AI Liability in SMEs: Governance Instead of Control

AI Liability in SMEs: Why Governance Is the New Control

In German boardrooms, I'm currently encountering a recurring concern: "If we deploy AI, I'll lose control over critical processes. And ultimately, I'll be personally liable for mistakes made by a machine."

At first glance, this fear seems understandable. But it's based on a fundamental misunderstanding of what control actually means in modern business processes.

The Uncomfortable Truth About Manual Control

Those who prevent AI-supported automation don't gain more control. They simply get less transparency. True control doesn't emerge from humans manually processing and signing off on every single transaction.

Real control comes from governance: clear decision rights, defined limits, structured approval processes, and a complete audit trail that's verifiable at any time.

The problem with purely manual processes? They are frequently:

Opaque: Who made which decision and why?
Inconsistent: Identical situations are handled differently
Undocumented: Gut decisions leave no audit trail
Not scalable: The more cases, the bigger the bottleneck

Real-World Example: Agentic Credit Management

Let me illustrate this with a concrete example from credit management that many mid-sized companies will recognize.

Initial situation: A long-standing customer places an order as usual. But warning signals appear in the system:

Payment terms have been extended from 30 to 90 days
Outstanding receivables have increased significantly in recent weeks
Payment behavior has deteriorated

The traditional approach: This information is scattered across different systems. Perhaps someone notices the change, perhaps not. A uniform assessment is missing. The decision ultimately falls to management, often under time pressure and without a complete data foundation.

The governance-based AI approach: An agentic workflow automatically detects the deviation and initiates a predefined playbook:

1. Automatic Limit Check

The system performs a risk classification based on defined criteria:

Current customer creditworthiness
Historical payment behavior
Outstanding receivables relative to credit limit
Industry-specific risk factors

All data used and evaluation criteria are documented.

2. Intelligent Escalation

Depending on severity, automatic notification goes to relevant departments:

Low risk: Monitoring without escalation
Medium risk: Notification to sales and finance
High risk: Immediate escalation to management with complete data foundation

Threshold values are predefined and transparent.

3. Complete Auditability

Every step is documented in an audit-proof manner:

What data foundation was used?
Which evaluation criteria were applied?
What decision was made?
Who initiated what and when?

This documentation is comprehensive and meets the highest compliance requirements.

Where Should Leadership Invest Their Time?

As a managing director, ask yourself the honest question:

Do you want to spend your time on:

Micromanaging data collection from various systems?
Manually reviewing routine cases that follow clear rules?
Tracing individual decisions without structured documentation?

Or would you prefer:

Making decisions on genuine exceptional cases that are properly prepared?
Strategically developing your governance structures?
Reviewing and optimizing your decision rules?

The Difference Between Routine and Exception

Let's be honest: Many so-called "gut decisions" are actually routine decisions following known patterns. They're based on empirical values that can certainly be translated into rules.

And precisely these routine matters belong in a clear operational framework, not on the managing director's desk. This isn't a loss of control, but rather the prerequisite for genuine strategic leadership.

Rethinking the Liability Question

Back to the original concern about liability: What are you actually liable for as a managing director?

Not for personally processing every single transaction. That would be simply impossible in growing companies.

But rather for ensuring that you:

Have established appropriate processes
Have defined clear responsibilities
Have implemented effective controls
Have identified and limited risks

This is precisely what well-designed AI governance delivers. It creates more legal certainty, not less.

Concrete Automation Potential in SMEs

Where are the greatest opportunities for governance-based automation in mid-sized companies?

A) Credit Limits and Payment Terms: Automatic risk assessment based on current data, with clear escalation rules when defined thresholds are exceeded.

B) Payment Approvals: Intelligent workflows with automatic verification against budgets, contracts, and authorizations. Only exceptions reach humans.

C) Discount and Condition Limits: Rule-based approvals within defined corridors, with automatic escalation for deviations.

D) Supplier Onboarding: Automated compliance checks, credit checks, and data validation before human approval.

E) Contract Clauses: AI-supported review for standard deviations and risks, with structured preparation for legal assessment.

The Governance Framework: Critical Success Factors

Implementing effective AI governance in mid-sized companies requires several key elements:

Clear Decision Architecture

Define precisely:

Which decisions can be fully automated?
Which require human review?
Which need management approval?
What are the escalation triggers?

Transparent Rules and Limits

Document all decision criteria:

Risk thresholds and their justification
Data sources and their reliability
Evaluation logic and weighting
Exception handling procedures

Comprehensive Audit Trail

Ensure every automated decision includes:

Complete data foundation
Applied rules and parameters
Decision outcome and reasoning
Timestamp and system version

Regular Review and Optimization

Governance is not static:

Continuously monitor decision quality
Adjust rules based on outcomes
Incorporate new risk factors
Update thresholds as business evolves

Overcoming Implementation Barriers

Many mid-sized companies hesitate to implement AI governance due to perceived obstacles:

"We don't have the data": You likely have more data than you think. Start with what you have and improve incrementally.

"It's too complex": Begin with one process. Credit management or invoice approval are excellent starting points.

"We lack the expertise": Partner with specialized providers who understand mid-market needs and regulatory requirements.

"It's too expensive": Compare the cost of implementation against the cost of inefficiency, errors, and audit failures in manual processes.

The Competitive Advantage of Good Governance

Companies with robust AI governance gain multiple advantages:

Faster decisions in routine matters
Better risk management through consistent application of criteria
Improved compliance with complete documentation
Scalability without proportional headcount growth
Management capacity for strategic rather than operational decisions

In an increasingly competitive market, these advantages can be decisive.

Conclusion: Control Through Transparency

The fear of losing control through AI is understandable but misguided. True control in complex business processes doesn't come from manual processing, but through:

Clear rules instead of gut decisions
Transparent processes instead of undocumented workflows
Structured escalation instead of bottlenecks
Complete documentation instead of audit gaps

AI isn't a risk to your control. It's the tool that finally makes control scalable, traceable, and verifiable.

The question isn't whether you can afford AI. The question is whether you can afford to forego this form of professional governance.

Your personal liability as a director is better protected by robust, documented, and consistent AI-supported processes than by manual intervention that lacks transparency and reproducibility.

How does your company handle this challenge? Where do you see the greatest automation potential?