38% of Finnish Companies Use AI, but 68% of Large Enterprises Do – How Growth Companies Close the Gap

38 percent of Finnish companies used AI in spring 2025. That share grew by 14 percentage points from the previous year (Statistics Finland, 2025). The number hides a large gap: among enterprises with 100 or more employees, 68 percent already used AI.

In information and communication activities, the share was 80 percent. For a mid-sized growth company, the picture looks different. There is no dedicated data team, and no budget for experiments that are allowed to fail without anyone losing sleep over it.

Growth company leaders don't buy technology. They buy saved time, new sales, and competitive advantage. If AI doesn't show up in results within a year, the project doesn't move to the next phase. This article covers why the gap exists and how growth companies close it.

Why do mid-sized growth companies fall behind on AI?

A mid-sized growth company falls behind because it lacks a large enterprise's dedicated data or AI team. Data sits scattered across CRM, ERP, and spreadsheets. According to Statistics Finland (2025), 45 percent of companies performed data analytics, but only 36 percent did so with their own staff.

In the rest, analytics was outsourced or never done at all. The same applies to AI: a pilot stays a single employee's initiative when nobody owns moving it into production. A sales director hears a competitor uses AI for quoting, but their own CRM data is too messy to feed any agent.

Skills gaps aren't the only cause. Just as often, a pilot stays a disconnected project without an owner or a timeline. Large enterprises have the resources to push even a failed pilot through to a conclusion. Growth companies usually don't.

What do large enterprises do differently from growth companies?

Large enterprises build AI on top of an existing foundation. According to Statistics Finland (2025), 79 percent of companies used cloud services and 58 percent used an ERP system. Large enterprises don't need to build these — they're already in place.

The foundation is ready before anyone tries to build an agent. A growth company doesn't need to copy a large enterprise's organization or budget. It needs the right sequence: fix the data first, then build the agent on top.

How does a growth company close the AI gap in practice?

A growth company doesn't need an enterprise-sized AI team. It needs three steps in the right order: mapping, connecting the data, and building the agent. Each step delivers value on its own, even if the next step doesn't start immediately.

Master Plan is an AI strategy sprint that maps where AI creates the most value for your company — measured in euros. It answers the question a large enterprise no longer has to ask: where to start.

Master Layer is a data foundation layer that connects your company's existing systems — CRM, ERP, documents — safely for AI to use. This step fixes the exact problem that keeps growth companies in the 38 percent group instead of the large enterprises' 68 percent.

Master Mind is a set of AI agents that operates on top of Master Layer's data and runs business processes independently. Custom solutions are delivered using an agile sprint model: one sprint equals 3 development days. First results appear within weeks, not after months of planning.

The same sequence explains why clear AI leadership in a growth company decides whether a project moves forward. The gap doesn't close by buying more tools. It closes when someone in the organization owns the decision to carry AI all the way into production.

The Aini AI assistant AIMASTER built for Jaajo Linnonmaa is an example of a solution already running in production, built with the sprint model without months of planning. It shows the sprint model works at growth-company scale too — not only with a large enterprise's resources.

How many Finnish companies use AI in 2025?

38 percent of Finnish companies used AI in spring 2025, up 14 percentage points from the previous year (Statistics Finland, 2025). Among enterprises with 100+ employees, the share was 68 percent, and 80 percent in information and communication activities. Growth is fast, but the gap between size classes remains wide.

Why does AI use concentrate in large enterprises?

Large enterprises already have cloud services, ERP and CRM systems, and their own data team. Building an agent only requires one extra layer on top of an existing foundation. In a growth company, the same building blocks often need to be built first, which slows adoption.

Where should a growth company start with AI adoption?

Start with a mapping exercise that shows, in euros, which process creates the most value from AI. This step is productized as Master Plan. Only after mapping does it make sense to connect the data and build the first agent.

How long does AI agent deployment take for a growth company?

Development moves in 3-day sprints, and first results appear within weeks. The overall timeline depends on how many systems need to be connected through Master Layer. The sprint model makes the schedule predictable from day one.

What does AI adoption cost a growth company?

Cost depends on scope and how many systems need to be connected. The sprint model makes cost predictable, because billing happens for completed 3-day sprints. The first step is mapping where AI creates the most value — that determines the budget, not the other way around.

The gap between large enterprises and growth companies doesn't close by waiting. It closes when the mapping gets done and the data gets connected for AI to use. Book a free Master Mind analysis and find out where AI creates the most value for your company.