According to Forbes, while 78% of organizations now use AI according to McKinsey research, only 1% describe their rollouts as “mature” and Deloitte found just 6% report ROI under a year. Brian Moran’s Chicago real estate firm iReal Estate Solutions spent an entire year codifying manual processes before deploying IBM’s watsonx AI platform, ultimately identifying the top 3% of homeowners most likely to sell who represented over $60 million in closed listings. Wharton research shows 88% of enterprises plan to increase AI spending yet struggle with deployment complexity, with projects scoped for three months actually requiring twelve to eighteen. Meanwhile, IBM’s study found half of major corporations admit rapid AI investments left them with disconnected technology, while smaller companies are performing better with only 16% of AI initiatives scaling at large organizations versus more successful deployments at resource-constrained firms.
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The counterintuitive advantage
Here’s the thing that most big companies completely miss: going slower actually makes you faster. iReal Estate Solutions didn’t just throw AI at their problems. They spent a year documenting everything – thousands of emails, follow-ups, testing tone and timing. Only then did they automate. What used to take three minutes per message now takes seconds. Agents produce three to five times more outreach in the same time. They’ve expanded from one workflow to four product lines in eighteen months.
This directly contradicts what most Fortune 500 companies are doing. They’re throwing money at the problem – 88% plan to increase spending – but they’re setting unrealistic timelines and expecting magic. Projects that should take a year get three-month deadlines. No wonder they’re failing.
<h2 id="why-small-wins”>Why being small helps
Smaller companies have this huge structural advantage that nobody talks about: fewer decision-makers. Large enterprises have to navigate this complex web of CISO, CIO, CDO, Chief Risk officers, multiple line-of-business owners. By the time everyone agrees on something, the technology‘s outdated.
Meanwhile, regional banks are automating away 200+ hours of grunt work annually. Healthcare startups are deploying AI diagnostics that rival human specialists. Resource-limited retailers are using AI demand forecasting to predict what customers want before they know it themselves. They’re not smarter – they’re just nimbler.
What the winners are doing
The successful 5% share three characteristics that have nothing to do with budget size. First, they weaponize their unique data – especially the messy, unstructured stuff competitors can’t access. IBM’s Vice Chairman Gary Cohn puts it bluntly: “leaders who aren’t leveraging AI and their own data are making a conscious business decision not to compete.”
Second, they cultivate leadership cultures that reward smart risks over guaranteed mediocrity. And third – this is huge – they train their people instead of assuming AI adoption happens by osmosis. Walmart’s fresh $1 billion investment in workforce AI training shows they get it. They’re fundamentally changing how 2.1 million employees work, not just deploying technology.
The real AI divide
So here’s the bottom line. The AI divide isn’t between companies that use AI and those that don’t. McKinsey’s 78% adoption rate combined with 80% seeing no return makes perfect sense when you realize most value happens in unmeasured places. It’s about measured versus unmeasured success.
IBM’s Chief Scientist Ruchir Puri says “much of the value is occurring outside formal pilots.” Companies are counting pilots launched instead of measuring how work actually changes. The companies capturing lasting value aren’t the ones with the flashiest announcements. They’re the ones paying attention to how work gets done when nobody’s watching. And right now, that’s mostly the little guys.
