Humans need to 'surf the wave' of AI rather than get swallowed by it, Chesky says at Communacopia | Enterprise workflow automation software
By Gammatek ISPL, Industrial Systems & Compliance Analyst at Gammatek ISPL
Last updated: September 2026 | 13 min read
Author block: Gammatek ISPL advises manufacturing, chemical, and pharma plants on compliance and operational technology adoption at Gammatek ISPL, drawing on direct client engagements across + industrial facilities.
Why This Matters to You Right Now
At this year's Goldman Sachs Communacopia + Technology Conference, Airbnb CEO Brian Chesky told the room something blunt: humans need to "surf the wave" of AI rather than let it swallow them. It's the kind of line that gets quoted everywhere and applied to nothing specific — a tech CEO talking to investors about a consumer platform. But strip away the Silicon Valley setting, and the underlying claim is directly relevant to anyone running a manufacturing, chemical, or pharma operation right now: the businesses that treat AI adoption as a deliberate, structured transition are pulling ahead of the ones waiting for the technology to "mature" before acting. If you're deciding whether this is a 2027 problem or a right-now problem for your plant, this is worth five minutes.
What Chesky Actually Said, and Why It's Not Just Tech-Industry Talk
Chesky's framing at Communacopia fits a pattern he's been building publicly for over a year. He's told investors that AI is "the best thing that ever happened" to Airbnb's business, crediting it directly for accelerating product development and improving the company's most recent quarterly results. He's been explicit that the companies willing to disrupt themselves — restructuring how they work around AI rather than bolting it onto old processes — are the ones that benefit, while the ones that wait get disrupted by someone else instead.
The "surf the wave, don't get swallowed" framing is really a restatement of a much older business truth, just sharpened by how fast this particular technology shift is moving: the risk isn't the technology itself, it's the gap between when a technology becomes genuinely useful and when your organization actually restructures around it. Companies that close that gap early get a compounding advantage. Companies that don't spend years playing catch-up, if they get the chance to catch up at all.
For a consumer tech platform like Airbnb, "surfing the wave" means AI-powered search, faster product cycles, and AI handling a third of customer service tickets. For a manufacturing plant, it means something different, but the underlying principle — restructure deliberately and early, rather than react late — applies just as directly.
Why Manufacturing Feels This Differently Than Consumer Tech
Most coverage of the "AI wave" framing focuses on software companies, media, and white-collar knowledge work. Manufacturing has its own version of this shift, and it looks different for a few structural reasons:
Physical infrastructure doesn't get replaced overnight. A consumer app can retrain its recommendation engine in a sprint cycle. A plant with decades-old equipment, legacy SCADA systems, and hard compliance requirements can't "move fast and break things" — every change has to be validated, documented, and often approved by a regulator before it goes live.
The stakes of getting it wrong are higher. A bad AI-driven recommendation on a booking platform costs a customer a mediocre experience. A bad AI-driven decision on a plant floor can mean a safety incident, a compliance violation, or a very expensive equipment failure.
But the upside is also more concrete. Where consumer AI often optimizes for engagement or conversion — somewhat abstract metrics — industrial AI adoption tends to show up in numbers plant managers already track closely: unplanned downtime, audit prep time, incident rates, and maintenance costs. That makes the business case, when it's real, unusually easy to measure.
This is exactly why "wait and see" is a more tempting strategy in manufacturing than it is in tech — the perceived risk of moving early feels higher, given the physical and regulatory stakes. But the data increasingly suggests the actual risk runs the other way: plants that keep pushing AI-assisted tools past the trial phase are pulling ahead on the metrics that matter, while the ones waiting for total certainty are accumulating exactly the kind of gap Chesky is describing.
The Practical Software Stack Behind "Surfing the Wave" in Manufacturing
Here's where the "surf the wave, don't get swallowed" idea stops being a slogan and becomes an operational question: what actually changes, day to day, inside a plant that's restructuring around AI rather than resisting it? In our work with manufacturing and pharma clients, it's rarely one dramatic AI system — it's a handful of specific software categories getting modernized in sequence, each solving a distinct piece of the adaptation problem.
Workforce and workflow automation. Enterprise workflow automation software is often the first serious step, because it's the layer that actually changes how work moves through a plant day to day — routing approvals, flagging exceptions, and reducing the manual handoffs that slow everything else down. Paired with enterprise workforce management software, this is usually where plants see the fastest, most measurable time savings, because it doesn't require replacing hardware or retraining on entirely new systems — it changes process, not equipment.
Predictive maintenance and monitoring. This is the layer most directly tied to "surfing the wave" rather than reacting to it. Enterprise CMMS software (computerized maintenance management systems) with AI-driven predictive capability shifts maintenance teams from responding to breakdowns to managing a queue of flagged risk signals — the same shift we described in our earlier piece on how AI is reshaping manufacturing roles. Plants running modern CMMS platforms are catching equipment issues before failure, not after.
Data protection for AI-dependent operations. As more of a plant's operational decisions depend on AI systems and the data feeding them, the cost of losing that data goes up sharply. Enterprise backup and recovery — and more specifically, enterprise backup software built for the always-on, high-volume data environment AI monitoring tools generate — stops being an IT afterthought and becomes core infrastructure. The same applies to corporate backup software protecting the training data, sensor logs, and compliance records that increasingly feed AI-assisted decision-making. Plants evaluating options here are typically comparing the best enterprise backup software available against their specific uptime and recovery-time requirements, not just picking whatever their IT vendor bundles in.
Contract and vendor management. AI adoption inevitably means more vendor relationships — AI monitoring platform providers, data processing agreements, integration partners. Enterprise contract management software (sometimes evaluated as an enterprise contract management system, depending on scale) becomes necessary almost immediately once a plant is juggling more than two or three AI-related vendor contracts, each with different data-handling terms that need tracking for compliance purposes.
Talent and hiring infrastructure. As we covered in our piece on the reshaped manufacturing workforce, new AI-adjacent roles are appearing in plant job postings that didn't exist two years ago. Enterprise recruiting software built for specialized, high-turnover hiring cycles is increasingly necessary to compete for this narrower pool of candidates who understand both plant operations and AI-assisted tooling.
Financial operations, modernized. Even the most unglamorous layer changes. Quickbooks Enterprise, adapted for manufacturing environments specifically, is a common upgrade path for plants whose financial reporting needs have outgrown basic accounting software as AI-driven operations create more granular, faster-moving cost data to track — particularly around maintenance spend, vendor contracts, and compliance-related expenses.
A Real Consideration: Sequencing Matters More Than Speed
Placeholder structure to fill in:
A specific plant (anonymized if needed) that adopted AI-related tooling in a deliberate sequence rather than all at once
What order they tackled these software categories in, and why that order worked
What measurable outcome resulted (downtime reduction, audit prep time, cost savings)
What they'd have done differently in hindsight
Implementation Considerations for Plant Managers
If Chesky's framing is right — and the manufacturing-specific data increasingly backs it up — the practical question isn't whether to adapt, it's how to sequence it without overwhelming your team or your compliance obligations:
Start with the process layer, not the flashiest AI system. Workflow automation and workforce management software tend to have the fastest payback and the lowest implementation risk, making them a reasonable first move even for plants still cautious about AI generally.
Treat data protection as a prerequisite, not an add-on. Before rolling out predictive maintenance or AI-driven compliance monitoring at scale, confirm your backup and recovery infrastructure can actually handle the increased data volume and criticality — retrofitting this after a failure is far more expensive than building it in upfront.
Don't let vendor contracts outpace your ability to track them. Plants that add AI-related vendors faster than they can manage the resulting contracts tend to lose visibility into data-handling terms exactly when compliance scrutiny of AI tools is increasing.
Budget for the hiring shift, not just the software. The software categories above only pay off if someone on your team actually knows how to run them — factor recruiting and training costs into the adoption timeline from the start, not as an afterthought once the tools are already live.
Document everything as you go. For regulated plants specifically, every step of this transition — what changed, when, and why — needs to be audit-ready from day one, not reconstructed after the fact when a regulator asks.
The Honest Version of "Surf the Wave"
Chesky's line makes for a good conference soundbite, but the manufacturing version of this advice is less dramatic and more useful: the plants pulling ahead right now aren't the ones making the biggest, most headline-worthy AI bets. They're the ones methodically modernizing the unglamorous layers — workflow, maintenance, data protection, contracts, hiring, finance — in a sequence that doesn't outrun their compliance obligations or their team's ability to actually use the tools. That's what "surfing the wave" looks like when the wave is hitting a factory floor instead of a boardroom.
Where Compliance Fits Into All of This
Every layer described above eventually touches the same question: can you prove, to a regulator or an auditor, that the changes you made were controlled, documented, and safe? That's the layer that sits underneath all of this software modernization — and it's exactly where a dedicated compliance platform earns its place, turning a fast-moving technology transition into something you can actually defend in an audit.
See how Gammatek's compliance platform helps plants document AI-driven operational changes → https://www.gammateksolutions.com/post/the-best-worst-and-strangest-ways-ai-is-really-being-used-at-work https://www.gammateksolutions.com/post/amazon-prime-members-just-got-a-huge-new-perk-here-s-how-to-use-it
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