everyone is talking about which AI model is best. which framework to use. whether to build agents or automations. whether chatgpt is better than claude.

none of that matters as much as when you start.

88% are using AI. almost nobody has scaled it.

mckinsey's november 2025 state of AI report found 88% of organizations report regular AI use. up from 78% in 2024 and 55% in 2023. adoption is near universal.

but nearly two-thirds are still in the experimenting or piloting stages. only about a third have begun to scale. and only 1% consider their generative AI strategy mature. mckinsey identifies a tiny 6% "high performer" group where more than 5% of EBIT comes from AI. for everyone else, it's noise.

gartner predicted over 40% of agentic AI projects will be canceled by end of 2027. everyone is experimenting. almost nobody is executing. that gap is the opportunity.

early adopters grow 2-3x faster

harvard business review studied 672 business and technology leaders across multiple technology waves: mobile, cloud, social, analytics. they split companies into three groups. pioneers who moved first. followers who invested once the technology was proven. cautious adopters who waited until it was established.

pioneers with revenue growth above 30% outnumbered followers by 2x and cautious adopters by 3x. 54% of pioneers saw technology lead to significant business model changes. only 10% of cautious adopters did.

the gap widened over time. it didn't shrink.

AWS launched in 2006, built a 31% global market share before azure and google cloud entered, and now generates $107.6B per year.

the compounding advantage

AI advantages compound in three ways.

data moats. every interaction, correction, and decision feeds your AI systems. a company with a 2-year head start has collected millions more data points than competitors. netflix earns roughly $1B per year from AI-driven recommendations built on years of compounding data. a new competitor starting today would need years to catch up.

workflow optimization. mckinsey found workflow redesign has the single biggest effect on an organization's ability to see profit impact from AI. only 21% of organizations have redesigned workflows around AI. the ones who do it first lock in operational advantages that compound quarter over quarter.

institutional knowledge. jp morgan chase has deployed 300+ AI use cases across operations. new implementations take weeks, not months. competitors need 18-24 months minimum to restructure and reach parity.

costs are moving in opposite directions

AI infrastructure is getting cheaper. LLM inference costs have dropped roughly 10x per year. GPT-4 equivalent performance went from $20 per million tokens in late 2022 to under $1 in 2025. a 20x+ reduction in 3 years.

AI talent is getting more expensive. AI engineer average salary hit $206K in 2025, a $50K jump from the prior year. AI roles command a 28% salary premium over traditional tech roles. PwC found workers with AI skills earned a 56% wage premium in 2024, more than double the 25% premium from the year before.

the tools get cheaper. the people who know how to use them get more expensive. every month you wait, the talent market tightens and the organizational capability gap grows.

what the next 3 years look like

right now: AI is a competitive advantage. a citizens bank survey found 82% of mid-market companies plan to increase AI investments over the next 5 years, up from 58% in 2023. but most are still figuring out what to build first.

in 18 months: AI becomes table stakes for growth-stage companies. early movers will have compounding advantages in data, workflows, and team capability. late movers will scramble to catch up at higher costs with less experienced teams.

in 3 years: gartner's 2026 strategic predictions forecast 90% of B2B buying will be AI agent intermediated by 2028 and 33% of enterprise software will include agentic AI. the edge won't be in having AI. it'll be in having had AI for 3 years. the data, the workflows, the institutional knowledge baked into your systems. that's the moat.

a good AI system deployed today will outperform a perfect system deployed a year from now. that's the math. and it's why timing matters more than technology.

what to do about it

start with where your business leaks time. that's it. you don't need to understand large language models or vector databases. you need to know which process burns the most hours every week.