The Case for Slow-Mo: Why Domo’s CDO Thinks Your AI FOMO is a Liability

If you feel like you’re drowning in a sea of “AI or Die” headlines, you aren’t alone. While the rest of the tech world is frantically trying to attach a large language model to everything from enterprise software to smart toasters, Chris Willis, the Chief Design Officer (CDO) at The Register, has a radical suggestion: just stop. Or at least, slow down significantly before you break something expensive.

During a recent sit-down, Willis voiced a sentiment that many of us have been whispering in the back of Zoom calls: the sheer volume of AI-induced anxiety is becoming toxic. He marveled at the lack of open resentment toward tech giants who have essentially shoved unvetted tools into the public square, forcing every C-suite executive to feel like their career is on a ticking clock. It’s a classic case of FOMO—Fear Of Missing Out—driving strategy, and as Willis puts it, that’s a recipe for driving your business at 100 mph through the night with the headlights turned off.

The Problem with the "Moonshot" Mentality

The industry is obsessed with "moonshots"—those grand, sweeping AI implementations meant to revolutionize entire industries overnight. But Willis argues that these high-stakes gambles often ignore the messy reality of business workflows. He points to the cautionary tale of Swedish fintech firm Klarna, which famously replaced customer service staff with AI, only to find themselves eventually bringing humans back into the loop. Why? Because, frankly, no customer actually wants to spend their afternoon arguing with a chatbot that doesn't understand nuance.

Instead of aiming for the stars and hitting a mountain, Domo’s design lead suggests a "slow-mo" approach. This isn't about being a Luddite; it’s about being deliberate. According to a report by Domo, the most effective strategies start with boring, tactical wins—like automating a spreadsheet or building an app to flag anomalies in invoices. These aren't flashy, but they work, they save money, and they don't hallucinate your company’s quarterly earnings into a fictional abyss.

Context is King, Not Just Code

The tech world loves to talk about "intelligence," but we rarely talk about "judgment." Willis insists that the bridge between knowing something (data) and doing something (action) requires a deep understanding of human processes that a raw API simply doesn't possess. Without clean, well-governed data, even the most expensive AI model is just a "very powerful engine" with no steering wheel. As noted by CIO, the goal should be augmentation, not just automation for the sake of a press release.

Ultimately, the "go slow-mo" philosophy is a call for sanity in a market currently defined by panic. If you don't understand your business’s manual processes today, throwing AI at them tomorrow won't fix the problem—it will just accelerate the chaos. It’s okay to let the hype cycle spin itself out while you focus on the unglamorous, foundational work that actually moves the needle. After all, the only thing worse than missing out on the AI revolution is being the one who led their company off a cliff in a rush to join it.

The Quiet Rebellion in the C-Suite: While the public-facing narrative of AI adoption looks like a triumphant march of progress, the view from the boardroom is increasingly colored by "implementation exhaustion." Chris Willis’s push for a slower cadence isn't just a design philosophy; it’s a direct response to the technical debt currently being accrued by companies that rushed to integrate Generative AI during the initial 2023-2024 craze. We are entering the "trough of disillusionment" where the bill for uncurated data and hallucinated outputs is finally coming due.

Historically, tech cycles follow a predictable pattern of explosion followed by consolidation. We saw it with the dot-com bubble and again with the move to the cloud. However, the AI cycle is unique because of its velocity. Stakeholders are realizing that while a developer can spin up a GPT-based internal tool in a weekend, the legal and ethical oversight required to keep that tool from leaking proprietary data takes months. This friction is exactly what Willis is highlighting—the disconnect between "can we build it?" and "should we deploy it?"

The Ghost in the Machine: Human Agency

A seasoned reporter looks for the human friction point, and in this case, it’s the "Agentic Workflow." The industry is currently pivoting from simple chat interfaces to AI agents that can actually perform tasks. But as discussed in deep dives by ZDNet, these agents lack the "tribal knowledge" that lives in the heads of long-term employees. If an AI makes a decision based on a dataset that hasn't accounted for a specific market anomaly from three years ago, the fallout falls on the human operator, not the software vendor.

This brings us to the "Domo way" of thinking about data governance. In the rush to be "AI-first," many organizations neglected the plumbing. Willis argues that "slow-mo" means auditing your data lineage before you ever let a model touch it. If your foundational data is siloed or messy, the AI isn't a silver bullet; it’s a magnifying glass for your existing organizational dysfunction. The smartest players in the room are currently pausing their "moonshot" projects to ensure their data architecture can actually support the weight of these new models.

Ultimately, the perspective of a CDO like Willis serves as a necessary guardrail. He isn't arguing against the technology itself, but rather against the loss of human intentionality. By shifting the focus from "how much AI can we use?" to "where does AI add the most friction-less value?", companies can avoid the burnout that comes from chasing every shiny new feature. The real winners of the AI era won't be the first to market, but the ones who built systems robust enough to last past the first round of hype-driven updates.

The High Price of the "Free" AI Pass: We have to stop pretending that AI integration is a cost-neutral evolution. The industry-wide assumption that you can simply layer intelligence over existing infrastructure without a massive "complexity tax" is the first lie we need to dismantle. Chris Willis’s plea for a "slow-mo" approach isn't just about avoiding bugs; it’s a warning about the hidden costs of maintenance. Every AI feature added in a panic is a new piece of infrastructure that requires monitoring, fine-tuning, and security patching—tasks that are already stretching IT budgets to their breaking points.

There is a glaring contradiction in the way enterprises are currently behaving. They demand "predictability" from their quarterly earnings while simultaneously deploying "stochastic" (probabilistic) AI tools that are, by their very nature, unpredictable. It’s a bit like trying to build a Swiss watch out of jelly. Willis points out that the FOMO-driven rush forces companies to ignore this fundamental incompatibility. When a CEO demands an "AI-first" strategy, they are often inadvertently asking for a "logic-second" reality, where the goal is the appearance of innovation rather than the substance of reliability.

The Skeptic’s Forecast: The Great Unbundling

Projecting forward, we are likely to see a "Great Unbundling" of AI hype. As the dust settles, the measured skepticism championed by leaders at Forbes suggests that the companies that will actually thrive are those currently being mocked for being "behind the curve." The irony is delicious: the laggards who focused on cleaning their data lakes and defining their manual processes are building a launchpad, while the early adopters are often busy untangling a "spaghetti mess" of disconnected APIs and hallucinating chatbots.

Furthermore, the implication of "going slow-mo" is a shift in power back to the designers and the end-users. For the last eighteen months, the engineers and the hype-merchants have held the con, but as Willis notes, a tool that nobody knows how to use—or trusts enough to keep turned on—isn't a tool at all; it’s an expensive paperweight. The next phase of the cycle won't be about who has the biggest model, but who has the most invisible AI. Success looks like a workflow that just works better, not a dashboard that screams "Powered by AI" in neon lights.

Ultimately, we need to treat AI with the same professional skepticism we applied to the blockchain or the metaverse. It is undeniably more useful than either, but that utility is precisely why it’s more dangerous if mishandled. Moving slowly doesn't mean you're losing the race; it means you're making sure you brought enough fuel to actually reach the finish line. In a world of sprint-to-fail, the crawl-to-win strategy might be the most radical thing a CDO can propose.

The "slow-mo" movement is, in essence, a return to the fundamentals of good product design: solve a real problem for a real person without making their life more complicated. If that means missing a few hype cycles and disappointing a few venture capitalists who are desperate for a quick exit, so be it. The goal is to build a business that lasts, not a demo that dazzles for five minutes before the first hallucination hits the fan.

"Chasing AI today is a bit like joining a high-speed car chase because you like the look of the sirens; eventually, you’re going to have to explain to your shareholders why you’re parked in a ditch with no map and a very confused robot in the passenger seat."

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn