What does your mind show you when you think about the word “legal”? Chances are, lengthy, tedious documents, piled high, the signing of which stand between you and that new app, subscription, or product.

Lawyers and attorneys at law firms, often billing by the hour, previously had to sift through these mountains of legal work with their keen eyes and deep legal expertise, summarizing hundreds of pages in days or weeks at great cost.

Artificial Intelligence has come to change this, and though those two words pique the interest of so many in 2026, it is an older and less-referenced system that has been improving the working lives of legal teams throughout the legal industry for years: Machine Learning.

Machine Learning Features That Matter

Machine Learning in this context could be considered a specialized type of AI that relates to the ability for a machine to analyze large amounts of data using machine learning algorithms to identify an ever-growing number of patterns. In recent years, this number has grown from dozens in the 80s and 90s to hundreds of millions and billions in the 2020s, as AI systems have grown increasingly sophisticated.

A well-calibrated Machine Learning solution will not replace legal teams; it will enhance human intelligence with features that free up their time to focus on meeting strategic objectives rather than completing specific tasks. The value of this for legal teams and other professionals cannot be overstated.

As the machine learns from data, AI can be used to help legal teams in specific areas, such as:

Automated data extraction

The primary legal team challenge Machine Learning overcomes is the ability to rapidly structure unstructured data via Optical Character Recognition (OCR) and Natural Language Processing (NLP). What this means is that the tool can quickly and accurately transform, for example, scans of handwritten documents into a searchable, indexed database that streamlines document review by highlighting errors, key dates, and other useful information.

Rather than analysts spending days compiling metadata (data about data), the machine can automate it in moments.

Clause identification

Two words that could strike fear into contract review teams: clause identification. This task used to mean manually searching through masses of paperwork to find relevant documents containing specific words or phrases, and could be compared to searching for a needle in a haystack.

Every time a new supplier is onboarded, a contract is negotiated, or an agreement is terminated, it is likely that legal teams - well-versed in legal language as they are - would have to search for similar but not identical clauses because different parties may use slightly different language. CTLR + F wasn’t going to cut it, so machine learning solutions arose in Search’s place, because they can recognize the intent of words, instead of just identifying exact matches.

Sophisticated machines will even cross-reference against a company’s existing playbook and highlight differences in structure and content between that and new suppliers or mergers and acquisitions. This process can even generate a redline showing precisely where a new contract deviates from the standard.

Contract summarization

A senior legal counsel’s time is expensive and valuable — especially when legal services are billed by the hour — and in 2026 (even before) should not be spent reading through dozens or hundreds of pages of an agreement searching for specific information.

Thankfully, modern technology (that utilizes both AI and Machine Learning) can rapidly provide executive summaries and valuable insight, tailored to specific stakeholders and comprehensive enough to provide value to every department of an organization, not just the legal team.

Risk scoring

A feature that quickly establishes the value of Machine Learning is the ability for legal teams to load the solution with existing historical data as well as anything new, providing it the ability to analyze data, make predictions, and highlight major risk factors with a high degree of accuracy.

Once learned, the machine can apply pre-determined risk factors to any new contract and provide a risk score to any relevant criteria, establishing a baseline for risk and highlighting, for example, particularly aggressive terms for other potential issues that could lead to a dispute. Legal teams can then focus their time on high-risk big-ticket items to drive better outcomes, whilst lower-risk score operations can be automated.

Choosing the Right AI Solution

There is no shortage of legal tech vendors saturating the market with new AI tools for contract analysis and management, and accurately determining those with real potential versus marketing noise will be a new challenge for legal teams. This will involve asking the right questions.

Evaluating business needs

Don’t just assume your business needs a solution like this (even though it does). As part of the proposal, it would be prudent to conduct an internal audit of key stakeholders, including legal professionals, and their needs that must be addressed by this new technology. This must happen before scheduling calls and booking demos with vendors.

Legal teams especially must understand whether they’re searching for a specific, point-solution to a problem or a broader platform for how they use AI to address many needs.

Questions to ask should include:

  • Does the tech solve a problem for the legal team?
  • Can it handle the type and volume of data we rely upon?
  • How easy is it to integrate?
  • How quickly can it provide value?

Measuring ROI

Beyond answering the sensible questions above, procuring software and the business case for doing so should also be tied to specific return on investment metrics. For legal teams, this can also relate to time and cost savings, direct returns, and a broader competitive advantage.

Legal teams should calculate the project ROI of at least these criteria:

  • Contract execution times within existing workflows (and how ML software may speed this up)
  • Time-cost savings (hours spent and the associated costs)
  • Any external capability expenditure that this procurement can bring in-house
  • Risk-prevention value related to avoided penalties, litigation, and the like

As the technology continues to evolve, projecting and then tracking these metrics before and after acquiring a tool helps embed it within your organization, improve client outcomes, and build a culture of transparency, efficiency, automation, and adoption of new technologies.

Schedule a demo today to find out just how much time your legal team could save by leveraging AI-powered, ML-enhanced software to help shape the future of the legal profession.