September 2026
90% already use AI.
Only 6% win with it.
Why the advantage is no longer adopting artificial intelligence, but having the method to capture it.
Julio Sánchez & Keyla Díaz · AI Tech Investments · Miami-Dade, Florida
Executive summary
- 90% of companies already use AI in at least one business function — but only 37% attribute any real impact on profit (EBIT) to it, and only 6% qualify as “high performers” (≥5% EBIT impact). [3]
- “Future-built” companies — the 5% that combine AI with genuine process redesign — grow revenue at twice the rate and achieve 40% greater cost savings than the 60% still lagging. Over three years, the gap is 1.7x in revenue, 3.6x in shareholder return and 1.6x in EBIT margin. [4]
- Large companies ($1B+ in revenue) scale AI agents at twice the rate of smaller ones: 40% versus 22%. [3]
- But when a U.S. small business does adopt AI with discipline, the results are dramatic: 85% growth in sales, 84% in profit, 82% in headcount — against 77% sales growth among those that stay at low technology adoption. [6]
- The economic opportunity is real, not marketing hyperbole: generative AI could contribute between $2.6 and $4.4 trillion annually to the global economy (up to $7.9 trillion including software productivity) [1]; Goldman Sachs projects +7% of global GDP — close to $7 trillion — over a decade. [2]
- In Florida and California, Latino-owned businesses accounted for more than 55% of all net new businesses between 2017 and 2023, with 86% growth in construction. That momentum faces more pressure today: federal-local immigration cooperation agreements rose 64% between Oct-2025 and Apr-2026, concentrated in Texas and Florida. [8] [11]
- The terrain is unforgiving: 22% of new U.S. businesses close in their first year, and 48.6% do not reach five years. [9] Operational inefficiency is no longer just a cost — it is a survival-risk multiplier.
The conclusion that runs through all the research cited in this document: the AI tool has already become a commodity. What is scarce — and what separates those who win from those who stay the same — is the method. That is exactly the layer the AITI Transformation Framework™ builds.
The opportunity is not marketing hyperbole
Before talking about method, it is worth establishing that the economic opportunity behind AI is not a public relations campaign run by big tech. It is one of the most heavily scrutinized estimates in the recent history of economic consulting.
McKinsey Global Institute estimates that generative AI could add between $2.6 and $4.4 trillion annually to the global economy across 63 use cases analyzed in detail — a figure comparable to more than the United Kingdom's annual gross domestic product. Adding the broader impact on software productivity, the range rises to $6.1–$7.9 trillion annually. [1]
Goldman Sachs Research reaches an independent conclusion of the same order of magnitude: generative AI could raise global GDP by 7% — close to $7 trillion — over a decade, with a 1.5 percentage point boost to productivity growth. Their analysis estimates that roughly two thirds of U.S. jobs are exposed to some degree of AI automation, with the potential to replace between a quarter and half of the workload in those roles. [2]
Neither firm claims this value distributes itself automatically. Quite the opposite: both warn that actually capturing it depends on reorganizing the work, not on the mere existence of the technology. That warning is the central theme of this document.
The value gap: almost everyone adopts, almost no one wins
The most recent McKinsey & Company survey on the state of enterprise AI (May–June 2026, 1,719 respondents across 97 countries) confirms what was already coming: adoption is no longer the problem.
9 out of 10 organizations already use AI in at least one business function. But that is where the good news ends. Only 37% attribute any measurable impact on operating profit (EBIT) to it — a figure unchanged from the previous year — and barely 6% qualify as “high performers”: companies reporting an EBIT impact of 5% or more and describing that value as “significant”. [3]
Boston Consulting Group documents the same gap from another angle, and gives it a name: the widening AI value gap — and it is growing, not closing. Only 5% of companies qualify as future-built: those combining aggressive AI investment with real redesign of how the work gets done. 60% remain laggards. The difference in results between the two groups is not marginal:
| Indicator | Future-built companies (5%) | Laggards (60%) |
|---|---|---|
| Revenue growth expected from AI in 2025 | Twice as much | Baseline |
| Cost savings where AI is applied | 40% greater | Baseline |
| Cumulative 3-year revenue growth | 1.7x | 1x |
| 3-year total shareholder return | 3.6x | 1x |
| 3-year EBIT margin | 1.6x | 1x |
| AI investment planned for 2025 | More than double | Baseline |
Source: BCG, “The Widening AI Value Gap: Build for the Future 2025”. [4]
What do future-built companies do differently? BCG identifies five levers — leadership with multi-year ambition, business redesign prioritized by value, “AI-first” operating models with human-machine collaboration, active talent development, and purpose-built data architecture. McKinsey finds the same pattern in its own survey: high-performing teams fully redesign workflows in 73% of cases, against only 25% of the rest. They are twice as likely to show real senior-leadership commitment, and twice as likely to allocate more than 15% of the IT budget to AI. [3]
Deloitte, surveying 1,854 executives (August–September 2025), adds the time dimension: most organizations need between 2 and 4 years to reach a satisfactory return on their AI investments — against the 7 to 12 months typical of a conventional technology investment. Only 6% achieve the return in less than a year. And only 20% of organizations qualify as true “ROI leaders”: those that measure with discipline, allocate more than 10% of the technology budget to AI, and use distinct measurement frameworks for generative and agentic AI. [5]
The pattern, repeated across three independent studies from three different firms, is the same: the tool is not what separates those who win from those who do not. It is the method — measure before investing, redesign the process instead of automating the mess, sustain the commitment beyond the first quarter.
The gap is worse for the small company — and the reward is greater if it closes
This is where the research becomes directly relevant to a small or midsize business, not just the Fortune 500.
McKinsey finds that large companies (more than $1 billion in annual revenue) scale AI agents beyond a pilot at 40%, against only 22% of smaller organizations — a rate essentially flat versus the previous year. [3] The reason is no mystery: scaling AI with method requires leadership time, measurement discipline and the capacity to redesign processes — resources that in a large company live in a dedicated department, and in a small service business usually do not exist at all. That is exactly the gap an external partner with a proven method fills.
But the other half of the data is what should matter to any owner of a service or industrial company: those who do close that gap win seriously. The 2025 report from the U.S. Chamber of Commerce (C_TEC), with a sample of 3,870 U.S. small businesses under 250 employees, found that AI adoption among small businesses more than doubled in two years — from 23% in 2023 to 58% in 2025, with an 18-point jump between 2024 and 2025 alone. [6]
The gap between adopting well and not adopting is no longer a difference in internal efficiency — it is a difference in business growth, measurable on the revenue line. [6]
The conclusion for a service or industrial small business in 2026 is no longer “should we use AI?” — 58% of its peers already do. [6] The question that decides the outcome is whether that adoption arrives with the method that makes it profitable, or stays as loose tools with no process or measurement behind them — which is exactly the pattern separating the 6% of high performers from the rest in McKinsey's research. [3]
Survival is no longer a background statistic
It is easy to read the figures in the previous sections as a conversation about growth and leave it there. But there is a second, less comfortable conversation underpinning the first: most small companies in the U.S. never reach the stage where these gains matter.
According to LendingTree's analysis of U.S. Bureau of Labor Statistics data (Business Employment Dynamics), of roughly one million businesses that open each year in the U.S.:
The most cited reasons are not surprising: lack of focus on who is served and what problem is solved, insufficient planning, high fixed costs, growing competition. [9] It is, in other words, almost always a problem of operating method, not bad luck.
What changes in 2026 is the speed at which that method problem becomes visible. When half the professional services market still reconciles payments by hand, recaptures the same data three times and depends on one person remembering the right criterion — while the other half is already redirecting that time toward what actually moves the business — the efficiency gap stops being a “we'll tidy it up someday” item and becomes a question of how much time is left for the company that does not. The AITI Assessment itself, applied in the field to Miami-Dade service companies, quantifies exactly that kind of operational exposure — Section 7 of this document shows that finding with real figures.
The Miami-Dade moment: Latino entrepreneurship, the 2025–2026 pressure, and why method matters more here
There is an angle to this research that rarely appears in the big consultancies' reports, and that is central to any Latino-owned company in South Florida: the last five years were years of real entrepreneurship — and the last two have been about sustaining that progress against an equally real headwind.
The 11th edition of the State of Latino Entrepreneurship report, from Stanford Graduate School of Business and the Latino Business Action Network (presented in April 2026, with data from more than 10,000 business owners), documents a sustained cycle of Latino entrepreneurship between 2017 and 2023. In California and Florida specifically, Latino-owned businesses accounted for more than 55% of all net new businesses created in that period, and in construction — a sector with a strong presence of Latino companies in Miami-Dade — growth was 86%. [8]
That five-year momentum is real, and it is also the one under the most pressure today. Between October 2025 and April 2026, cooperation agreements between federal and local agencies on immigration enforcement (the 287(g) program) rose 64% in six months, and Texas and Florida together account for close to 40% of the national total — meaning the operational intensity of that environment is particularly high in the same states where Latino entrepreneurship grew fastest. [11] In parallel, Hispanic household spending barely grew in the year ending June 2025, according to Numerator data cited by Bloomberg News — a slowdown analysts attribute to accumulated price increases, a cooling labor market, tariff uncertainty and concern tied to the immigration environment. [12]
AI adoption, however, did not stop: AI use among Latino-owned businesses more than doubled between 2024 and 2025 — the willingness to modernize holds firm even in an environment that is harder to sustain. [8]
Where there is a structural gap, and one that changes the right strategy, is access to capital: Latino-owned businesses received less than 2% of all venture capital funding in 2025 — a gap that already existed before conditions got difficult, and that a more restrictive environment does not reduce. [8]
That matters because the two most heavily promoted transformation paths in the market — an enterprise consultancy charging per phase of a two-year roadmap, or an investment round to build an internal AI team — are structurally less accessible to this segment, not for lack of ambition, but for lack of access to the capital those paths demand.
This is exactly what makes a third path relevant: a low-risk assessment, with returns measured in months rather than funding rounds, executed by a partner who understands the language, the market and — with the AITI Transformation Framework™ — brings the method without demanding the capital of an enterprise transformation.
Why method decides: the AITI Transformation Framework™
Three independent research firms — McKinsey, BCG and Deloitte — arrive, from different surveys and methodologies, at the same diagnosis: what separates the companies capturing real value from AI from those that are not is not which model they use. It is whether they have, before automating anything, a discipline of:
- Measuring with real data before investing — not with assumptions. (McKinsey, BCG) [3] [4]
- Redesigning the process, not just bolting AI on top — 73% of high-performing teams redesign the entire workflow, against 25% of the rest. [3]
- Sustaining leadership and budget commitment beyond the pilot — ROI leaders allocate more than 10% of the technology budget to AI and use explicit measurement frameworks. [5]
- Measuring the return against a baseline, with realistic time expectations — 2 to 4 years, not 2 to 4 months. [5]
Those four disciplines, named independently by three different consultancies, are — word for word — the four phases of the AITI Transformation Framework™:
We measure before touching anything. Task-by-task timed studies, real volumes, real money. It is discipline #1 separating the leaders in McKinsey's and BCG's research.
Deliverable: AITI Business Assessment — a roadmap prioritized by impact.
We redesign the process before automating it. The standard, the criterion, the sequence — before AI executes anything. It is the practice 73% of McKinsey's high-performing teams apply and the rest do not.
Deliverable: Transformation Blueprint — a complete implementation plan.
We implement alongside the team, with governance and real adoption, not a pilot that quietly dies. It is the sustained budget and leadership commitment Deloitte finds in its “ROI leaders”.
Deliverable: Business Transformation Underway — solutions running in the real operation.
We measure the result against the Assessment baseline, over a realistic time window. It is the measurement discipline only 20% of the companies in Deloitte's study apply rigorously — and the one that decides whether McKinsey's 6% of high performers becomes a figure that grows every year.
Deliverable: Impact Report — evidence that the transformation generated value.
It is not a marketing coincidence that the framework has four phases. It is the operational translation, on the ground of a service or industrial small business, of what Fortune 500-level research already proved works — without demanding the budget or the internal team that research assumes as a starting point.
Our own evidence: what the AITI Assessment finds in the field
The research cited so far is external, and that is precisely what makes it valuable: it does not depend on believing AITI, it depends on believing McKinsey, BCG, Deloitte and Stanford. But AITI does not just read the research — it applies it, and measures what it finds.
During the AITI Assessment at a professional services company in Miami-Dade (25 people, USD $2.5M in annual revenue), the task-by-task timed study found an estimated annual exposure of USD $490,000 — close to 20% of the company's revenue — spread across twelve loss categories: from manual recapture of the same data (30 minutes per person per day) to accounts receivable overstated by $264,000 because payments were not applied to invoices automatically. None of those categories appears as a single line on an income statement — and that is why none of them corrects itself.
After the AITI Transformation, implementation experience puts realistic recovery between 50% and 70% in the first twelve months — a figure consistent with the time range Deloitte documents for the return on well-managed AI investments (2 to 4 years for the full return, with measurable partial results from the first year). [5] [10]
This is exactly the kind of evidence — measured, not assumed — that the research cited in this document identifies as the discipline separating the companies that capture real value from those that do not.
The window is open, but not indefinitely
All the evidence in this document points the same way: adopting AI has stopped being a competitive advantage — 90% of companies already use it. [3] The real advantage, the one separating the 6% of high performers from the rest, is having the method before having the tool. [3] [4] [5]
For a service or industrial company — in Miami-Dade or any other market, because AITI implements remotely — the question is no longer whether competitors are adopting AI; most already have. [6] [8] The question is whether that adoption is generating the double-digit growth reported by the small businesses doing it with method [6], or whether it is stalling, as it does for 94% of the organizations in McKinsey's research, as one more tool with no measurable impact on the result. [3]
In twelve months, that difference will be visible on the income statement in a way it still is not today. The question worth taking to the decision-making table is not whether to transform the operation — it is how much each postponed quarter will cost, on the line of revenue not captured, before the decision to measure it gets made.
A 30-minute session with AITI reviews the largest cost categories in the operation and delivers a real number to take to that table — whether or not you work with AITI afterward.
References
- McKinsey & Company, “The economic potential of generative AI: The next productivity frontier” (June 2023). mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
- Goldman Sachs Research — Briggs, J. & Kodnani, D., “The Potentially Large Effects of Artificial Intelligence on Economic Growth” (April 2023). goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent
- McKinsey & Company, “The State of AI” (2026 edition; survey May–June 2026, n=1,719 across 97 countries). mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- Boston Consulting Group, “The Widening AI Value Gap: Build for the Future 2025” (September 30, 2025). bcg.com/press/30september2025-ai-leaders-outpace-laggards-revenue-growth-cost-savings
- Deloitte, “AI ROI: The Paradox of Rising Investment and Elusive Returns” (survey August–September 2025, n=1,854 executives, Europe and the Middle East). deloitte.com/global/en/issues/ai/ai-roi-the-paradox-of-rising-investment-and-elusive-returns.html
- U.S. Chamber of Commerce, Technology Engagement Center (C_TEC), “Empowering Small Business: The Impact of Technology on U.S. Small Business” (August 2025; survey June 2025, n=3,870 U.S. small businesses under 250 employees).
- Stanford Institute for Human-Centered AI (HAI), “AI Index Report 2025” (April 2025). hai.stanford.edu
- Stanford Graduate School of Business — Latino Business Action Network, “State of Latino Entrepreneurship” (11th edition, presented at the SOLE Summit, April 2026). gsb.stanford.edu
- LendingTree, analysis of U.S. Bureau of Labor Statistics data — Business Employment Dynamics (2025). lendingtree.com/business/small/failure-rate
- AITI, findings from the AITI Assessment at a professional services company in Miami-Dade (2026) — internal data, also cited in “The hidden cost of workflow” (AITI, 2026).
- Brookings Institution, “From policy volatility to stability and growth: Rebuilding the foundations for Latino entrepreneurs and small businesses” (May 2026). brookings.edu
- Numerator, data cited by Bloomberg News (September 6, 2025) and PYMNTS (September 7, 2025), “Hispanic Consumer Spending: Caution Replaces Confidence”. numerator.com
Method note: the data cited in sections 1 through 5 comes from public third-party research — McKinsey, Goldman Sachs, BCG, Deloitte, the U.S. Chamber of Commerce, Stanford, the Brookings Institution and Numerator — and is presented with its source, date and sample size wherever the original material reports them. No data in those sections was generated or modified by AITI. Section 7 is the only one reporting AITI's own findings, and is explicitly distinguished from the rest. This document is general informational material and does not constitute legal, accounting or investment advice.
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