Using AI in Due Diligence Without Missing Red Flags

Due diligence is one of the most obvious places to use AI: repetitive documents, tight timetables and clients who want clear answers, not just lever-arch files.

The risk is equally clear: missing red flags because you relied too heavily on automation, or being unable to explain to a court or regulator how checks were carried out.

This article sets out a practical approach to using AI in due diligence for UK transactions while keeping lawyers firmly in control.

What you are really trying to achieve

In most deals, the real aims of due diligence are to:

identify issues that could change price or deal structure;
flag risks that need warranty or indemnity protection;
confirm that the target can actually do what the transaction assumes; and
avoid nasty surprises post-completion.

AI can help by:

accelerating document triage and extraction;
spotting patterns across large volumes of agreements; and
helping to draft clear, structured reports.

It cannot decide how serious a finding is in context – that remains a matter for human legal and commercial judgment.

Building an AI-assisted due diligence workflow

1. Scoping and planning

Begin with the traditional questions:

What is in scope (corporate, commercial, IP, employment, regulatory)?
Which data rooms and sources will be used?
What is the timetable and budget?

Then decide where AI will and will not be used, for example:

yes: clustering contracts by type, extracting key fields, suggesting issues lists;
maybe: first-pass flagging of change-of-control or non-compete clauses;
no: final sign-off on whether an issue is “deal breaking”.

Document this in a short protocol so the team knows what to expect.

2. Normalising and indexing documents

AI systems work best with clean text. Early steps should include:

converting scanned PDFs to searchable text where possible;
ensuring documents are correctly dated and labelled;
de-duplicating obvious copies.

An AI-enabled platform can then:

classify documents by type (leases, supply contracts, employment agreements);
extract key entities (parties, governing law, durations, renewal provisions);
identify potentially unusual clauses.

3. Extracting data you can actually use

The biggest win is often a structured dataset of key fields, for example:

contract counterparties, terms and termination rights;
change-of-control, assignment and consent requirements;
caps, exclusions and indemnities;
key IP ownership and licence terms.

AI models can propose this extraction, but lawyers should:

review samples for accuracy;
refine extraction rules or prompts; and
decide which fields genuinely matter for the transaction.

Avoid collecting everything just because you can; focus on what will drive advice.

4. Issue spotting and escalation

With structured data in place, AI can help you:

filter for contracts with unusual durations or auto-renewals;
surface agreements that contain particularly restrictive covenants;
highlight clusters of contracts with the same counterparty or governing law.

However, human reviewers should make the call on whether a finding is:

a minor drafting quirk;
something to cover with a disclosure or warranty; or
a serious red flag that might change the deal.

Build escalation routes into your review plan, so juniors know when to involve senior lawyers and, where relevant, client decision-makers.

5. Reporting clearly to clients

AI can also assist with the reporting stage by:

drafting sections of the due diligence report based on structured findings;
generating tables of key contracts or risks;
suggesting ways to group issues by severity or impact.

As always, partners should review and edit the narrative so that:

recommendations are clear and prioritised;
caveats and assumptions are properly expressed; and
there is a sensible link between findings and proposed protections in the SPA or ancillary documents.

Managing confidentiality and privilege in due diligence

Data rooms and due diligence platforms already involve extensive sharing of confidential information. Adding AI features introduces extra questions:

Is data kept within the agreed jurisdictions?
Does the vendor use deal documents to train general models?
How are access controls and logs managed?

Treat AI-enabled due diligence platforms as you would other key vendors:

review contracts carefully;
insist on clear DPAs and confidentiality terms; and
understand how to remove data after the transaction if required.

How OrdoLux can support the process

OrdoLux is not a replacement for full-scale due diligence platforms, but it can:

hold your issue lists, extraction templates and escalation rules centrally;
keep notes, AI-assisted findings and reports attached to matters; and
link due diligence work to time recording and task management.

Over time, firms can build their own playbooks for AI-assisted due diligence – recording what worked, what did not and how to improve on the next deal.

This article is general information for practitioners — not legal advice.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined-up system. Learn more on the OrdoLux website.