# Paul Storm

Paul Storm writes and speaks about practical business uses of AI, including business process automation, enterprise adoption and marketing operations. Storm also co-runs Dr. Storm Advisory GmbH and publishes DIGITAL STORM weekly, featuring interviews with business and technology leaders.

- Profile: https://b2binfluencer.marketing/creators/paul-storm/
- LinkedIn: https://www.linkedin.com/in/paul-storm
- LinkedIn followers: 670,582
- Topics: artificial-intelligence, ecommerce, agentic-ai, marketing-automation, ecommerce-marketing, enterprise-ai-adoption
- Based on 30 dated posts, latest 2026-10-08

## Summary

Most useful for practical AI workflow and enterprise adoption content, especially interviews and posts on turning pilots into working processes.

- Best for: A workflow automation or AI governance platform sponsoring an enterprise AI adoption series.
- Not for: A consumer product launch needing a high volume short video campaign.
- Check first: Confirm how product and results claims are independently verified, and ask for audience data.

### Takeaways

- Storm argues reusable setup like business memory, connectors and checkpoints matters more than clever one-off prompts.
- Heineken CIO says 95% of GenAI projects die in pilot stage, citing ownership and governance.
- A viktor.com post cites $133,752 in new recurring revenue in thirty days.

## Main ideas

- **Give AI a job, not just a prompt**: Storm argues that teams often have capable AI tools but no clearly scoped work for them. 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.
- **Better AI systems matter more than better prompts**: Storm’s Claude tutorials draw a recurring distinction: results depend less on asking one clever question than on setting up reusable context and connections. They present memory files, reusable skills, connectors, planning, checkpoints, and parallel workflows as ways to make a tool useful across repeated tasks.
- **Enterprise AI needs ownership and preparation to scale**: Storm’s posts frame stalled AI projects as an ownership problem; their interview summaries emphasize sponsorship, data foundations, and governance. 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.
- **Put AI inside the workflow, while keeping people responsible for decisions**: Storm favors AI connected to tools and operational data over another standalone destination. 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.
- **Automation pressure often falls on repetitive back-office work**: Storm challenges the idea that high-profile creative or technical jobs will necessarily be automated first. They point readers toward routine, rules-based work, using labor-market data and a company example to make the consequences concrete.

## Work to read

- [DIGITAL STORM weekly: Heineken's APAC CIO on why GenAI pilots stall](https://www.linkedin.com/posts/paul-storm_heineken-cio-apac-95-of-genai-projects-activity-7493275184830013441-X_uS)
- [Dr. Storm Advisory on the ownership gap behind stalled AI rollouts](https://www.linkedin.com/posts/paul-storm_how-an-agency-added-133752-in-new-recurring-activity-7493637494601674753-MWuL)
- [Most people are using Claude wrong: setup over prompts](https://www.linkedin.com/posts/paul-storm_most-people-are-using-%F0%9D%97%96%F0%9D%97%B9%F0%9D%97%AE%F0%9D%98%82%F0%9D%97%B1%F0%9D%97%B2-wrong-activity-7503421957145608193-LNkz)
- [Microsoft's 30 million Copilot seats and the difference between procurement and preference](https://www.linkedin.com/posts/paul-storm_ai-microsoft-copilot-activity-7496174304032759808-c8Sz)
- [The World Economic Forum jobs data, read through what is easy to automate](https://www.linkedin.com/posts/paul-storm_%F0%9D%97%95%F0%9D%98%86-%F0%9D%9F%AE%F0%9D%9F%AC%F0%9D%9F%AF%F0%9D%9F%AC-%F0%9D%97%BC%F0%9D%98%82%F0%9D%97%BF-%F0%9D%97%B7%F0%9D%97%BC%F0%9D%97%AF-%F0%9D%97%BA%F0%9D%97%AE%F0%9D%97%BF%F0%9D%97%B2%F0%9D%98%81-activity-7479165142589542400-NKnU)
- [Higgsfield AI's API and the shift from using AI to building it into workflows](https://www.linkedin.com/posts/paul-storm_ai-generativeai-apis-activity-7509187499899097088-up51)

## Brand relationships

- **Dr. Storm Advisory GmbH** (owned_business)
- **Apollo.io** (disclosed_partnership)
- **Omnisend** (disclosed_partnership)
- **Viktor** (disclosed_partnership)
- **Accio** (disclosed_partnership)
- **Higgsfield AI** (disclosed_partnership)
- **CoreAIVideo** (affiliate)
- **HEINEKEN** (mention)
- **ServiceNow** (mention)

## Partnerships

Paul Storm's posts feature brand placements and tracked-link promotions, making partnership work visible. The clearest disclosed example is a LinkedIn post of 30 July 2026 labeled a paid partnership with Apollo.io, showing the product working inside a ChatGPT session. Omnisend placements ran as a series of at least four posts between 25 August and 7 October 2026, each carrying the same free-migration offer and link. Discount-code and product links also appear for CoreAIVideo, Higgsfield AI and Accio, and a 13 August 2026 post promotes viktor.com with a $133,752 recurring-revenue case study. The Digital Storm weekly series adds a separate interview format, split between a LinkedIn teaser and a longer Substack breakdown. Ask directly for pricing, disclosure wording and link requirements; no rate card, media kit or partnership inquiry page appears in their posts.

## Alternatives

- Denis Panjuta: Choose Denis Panjuta when the sponsored series is about wiring an AI assistant into a sales and lead-generation stack, and the sponsor is happy to sit inside a long list of named tools. 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.
- Bojan Radojicic: Choose Bojan Radojicic when the sponsor’s buyer sits in finance rather than central IT, and the series needs a build story with controls attached. 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.
- Tahnee Perry: Choose Tahnee Perry when the sponsor’s product is bought by a revenue or RevOps team and the series can be sold under AI adoption while speaking about pipeline. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/