Where to Start with AI Automation in a Mid-Market Company

1. Start with the most boring processes

The best AI automation wins are rarely glamorous. They live in repetitive work: data entry, document classification, invoice matching, ticket triage, and follow‑ups. These processes have high volume, low risk, and measurable impact.

Many mid‑market firms discover that 30–50% of back‑office workload is repetitive. That is the fastest ROI surface, not advanced predictive models.

2. Score processes by automation potential

Use a simple score: volume × repetitiveness × error rate. Anything that is high on all three dimensions is a candidate for automation. This removes politics and replaces “cool ideas” with measurable ROI potential.

A scoring workshop typically reveals 10–15 opportunities. You only need three to start. Choose the ones with clear owners and clean data sources.

3. Run three pilots in 90 days

The fastest path is three parallel pilots with lightweight governance. Each pilot should have a success metric (time saved, error reduction, or cash impact) and a human‑in‑the‑loop control for higher‑risk outputs.

Most teams can measure ROI in 6–12 weeks. That is enough to decide whether to scale or stop.

4. Governance is not optional

EU AI Act guidance and industry best practices require clear accountability. That means an AI acceptable‑use policy, approved tools list, and escalation rules for sensitive workflows. Governance prevents Shadow AI and protects the company as adoption accelerates.

The governance package does not slow you down. It actually accelerates adoption because teams know what is allowed and safe.

5. What we deliver

• Process audit and automation scoring matrix.
• Three AI pilots deployed within 90 days.
• AI acceptable‑use policy and governance controls.
• ROI dashboard to decide scale vs. stop.

This is executed with our AI agents and a fractional expert to align stakeholders and avoid Shadow AI.

6. Expected ROI timeline

In most mid‑market companies, ROI appears quickly because the baseline is manual and inefficient. A 20% time reduction in a 10‑person operations team is often a €150K annual impact. Scaling that across finance and customer service can multiply the gains.

The goal is not just cost savings. It is decision speed, fewer errors, and the ability to redeploy teams to higher‑value work.

7. Avoid the three classic mistakes

First, avoid starting with a high‑risk process (pricing, compliance, or HR decisions). These create governance pressure and slow adoption. Start with low‑risk workflows to build trust.

Second, do not launch pilots without owners. Each pilot needs a business owner, a success metric, and a clear “stop” decision. Without that, pilots turn into endless experiments with no ROI.

Third, resist tool sprawl. Use the minimum number of tools needed for the first 90 days. Once ROI is proven, you can standardize and scale. This keeps IT and security aligned while the business sees impact quickly.

8. Data readiness checklist

You do not need perfect data to start, but you need a minimum baseline. Use this checklist:

• Data exists in one system of record (even if messy).
• Process owners agree on “what good looks like.”
• Sample data can be shared without compliance risk.
• Success metrics are defined before pilots start.

If you meet at least three of these four, you can safely launch pilots. If not, start with a two‑week data cleanup sprint and then proceed.

Treat this checklist as a go/no‑go gate. It prevents teams from blaming the model when the real issue is missing ownership or inconsistent data. That alone saves weeks of wasted effort.