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MOVING STALLED AI PROJECTS FORWARD

The AI gives answers. Can you rely on them?

A knowledge assistant, chatbot or AI agent needs to work reliably in everyday business. We investigate why results vary, workflows break down or the value falls short of expectations.

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THE STARTING POINT

A good test run does not guarantee everyday reliability.

A prototype often works with selected documents and a few familiar questions. In everyday use, data changes, users have different permissions and exceptions become routine. Answers that sound plausible may still be incomplete or factually wrong.

With AI agents, results can also trigger further actions. Alongside answer quality, permitted actions, approvals, stopping conditions and error handling therefore need to be assessed.

THE ESSENTIAL FOUNDATIONS

Where AI projects get stuck.

  • Data and knowledge access

    Outdated, contradictory or unsuitable sources can contribute to unreliable answers. We review source selection, updates, discoverability and attribution.

  • Permissions

    Who may see which information and perform which actions? User permissions must remain effective during data retrieval and within connected tools.

  • Output quality and limits

    We examine typical tasks, known errors and situations in which the system should not provide a definitive answer. A structured handover to people may form part of the workflow.

  • Integration and economics

    Data exchanges, external services, response times and running costs are considered together. More model calls alone will not fix unclear processes or missing integrations.

MAKING QUALITY ASSESSABLE

Turn expectations into testable criteria.

Together, we define which tasks the application should perform and what makes a result useful. A test catalogue includes representative cases, exceptions and tasks the system should decline or escalate.

Depending on the use case, checks may cover factual correctness, grounding in sources, handling of missing knowledge, compliance with permissions and correct execution of approved actions. Reference answers or subject-matter evaluations need responsible people to provide them.

Changes to models, instructions, data or tools are checked against these criteria. This creates a basis for deciding on controlled use — without promising that the system will never make mistakes.

THE GOAL

A realistic path from pilot to production.

Explore ongoing quality checks for AI features
  • Identified causes of unreliable results or workflows.
  • Quality criteria tailored to the use case and representative tests.
  • Prioritised improvements to data, permissions and integration.
  • An assessment of operational effort, costs and remaining limits.

FREQUENTLY ASKED QUESTIONS

What you
should know.

Possible causes include unsuitable or outdated data, changing tasks, unresolved permissions, missing integrations or undefined quality standards. Which cause matters in a particular case needs to be assessed.

There are often specific opportunities, such as source selection, document updates or handling questions outside the intended knowledge base. Whether these are sufficient is determined through an assessment using representative tasks.

It needs clearly limited tools and permissions, traceable actions and defined error handling. Consequential steps may require human approval. Appropriate controls depend on the specific use and its possible effects.

Yes. If quality, data access, integration effort or running costs do not support a viable use case, this must be part of the recommendation. Narrowing the use case or discontinuing it may be the more sensible decision.

THE NEXT STEP

Let’s make the limits of your AI clear.

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