How to Get Started with AI – A Practical Guide for Small and Medium-Sized Businesses

AI has quickly become one of the most talked-about tools for efficiency and innovation. At the same time, it’s easy to take a wrong turn: either AI becomes a technology-driven initiative with unclear value, or organizations get stuck in investigations and policy documents before anything is ever tested.

A more effective way to approach AI is to see it as a capability the organization builds over time: the ability to identify the right problems, run small experiments, measure impact, manage risk, and then scale what works.

“AI is not a project – it’s a capability.”

In this article, you’ll find a practical approach tailored for small and medium-sized businesses: from the first question (“where can AI create the most value?”), to the first pilot, and onward to making AI part of everyday work rather than just a demo.

1) Start in the right place: what should AI improve?

The fastest way to get stuck is to start with “we need to implement AI.” Instead, start with what you want to improve. At its core, AI is a way to automate, augment, or improve behaviors in a process: writing, reading, summarizing, classifying, searching, prioritizing, detecting anomalies, or making recommendations.

A simple way to find the right starting point is to ask three questions:

• Where does time leak every week? (e.g. manual administration, repetitive questions, duplicate work)

• Where do errors occur or quality vary? (e.g. case handling, documentation, quotes, reporting)

• Where are decisions made too late or with insufficient data? (e.g. forecasting, prioritization, planning)

Aim for an area where the business already feels the pain. If the “problem” isn’t important enough for someone to want to own it, it’s rarely a good AI case.

2) Choose the first use case: “small enough to succeed, big enough to matter”

Your first use case sets the tone for everything that follows. Choose something that can be tested without redesigning half the organization, but that still delivers a clear, visible effect.

Criteria for a good first case:

• A clear business owner (someone who wants it to happen, not just “supports the idea”)

• Easy to scope (one process, one team, one channel, one data source)

• Measurable value (time, quality, response time, throughput, customer satisfaction, cost)

• Manageable risk (low data sensitivity, clear controls)

• Testable within 2–6 weeks

Real-world examples:

• Customer service: AI suggests replies and links relevant knowledge articles

• Sales / delivery: summarize meetings and suggest next steps or tasks

• Internal efficiency: chatbot for policies, handbooks, and requirements

• Finance / procurement: detect invoice anomalies or categorize costs

3) Secure the foundations: data, security, and rules (before you “accelerate”)

Many AI initiatives slow down not because the technology is hard, but because it’s unclear what is allowed. Clear boundaries early on make it easier to do the right thing.

Data (practical, not theoretical):

• Where is the data you need? (case systems, CRM, Teams/SharePoint, email, file servers, BI)

• Who owns the data and who can grant access?

• Does anything need to change in how data is stored or handled?

• What is the data quality like? (missing fields, inconsistent formats, duplicates, outdated data)

Security and GDPR (minimum level to start testing):

• What can be sent to external services – and what cannot?

• Is personal data involved? How are logs, retention, and access handled?

• Do datasets need to be anonymized, masked, or restricted?

• Is a supplier review or DPA required before going live?

At minimum, create an “AI policy light” (one page that helps people act correctly):

• Examples of what is “okay” and “not okay”

• Review requirements (e.g. AI may suggest, humans publish)

• Who to contact in case of uncertainty

The goal is not perfect documentation, but removing friction so you can test responsibly.

4) Set up a minimal team and way of working

SMEs rarely need a large AI center of excellence to get started. What you need is a small team with clear roles and a pace that drives progress.

Typically:

• Business owner: prioritizes, makes decisions, owns the impact

• Process / product lead: aligns goals, scope, change, and follow-up

• Tech / AI: prototypes, integrates, quality-assures

• Security / legal (on demand): reviews risks and frameworks when needed

A common and effective approach is to run pilots in short iterations—one to two-week loops—where you test in real workflows, adjust, and test again.

Work from clear hypotheses rather than vague ambitions, such as: “If we do X, Y should decrease by Z.” This makes it possible to evaluate whether the pilot actually delivered value.

Plan for human-in-the-loop early on: AI suggests, humans review and take responsibility.

Collect structured feedback from users: what saved time, what went wrong, and what felt unclear or unsafe.

Once this way of working is established, it becomes easy to add more use cases to a small portfolio—without every new initiative feeling like starting from scratch.

5) Build the first pilot: prove value with measurable impact

A good pilot should answer two questions: Does it work? and Is it worth continuing? That requires a baseline, metrics, and clear scope.

Pilot steps:

• Define the baseline: e.g. average handling time, response time, cases per person, error rate, documentation time

• Define what “better” means: time saved, improved quality, fewer interruptions, higher accuracy, higher customer satisfaction

• Build the simplest testable version: often an internal feature in an existing tool or a simple UI prototype

• Safety nets: clear labeling (“AI suggestion”), review requirements, logging, and a way to report errors

• Test with a small group: 5–15 users is often enough if they work in real flows

The goal is not 100% automation, but to identify where AI creates strong leverage—and where the risks are.

6) From pilot to everyday use: integrate, train, and change

What works in a pilot must translate into everyday behavior. Many AI initiatives get stuck in endless pilot mode because production and scaling were never planned.

Make AI easy to use:

• Integrate where work happens (case systems, CRM, intranet, document tools)

• Standardize prompts and templates where relevant

• Be clear when AI is used and what it is based on

Create simple governance:

• Who owns the model or solution?

• How do you monitor quality over time?

• How do you handle new data sources, new risks, or changed processes?

With this in place, you can scale without every new AI case becoming a one-off solution.

Summary: a simple starting plan

In simplified terms, these are the most important steps to get started with AI in a sound way:

• Choose a problem that hurts (time, quality, or decisions)

• Set the boundaries (data, security, what’s allowed)

• Appoint a minimal team and a clear owner

• Run a 2–6 week pilot with clear metrics

• Decide based on results: stop, improve, or scale

An independent conclusion: the companies that succeed are rarely those that start with the most technology—but those that start with clear value, test quickly, and learn systematically.

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