Principles for integrating AI into business processes

Using AI in business automation can create real value, but only when it’s applied with clear principles in mind. AI is powerful, but also probabilistic, so it needs structure and guardrails to support reliable processes.

Below are the principles to implement AI in a way that consistently delivers business value, supports digital transformation, and strengthens process automation.

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From unstructured data to structured

Large Language Models (LLMs) and similar AI technologies are probabilistic. Their power lies in transforming unstructured data (texts, images, audio) into structured data such as numbers, labels, and categories.

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Structured data is essential for deterministic automation. Once unstructured data is converted, it can feed into reliable business logic, including:

• Profile scoring
• Document analysis
• Classification and routing
• Automated compliance checks

This principle makes unstructured data usable in traditional automation flows without losing control over outcomes. It also enables scalable workflow automation, data enrichment, and AI-assisted decision support.

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Prompting: use the probabilistic nature of LLMs the right way

LLMs do not behave deterministically. Outputs may vary, and inaccuracies can occur. To handle this, we follow a prompting principle:

Provide large descriptive input and request a small, specific output.

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This approach increases reliability. Examples include:

• Scoring a profile on a single property from 0–10
• Extracting a small set of fields from a long text
• Assigning a category based on a detailed description

By narrowing the required output, you reduce ambiguity and variance — key for stable business automation, workflow optimization, and enterprise AI integration.

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AI is not a miracle worker

AI adds value, but it should not replace sound process design or human judgment. When automating a business process, we follow three guidelines:

1. Automate traditional process steps using structured data. Keep core business logic deterministic. This ensures transparency, auditability, and predictable outcomes.
2. If data is unstructured, use AI to transform it into structured data. Think of AI as an input processor, not the decision-maker. Use it for extraction, classification, or enrichment before the process continues.
3. Use human intelligence for the most important and impactful decisions. Humans remain essential for exceptions, final approvals, and context-heavy decisions.

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Keep AI steps as small as possible

Because probabilistic errors can compound, AI tasks should be minimal and clearly defined. Breaking AI actions into small, contained steps prevents divergence and keeps automation flows stable.

Small tasks also make AI performance easier to monitor and improve over time, supporting continuous optimization and reliable business process automation.

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Final thoughts

These principles help organizations use AI in a controlled, value-driven way. By converting unstructured data, designing effective prompts, limiting AI decision-making, and keeping tasks small, businesses can safely integrate AI into their no-code automation workflows and accelerate digitalization.