A practical AI workflow for Slovak SMEs

Artificial intelligence is already useful for routine work such as analysing text, preparing first drafts, classifying requests or finding information. The harder question for a small or medium-sized business is not whether a tool can produce an impressive result once. It is whether the same task can be completed reliably next week, by another colleague, without exposing confidential information or creating more review work than it saves.

This distinction matters in Slovakia. The European Commission’s 2025 Digital Decade report says that Slovak businesses still show low adoption of advanced technologies and that many SMEs have low digital intensity. Across the EU, meanwhile, Eurostat reports that 20% of enterprises with at least ten employees used AI in 2025; among SMEs, the figure was about 19%. Adoption is moving quickly, but dependable implementation remains a management task rather than a software purchase.

1. Define a business result, not an “AI project”

Start with a visible problem: quotations take too long to prepare, customer enquiries are repeatedly sorted by hand, meeting notes disappear into folders, or product descriptions vary in quality. Describe the current process, its owner, its frequency and the cost of delay or rework.

A useful target is specific enough to test: “reduce the preparation time for a standard first draft from 45 to 20 minutes while every final document is approved by the account manager.” This creates a baseline and prevents the pilot from being judged by novelty alone.

2. Choose one bounded, low-consequence use case

The first workflow should be frequent enough to measure but limited enough to stop safely. Internal summaries, draft structures, categorisation and retrieval from approved reference material are often easier starting points than autonomous decisions, employment matters, creditworthiness, health information or communication that is published without review.

Before testing, write down what the system may do, what it must never do and when a person must take over. A narrow boundary makes training, quality control and later improvement much easier.

3. Map the data before selecting the tool

List the information entering the workflow: public material, internal operating information, personal data, customer documents, contracts or trade secrets. Then decide which categories may be processed in which environment. Do not assume that a convenient consumer tool is suitable for confidential business data.

The practical checks include contractual terms, access rights, retention, the provider’s use of submitted data, account administration and a deletion or export route. Apply data minimisation: if a task can be completed with names and identifiers removed, remove them before processing.

4. Design a workflow with evidence and human review

A robust process specifies the approved input, the instruction or template, the expected output format, the reviewer and the record that is retained. For factual work, require links or references to the approved source material and make “not enough information” an acceptable output.

Human review must be real, not ceremonial. The reviewer needs the knowledge, time and authority to reject the result. ENISA’s guidance for SMEs emphasises basic cyber hygiene, risk assessment, access control and business continuity; an AI-supported process should fit those controls rather than bypass them.

5. Run a measured pilot

Test the workflow on a representative sample for two to four weeks. Record at least four dimensions:

Time: total minutes including correction and approval.
Quality: errors, omissions and compliance with the required format.
Risk: sensitive-data incidents, inappropriate outputs and access problems.
Use: how often colleagues follow the process and where they work around it.

Compare the results with the baseline. If the tool saves drafting time but doubles review time, the process has not yet improved. Adjust one component at a time so that the cause of a change remains visible.

6. Establish ownership before scaling

Once the pilot works, assign an owner for the workflow, an owner for access and data, a review interval and a response plan for provider or model changes. Keep a short register of approved AI uses, responsible people, data categories and controls. Train users on both operation and limitations.

The EU AI Act has applied in stages and most provisions became applicable on 2 August 2026, while specific high-risk rules have later dates. Transparency duties can also matter when people interact with AI or encounter generated or altered content. A company should therefore classify its actual role and use case instead of assuming that every AI tool has the same obligations. For consequential or regulated applications, obtain qualified legal and data-protection advice.

A practical 30-day sequence

Week 1: document the task, baseline, data and boundaries.
Week 2: configure the smallest viable workflow and train two or three users.
Week 3: test representative cases and record time, quality, risk and usage.
Week 4: compare results, correct weaknesses and decide whether to stop, revise or scale.

Slovak companies do not have to solve this alone. The Slovak European Digital Innovation Hub Hopero, for example, offers an AI maturity self-assessment and services for AI projects. The European Commission also recommends continued support for Slovak SMEs through EDIHs and related programmes. External support is most valuable when the company still retains clear internal ownership of the decision and the workflow.

Sources and further reading

Facts and regulatory context were checked against the following sources on 21 August 2026. The practical recommendations and wording in this article are original editorial work by Merkle s. r. o.

This article provides general business information, not legal advice. Requirements depend on the specific system, data, role and use case.