From Text to Trial: Why Multimodal AI is the Next Big Leap in Legal Tech

The legal profession relies on a mix of documents, images, video evidence, and voice recordings. AI has improved text-based tasks like contract analysis and legal research, but real-world cases involve more than text.

Multimodal AI allows legal teams to process and connect different types of evidence in a single system. Instead of treating a video deposition, a contract, and an email separately, AI can analyse them together, flag inconsistencies, and find links that might otherwise be missed.

This has significant implications for disclosure, trial preparation, and regulatory compliance.

Finding the Needle in a Digital Haystack

Disclosure is resource-intensive. Law firms must review and disclose vast amounts of emails, contracts, meeting notes, and surveillance footage. Finding key evidence in this sea of information is difficult, especially when data formats need cross-referencing.

Scenario: Investigating Financial Misconduct

A UK-based investment firm faces scrutiny over insider trading. Regulators request communications, transaction records, and any evidence that key staff may have shared confidential information. The legal team must review:

Thousands of emails and WhatsApp messages between traders and external contacts.
Call recordings from the firm's trading desk.
Financial transaction logs to spot unusual trading activity.

Multimodal AI can:

Cross-reference messages with transaction data to detect suspicious patterns.
Transcribe and analyse voice calls, flagging hesitant or contradictory statements.
Surface hidden relationships between people and data that might be overlooked.

This reduces the time spent reviewing irrelevant documents and ensures key evidence is found.

Conversations to Have with Clients:

Where is their data stored, and in what formats? If key documents, recordings, or messages aren’t centralised, retrieval slows down disclosure.
Do they have structured data management practices? If not, firms may need better categorisation and metadata tagging.
What new data sources should be considered? Clients may not realise that CCTV footage, WhatsApp logs, or CRM data could be valuable evidence.

Building a Stronger Case

Preparing for trial means piecing together a narrative from multiple types of evidence. Lawyers must ensure consistency across witness statements, contracts, CCTV footage, and expert reports.

Scenario: A Workplace Injury Claim

A construction worker in Manchester sues their employer after a serious accident. The company claims safety procedures were followed, but the legal team needs to establish liability.

Multimodal AI can:

Analyse health and safety records for past warnings about unsafe conditions.
Process CCTV footage to check safety measures at the time of the accident.
Transcribe and compare witness statements, flagging contradictions.
Match maintenance logs with footage to determine if faulty equipment played a role.

AI automates cross-referencing, ensuring a clearer and more complete picture.

Conversations to Have with Clients:

How do they track workplace incidents and safety records? If these aren’t well-documented, proving liability is harder.
Is there a policy for preserving video and audio evidence? If recordings are routinely deleted, crucial evidence could be lost.
Can witness interviews be recorded and transcribed early? AI-assisted transcription works best with high-quality data.

Catching Issues Before They Escalate

Regulated industries like banking, healthcare, and telecoms require compliance with strict rules on data protection and transparency. Compliance risks often hide in multiple formats, including text reports, emails, invoices, and phone conversations.

Scenario: A Corporate Bribery Investigation

A UK based engineering firm is investigated for bribery after securing government contracts overseas. Regulators suspect intermediaries were used for kickbacks, requiring an internal review.

Multimodal AI can:

Scan financial records for unusual payments.
Analyse internal emails and messages for coded language or vague references to special arrangements.
Process phone call recordings between UK and overseas staff, detecting inconsistencies.

AI allows companies to proactively identify risks before regulators intervene, helping them respond effectively.

Conversations to Have with Clients:

How do they track and audit payments? If records are fragmented, bribery risks may be harder to detect.
Are all key conversations and decisions logged? If compliance relies solely on text-based reports, verbal discussions may create legal blind spots.
What early warning signs should they monitor? AI can be trained to flag patterns of risk before they become major legal issues.

How Law Firms Can Prepare for Multimodal AI

The benefits of multimodal AI are obvious, but how do firms start integrating it? The key is changing the way legal teams expect to work with data, collaborate with clients, and build AI-assisted workflows.

1. Identify High-Impact Areas First

Feels obvious but firms should start where the biggest efficiency gains can be made, such as disclosure, compliance monitoring, or trial preparation.

2. Audit Existing Data and Evidence Management

Multimodal AI is only as good as the data it can access. Firms should assess:

Where different types of evidence are stored.
Whether case materials are structured in a way AI can process efficiently.
Gaps in metadata or labelling that could limit AI’s ability to link files.

3. Choose the Right AI Tools and Partners

Look for solutions that:

Handle structured and unstructured data.
Allow customisation for firm-specific workflows.
Offer explainability and compliance with legal AI regulations.

4. Train Legal Teams on AI-Assisted Workflows

AI should enhance legal expertise, not replace it. Firms should:

Train lawyers on how AI can surface insights faster.
Ensure compliance teams understand where AI fits within regulatory obligations.
Develop best practices for AI-assisted decision-making.

5. Start with a Pilot Before Scaling

A small-scale project in a focused area can help teams:

Build confidence in AI’s ability to support legal tasks.
Identify practical challenges before full-scale adoption.
Develop firm-specific guidelines for AI integration.

The Future is Multimodal

Legal work involves a mix of documents, recordings, financial data, and video evidence. Legal professionals need tools that work across all of them.

Multimodal AI isn’t just an upgrade to traditional legal AI, it’s a shift in how legal teams handle evidence, build cases, and ensure compliance. Firms that adopt this technology will work faster, reduce costs, and make stronger legal arguments.

The question isn’t whether multimodal AI will reshape legal tech, it’s how soon your firm will start using it.