# Cory Cachola

Cory Cachola helps consultants turn their distinctive ideas into a clear, repeatable storytelling system. They also write about how AI is changing B2B strategy, business operations, and the value of marketing.

- Profile: https://b2binfluencer.marketing/creators/cory-cachola/
- LinkedIn: https://www.linkedin.com/in/corycachola
- Topics: artificial-intelligence, startup
- Based on 18 dated posts, latest 2026-10-06

## Summary

Cory Cachola is useful for sharpening AI positioning and messaging around operating-model redesign.

- Best for: A B2B SaaS campaign to sharpen enterprise AI positioning and messaging.
- Not for: Consumer packaged-goods paid social and short-form video campaigns.
- Check first: Ask for recent audience breakdown by industry, job function, and seniority.

### Takeaways

- AI is a structural market shift, not a bubble or fad.
- Adopt AI by redesigning one workflow, not by announcing AI use.
- AI commoditizes utility, so identity and narrative become the moat.

## Main ideas

- **AI is a structural market shift, not a bubble or a fad**: Cachola's most repeated argument is that debating whether AI is overhyped misses the point. They call the “AI bubble” framing intellectually lazy because it gives executives an excuse to postpone the redesign their institutions already need. Even firms that never call themselves AI companies are now standing on a new playing field. Cachola treats AI as a market-structural shift, like the internet, the PC and the smartphone before it, and argues that the honest question for leadership is whether they are paying attention, taking it seriously and acting—not whether the hype is justified.
- **Guest framing: “AI is not a technology shift, it is a market shift”**: Cachola relays and endorses an argument from a webinar rather than claiming it as their own. The post attributes the line—“market shifts don't reward optimization, they reward reinvention”—to Laura Stevens, speaking for the Board of Innovation about how AI disrupts business models. Cachola uses it to challenge the common “AI will free humans from boring tasks” pitch and argue that AI changes what customers perceive as valuable, not just who the competitors are. They connect this to category creation, which most B2B marketers avoid because it is risky and poorly understood, and to Amazon's continued heavy spending on AI infrastructure despite near-term shareholder pressure.
- **Adopt AI by redesigning one workflow, not by announcing you do AI**: Cachola's practical method is deliberately narrow. They argue that useful AI adoption is an operating-model decision, not a branding exercise, software procurement cycle or top-down declaration. The work starts with diagnosis: identify where judgment belongs, where documentation must improve, where decision support can accelerate work, and where manual drag is quietly eroding service, consistency and capacity. Only then do they recommend isolating one consequential workflow, defining what matters and building institutional confidence from observed results—making the advice more operational than a general call to adopt AI.
- **Storytelling should model a system, not decorate a message**: Cachola argues that the standard advice to make AI stories emotional or lead with customer pain is incomplete in a pre-AI world. What's missing, in their view, is metaphor rooted in real system design—a way to understand how something works before explaining it technically. They distinguish slogans from system models and urge marketers to become systems thinkers who describe futures that are actually unfolding.
- **AI commoditizes utility, so identity and narrative become the moat**: Cachola's commercial argument is that when a competitor can spin up a clone of a solution, useful features are no longer a moat. The alternative is to reassemble the offer around identity rather than utility: connect a shared belief to a person instead of a feature to a pain point, and build a lasting story instead of a one-off transaction. They apply this to B2B, anticipating and dismissing the objection that identity and narrative are consumer tactics that don't transfer. They make the same case about content: AI has destroyed the premium on effort-heavy assets like LinkedIn infographics and pushed it higher up the stack, to conviction and clarity.
- **AI entered a content system that was already built to reward cheap output**: Cachola argues that AI did not corrupt social media; it slotted into a system that spent decades rewarding constant posting, fast responses, confidence and shareable packaging. The consequence is uncomfortable for writers: polished content is no longer evidence that someone read closely or thought deeply. They distinguish two uses of AI—adding more content to the feed, versus using it to explore a question, test ideas and find connections—and reject the anti-AI position that this is avoidable or reversible. The tension in their writing is clear: Cachola is not anti-AI, but is unimpressed by AI-saturated output.
- **Layoffs are about the economics of task execution, not only greed**: Cachola pushes back on the popular explanation that recent tech layoffs are purely corporate greed or a COVID over-hiring correction. They concede both—that companies over-hired and the cuts can reasonably be called greedy—but argue that ignoring the mechanics keeps leaders stuck in denial. Their claim: AI changed the unit economics of task execution, allowing a company to cut thousands of roles without the product breaking or users leaving. Cachola frames AI as a connectivity technology for humans and machines, and says tomorrow's leverage differs from the leverage of the last cycle.
- **The founder is part of the infrastructure, so sustaining them is strategy**: One post shifts from market analysis to personal sustainability, which Cachola frames as a business argument rather than a wellness one. They reject the equation of belief with grinding—the always-on B2B posting culture they compare to Las Vegas—and argue that neglecting your own state degrades the company's primary asset. Their prescription: protect the machine that builds the mission, listen to hopes rather than dash them, and build rest into the routine as a requirement rather than a reward. The post also makes clear what Cachola sells: helping consultants extract their “source code” and install it as a Storytelling Operating System.

## Work to read

- [AI adoption as operating-model redesign](https://www.linkedin.com/posts/corycachola_a-lot-of-smart-leaders-still-think-ai-is-activity-7471502371911581696-yH6m)
- [AI as a market shift, not just a technology shift](https://www.linkedin.com/posts/corycachola_i-dont-always-post-about-webinars-i-attend-activity-7434679422147883008-ifxh)
- [Why AI stories need system-based metaphors](https://www.linkedin.com/posts/corycachola_everyone-agrees-that-ai-companies-need-better-activity-7448088066453413888-6Lg9)
- [What AI changes about the economics of work](https://www.linkedin.com/posts/corycachola_there-is-a-nice-and-easily-digestible-lie-activity-7433991547752284162-nz2h)
- [Why content volume is not the same as thought](https://www.linkedin.com/posts/corycachola_its-time-to-talk-about-people-on-li-complaining-activity-7502786187993124866-qUea)

## Brand relationships

- **Board of Innovation** (mention)
- **Storytelling Operating System** (owned_business)

## Partnerships

No partnership or sponsorship program is described in Cachola's posts. They publish no rate card, media kit, sponsored-post format, or brand-collaboration policy, and the posts disclose no sponsored content or paid brand partnership. Cachola does offer a direct service: helping consultants turn a distinctive idea into a repeatable storytelling system, which they call a Storytelling Operating System. They describe installing it as operating infrastructure to keep a person's message clear as AI pushes more generic content into the market. They also publish a steady argument about AI as a market shift that changes what buyers value, aimed at B2B marketers, advisers, and consultants. For a brand considering a talk or advisory session, treat the engagement as direct consulting arranged by personal contact, not as a packaged sponsorship product. No terms, deliverables, pricing, availability, or booking process are published; ask directly about scope and budget.

## Alternatives

- Nick Woolley: Choose Woolley when the need is internal adoption rather than reputational positioning. His posts describe running an AI-first operating model inside a company in four phases — principles, playbook, a whole-team hackathon, then an agentic operating model where agents carry the operational layer and people own judgment — with live examples in policy, support, marketing, engineering, and procurement. If the client wants a speaker who can describe what adoption actually looked like across functions, or sells into energy and utilities and needs a message that addresses grid strain and deployment speed, Woolley fits better than Cachola.
- Aashiesh Chopra: Choose Chopra when the client's buyers sit in banking, payments, fintech, or asset infrastructure and the positioning question is where AI agents, stablecoins, and tokenized assets sit in the financial stack. His posts give that audience a working vocabulary — identity, intelligence, authority, money, settlement; mandate share; a licence to delegate — and a concrete argument that the banking stack is being decomposed into fintechs becoming banks, infrastructure firms moving inside the banking perimeter, and platforms owning the relationship while regulated banks sit underneath. If the message has to land with treasury, risk, or payments decision-makers, that specificity beats a general narrative session.
- Andrew Ng: Choose Ng when the audience is engineering leaders, developer-tool buyers, or a company whose AI story is really about how software gets built. His posts carry a specific, arguable thesis — the product-management bottleneck, engineers expanding into product work, engineer-to-PM ratios moving toward 1:1, and design, marketing, and legal becoming the next constraints once coding speeds up — and tie it to a named event, the AI Developer Conference in San Francisco on 28-29 April, on the future of software engineering. If the session has to fill a room of technical leaders with a contested industry argument, he fits better.

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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/