Using AI in Workplace and Corporate Investigations

AI Is Changing How Investigations Are Conducted

Artificial intelligence tools are becoming a practical feature of workplace and corporate investigations. Transcription software converts audio recordings to text. Document review tools search large volumes of electronic communications for keywords and patterns. Drafting assistants help structure and clarify written reports. Scheduling and project management tools coordinate the logistics of complex investigations. These are not future developments. They are in use now, and investigators who do not engage with them risk being left behind in terms of efficiency.

But the integration of AI into workplace investigations raises important questions that every investigator and commissioning organisation needs to think through carefully. What can AI legitimately assist with? Where does its use introduce risk? How should that risk be managed? And how should AI use be documented so that the investigation remains transparent and defensible?

This chapter draws on The Workplace and Corporate Investigator’s Handbook to address each of these questions directly.

This chapter is part of the Workplace and Corporate Investigations Knowledge Guide. If you need an independent investigator see my independent workplace investigator page.

The Fundamental Principle: AI as an Assistive Tool

The starting point for all thinking about AI in workplace investigations is a principle that is straightforward to state but requires consistent application in practice: AI is an assistive tool, not a determinative one. It can support good investigative practice. It cannot replace it.

The decisions that matter in a workplace investigation, what evidence to gather, how to assess competing accounts, whether a witness is credible, what weight to give particular evidence, and what the findings should be, are judgment calls that belong to the investigator. They require contextual understanding, ethical reasoning and human accountability that AI cannot provide. An investigation whose conclusions have been reached by AI rather than by the investigator exercising their own judgment is not an investigation that will withstand scrutiny.

This principle does not make AI useless. Far from it. Applied correctly, AI tools can save substantial time, improve accuracy in certain tasks, and free the investigator to focus on the analytical work that only they can do. The key is understanding which tasks benefit from AI assistance and which must remain entirely within human judgment.

Where AI Can Add Genuine Value

Transcription

The most established and widely used application of AI in workplace investigations is the automated transcription of audio-recorded interviews. AI transcription tools can produce a verbatim text of an interview in a fraction of the time manual transcription would require. This is a significant efficiency gain, particularly in investigations with multiple lengthy interviews.

However, AI-generated transcripts must always be reviewed and corrected by the investigator before they are relied on. Transcription software makes errors, particularly with specialist terminology, strong accents, multiple speakers talking simultaneously, and quiet or poorly recorded speech. A transcript that has not been reviewed and corrected is not a reliable record. It is a draft that requires human verification before it can be used as evidence.

Document review and pattern identification

In investigations involving large volumes of electronic communications, emails, messages, system logs or other digital records, AI-assisted search and review tools can identify relevant documents far more efficiently than manual review. They can search for keywords and phrases across thousands of documents, identify patterns of communication between individuals, flag anomalies in transaction data or system access records, and create timelines from date-stamped materials.

These capabilities are particularly valuable in investigations involving allegations of dishonesty, fraud or data security breaches, where the relevant evidence may be buried in large volumes of routine communications. For detailed guidance on non-witness evidence gathering see the chapter on non-witness evidence in workplace investigations.

As with transcription, AI-assisted document review requires human oversight. Search results must be reviewed critically. AI may surface irrelevant material alongside relevant material, and may miss material that is phrased in ways the search algorithm does not recognise. The investigator remains responsible for determining what is relevant and what weight it carries.

Interview planning and question preparation

AI tools can assist with structuring interview preparation by identifying themes from a review of the available evidence, suggesting questions that address identified gaps, and helping organise the preparation for complex multi-witness investigations. This can be useful as a starting point for preparation, particularly in complex cases.

However, AI-generated interview questions must be reviewed and adapted by the investigator before use. They will not reflect the specific dynamics, sensitivities and evidential nuances of the particular case with the precision that the investigator’s own judgment brings. AI generates generic frameworks. The investigator adapts them to the specific situation.

Drafting support

AI drafting tools can assist with structuring the investigation report, improving the clarity and consistency of language, identifying internal inconsistencies in a draft, and suggesting how complex reasoning might be expressed more clearly. These functions can genuinely improve the quality and readability of a report that the investigator has drafted based on their own analysis.

The critical point is that AI should be assisting the drafting of conclusions the investigator has already reached through their own analysis, not generating those conclusions. An AI-drafted report that presents findings the investigator has not independently reached is not a legitimate investigation report. It is a fabrication wearing the appearance of one.

Evidence bundle management

AI tools can assist with building and organising evidence bundles, including automatic indexing, document categorisation and the creation of hyperlinked navigation. These functions reduce administrative burden and improve the accessibility of the bundle without affecting the substance of the investigation. For detailed guidance on evidence bundle creation see the chapter on writing the investigation report and creating the evidence bundle.

Where AI Must Not Be Used

The boundaries of appropriate AI use in workplace investigations are as important as understanding where AI can help. The following functions must remain entirely within human judgment and must not be delegated to AI tools.

Assessing credibility. Whether a witness is telling the truth, whether their account is reliable, and how their evidence should be weighted against competing accounts are quintessentially human judgments that require contextual understanding, knowledge of the specific case, and ethical accountability. AI has no capacity to assess credibility in any meaningful sense. An AI tool that purports to assess witness credibility based on linguistic patterns or other proxies is not performing a genuine credibility assessment. It is generating an output that carries the risk of significant bias and error while appearing authoritative.

Determining intent. Whether a person acted deliberately, recklessly or accidentally is a judgment about human motivation and state of mind that requires the kind of contextual understanding and reasoning that AI cannot provide. Findings about intent must be grounded in the investigator’s own assessment of all the evidence.

Weighing competing evidence. The determination of how much weight to give to each piece of evidence, and how to resolve conflicts between competing accounts and materials, is the core analytical task of the investigation. It requires judgment, not pattern matching. It cannot be outsourced to AI.

Reaching investigative conclusions. The findings of fact that form the conclusions of the investigation must be the investigator’s own, reached through their own reasoning process applied to the evidence. AI can help document and present conclusions the investigator has already reached. It cannot reach them.

The Risks of AI Use in Investigations

False authority

AI-generated material can appear confident, polished and analytical in a way that creates an unjustified impression of reliability. An AI-produced analysis that presents uncertain conclusions with apparent authority is particularly dangerous in an investigation context, where the quality of the reasoning directly affects the fairness of the outcome. Investigators must not treat AI outputs as findings, analysis or conclusions. They are drafts and suggestions that require independent verification and critical assessment.

Bias amplification

AI tools reflect the biases present in their training data and in the way prompts are framed. In workplace investigations, which frequently involve protected characteristics, power imbalances and sensitive personal circumstances, the risk that AI outputs reinforce rather than challenge the investigator’s existing assumptions is significant. Using AI tools uncritically can amplify unconscious bias rather than providing the independent check that AI is sometimes assumed to offer. For a detailed treatment of unconscious bias in investigations see the chapter on evidence, credibility and decision-making.

Over-reliance

Excessive dependence on AI tools can weaken investigative rigour by encouraging superficial analysis, reducing the investigator’s engagement with the nuance of the evidence, and creating a false sense that the analytical work has been done when it has only been approximated. The risk is particularly acute in organisations that are cost-conscious and may be tempted to use AI to reduce the time spent on investigation work that genuinely requires skilled human judgment.

Confidentiality and data protection

Some AI tools process or store information on external servers, creating potential exposure of sensitive, confidential or legally privileged material. Investigators must understand how any AI tool they use handles data before using it in an investigation context. Where there is a risk that confidential or privileged material might be exposed through AI use, that tool should not be used. Data protection obligations under the UK GDPR must be considered and complied with throughout.

Transparency and Defensibility

One of the most important practical requirements for AI use in workplace investigations is transparency. Organisations and investigators may be asked, whether in tribunal proceedings, regulatory scrutiny or internal review, whether and how AI was used in the investigation and what role it played in producing the report and findings.

The investigator should be able to answer these questions clearly and accurately. Where AI has been used, the nature of its use should be capable of being explained: what it assisted with, what it did not assist with, and how the investigator verified and validated its outputs before relying on them. Vague or evasive answers to questions about AI use are more damaging than straightforward disclosure of appropriate and limited use.

Documenting AI use as part of the investigation record, in the same way that other methodological decisions are documented, is good practice and provides a basis for the transparent account that may later be required.

A Practical Framework for AI Use in Investigations

The simplest practical framework is to treat AI tools in the same way as any other investigative tool, such as a checklist or a template, but with heightened awareness of the specific risks they carry. Ask, for each proposed use of AI, whether it is assisting the investigator’s thinking or replacing it. Whether it is improving efficiency on a task that does not require human judgment, or delegating a task that does. Whether its outputs will be independently verified before being relied on. Whether its use is consistent with data protection obligations and the confidentiality requirements of the investigation. And whether it can be transparently documented and explained if required.

Where the answer to the first question in each pair is yes, the use is likely to be appropriate. Where the answer to the second is yes, it is not.

Published Resources

My book The Workplace and Corporate Investigator’s Handbook covers this chapter in full, including detailed guidance on specific AI tools and their appropriate use at each stage of the investigation process, the full risk framework for AI in investigations, the data protection considerations that apply to AI use, and real examples of how AI can assist and how it can go wrong in an investigation context.

See all books.

Frequently Asked Questions

Can AI be used to transcribe investigation interviews?

Yes, and this is one of the most valuable and legitimate uses of AI in the investigation process. AI transcription tools can produce a verbatim record of an interview in a fraction of the time manual transcription requires. The transcript must always be reviewed and corrected by the investigator before it is relied on, because AI transcription tools make errors that require human verification.

Can AI assess whether a witness is telling the truth?

No. Credibility assessment is a quintessentially human judgment that requires contextual understanding, knowledge of the specific case and ethical accountability. AI tools that purport to assess credibility through linguistic analysis or other proxies carry a significant risk of bias and error and must not be used for this purpose in workplace investigations.

Does using AI in an investigation make it less defensible?

Not if AI is used appropriately and transparently. Appropriate use means limiting AI to assistive functions that do not involve judgment calls, verifying all AI outputs independently before relying on them, and documenting how AI was used so that its role can be explained clearly if required. Inappropriate use, particularly using AI to reach conclusions or assess evidence, significantly undermines the defensibility of the investigation.

What data protection issues arise from using AI in investigations?

The main risks are that AI tools may process or store personal data on external servers, potentially exposing confidential or sensitive information about the parties, witnesses and the subject matter of the investigation. Investigators must understand how any AI tool handles data before using it, ensure that its use is consistent with the organisation’s data protection policies, and consider whether specific AI tools are appropriate for use with sensitive or legally privileged material.

Should AI use in an investigation be disclosed to the parties?

Where AI has been used in a way that could affect the findings or the process, transparency about its use is good practice and reduces the risk of later challenge. The investigator should be able to explain clearly what AI was used for, how its outputs were verified, and what role it played in producing the report. Straightforward disclosure of appropriate and limited AI use is significantly less damaging than vague or evasive answers to questions about it.

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Important disclaimer: This page is provided for general information and educational purposes only and does not constitute legal advice. The content may not be legally accurate for your specific situation. You must not rely on anything on this page in respect of your legal rights or obligations. Always seek independent legal advice before taking or refraining from taking any action. The author accepts no responsibility for any decisions made or outcomes arising from use of this material. If you would like specific advice, contact me here.

AI in modern workplace investigations