# Corey Tate

Corey Tate creates LinkedIn content about AI news and practical ways businesses and marketing teams can use AI, from operational workflows to creative production.

- Profile: https://b2binfluencer.marketing/creators/corey-tate/
- LinkedIn: https://www.linkedin.com/in/coreytate
- LinkedIn followers: 4,113
- Topics: artificial-intelligence, ai-adoption
- Based on 11 dated posts, latest 2026-10-16

## Summary

Corey Tate is most useful for practical AI education on operations, model risk, and creative production workflows.

- Best for: A B2B AI consultancy keynote or workshop on operational execution and practical creative AI workflows.
- Not for: A creative AI vendor wanting a paid comparative benchmark review with measured claims and a short-form video demo.
- Check first: Confirm past session recordings, references, audience analytics, and commercial terms before booking.

### Takeaways

- AI demos are over; the next contest is operational execution.
- Reliability, not the top benchmark score, determines which model a company should run.
- Use creative AI to speed up production, then hand off to professional editing tools.

## Main ideas

- **AI demos are over; the next contest is operational execution**: Corey Tate argues that the AI conversation has moved from models and benchmarks to deployment. Companies win by securely spreading AI across workflows, employees, and customer interactions before competitors do. The post lays out four priorities: cybersecurity as the foundation, because each autonomous agent is another doorway into the business; workflow automation as the first source of measurable returns, removing repetitive work rather than replacing whole departments; autonomous customer experiences that raise the bar to the best AI experience a customer has had; and workforce investment in AI literacy, with humans overseeing the systems. Tate pushes back on job-replacement narratives, saying AI agents remain far from replacing most jobs and that small and midsize businesses would rather keep staff and raise productivity with AI tools.
- **Reliability, not the top benchmark score, determines which model a company should run**: Corey Tate argues that production AI depends on predictable behaviour: validated workflows, security reviews, internal testing, compliance, and prompts that will not suddenly behave differently after a model change. Organisations are deliberately choosing to run one generation behind the newest frontier model, treating reliability as a competitive advantage rather than a sign of falling behind. A vendor's release schedule is part of a customer's operational risk.
- **Use creative AI to speed up production, then hand off to professional editing tools**: The post offers a practical guide to using Adobe Firefly for images, video, audio, voiceovers, and design assets. The process is consistent: generate early to explore directions cheaply, fix and extend assets after production, repurpose a strong asset across formats, and prototype video before committing budget. Firefly speeds up production; conventional tools such as Photoshop or Premiere Pro handle the polish. The aim is to cut repetitive production work and return time to strategy, storytelling, and creative direction. The audience includes creators, marketing teams, agencies, e-commerce brands, social media managers, freelancers, small businesses, and YouTubers.
- **Move fast with creative tools; move deliberately with frontier models**: Read together, the posts reveal a tension worth understanding. Corey Tate encourages teams to adopt generative tools in day-to-day creative work and start early in the process, but cautions against chasing the newest frontier model. The dividing line is production risk: a thumbnail concept carries little downside, while a model change in a live process brings security, compliance, and behaviour-change risks. That distinction makes the advice more useful to marketing and operations teams than a blanket position would be.
- **Weekly tracking focused on transparency and content distribution**: A weekly scan of AI news distills developments into one-line items, each linking to a longer blog edition. The selection reveals priorities rather than offering a neutral feed: two themes recur—provenance and disclosure, and platform changes that reshape how content reaches people. The coverage also leans toward tools that affect content, marketing, and social teams.

## Work to read

- [The Next AI Race Is Operational Execution](https://www.linkedin.com/posts/coreytate_the-next-ai-race-is-operational-execution-activity-7484940206488580096-OATI)
- [How To Use Adobe Firefly As Your Full Creative AI Studio](https://www.linkedin.com/posts/coreytate_how-to-use-adobe-firefly-as-your-full-activity-7477337580573782016-QLZ4)
- [The Upcoming ChatGPT 5.6 Release Isn't Just an AI Upgrade. It's a Risk Test.](https://www.linkedin.com/posts/coreytate_the-upcoming-chatgpt-56-release-isnt-activity-7480953962054017026-XutI)
- [The Week in AI News — July 27–31, 2026](https://www.linkedin.com/posts/coreytate_the-week-in-ai-news-july-27-31-2026-activity-7488926481964785665-R_5z)
- [The Week in AI News — August 3–7, 2026](https://www.linkedin.com/posts/coreytate_the-week-in-ai-news-august-3-7-2026-activity-7491466930852085760-J3Ft)

## Brand relationships

- **Promethean AI** (disclosed_partnership)
- **Adobe** (mention)
- **OpenAI** (mention)

## Partnerships

Corey Tate’s posts combine AI news and tool explainers with promotion of the Promethean AI services business, and one explicitly invites social media collaborations. The posts do not publish a sponsorship policy, rate card, deliverable list, audience specification or dedicated partnership page. Brands can approach Tate about a paid collaboration, but should ask directly about commercial terms, turnaround, disclosure practices and available formats. Early scoping is therefore essential, unlike with a creator who publishes a media kit.

## Alternatives

- Raphael Guilhem: A stronger choice when the buyer wants a hands-on creative production session rather than a strategy framework. Raphael Guilhem walks through a full AI ad pipeline: upload a product, use a search-enabled model to study the brand and write a creative brief with a matching persona, then generate the video with Sora 2. A second post shows a Claude-based skill that turns a static ad into motion design while keeping text and product typography intact, and explains why describing what is not visible at 0.0s makes elements pop in. For a marketing team that needs to see an actual toolchain produce assets on the day and weigh an AI stack running roughly $100–$1,000 per campaign against hired creators at $50,000-plus a month, this is closer to the production work than a conceptual adoption talk.
- Cole Medin: A stronger choice when the audience is technical leaders who build and run AI systems inside a software organization, not marketing teams. Cole Medin describes running an AI software factory where a GitHub issue goes in and a pull request comes out, with holdout tests the builder never sees, parallel workstreams, and a manager agent that flags genuine blockers every couple of hours. A second post argues the next big thing in AI coding is exactly this factory model and that it can maintain a shipped app the creator has never read. For a workshop on making coding agents reliable and designing human oversight into an engineering pipeline, this is directly on point.
- Nick Maciel: A stronger choice when the session must center on paid media and performance creative — Google Ads, Performance Max, and Demand Gen — and how generative AI changes ad production and testing. Nick Maciel lays out numbered playbooks: use AI image expansion and background generation inside PMax to turn three to five product photos into fifty-plus assets, structure creative into blocks the system can mix, run a control-versus-variant experiment and read asset ratings, and review AI images for off-brand colors, odd hands or faces, and false product claims. A marketing team focused on shipping and testing more ad creative would find this more immediately usable than a broad adoption framework.

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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/