Legal Tech in 2026: 10 major shifts and what they mean for 2027

The year’s most important developments in contract management, legal AI, and regulation, plus a look ahead to 2027.

I’ve followed developments in legal tech and contract management more closely this year than perhaps ever before. There’s a good reason for that. 2026 has been an unusually eventful year for the industry. Legal AI tools have made substantial progress, company valuations have climbed into the billions, and contract management software has started taking a more active role in getting work done.

Although the year isn’t over yet, it already feels like a good time to take stock of what’s happened.

This review covers ten of the most important developments of 2026, grouped by theme. I’ll also share a few thoughts on what they could mean for 2027.

Just a few years ago, the main promise of AI was to help people read documents and find information faster. In 2026, the conversation shifted toward agentic AI: systems that can plan a task, work with multiple sources and tools, and prepare or carry out actions.

That shift has raised new questions. Where does AI get reliable information? How does it account for previous contracts and an organization’s approved practices? Who is responsible for decisions, and how can the results be checked?

These questions matter particularly in contract management. Contracts govern a company’s obligations, rights, and business relationships for years. AI becomes more useful when it can work with that long-term information, too.

1. AI agents moved from legal experiments into everyday workflows

The most visible technology trend in 2026 was the rise of agentic AI in legal tech products.

A conventional AI assistant answers a question or performs a specific task. An AI agent can break a broader assignment into stages, use different tools, gather additional information, and work toward a defined outcome.

In May, legal AI company Harvey introduced more than 500 ready-made legal AI agents. They cover a range of legal use cases, while its Agent Builder lets organizations tailor agents to their own processes.

In August, Thomson Reuters launched a new version of CoCounsel Legal. Its agent-based architecture brings legal research, analysis, document drafting, and source checking into the same workflow.

In October, Legora introduced Intake, bringing agents into the day-to-day service process of in-house legal teams. The Slack-based tool can receive legal requests, collect the information needed to handle them, and prepare work for the legal team’s review.

Large general-purpose software providers also became more visible in legal AI. Microsoft introduced Legal Agent in Word through its limited Frontier program, while Google previewed Gemini Enterprise for Legal in August.

What does this mean in practice?

A legal team might ask AI to review a supplier agreement against the organization’s approved contract terms, identify deviations, and draft proposed changes.

Work that previously required several separate prompts and manual steps can be brought together into one guided process.

How independently agents can operate still varies considerably. A product launch doesn’t mean every legal process can now be automated reliably. Human review, approval, and accountability remain essential when decisions have significant consequences.

2. Contract management systems began taking a more active role

The shift toward agents was particularly clear in contract lifecycle management (CLM) software.

Traditionally, contract management has focused on storing agreements, managing metadata, searching documents, handling approvals, and tracking deadlines and obligations.

In 2026, several major CLM providers introduced features that allow AI to prepare and initiate contract-related actions as well.

In May, Docusign announced its Iris AI assistant, AI agents, and Agent Studio. These capabilities can help users review agreements against organizational requirements, identify obligations, and automate follow-up actions.

In June, Icertis introduced a new contracting experience built around Vera, combining a conversational interface with agent-based contract work and portfolio analytics.

In October, Ironclad launched Ironclad Agent, bringing together contract search and agents for different tasks. Users can, for example, initiate a contract review or investigate upcoming renewals using natural language.

In October Zefort introduced Zefort Agent in 2026, bringing agent-based capabilities into contract management.

Zefort Agent can retrieve contract information, compare documents, review groups of agreements, and prepare recommendations. It can also suggest updates to metadata, contract parties, tasks, and reminders. Users review these proposals before approving them.

These launches share a common ambition: to make the contract management system a place where contract information can be used to carry out work, rather than simply stored and retrieved.

Vendors approach this in different ways, and the maturity of their offerings varies. Some focus on the early stages of contracting, including drafting and negotiation. Others concentrate on managing existing agreements and the work that follows signature.

For buyers, the useful question is which actions the system can perform reliably and how much work still falls to the user.

3. Organizational legal memory and internal policies began guiding AI

One of the more interesting developments of 2026 concerned AI memory and the use of an organization’s own information.

Previous decisions matter in contract work. When a lawyer reviews a new agreement, they may need to know which terms were accepted in similar situations, what was negotiated, and why a particular decision was made.

That information may be scattered across agreements, emails, negotiation notes, and individual experts’ memories.

In January, Luminance announced a new approach built around institutional memory. The aim is to retain negotiation history and the reasoning behind legal decisions so AI can draw on that context in future work.

In August, Harvey introduced Harvey II, enabling agents to make use of the context of an assignment as well as a user’s previous working methods and preferences.

In October, Ironclad introduced Contract Knowledge Graph, designed to connect agreements with approved terms, workflows, and past negotiation decisions.

Organizations can also make their legal knowledge available to AI by defining clear policies and acceptance criteria. For example, Zefort Playbooks allows contracts to be reviewed against an organization’s own contract policies and requirements. AI assesses agreements against defined rules, identifies potential deviations, and shows supporting contract text. This allows the same policies to be applied consistently to an individual agreement or a larger portfolio.

This direction matters because an organization’s legal knowledge extends beyond the documents themselves.

That makes the quality of an organization’s contract information increasingly important. AI can use past information, but it needs to distinguish between a clause found in a document, a system’s interpretation of that clause, and information the organization has actually reviewed and approved.

4. Legal AI and CLM systems started connecting

Another important development was the closer connection between different systems.

Legal work doesn’t happen in a single application. Contracts live in CLM systems, documents are edited in Word, legal research takes place in dedicated services, and business information is held in CRM and ERP systems.

Several announcements in 2026 pointed toward bringing these environments closer together.

In June, Legora and Ironclad announced a strategic partnership intended to connect Legora’s legal research and analysis with Ironclad’s contract information and workflows.

In October, Legora and Sirion announced a similar partnership. The integration is designed to let Legora use agreements and business context stored in Sirion, while Sirion users can access Legora’s legal analysis capabilities. The rollout is taking place in stages.

That same month, Legora also announced an ecosystem of more than 30 integrations, allowing its agents to work with other legal and business systems.

One technology making these connections possible is Model Context Protocol (MCP). It provides a way to connect AI assistants with external data sources and the functions offered by other systems.

In September, Zefort introduced an MCP connection that allows contract information to be accessed through tools such as Claude and Microsoft 365 Copilot. Users can search agreements, request summaries, and compare contract information from their own AI assistant.

Docusign also opened an MCP connection for AI agents in September.

These developments raise an interesting question about the future of CLM. Will the CLM platform itself always be the main interface for contract management, or will more work happen through AI assistants that companies already use?

Both approaches will probably have a role. Contract information still needs to be maintained in a controlled system, even when other tools are used to work with it.

This makes access permissions, up-to-date records, and traceability of the information used by an agent increasingly important.

5. Autonomous contract negotiation advanced, but trust remained a concern

Can AI negotiate a contract on its own?

The question became more concrete in 2026 as agents designed for contract negotiation developed further.

In March, Luminance announced improvements to its Autonomous Negotiation product. The updates aim to make the reasoning behind negotiation decisions more visible and extend the tool’s use beyond legal teams.

The idea is particularly relevant to businesses that handle large numbers of similar agreements. For a nondisclosure agreement, for example, negotiation can follow predefined acceptable terms and limits.

More complex agreements are a different matter. One provision may affect other contractual terms, commercial objectives, or the company’s appetite for risk.

According to Icertis’ State of Contracting 2026 report, 53% of the executives surveyed expect AI agents to negotiate customer and supplier contracts autonomously within the next 12 months.

In the same research, 44% of executives cited a lack of trust in AI’s ability to perform tasks independently as a significant concern.

Expectations are high, then, but trust doesn’t automatically keep pace with technical progress.

The developments of 2026 suggest that automated negotiation is becoming a practical option for defined, repeatable use cases. They don’t establish that autonomous AI is suitable for negotiating every type of agreement.

6. AI adoption grew rapidly, but organizations moved at different speeds

Research published in 2026 suggests that AI has become a regular part of many legal professionals’ work.

In Wolters Kluwer’s Future Ready Lawyer 2026 survey, 92% of respondents said they used at least one AI tool in their daily work. The survey covered 810 legal professionals from law firms and in-house legal departments across 11 countries.

That figure shows how widely AI is being used. It tells us less about how deeply it has been integrated into shared organizational processes.

In Thomson Reuters’ 2026 AI in Professional Services report, 40% of respondents across professional services said their organizations were using generative AI. Only 15% reported organizational use of agentic AI.

According to Icertis’ State of Contracting report, 44% of organizations had adopted or were actively implementing AI in their contracting processes.

These studies measure different things and draw on different groups of respondents, so their percentages shouldn’t be compared directly. Together, though, they suggest a market moving at two different speeds.

Individual AI tools are already part of everyday work in many organizations. Integrations between systems, independent agent actions, and consistent governance practices are still developing.

That distinction matters when buying technology. Introducing an AI tool is relatively straightforward compared with redesigning an entire contracting process around it.

The latter requires agreed ways of working, sufficiently reliable data, clear responsibilities, and change management.

7. Funding for legal AI reached a new level

The pace of technological change was also reflected in legal tech funding.

In September, US-based Harvey announced a $550 million funding round, valuing the company at $15.5 billion.

On October 21, Swedish-founded Legora announced another $550 million round, at an $8.8 billion valuation.

These are unusually large figures by historical legal tech standards. They reflect investors’ expectations of the commercial potential of automating legal work.

Large funding rounds also give vendors the resources to invest heavily in product development, international expansion, and serving enterprise customers.

Funding growth doesn’t tell us directly how much value customers are getting from these products, or which vendors will succeed in the long run.

For buyers, the faster-moving market means more options and products that are changing quickly. It also makes it important to consider how well a vendor will fit the organization’s working environment over time.

8. Growing workloads pushed legal teams to rethink how they work

All this technology is arriving at a time when in-house legal teams are being asked to do more.

According to the Corporate Legal Operations Consortium’s State of the Industry 2026 report, legal workloads are growing particularly in regulatory and cybersecurity matters.

In the survey, 63% of legal departments reported an increase in regulatory work, and 58% reported more cybersecurity-related work. Only 32% expected to increase their number of lawyers.

That gap helps explain some of the expectations surrounding AI.

Legal teams need to handle more agreements, support business decisions, and respond to new regulatory requirements without equivalent growth in headcount.

The way external legal services are delivered is also under scrutiny. As AI reduces the time needed for certain tasks, clients are looking more closely at how legal work is priced and what they receive in return.

In Wolters Kluwer’s 2026 survey, 62% of in-house legal respondents believed that efficiency gains from AI would significantly reduce the prevalence of hourly billing.

The implications go beyond costs, however.

Consider the management of contractual obligations. A legal team may be able to move from responding to individual questions toward a more continuous review of exceptions and risks. A system might flag an approaching deadline or a contract provision that needs attention. A person still decides what should happen next.

Organizations see the benefit when technology becomes part of a workable process, rather than remaining a tool that individuals use in isolation.

9. AI reliability became something to measure

In 2026, there was also growing interest in how well AI actually performs legal tasks.

In May, Harvey released the Legal Agent Benchmark (LAB), designed to evaluate AI systems on tasks resembling real legal work, where the system must produce an assessable deliverable.

In July, Artificial Analysis published an independent evaluation based on 120 legal tasks.

In the original July results, the leading model met every evaluation criterion in 14.2% of tasks. The assessment used a particularly strict approach: if even one criterion was missed, the entire task was counted as unsuccessful. The figure therefore isn’t a general accuracy rate for AI.

The benchmark has since been updated, and results from different versions aren’t directly comparable.

What makes the research useful is the distinction between getting an individual answer right and completing an entire legal assignment to an acceptable standard.

AI might find the right contract clause but miss a later amendment that changes its meaning. It might also identify a risk correctly while recommending an action that conflicts with the organization’s approved policy.

Evaluating legal AI therefore requires looking at the complete task, the traceability of sources, and whether the system’s conclusions can be checked.

In contract management, it’s equally important to distinguish information found in the original agreement, an AI-generated interpretation, and information reviewed by the organization.

Reliability doesn’t depend on the language model alone. The underlying information, system permissions, workflow design, and approval procedures also affect the result.

10. European data and AI regulation moved forward

In Europe, legal tech is shaped by regulation as well as technology.

Three regulatory frameworks were particularly relevant in 2026: the EU AI Act, the Data Act, and the Cyber Resilience Act.

The EU AI Act entered a new phase of application

A major phase of the EU AI Act began to apply on August 2, 2026.

This included certain transparency requirements for AI systems, while oversight and enforcement moved into new stages.

However, the Digital Omnibus on AI reform adopted in 2026 changed parts of the original timeline. Requirements for certain high-risk AI systems will apply from December 2027 or August 2028, depending on the type of system.

The European Commission’s AI Act implementation timeline provides an overview of the key dates.

For in-house legal teams and technology providers, the important task is to determine which requirements apply to each AI use case.

Legal AI tools and CLM systems are not automatically classified as high-risk systems under the AI Act.

The Data Act affected data agreements and provider switching

Most of the EU Data Act started to apply in September 2025, but organizations continued to interpret and implement its requirements throughout 2026.

Among other things, the Data Act seeks to improve access to and use of data while reducing barriers to switching between data processing service providers.

For contract management, the most relevant areas include data-sharing clauses, cloud service contract requirements, data portability, and the terms governing a change of service provider.

Another important milestone arrives in January 2027: switching charges for data processing services covered by the Data Act, including applicable data egress charges, will be prohibited.

The European Commission explains the requirements in more detail in its Data Act overview.

Cybersecurity regulation introduced new obligations

The EU Cyber Resilience Act also progressed in 2026.

In September, reporting obligations began to apply to manufacturers of products with digital elements for certain vulnerabilities and serious security incidents. Most of the regulation’s other substantive requirements will apply from December 2027.

Cybersecurity regulation affects the security requirements that companies set for technology suppliers and how those requirements are reflected in contracts.

For legal teams, the work goes beyond drafting clauses for new agreements. They also need to understand what existing contracts say about information security, incident reporting, audit rights, and supplier responsibilities.

That creates a need to keep contract information available when regulation or business requirements change after an agreement has been signed.

What will happen in 2027? Five predictions for legal tech and contract management

Developments in 2026 offer some clues about where legal tech may be heading next.

The following five predictions are based on the technology launches, market signals, and regulatory changes we’ve seen this year. They are expectations, not certain outcomes.

1. AI agents will become more closely involved in everyday legal work

In 2026, agents became a visible part of the user interface in many legal tech products. In 2027, they may take on a larger role in carrying out the work itself.

This could include preparing contract review requests, coordinating approvals, comparing amendments, and handling tasks related to contractual obligations.

The most useful measure of progress won’t be how many different agents a platform offers. It will be how reliably an agent completes a repeatable process and how safely it handles exceptions.

2. CLM platforms will become even more important as sources of trusted contract information

As agents become available across more applications, users won’t necessarily need to work with contract information through the CLM platform’s own interface every time.

That doesn’t make contract information management any less necessary.

On the contrary, the different tools using that information need a reliable source. A company has to know which version of an agreement is in force, who the parties are, what obligations apply, and how the contract has been amended over time.

In 2027, we may see more architectures where the CLM platform acts as the system of record for agreements and approved contract information, while AI agents use that information in different working environments.

This could make integrations, open interfaces, and access controls more important criteria when selecting CLM software.

3. Organizational legal memory will become a more important differentiator

In 2026, many legal tech vendors began emphasizing the use of previous decisions, contracts, and negotiation history.

In 2027, that development could lead to AI tools that adapt more closely to each organization’s way of working.

Legal teams want more than generally well-written contract drafts or analyses. The results should also reflect their organization’s policies, risk principles, and previously approved decisions.

That raises new information management questions. What should AI remember? Who can approve a new policy? How can organizations prevent outdated or exceptional decisions from automatically becoming standard practice?

In legal work, useful memory also requires a way to distinguish reliable, approved information from information that is uncertain or out of date.

4. Buyers will ask for evidence that AI can complete entire workflows

The announcements of 2026 showed how quickly agent capabilities are developing. Research also revealed gaps in how organizations measure reliability and track the return on AI investments.

In Thomson Reuters’ 2026 survey, only 18% of respondents across professional services said their organizations measured the return on investment from AI.

In 2027, technology vendors may face greater pressure to demonstrate how much work their products can actually automate and what review steps are still required.

For example, a contract review agent can be evaluated on how consistently it identifies material deviations, what kinds of mistakes it makes, and how much of its work a user needs to correct.

That kind of evaluation may tell a buyer more than a general demonstration of an AI assistant’s conversational abilities.

5. New regulatory deadlines will create more reasons to review existing agreements

Several important regulatory dates are already on the calendar in Europe for 2027.

In January, the removal of switching charges under the Data Act will affect the terms of service agreements covered by those rules.

In December 2027, certain high-risk AI Act requirements and the main requirements of the Cyber Resilience Act will become applicable.

For companies, regulatory changes can create a need to review agreements that have already been signed.

They may need to establish what obligations have been agreed with a supplier, who is responsible for meeting the requirements, and whether changes in regulation call for amendments.

In those situations, being able to find, compare, and work with current contract information becomes especially important.

AI can help identify agreements that need attention and prepare the analysis. Legal interpretation, risk assessment, and the decisions that follow remain the organization’s responsibility.

What should we remember about legal tech in 2026?

Legal tech made progress on several fronts at once in 2026.

Agent-based tools expanded from individual tasks into multi-step workflows. Contract management platforms gained new AI capabilities, and connections between different systems improved. The market also attracted major new investments that could accelerate product development further.

Adoption, however, didn’t progress at the same pace everywhere. Many organizations already use AI in daily work, but agents capable of independently completing entire processes are still at an early stage.

One of the important questions for the next few years is how organizations manage the quality and governance of their information.

Contracts provide a long-term foundation for companies’ rights, obligations, and business relationships. As AI becomes able to use that information more widely, managing contract information systematically will become even more important.

During 2027, we’ll probably see more evidence of which agent-based tools really work in day-to-day business and which technology approaches can cope with changing business and regulatory requirements.