AI-powered document review: How GenAI can benefit corporate resilience

Compliance documents, emergency plans, contract documents, guidelines and audit reports: in many companies, the volume of documentation is growing faster than it can be properly reviewed. This blog post shows how AI-powered document review can help to assess requirements in a more structured way, highlight gaps more quickly and ease the burden on specialist departments in the compliance environment.

Resilience becomes verifiable when requirements are documented in a traceable manner

Corporate resilience is not something companies can simply tick off as an afterthought. Regulatory requirements are becoming more stringent, audit obligations are increasing, and expectations regarding traceability are rising. This particularly affects areas such as information security, data protection, outsourcing management and business continuity management.

The problem is rarely that companies have no documents at all. Often, the opposite is true.

For example, there are guidelines, policies, process descriptions, evidence of controls, logs and much more. Some documents are up to date, whilst others have evolved over time, been amended several times or are scattered across different departments. This is precisely where the real work begins: are all requirements covered? Do the documents align with one another? Are there gaps, contradictions or outdated information?

This review is a laborious task. It takes time, ties up specialist knowledge and is prone to errors. After all, anyone reviewing a large number of documents manually must not only understand individual pieces of content but also recognise the broader context. This is precisely where AI can be a valuable tool.

What AI-supported document review can achieve

AI-supported document review does not mean that artificial intelligence takes over compliance independently. That would be short-sighted and, in regulated environments, would not be robust enough.

The real added value lies elsewhere: AI can search through large volumes of documents in a structured way, compare content against defined requirements and highlight potential anomalies. It does not act as the final decision-maker, but rather as a support system for experts.

In practice, this might look something like this:

• Documents are uploaded or made available.
• A defined set of questions or requirements specifies what is to be checked.
• The AI searches for relevant text passages in the documents.
• The results are assessed, justified and linked to source references.
• Experts then review the results and make the final assessment.

The advantage is clear: the initial review becomes faster, more structured and more traceable. This can significantly reduce the workload, particularly in the case of recurring checks or extensive documentation.

Why this is particularly exciting in business continuity management

Business Continuity Management, or BCM for short, is a good example of an environment in which documents are highly interdependent.

A functioning BCM consists of more than just a contingency plan. It includes, amongst other things, guidelines, role descriptions, business impact analyses, risk analyses, business continuity strategies, alerting procedures, communication plans, recovery plans, exercise concepts and evidence of continuous improvement.

These documents must be consistent with one another. If a process is assessed as time-critical in the business impact analysis, this must be followed by appropriate recovery times, resource requirements and emergency measures. If roles are described in a guideline, they should also be reflected in alerting and escalation plans. If service providers are relevant to critical processes, these dependencies must be clearly documented and assessed.

This is precisely the sort of task where AI can provide support. Not because it understands better than BCM experts how a company must function in an emergency, but because it can help to systematically check documents against defined requirements.

In this way, a vast and unorganised volume of documents is transformed into a structured basis for technical assessment.

Compl.AI as an example of practical AI in the compliance environment

A concrete example from the adesso environment is Compl.AI. The basic idea behind it is simple: documents are not merely summarised, but analysed against specific criteria. This makes the approach particularly suitable for compliance issues where it is not only relevant to ask ‘What does the document say?’, but also ‘Is a specific requirement met?’.

In the context of DORA, adesso demonstrates with Compl.AI how such an approach can be put into practice. Under the Digital Operational Resilience Act, financial firms must take a structured look at their information and communications technology service providers and their contracts. This is precisely where Compl.AI comes in. The tool provides AI-supported assistance in reviewing contracts against DORA requirements.

You can find out more on the adesso page about Compl.AI.

However, the real significance goes beyond DORA alone. This approach demonstrates how AI can be utilised in the compliance environment when three elements come together:

• Firstly, there needs to be a clear set of technical requirements. The AI must know what to look for and what to assess.
• Secondly, the evaluation must be traceable. A result without a reference to its source is of little use in compliance contexts. Experts must be able to see how an assessment was arrived at.
• Thirdly, a secure technical framework is required. Compliance, contractual and BCM documents, in particular, often contain sensitive information. Data protection, secure processing and controlled operation are therefore not optional extras, but fundamental prerequisites.

Compl.AI thus demonstrates a realistic approach: AI is not used as a gimmick, but as a tool for specific review and analysis processes.

What concrete benefits do companies gain from this?

The benefit does not lie in AI taking over the entire responsibility for compliance. It cannot and should not do so. The benefit lies in the fact that it supports specialist departments, compliance teams, BCM managers and consultants in their preparatory work.

This brings four main advantages.

• 1. Faster initial assessment
  Instead of reading documents one by one and manually searching for relevant statements, an AI-supported review can provide indications as to which requirements are met, partially met or possibly not met. This saves time and helps to prioritise the audit workload more effectively.
• 2. Better preparation for audits and reviews
  Audits rely on evidence. Anyone who can demonstrate where specific requirements are documented is significantly better prepared. An AI-supported analysis can help to find relevant sources more quickly and identify outstanding issues at an early stage.
• 3. Greater transparency regarding gaps
  Many gaps do not arise because nobody was aware of the issue. They arise because information is scattered or dependencies are overlooked. AI can help to highlight such anomalies. For example, if a process is described as critical but no suitable business continuity plan is in place.
• 4. Repeatable audits
  Compliance is not a one-off project. Requirements change, documents are updated and new supporting evidence is added. A structured, AI-supported audit process can be used repeatedly. This transforms a one-off review into a continuous improvement process.

Where the limits lie

However useful AI-supported document review may be, it does not replace professional responsibility.

AI can only work with the information made available to it. If documents are missing, poorly maintained or requirements have been formulated unclearly, the result will also be of limited reliability. Furthermore, AI may misinterpret connections or overemphasise passages of text if the subject-matter context is lacking.

For this reason, AI should always be viewed as a support system in compliance and BCM contexts. It provides guidance, preliminary assessments and structure. The final assessment remains the responsibility of qualified specialists.

This is not a weakness of the approach. It is a realistic way of dealing with it.

Conclusion: Better document review, better basis for decision-making.

AI-supported document review is no substitute for audit expertise. It is a tool for applying this expertise in a more targeted manner.

Particularly in business continuity management and other document-intensive areas of compliance, AI can help to structure large volumes of information, check requirements in a traceable manner and identify gaps more quickly. This does not relieve specialist departments of their responsibility, but rather provides them with better support.

The key point is this: AI should not be seen as an autopilot. It is a co-pilot for document review. Used correctly, it saves time, increases transparency and creates a better basis for well-informed decisions.

And this is precisely where its value lies for modern compliance work.