AI in your company, where to start in 2026

In July 2025, MIT NANDA published a report, "The GenAI Divide: State of AI in Business". The number that shook the market: 95% of generative AI deployments in enterprises generate no measurable return on investment. After spending thirty to forty billion dollars.

This is not a single statistic. It is a pattern.

Three sources, the same number

Gartner, in a report from April 2026 ("AI Projects in I&O Stall Ahead of Meaningful ROI Returns"), reported that only 28% of AI deployments in infrastructure and operations achieve a return on investment. 20% end in outright failure. 57% of IT leaders report at least one failed initiative.

RAND Corporation, after analyzing 65 enterprise deployments (end of 2025), gave its own number: 80% of AI projects fail to deliver the promised business value.

Three different methodologies, three different samples, the same conclusion: most companies deploying AI in 2025-2026 are spending money with no effect.

Why

RAND identified three recurring causes:

• Data quality problems. Data scattered across many systems, disorganized, with no owner. AI learns the chaos and replicates it.
• Organizational immaturity. No governance, no process owners, no alignment of teams around the goal of the deployment.
• Scope drift. A project starts with one goal and ends as an "AI platform for everything". No priorities. No metrics.

None of these causes is about the AI model. All of them are about the company.

What this means in practice

Most Polish companies in 2026 approach AI along one of three paths.

Path 1: let's start with a pilot

The company buys AI consulting, starts a pilot, the pilot shows "promising results" in a demo, but does not scale to production. After 6-9 months the budget runs out and the project goes quiet. A post-mortem? None.

Path 2: let's hire an AI specialist

The company recruits an ML engineer or an "AI Officer". The specialist arrives, sees the chaos in the data and processes, tries to fix it, collides with company politics, and leaves after a year. Next one.

Path 3: let's buy an off-the-shelf solution

The company buys a SaaS product with an AI label (chatbot, copilot, documentation). The deployment goes quickly, because the SaaS is ready. The business effect? Hard to say, no baseline, no metric.

All three paths end the same way: a costly deployment with no measurable ROI.

What works instead

The five percent of companies that do achieve ROI from AI do something different. The pattern:

• They choose one business problem to solve, not ten.
• They have a baseline, they know what the process costs today.
• They have an owner, one decision-maker on the business side.
• They have governance, a process for evaluating results and deciding whether to scale.

In other words, they are ready before they start.

What "readiness" means

Readiness for AI is not a matter of technology. It is a matter of five organizational conditions.

Mapped processes. You know exactly how the process you want to support with AI works. You have documentation. You have an owner. You have metrics.

Data in one place. The data needed for AI is not scattered across twelve systems. It has a clear architecture. It has governance.

The team knows why. The people whose work AI changes understand the goal. They know what AI is supposed to do, and what it is not. They are not afraid of being replaced, because they know how their role is changing.

ROI is measurable. You know what the process costs today (time, people, errors). After the AI deployment you will know what it costs now. Without that, "success" is an opinion, not a fact.

Governance exists. You know who approves new models, who monitors risks, who responds to incidents.

Without these five conditions, do not deploy AI. Put your house in order first.

Where to start in 2026

Months 1-2: a readiness audit. An independent auditor (external, because an internal one has a conflict of interest) assesses 8 areas. The result: a number from 0 to 100, a list of priority blockers, a roadmap.

Months 3-4: quick wins or structure. If the audit result shows readiness above 70, you start an AI pilot on a specific process. If below 70, you start by putting things in order.

Months 5-6: pilot or continued structure. You measure ROI weekly, not quarterly. You scale only if the ROI is positive.

Do not start with a deployment. Start with a question: are we ready.

Closing

Deploying AI in 2026 is not a matter of technology. The models are good. The tooling is good. Vendors are available.

The issue is organizational. Most companies are not ready, not because they "do not understand AI", but because they have no order in their processes, data, and teams.

Order first. Then AI.

This is not a slogan. It is a statistic.