SMEs Lag on AI, but New Businesses Are Moving Fast — Here's How to Close the Gap

While many established SMEs lag in AI adoption due to cost, data, and skills barriers, new businesses are integrating AI from day one. Owners can close this competitive gap by diagnosing their specific challenges and leveraging tailored support programs.

Isabella Rossi

June 23, 2026 · Updated October 22, 2026 · 5 min read

Most small and medium-sized enterprises still haven't meaningfully adopted AI, and the reasons are well documented: money, data, skills, and unclear returns. But the picture is changing fast at one edge of the market. Firms launched in 2024 and 2025 are far more likely to use AI from day one than businesses started just a few years earlier, according to JPMorgan Chase Institute analysis of small-business payment activity. That creates a widening divide between established SMEs and new entrants — one that owners can close by diagnosing their specific barrier and matching it to a concrete support program.

The AI Adoption Landscape for SMEs: Lag and Acceleration

The headline trend is a sharp generational split. The JPMorgan Chase Institute tracked the share of firms that paid for an AI tool within their first month of activity, by cohort year. From 2019 through 2023, that first-month adoption rate stayed low and flat: 1.2 percent for the 2019 cohort, 1.7 percent in 2020, 1.7 percent in 2021, 1.5 percent in 2022, and 1.6 percent in 2023.

Then the curve bends. First-month AI adoption rose to 4.1 percent for firms starting in 2024 and 6.5 percent for firms starting in 2025 — roughly four times the 2019 rate. Newer businesses are simply incorporating AI from the onset rather than retrofitting it later.

For established SMEs, the implication is uncomfortable. If existing firms don't address their adoption barriers, the competitive gap between them and AI-native newcomers could keep widening. The OECD, in its December 2025 report on AI adoption by small and medium-sized enterprises, notes that SMEs often lag on precisely the enablers — connectivity, finance, data, compute, and skills — that larger firms take for granted, and that these gaps justify support tailored to firms' needs, digital maturity, and aspirations.

Key Barriers Preventing SME AI Integration

What's actually holding SMEs back? The evidence points to four recurring obstacles, and they compound each other.

Financial constraints. Upfront capital expenditure for AI is a real burden, particularly for smaller firms with limited capital. A 2025 academic review of SME AI adoption published in Applied Sciences found that the perceived and real financial burden is a central reason adoption stalls.
Data readiness. Poor data quality or availability prevents effective AI model training and deployment. Many SMEs haven't digitized core records or standardized and labeled their data — a prerequisite for any serious AI use.
Skills and expertise gaps. The shortage extends beyond technical development to the strategic understanding of AI's business value. Many SME leaders can't yet assess where AI would pay off.
Low ROI visibility and high perceived risk. Uncertain returns make it hard to justify the investment, which feeds back into the funding problem.

No single solution addresses all four. The academic review's authors argue that SMEs need a combination of financial strategies and risk-minimizing implementation models — not one big bet.

Actionable Strategies and Support Programs for SMEs

So what does real help look like? Canada offers the most concrete named examples in the current evidence, drawn from a 2025 G7-focused report on AI adoption hosted by Mila, the Quebec AI institute.

AI Assist Program. A $100 million federal program supporting innovative SMEs developing or integrating generative AI and deep learning by funding research, product development, and testing.
AI Compute Access Fund. Launched in spring 2025 with $300 million, it helps SMEs in life sciences, energy, and advanced manufacturing access high-performance computing, covering up to two-thirds of eligible costs.
Data to AI Program (BDC). Launched by the Business Development Bank of Canada in December 2024, it helps Canadian SMEs adopt AI, improve productivity, and enhance competitiveness through tailored guidance, training, and financing — covering AI integration, automation, and cybersecurity.

Alongside public money, the market itself is lowering entry costs. The Applied Sciences review highlights shared-resource models — AI-as-a-Service and pay-as-you-go platforms — as ways to reduce upfront capital expenditure, alongside public grants, low-interest innovation loans, and tax incentives. Its recommended approach is phased adoption: start small, prove value, then scale.

SME AI Adoption Decision Framework

Use this matrix to diagnose your firm's primary barrier and identify the most relevant type of solution or support program. Find the row that best matches your situation, then investigate the corresponding program category. The mapping is drawn from the OECD's enabler analysis, the 2025 academic review of SME adoption strategies, and the G7 report's Canadian program examples.

Mapping SME AI adoption barriers to strategies and example programs
Barrier	What it looks like	Recommended strategy	Example program or tool
Financial constraints	Limited capital for AI investment; acute for smaller firms	Flexible financing: public grants, low-interest innovation loans, tax incentives; shared-resource models to cut upfront cost and risk	AI Assist Program (Canada) — $100 million funding for SMEs developing or integrating generative AI
Data readiness	Poor data quality or availability blocks model training and deployment	Data-to-AI programs, cloud access, and data-sharing mechanisms; digitize and standardize core records first	Data to AI Program (BDC, Canada) — tailored guidance, training, and financing for data preparation and AI integration
Compute and infrastructure	Can't access high-performance computing; connectivity gaps by region or firm size	Subsidized compute access; connectivity and cloud initiatives	AI Compute Access Fund (Canada) — $300 million, covering up to two-thirds of eligible costs in life sciences, energy, and advanced manufacturing
Skills and expertise gap	Lack of internal capability to implement AI and judge its business value	External expertise, training programs, and AI-as-a-Service platforms that require less specialized knowledge	AI-as-a-Service and pay-as-you-go platforms; BDC-style training components

Mapping SME AI adoption barriers to strategies and example programs

Barrier

What it looks like

Recommended strategy

Example program or tool

Financial constraints

Limited capital for AI investment; acute for smaller firms

Flexible financing: public grants, low-interest innovation loans, tax incentives; shared-resource models to cut upfront cost and risk

AI Assist Program (Canada) — $100 million funding for SMEs developing or integrating generative AI

Data readiness

Poor data quality or availability blocks model training and deployment

Data-to-AI programs, cloud access, and data-sharing mechanisms; digitize and standardize core records first

Data to AI Program (BDC, Canada) — tailored guidance, training, and financing for data preparation and AI integration

Compute and infrastructure

Can't access high-performance computing; connectivity gaps by region or firm size

Subsidized compute access; connectivity and cloud initiatives

AI Compute Access Fund (Canada) — $300 million, covering up to two-thirds of eligible costs in life sciences, energy, and advanced manufacturing

Skills and expertise gap

Lack of internal capability to implement AI and judge its business value

External expertise, training programs, and AI-as-a-Service platforms that require less specialized knowledge

AI-as-a-Service and pay-as-you-go platforms; BDC-style training components

The reasoning path is straightforward: each barrier identified in the evidence has a corresponding support structure, and the Canadian programs show that governments are already funding the bridge. The effectiveness of any program, though, depends on its accessibility and alignment with your specific needs — check eligibility before committing.