By Greg Woolf, AI RegRisk Think Tank

The mantra of the moment is “AI-first.” Two years after companies began chanting it, the early returns are mixed. Some firms boast eye-popping productivity gains; others are already walking back aggressive cost-cutting as quality slips and customers revolt.  

This article examines the most prominent successes—and disappointments—in large-scale AI adoption. 

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What “AI-First” Really Means 

An AI-first company treats artificial intelligence as the starting point for everything it builds—strategy, products, org charts, even culture. Instead of bolting ChatGPT onto old workflows, leaders begin every initiative by asking, “How would an intelligent system solve this from scratch?” and then re-architect teams, budgets, and metrics around the answer. 

The goal is not efficiency alone but transformative throughput: unlocking capabilities that were impossible—or unaffordable—under traditional software and human-only labor. 

Crucially, AI-first does not mean AI-only: where models hallucinate or lack context, humans supply judgement, ethics, and creative direction. 

By weaving AI into every core workflow—customer support, supply-chain planning, product design—these companies create feedback loops that let their models learn faster than rivals who adopt AI piecemeal.  

The result is a business whose edge is the rate at which its algorithms and people co-evolve—making it, in effect, an intelligence company rather than merely a software company.  

Klarna & Duolingo: Cautionary Headliners 

Klarna removed 1,200 SaaS tools and let an OpenAI-powered bot do work equal to roughly 800 full-time staff. CEO Siemiatkowski now concedes the fintech “went too far” and must re-hire humans for nuanced customer service. 

Duolingo made AI proficiency mandatory for new hires and performance reviews—then terminated contractors whose tasks could be automated. The backlash was swift enough that founder Luis von Ahn publicly clarified: AI will augment, not replace, employees. 

Artizan used a “Stop Hiring Humans” billboard campaign in San Francisco to spark debate—and still drove $2 million in new annual revenue. 

Shopify upgraded its Sidekick assistant with voice chat, screen-sharing, and a prompt-based store builder, proving that “AI-first” can create net-new product lines, not just head-count freezes. 

From Efficiency AI to Opportunity AI 

Early adopters are racing through three phases: 

Experimental – “It runs without crashing.” 
Efficiency – Automate rote tasks, shrink cycle times, save OPEX. 
Opportunity – Launch services that were impossible before AI. 

Venture investors already peg vertical AI agents—domain-specific copilots funded from labor budgets—as a market potentially ten times larger than SaaS. Success, however, hinges on avoiding two classic traps: first, the “new-wine-in-old-bottles” mistake of pasting a chat widget onto legacy screens, leaving both users and the balance sheet cold; and second, the lure of magic-thinking that forgets LLMs hallucinate and still need deterministic code to close the books.  

The strongest leaders flip the script by reinvesting productivity gains into new lines of business and retraining talent. 

The Coming AI-Only Frontier 

Siemens EDA’s roadmap envisions design teams moving from a one-to-one engineer-agent pairing to a 1:100 human-to-AI ratio, with people acting as strategic supervisors of swarming bots.  

Relay.app already runs customer workflows with nine employees and more than 100 agents. EY—at roughly 400,000 staff—is investing $1 billion to push revenue toward $100 billion by adding 200,000 AI agents. 

The Fully Autonomous Business 

Even bolder than “AI-first” are nascent “AI-only” experiments that invert the staffing pyramid. The trend is turning into a sport: the $1 million AI Agents Global Challenge crowned a fully autonomous workflow last year, while Ready Tensor’s 2025 Agentic AI Innovation Challenge spotlights low-supervision systems.  

Reuters now predicts that autonomous agents—and the profitability metrics they unlock—will dominate the 2025 AI agenda. 

 Whether these ventures become enduring businesses or cautionary tales, they underscore a simple truth: AI-first companies begin by amplifying humans, but the boldest are already asking how few humans a business can run on.  

The market will decide which vision prevails—yet the experiments are well under way. 

Greg Woolf is an accomplished innovator and AI strategist with over 20 years of experience in founding and leading AI and data analytics companies. Recognized for his visionary leadership, he has been honored as AI Global IT-CEO of the Year, received the FIMA FinTech Innovation Award, and was a winner of an FDIC Tech Sprint. Currently, he leads the AI Reg-Risk Think Tank, advising financial institutions, FinTech companies, and government regulators on leveraging AI within the financial services industry. https://airegrisk.com

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