determine whether anything scales; and governance belongs before the build, not after it.
• Storm frames the episode as a grounded look at enterprise AI adoption, including projects stalled by unclear ownership.
• The post links the episode on YouTube, Amazon Music, Apple Podcasts, Spotify and RSS.

For brands planning a campaign

For a sponsored series on enterprise AI adoption, the working shape is already here: Storm supplies the executive and the operating question, while a sponsor’s talking points sit alongside a CIO’s account of stalled projects rather than replace it. Any brief that needs a named customer voice, with governance and ownership as themes, should fit this episode format.

It defines the “ownership gap” language that recurs across the material and shows a client-side result, not just a tool description.

Storm writes in the first person as co-runner of Dr.

• Storm Advisory GmbH, describing a pattern seen across client work: a team subscribes to several assistants, builds a prompt library, runs a pilot, and within six weeks the tool is a tab nobody opens.
• Nothing failed technically; nobody gave it a job with a beginning and an end.
• The prescription is scope, not capability: pick a job that already exists, has a clear output and currently has no owner.
• The post cites Authority Makers founder Nico Torres adding $133,752 in annual recurring revenue in thirty days with no new hires, using an AI employee called Viktor that works inside Slack and returns a proposal for a person to approve.
• It closes with a promotion for viktor.com, including $100 in credits and no card required.
• Ask whether the promotion is paid.

For brands planning a campaign

Sponsors get a reusable framing — give the AI a job, not a prompt — plus a concrete revenue story a brand can borrow. Storm also places a vendor offer directly in the post, so agree in advance how much of that the sponsor wants and where the disclosure sits.

It shows how Storm teaches, promising a concrete setup guide rather than a product recommendation.

Storm opens with an analogy — paying for a Ferrari and then driving it in a parking lot — and argues the problem is the setup, not the model.

• Eight numbered Claude features follow, each with a short benefit line and an outbound link: the memory file, reusable skills, MCP connectors, plugins, plan mode, slash commands, checkpoints and subagents.
• The argument lands on “the AI gap isn't model quality anymore.
• It's user sophistication.”
• The post ends by asking readers to comment “SYSTEM” to receive a setup guide and points to a free newsletter.
• Near-identical text appears in a second post in the same set.

For brands planning a campaign

The post argues that value comes from systems, not tools, setting a clear bar for sponsored content: the sponsor has to deliver an actual workflow or asset, not just a mention. A B2B SaaS sponsor selling an integration, connector or agent setup fits this framing; a sponsor with nothing to hand over is a weaker fit.

Read this to judge whether the writing can carry an argument rather than summarize a press release.

Storm takes a vendor statistic — Microsoft’s reported 30 million paid Copilot seats — and separates it from the usual conclusion.

• Distribution is real: Copilot sits inside Microsoft 365, IT can approve it centrally, and procurement knows the vendor.
• But Storm cites a PagerDuty survey in which 66% of office professionals said they had used AI tools at work even when they believed company policy did not allow it, concluding that a paid seat proves procurement, not preference.
• The closing argument: enterprise AI will not be won by distribution alone; the winning product is the one people reach for before IT tells them to.
• Near-identical text appears in a second post in the same set.

For brands planning a campaign

A sponsor pitching adoption metrics should expect the counterargument in the same post.

• That can be a reason to buy the placement or avoid it, depending on whether the campaign aims to invite debate or avoid it.
• It’s a strong template for a point-of-view piece where a challenger brand wants the incumbent’s dashboard number debated on the page.

Readers focused on the workforce side of AI adoption should start here.

Storm takes the World Economic Forum's Future of Jobs Report headline — 92 million jobs disappearing and 170 million created by 2030, a net gain of 78 million — and splits it into three lists: fast-growing roles in the physical economy, such as sales assistants, drivers and construction trades; AI-adjacent roles, such as data and machine learning specialists, developers and security specialists; and the steepest declines in rules-based work, such as receptionists, legal secretaries, data entry clerks, bookkeepers and contact centre staff.

• The point: clerical roles are not bad, just easy to automate.
• The post ends with three audience-specific actions — reskill, redesign roles around judgment, make workforce transformation the default — and asks readers what they would pivot to.

For brands planning a campaign

A sponsor selling reskilling, enablement or workforce planning can use this structure: one credible dataset, role lists readers can locate themselves in, then actions and a question. The piece also offers a ready-made line — automation is landing on repetitive work first — that a B2B SaaS audience will already accept.

Readers weighing a sponsor whose product is an API or embedded capability should read it alongside the Claude tutorial post.

Storm reports a vendor claim — Higgsfield AI saying it crossed $1 billion in annualized revenue 18 months after launch while powering campaigns for 390 Fortune 500 companies — then turns to the part that interests them: an API giving developers one key to 50-plus image and video models, with no separate integrations and per-generation billing.

• The argument: the next phase of creative AI will be won not inside standalone tools but embedded in products, marketing systems, ecommerce workflows and internal enterprise software.
• The post closes with a link to try Higgsfield and asks whether readers would rather use a tool directly or build it invisibly into existing products.

For brands planning a campaign

For a B2B SaaS sponsor whose value lies in being embedded in someone else’s workflow, this is the closest existing example of the argument the sponsor wants made, already in Storm’s voice. It also shows the vendor-adjacent register Storm uses for commercial posts: a company result, then an industry claim, then a reader question.

Two posts carry the same text: they contrast a user who opens Claude, asks one question, gets one answer and closes the tab with people who "build better systems", then walk through eight features — CLAUDE.md as a business memory file, skills, MCP connectors, plugins, plan mode, slash commands, checkpoints and subagents.

• Storm calls CLAUDE.md the biggest unlock because five minutes of setup saves hundreds of hours of prompting.
• A third post applies the same logic to tool sprawl, arguing that a business needs not twenty AI tools but one system connected to everything.
• It lists roughly twenty connectors grouped by work area: Google Drive, Notion, Microsoft 365, Gmail, Slack, Intercom, Google Calendar, Asana, Linear, HubSpot, Apollo, Clay, MailerLite, Canva, Gamma, Granola, Zapier, Make, n8n and Stripe.

Numbered feature list, with a one-line definition and an external link for each entry, followed by a comment-keyword lead magnet ("Comment SYSTEM and I'll send the setup guide") and a newsletter call to action.

For brands planning a campaign

A productivity or developer tool sponsor can appear in the list alongside unfunded entries: the format treats every connector as a workflow component rather than an advertisement. The comment-to-receive-guide mechanism also gives a sponsor a measurable interaction beyond impressions.

Examples: Source 1 · Source 2 · Source 3

They interview transformation leaders and distill their points into bullets.

• Ralph Ostertag, CIO of Heineken Asia Pacific, is quoted saying 95% of GenAI projects die in the pilot stage and offering "think big, start small, scale fast".
• The interview's takeaways cover business sponsorship; evaluating use cases for value, feasibility, risk, sponsorship and scalability; strong data foundations; and early governance.
• Dirk Ramhorst of ServiceNow is quoted saying "technology is ready, most organizations aren't".
• The takeaways: AI agents execute rather than assist, data quality and governance determine success, and transformation is about preparing people.
• In their own post, Storm says they co-run Dr.
• Storm Advisory, describe weeks spent with companies that "have already bought the AI and cannot get it to do anything", and name the pattern bluntly: organisations do not have a capability gap, they have an ownership gap.

Interview recap built around a named guest and a quoted line, followed by five to six bullet takeaways and links to the full episode on YouTube, Amazon Music, Apple Podcasts, Spotify and RSS.

For brands planning a campaign

This is the strongest existing base for a sponsored series on enterprise AI adoption: the format already brings an outside executive's credibility and a defined takeaway structure. A sponsor can fund an episode or workflow piece and feature its tool as the one that receives a "job with a beginning and an end", language Storm already uses.

Examples: Source 1 · Source 2 · Source 3 · Source 4

One post cites the World Economic Forum's Future of Jobs Report — 92 million roles disappearing and 170 million created by 2030 — and divides fast-growing jobs into physical roles driven by demographics and logistics (shop assistants, delivery drivers, construction trades, food processing, farmworkers) and AI-driven specialist roles (data and analytics, machine learning, software development, security, electric vehicles, renewable energy engineers).

• It then lists the steepest declines in rules-based clerical work: receptionists, legal secretaries, data entry clerks, postal clerks, bank tellers, bookkeeping and payroll clerks, telemarketers and contact centre staff, claims adjusters.
• Another post directly challenges Anthropic CEO Dario Amodei's claim that coding would be automated first, citing Freehand's accounts-payable work for Meta, Apple, J&J, Pfizer and P&G, a drop in invoice workers at Meta from 250 people to 3, and the exposure of the Philippines and India back-office economies.

The job-loss framing is blunt and names two national economies as directly exposed. It suits sponsors comfortable with a hard efficiency message, not those who need an upbeat tone.

For brands planning a campaign

A sponsor selling workflow automation or finance operations software can tie its product to a concrete target — invoice and record-checking work — backed by a sourcing line from the World Economic Forum and named customer examples.

Examples: Source 1 · Source 2

One covers segmentation: ask the AI to read contact data, purchase history and engagement, recommend high-impact segments, and explain why each matters.

• The point: the bottleneck is turning data into a decision fast enough to use.
• A second asks for a monthly executive summary covering what worked, what did not, and how campaigns, automations, revenue and subscribers changed.
• AI removes the scavenger hunt, not the need for judgment.
• A third uses the same connection for a plain-language deliverability health check on bounce rates, spam complaints and unsubscribes, positioning it as an early-warning conversation rather than a subject-line generator.
• A fourth targets switching costs directly, comparing Klaviyo's SMS price increase with Omnisend lowering SMS prices and offering to migrate flows, segments, forms and templates for free in five days.

Each post starts with a familiar manual chore, contrasts it with a specific request typed into the connected assistant, preserves the marketer's decision rights, and closes with the same migration offer and a discussion question.

For brands planning a campaign

This is the account's clearest documented sponsored-series format: a run of same-brand application posts, each tied to one product integration and one repeatable offer. A sponsor can commission a comparable sequence and expect a consistent structure, a defined call to action and an audience primed for operational software.

Examples: Source 1 · Source 2 · Source 3 · Source 4

In a paid partnership post about Apollo.io Anywhere, Storm describes researching companies, finding verified decision-makers, adding contacts to sequences and running outreach without leaving ChatGPT.

• They frame the point as friction removal: "the best software in 2026 won't be the software you open, it will be the software that quietly appears exactly where you're already working."
• A post on Higgsfield AI reports $1 billion in annualised revenue eighteen months after launch, campaigns for 390 Fortune 500 companies, and an API giving developers one key to more than fifty image and video models.
• It argues the shift is from using AI to make content to building generation into existing products.
• A post on Alibaba's Accio Work describes agent teams that analyse product trends, select products, create listings, launch campaigns and run daily operations.
• A demo store reaches $10,000 in thirty days, while a spa owner gets layouts, designs, sourcing, negotiations and logistics from a photo of an empty space.
• A Copilot post argues Microsoft's 30 million paid seats prove procurement, not preference, citing a PagerDuty survey finding 66% of office professionals had used AI tools at work against company policy.

Most posts open with one workflow example in concrete bullet steps, state the broader shift in one line, and close with a direct question to readers.

For brands planning a campaign

For a B2B software sponsor, the recurring frame offers a ready-made campaign angle: show the product working inside a system the buyer already has, rather than arguing for another destination app.

• The Copilot post also shows Storm will make a critical argument about a large incumbent.
• A challenger brand could sponsor it; a Microsoft-facing campaign could not.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

One post argues that tools are commoditised and access is no longer the edge; the winners will be the fastest adapters, who redesign workflows and multiply output rather than effort.

• Another weekly roundup uses the same idea as a hook — "most people are still using AI like a chatbot" — then lists five signals: AI agents as an identity security problem once they can reach email, code, CRM and databases; a football club where workflows, data quality and ownership matter more than flashy models; two competing models costing the same on paper but differing by up to four times on real workload cost; token efficiency as the new pricing battleground; and the unreliability of vendor benchmark charts.
• Storm spells out the structure: LinkedIn gives the quick signal, Substack the full breakdown, and the paid tier the operating model, prompts, tools, frameworks and deals.

Weekly roundup with five short numbered items, each pairing a claim with a consequence, followed by a split between the free signal and the paid system.

For brands planning a campaign

A sponsor can join the newsletter pipeline rather than a single post, using the free LinkedIn item as an entry point and the paid issue for deeper placement. The token-cost and benchmark-criticism angles also suit infrastructure or model-serving vendors whose message is cost per finished task, not price per million tokens.

Examples: Source 1 · Source 2

Posts open with a "">>" marker and a bold stylised headline, often set in unicode glyphs, then break into short numbered or bulleted blocks, then close with a call to action — follow, repost, newsletter subscription or a product link — followed by hashtags.

• Commercial posts are disclosed in the post itself: the Apollo post carries "#ApolloPartner #ApolloAnywhere - Paid partnership with Apollo.io".
• Named products appear across the corpus for different reasons: Apollo, Omnisend, Higgsfield, Accio, Viktor, CoreAIVideo, Claude and Microsoft Copilot.
• Storm also names Storm's own business, Dr.
• Storm Advisory GmbH, and refers to a podcast and newsletter audience in the hundreds of thousands.
• One post is a conference attendance record for Gastech 2026 in Bangkok covering energy demand, LNG, hydrogen and AI for energy, showing the account also carries event and industry-vertical content.

Energy appears in one event post; ask for more examples before treating it as a standing beat.

For brands planning a campaign

A sponsor can expect a predictable format, an in-post disclosure line and a planned CTA stack after the content. The event and vertical posts also point to openness to conference-linked partnerships beyond pure AI software.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5

Storm Advisory GmbH and spend most of their week with companies that have bought AI tools but cannot get them into use. The post credits a client example — $133,752 in new annual recurring revenue at Authority Makers — to a scoped handover of outbound work, not to the tool alone.

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Storm describes researching companies, finding decision-makers and adding contacts to active sequences without leaving ChatGPT. The disclosure is the clearest commercial statement in these posts.

Original disclosure

#ApolloPartner #ApolloAnywhere - Paid partnership with Apollo.io

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Each ends with a link inviting readers to switch or check it out. Ask whether the posts are paid or otherwise affiliated with the company.

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Storm Advisory GmbH, Storm promotes Viktor, an AI employee that works inside Slack and reaches around 3,000 tools, with a signup link and $100 in credits. Ask whether the promotion is paid or otherwise affiliated with the company.

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The post carries a campaign hashtag and offers a limited two-week promotion plus access to Claude Opus 4.6 and GPT 5.4 through their link. Ask for the paid terms.

Original disclosure

#MyAccioWorks

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Ask whether the link is paid or otherwise affiliated with the company.

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The post presents the link as their own referral rather than a paid campaign.

Original disclosure

use my link to get 20% off

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Ralph Ostertag, CIO of The HEINEKEN Company Asia Pacific, on why most GenAI projects stall and what makes the rest scale. The post promotes Storm's newsletter and podcast.

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The post promotes Storm's newsletter and podcast.

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Both creators argue that the value of AI comes from connecting it to tools a team already uses rather than adopting another destination app, and both close posts with a community or resource call to action.

Panjuta's lists sit inside a sales and lead-generation system and end in a paid community, while Storm's connector writing focuses on general workflow design and enterprise adoption. Their commercial pieces are disclosed vendor partnerships tied to a single product.

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One post describes using Claude, Cloudflare and Git/GitHub so a finance team can build its own reporting app, with the rule "code calculates, AI explains" and a human review step before commentary reaches the CFO; another describes auditing a team whose month-end reporting had become a fire drill. Both teach through concrete worked problems and keep a person accountable for the output.

Radojicic's audience is finance and FP&A professionals, and his examples stay inside month-end reporting and forecasting templates. Storm covers marketing, ecommerce operations and enterprise AI adoption more broadly, and carries sponsor-funded posts.

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One post lays out an agent investigation that recovered more than $100,000 in pipeline leakage, splitting the work across agents with clearly limited tasks; another describes building a Claude pipeline-scoring model with defined inputs, a score, a pick and a 90-day plan. Both write numbered, step-by-step frameworks with a stated output for each step.

Perry focuses on revenue operations and broken buyer journeys inside a sales stack, with instructional frameworks for practitioners. Storm ranges across Claude setup, enterprise adoption, workforce change and vendor partnerships.

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His two posts do the same job twice: one lists fifteen Claude connectors for a lead-generation workflow — Apollo.io, HubSpot, Outreach, Clay, ZoomInfo and others — and the other lists twenty Claude tool combinations grouped by communication, projects, design and automation.

• Both lay out the stack, explain that the value appears when the tools are combined, and point readers to a Skool community for step-by-step instruction.
• A sponsor whose product is one of the listed connectors can appear in a category the reader is already scanning, as a single line item rather than the focus of one story.
• If the campaign values volume of named integrations and a repeatable post format over a single deep account of enterprise rollout, this is the cheaper, more direct option.

What a sponsor gives up is the enterprise-adoption argument Paul Storm already publishes.

• Paul Storm's posts about Panjuta name tools but do not name a buyer-side practitioner, discuss governance, ownership or why pilots stall, or give a reason to prefer one connector over another beyond how the workflow fits together.
• There is also no audience, engagement or campaign-performance data published for this account, and the posts contain no disclosed sponsorship, so a sponsor cannot compare reach or judge how a paid placement would be labelled.
• The offer the reader is sent to is a community, not a product trial, which means a sponsor would be supplying the trial mechanic rather than borrowing a proven one.

Examples: Source 1 · Source 2

His October 2026 post walks through a finance team building its own reporting app with Claude Code, Cloudflare and GitHub: rules are explicit, code calculates and the AI only writes words, every figure is reconciled to the trial balance, and a named line owner edits each comment before it reaches the CFO.

• The build also includes company login with MFA, role-based access, a full audit trail and a developer review before real data goes in.
• “Realistic first version,” he writes, is ten to fourteen weeks.
• His other post is a finance-ops audit: teams on seventy-hour weeks, static spreadsheets, broken lookups, three days of manual variance commentary, and templates that turn a fire drill into a short update.
• A sponsor selling an approval or governance layer can use controls vocabulary — human in the loop, review, audit trail — in a creator’s own terms, for readers who already see manual finance work as the problem.

The audience is materially narrower.

• These posts focus on finance and FP&A process, so an enterprise-AI-adoption sponsor would be buying a finance readership.
• The case for handing an agent real work must be made in accounting terms, rather than the general terms Storm uses with CIO and transformation guests.
• The self-build framing is also a tradeoff: readers see a team assembling its own app, which positions vendors as components rather than the product being bought.
• There are two posts, no published audience, engagement or campaign data, and no disclosed sponsorship.
• The calls to action are a template download and a newsletter signup; the post describes the newsletter as reaching 90,000 finance readers.
• A sponsor would need to supply its own product-trial mechanic.

Examples: Source 1 · Source 2

Her August 2026 posts offer procedures rather than commentary.

• One sets out a five-step method for finding lost pipeline with a team of agents: rebuild one buyer journey by hand first, fix a daily snapshot as the source of truth, give each agent a bounded job, add a verification step against approved examples, then turn the diagnosis into a cross-functional fix.
• It names a practitioner at Zapier and a result — weekly pipeline leakage falling from the high six figures to the thousands, with more than $100,000 recovered.
• The second describes a scoring prompt run against a client’s CRM that picks one channel for ninety days, cuts two the author says she was paying for, and sets weekly leading indicators.
• For a sponsor seeking readers who will run the method themselves and use a specific number as proof, this is closer to the buyer’s own task than Storm’s enterprise-adoption interviews.
• The comment-a-keyword ask also gives the campaign an interaction beyond impressions.

The subject is pipeline leakage and channel focus, so an enterprise-AI series sponsor would be paying for a revenue-operations audience and a story in which AI is the tool, not the theme.

• The named company is a software business, not an industrial enterprise, so the proof point speaks to selling motions rather than the CIO, governance and data-foundation territory Storm’s interviews occupy.
• The comparison rests on two posts.
• No audience size, engagement or campaign data is published, and no sponsorship is disclosed, so a sponsor cannot compare reach with Storm’s follower figure or predict how a paid series would perform.

Examples: Source 1 · Source 2