Where should a mid-size company start with AI?

Start by identifying one repetitive, time-consuming process that relies on data you already have. Common first projects include customer support automation, document processing, or internal knowledge search. Pick something small, measurable, and valuable — then prove it works before expanding.

How much should a mid-size company budget for AI in year one?

A realistic first-year budget is €10,000 to €50,000. This covers a discovery phase, a pilot project, and initial deployment. You do not need to allocate a percentage of revenue — start with what you can invest in one focused project and grow from proven results.

Do we need to hire AI specialists?

Not initially. Most mid-size companies get better results by partnering with an AI consultancy for their first project. This gives you a working system and practical knowledge without the overhead of hiring. Once you understand what works, you can decide whether to build internal capability.

How long before we see ROI from AI?

A well-chosen pilot project can show measurable results in 4 to 8 weeks. Full ROI on a production system typically becomes clear within 3 to 6 months. The key is picking a use case with clear, measurable metrics from the start.

What are the biggest mistakes mid-size companies make with AI?

Three common mistakes: starting too big (trying to transform everything at once instead of proving one use case), focusing on technology instead of business problems (buying tools before understanding needs), and not involving the team early enough (the best AI system fails if nobody uses it).

AI Strategy for Mid-Size Companies: A Practical Guide

Most mid-size companies do not need a massive AI transformation. They need one clear use case, a small pilot, and a partner who helps them grow from there.

Here is the reality: 98% of mid-size company leaders believe AI has value for their business, but only 7% have an actual strategy in place. The gap is not about belief — it is about knowing where to start. The good news is that starting is simpler than most people think. You do not need a dedicated AI team, a six-figure budget, or a 12-month roadmap. You need one clear problem, a small pilot, and the willingness to learn from results.

This guide walks you through a practical approach to AI strategy that works for companies with 20 to 500 employees. No buzzwords. No theoretical frameworks. Just the steps that actually work.

Step 1: Find Your First Use Case

The best first AI project is not the most exciting one — it is the most obvious one. Look for processes that are:

Repetitive and time-consuming. Tasks that eat hours every week and follow predictable patterns.
Data-rich. You already have the information needed — customer emails, support tickets, documents, product data.
Measurable. You can clearly define what “better” looks like — faster response times, fewer errors, less manual work.

Common first use cases that work well:

Customer support: Automating responses to frequently asked questions, routing tickets to the right person, or drafting reply suggestions.
Document processing: Extracting information from invoices, contracts, or forms that someone currently handles manually.
Internal knowledge search: Making it easy for your team to find answers in company documentation, policies, or past projects.
Content and communication: Drafting emails, translating documents, or summarizing meeting notes.

The rule of thumb: If someone on your team spends more than 5 hours per week on a repetitive task that involves text, data, or decisions based on patterns — that is a candidate for AI.

Step 2: Run a Small Pilot (4-6 Weeks)

Do not plan for months. Pick your use case and test it quickly.

What a good pilot looks like:

Scope: One use case, one team, one measurable goal
Timeline: 4-6 weeks from start to results
Budget: €5,000 – €15,000 (including discovery and prototype)
Success metric: Something concrete — “reduce average response time from 4 hours to 30 minutes” or “process 80% of invoices without manual intervention”

What a good pilot does not look like:

A 6-month “exploration phase” with no clear deliverable
A technology evaluation that compares 15 vendors without testing any
An internal project where someone “plays with ChatGPT” without a business goal

The purpose of a pilot is to answer one question: Does AI solve this specific problem well enough to justify further investment? If yes, you scale. If no, you learned something valuable for €10,000 instead of €100,000.

Step 3: Build on What Works

Once your pilot proves value, expand deliberately. This does not mean launching five AI projects at once. It means:

Optimize the pilot. Take your working prototype and make it production-ready. Add monitoring, handle edge cases, train your team.
Measure the impact. Document the actual ROI — time saved, errors reduced, customer satisfaction improved. These numbers justify the next investment.
Identify the next use case. Based on what you learned, pick the next highest-value opportunity. Each project gets easier because you understand the process.

A realistic 12-month timeline:

Months 1-2: Discovery and pilot (one use case)
Months 3-4: Production deployment of first system
Months 5-8: Second use case, building on lessons learned
Months 9-12: Third use case or expansion of existing systems

Most companies that follow this approach have 2-3 working AI systems within a year, each delivering measurable value.

What to Budget

Year one is about learning, not transforming. A realistic budget:

Discovery and pilot: €5,000 – €15,000
First production system: €15,000 – €40,000
Ongoing costs: €300 – €1,500/month
Total year one: €25,000 – €60,000

This is not a percentage of revenue or a board-level strategic investment. It is the cost of solving one or two real business problems with AI. If the ROI is there, year two budgets justify themselves.

You do not need to hire anyone. A specialized partner handles the technical work. Your team provides domain knowledge, feedback, and testing. Over time, you build internal understanding naturally.

The Three Things That Actually Matter

After working with dozens of mid-size companies, these are the factors that separate successful AI adoption from expensive experiments:

1. Executive Sponsorship (But Not Executive Micromanagement)

AI projects need someone senior enough to remove blockers and allocate resources. But they do not need weekly steering committees or 50-page strategy documents. The best executive sponsors say: “Here is the problem, here is the budget, here is the team — show me results in 6 weeks.”

2. Team Involvement From Day One

The people who will use the AI system need to be part of building it. Not as developers — as domain experts. They know the edge cases, the real workflows, and the reasons why “the obvious solution” does not work. Involve them early, and they become champions. Surprise them with a finished system, and they become resistors.

3. Willingness to Start Imperfect

The first version of your AI system will not be perfect. It will handle 70-80% of cases well and struggle with the rest. That is fine. The goal is not perfection — it is proving that AI adds value. You improve from there.

What Not to Do

Do not start with a “comprehensive AI strategy.” Start with one project. Strategy emerges from experience.
Do not buy tools before understanding problems. The technology is not the hard part. Understanding your data and processes is.
Do not try to build everything in-house. Your first AI project should be with a partner. Build internal capability after you know what works.
Do not wait for perfect data. Your data does not need to be perfect. It needs to be good enough for one specific use case.
Do not compare yourself to tech giants. Google’s AI strategy is irrelevant to your business. Focus on your specific problems and customers.

Key Takeaways

Start with one problem, not a strategy. Pick a repetitive, data-rich process and test AI on it.
Run a pilot in 4-6 weeks. Spend €5,000 – €15,000 to prove whether AI works for your use case.
Build on proven results. Scale what works. Drop what does not. Each project gets easier.
Budget €25,000 – €60,000 for year one. That gets you 1-2 working systems with measurable ROI.
You do not need an AI team. Partner for the first project. Build internal knowledge over time.