5 AI myths every legal professional should know

5 AI myths every legal professional should know

Learn the truth behind common AI myths in the legal sector. See how lawyers can use AI safely, avoid risks and improve confidence with practical guidance.

AI is becoming a normal part of everyday work across the legal profession.

Research tasks are faster, drafting is easier, and document reviewing is more accurate.

Yet many lawyers still feel uncertain about where the real risks sit, especially from a lack of clear guidance on responsible AI use.

This blog brings together common myths heard in family law, criminal defence, conveyancing and dispute resolution. It aims to support safe and confident AI adoption across the sector.

"Recent research shows that 61% of UK lawyers now use generative AI tools in their work, an increase from 46% at the start of 2025."
FOIL Update – The Rise of AI in UK Legal Practice: Adoption vs Integration, 2025

Myth 1. Using AI could get me in trouble with the Bar

There is no blanket rule that stops Barristers or Solicitors from using AI in legal services.

The issue is not the technology. The issue is how it is used. Regulatory concern focuses on confidentiality, accuracy and supervision.

The Bar Council has already confirmed that there is nothing improper about using reliable AI tools as long as lawyers protect client information and verify outputs.

Strong governance is what keeps legal AI adoption safe. This includes reviewing prompts, checking every output and keeping privileged information away from unsecured public models. Vendor checks and training help reduce risk. Updating engagement terms can also build trust by explaining how the firm uses AI to support client work.

Responsible use sits at the centre of compliance. Not the technology itself.

Myth 2. Generative AI will always give accurate legal advice

AI is powerful at recognising patterns. It can process large volumes of case law and documents in a fraction of the time. It cannot apply legal judgement. It cannot understand commercial nuance. It cannot replace experience.

This is why the best results come from combining AI for research and drafting with human oversight.

Lawyers verify sources. They check reasoning and ensure the advice reflects real world context.

Family law shows this clearly. Matters linked to safeguarding, domestic abuse or child arrangements rely heavily on empathy and instinct.

AI can support factual summaries and routine documents. The decisions that shape lives must remain with trained professionals who understand the emotional weight and complexities behind every case.

Myth 3. AI will automatically reduce legal costs

AI can improve efficiency. It can support fixed fee services and reduce time spent on repetitive tasks. This can help widen access to legal support. But financial savings only appear when firms prepare properly.

"Firms predict 16% of average hours saved from AI adoption, yet 92% of the Top 100 firms are concerned about cyber risk."
PwC Law Firms’ Survey 2025

If the data is poor or the workflow unclear, AI can introduce errors that require costly remedial work. If staff are not trained, outputs may be treated as authoritative when they are not. Without governance, the risks outweigh the benefits.

Firms that see real gains use AI for high volume and low risk tasks first. They add quality checks to every workflow and keep mandatory human review in place.

When managed carefully, AI becomes a reliable support tool that saves time for both firms and clients.

Myth 4. AI can handle the entire conveyancing process

Conveyancing teams can use AI to speed up research, extract key information and manage routine enquiries. It can handle administrative tasks with consistency. This improves accuracy and frees teams to focus on more complex matters.

But conveyancing is built on relationships and judgement. Clients need reassurance. Lenders expect clarity. Local knowledge and commercial instinct still guide the difficult parts of a transaction.

Laws and tax rules also change often. Stamp duty bands. Second home rules. Buy to let criteria. Each update carries important implications for clients.

AI supports research but it cannot replace the human understanding needed to navigate shifting legislation.

Myth 5. AI will eliminate the need for professional legal representation

AI helps people better understand their options. It breaks down barriers that once made legal information difficult to access. This is good news for the justice system.

However legal representation remains essential. Criminal defence, family matters and commercial disputes all involve strategic decisions, ethical judgement and detailed advocacy. These are responsibilities that cannot be automated.

AI improves the work of legal teams. It does not replace them.

How to myth bust with an AI Readiness Assessment

A good way to cut through common myths about AI in legal services is to look at the practical foundations that make adoption safe and effective.

An AI Readiness Assessment gives firms a structured way to understand where they are now and what conditions need to be in place before scaling new tools or workflows.

"While 87% of UK legal professionals expect AI to have a transformational impact on the profession within five years, only 38% anticipate that level of change inside their own organisation this year. This gap shows why readiness varies and emphasises the need for structured assessment."
Thomson Reuters: UK lawyers and generative AI survey, 2025

The assessment examines six areas that influence responsible AI use:

  1. Governance and ethics.
  2. Leadership and strategy.
  3. Data quality and structure.
  4. Team skills and confidence.
  5. Culture and willingness to explore new approaches.
  6. Technology and tools.

Each area contributes to overall readiness and highlights where further work may be helpful.

This provides a clearer view of your current maturity. It helps you see which parts of your organisation are well positioned for AI and which may benefit from improved data processes, updated policies, additional training or more targeted experimentation.

It is a practical way to replace broad assumptions with a measured understanding of what successful AI adoption requires. It supports better planning, reduces risk and ensures that any future implementation builds on a stable foundation.

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