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

I’ve followed legal tech and contract management developments 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 and what they could mean for 2027.

1. AI agents moved from legal experiments into everyday workflows

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 on your behalf.

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.

General-purpose software providers also expanded into legal AI. Microsoft introduced Legal Agent in Word through its limited Frontier program, while Google previewed Gemini Enterprise for Legal in August.

How independently agents can operate still varies considerably. Before relying on them, legal teams need to ask: What information do they use? Can they account for existing contracts and approved practices? Who checks the results and remains responsible for decisions?

2. Contract management systems began taking a more active role

The same shift was happening in contract lifecycle management (CLM) software.

Alongside established functions such as document storage, search, approvals, and deadline tracking, several major providers introduced AI features in 2026 that can prepare and initiate contract-related actions.

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, bringing contract search, review, and follow-up into a conversational workflow. Users can compare agreements, review groups of contracts, and prepare updates to contract data, tasks, and reminders for approval.

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.

The scope and maturity of these offerings vary. Some focus on drafting and negotiation; others concentrate on existing agreements and the work that follows signature. For buyers, the useful question is which actions a 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.

The value lies in preserving the reasoning behind an agreement, not just its wording.

For AI to use that history reliably, it needs to distinguish between what a contract says, why a decision was made, and what the organization currently approves.

4. Legal AI and CLM systems started connecting

Legal work spans several applications. Contracts live in CLM systems, documents are edited in Word, legal research takes place in dedicated services, and business information sits in CRM and ERP systems. Several announcements in 2026 aimed to connect more of that work.

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 lets tools such as Claude and Microsoft 365 Copilot access contract information. Users can search agreements, request summaries, and compare contract information through their AI assistant.

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

These connections raise a question about how people will use CLM software: will they work mainly within the platform, or increasingly through AI assistants they already use for other tasks?

Both approaches may coexist. The CLM system can maintain contract records and access controls while an AI assistant provides another way to work with that information. Teams still need to know whether the records are current, what the assistant is allowed to access, and which sources support its answers.

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 contract-negotiation agents advanced.

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 executives surveyed expect AI agents to negotiate customer and supplier contracts autonomously within the next 12 months. Yet 44% lack sufficient trust in AI’s autonomous capabilities.

G2’s 2026 AI Agents Insight Report shows a similar tension across business uses of AI agents. While 63% of respondents plan to give agents more autonomy, only 53% trust them to act fully autonomously in low-risk workflows. Oversight also affects the returns: 45% say reviewing and correcting agent work consumes more than a quarter of their expected productivity gains.

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, even as organizations continue to work out how to integrate it into shared processes.

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 measures individual use. Organizational adoption presents a different picture.

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.

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

These studies cover different populations and measure different forms of adoption, so their percentages aren’t directly comparable. They nevertheless highlight an important distinction: using AI for individual tasks is different from making it part of an organization’s established workflows.

A lawyer can start using an assistant to summarize documents without changing how the wider team operates. Redesigning a contracting process around AI requires agreement on which tasks it handles, what information it uses, who reviews its work, and how exceptions are resolved.

For buyers, that means assessing both the software’s capabilities and the organizational work needed to put them into practice.

7. Funding for legal AI reached a new level

Investment in legal AI produced some striking figures in 2026.

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 rounds reflect investors’ expectations for legal AI and give the companies resources to expand their products, enter new markets, and support enterprise customers. They don’t establish how much value customers are getting or which vendors will succeed over time.

For buyers, a faster-moving market means more options and rapidly changing products. The key questions remain whether a solution works reliably with the organization’s contracts, systems, and processes, and whether the vendor can support it over the long term.

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

This technology is arriving as in-house legal teams take on more.

According to the Corporate Legal Operations Consortium’s State of the Industry 2026 report, 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 pressure helps explain the interest in AI. Teams need to support business decisions and respond to new requirements without a proportional increase in headcount.

It also raises questions about external legal fees. As AI reduces the time needed for certain tasks, clients are examining how those services are priced. In Wolters Kluwer’s Future Ready Lawyer survey, 62% of in-house legal respondents believed AI efficiency gains would significantly reduce the prevalence of hourly billing.

Within legal departments, the opportunity extends beyond completing individual tasks faster. In obligation management, for example, a system could monitor agreements for approaching deadlines and flag issues for review. The team would need an agreed process to assess those alerts, assign responsibility, and follow through.

The practical question is where automation can reduce routine work and where the team’s limited time and judgment are most needed.

9. AI reliability became something to measure

As briefly noted above, evaluating AI’s performance became more important as it took on more complex legal work.

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. The leading model met every evaluation criterion in 14.2% of tasks. The scoring was strict: missing even one criterion meant the entire task counted as unsuccessful. That figure is therefore not a general accuracy rate for AI.

The useful distinction is between answering an individual question correctly and completing a legal assignment to an acceptable standard. AI might find the right contract clause but miss a later amendment that changes its meaning. It might identify a risk correctly but recommend an action that conflicts with approved company policy.

For contract teams, evaluation therefore needs to cover the whole task: whether the system finds the relevant records, supports its conclusions with sources, and follows the rules governing what happens next. Reliability also depends on the information, permissions, workflows, and approval procedures surrounding the model.

That includes deciding when an AI assessment needs human review. Zefort Playbooks’ AI engine, for example, separately assesses evidence that an agreement meets or fails a user-defined rule, citing relevant contract text. When the difference between those assessments doesn’t exceed an administrator-defined threshold, it marks the result “Unclear” for review. Teams can adjust that threshold to control when the system assigns a label and when a person needs to assess the evidence.

10. European data and AI regulation moved forward

In 2026, European regulation changed the requirements surrounding AI use, data access, and digital product security. Three frameworks were particularly relevant to legal and contract teams.

The EU AI Act entered a new phase of application

On August 2, 2026, further provisions of the EU AI Act began to apply, including transparency requirements under Article 50.

The Digital Omnibus on AI amendments also changed the timetable for high-risk systems. The Commission’s updated implementation timeline sets December 2, 2027, for the high-risk use cases listed in Annex III, and August 2, 2028, for high-risk AI embedded in regulated products covered by Annex I.

For legal teams and technology providers, the practical task is to identify which requirements apply to each use case. Legal AI tools and CLM systems are not automatically high-risk: classification depends on their intended purpose and the Act’s specific criteria.

The Data Act affected data agreements and provider switching

The EU Data Act has applied since September 12, 2025. Its requirements concern access to data, fairness in data-sharing agreements, and barriers to switching between data processing services.

For contract teams, this brings attention to data-sharing clauses, cloud contract terms, export capabilities, and the responsibilities of providers during a switch. Another milestone follows on January 12, 2027, when switching charges, including data egress charges associated with switching, will be prohibited for services covered by these requirements. The Commission’s Data Act overview explains the scope in more detail.

Cybersecurity regulation introduced new obligations

From September 11, 2026, the Cyber Resilience Act requires manufacturers of products with digital elements to report actively exploited vulnerabilities and severe incidents affecting product security. Most of its other substantive requirements apply from December 11, 2027.

For contract teams, these developments make supplier security commitments worth revisiting. Existing agreements may need to be assessed for provisions on incident notification, security updates, cooperation, and responsibility when something goes wrong.

Across all three frameworks, the work extends beyond updating templates. Teams need to identify affected agreements, understand the commitments already in place, and decide where changes are needed.

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

2026 launches, research, and regulatory changes suggest five directions for the year ahead. They are expectations, not certain outcomes.

1. AI agents will take on more routine legal work

I expect agents to take on more routine legal work, including preparing contract review requests, coordinating approvals, comparing amendments, and following up on contractual obligations.

Progress will depend on how reliably they complete these tasks and recognize exceptions that need human attention. An agent that handles a recurring process consistently is more useful than one that attempts a wider range of work but needs frequent correction.

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

Working at Zefort, I have a stake in this prediction. It reflects both where I see the market heading and the role we’re building our platform to serve.

As agents connect to more applications, users may increasingly work with contracts through assistants outside the CLM platform.

Those assistants still need reliable records. They need to identify the agreement in force, its parties and obligations, and any amendments that change its meaning.

I expect more organizations to use their CLM platform as the authoritative source for this information while giving agents access through other working environments. Integrations, open interfaces, and permissions will consequently carry more weight in purchasing decisions.

3. Organizational legal memory will become a stronger differentiator

AI tools may become better at drawing on an organization’s negotiation history, policies, and previous decisions. The value will depend on how well they distinguish useful precedent from an exception that should stay an exception.

A previously accepted clause may reflect an unusual commercial arrangement or a policy that has since changed. Systems will need to preserve that context and give teams ways to review, update, and retire the guidance AI uses.

I expect buyers to ask more specific questions about what these tools remember, where that information comes from, and who controls it.

4. Buyers will demand evidence of results across complete workflows

In Thomson Reuters’ AI in Professional Services report, only 18% of respondents said their organizations tracked AI’s return on investment. Another 40% didn’t know whether it was measured.

As spending grows, I expect more pressure to demonstrate what remains of the promised gains after review, corrections, and exception handling.

For a contract review agent, that means measuring whether it identifies material deviations consistently, which mistakes it makes, and how much time a reviewer spends correcting them. Buyers will want evidence from agreements and processes resembling their own.

5. Regulatory deadlines will prompt reviews of existing agreements

I expect the regulatory deadlines outlined in trend #10 to prompt more targeted reviews of existing supplier and service agreements in 2027.

Teams will need to identify affected contracts, check how responsibilities are allocated, and prioritize amendments. This could increase demand for tools that assess groups of agreements against specific requirements and help teams track the resulting work.

In 2027, I’ll be watching how much of this progress holds up in everyday work: which tasks agents complete reliably, how much review they need, and whether they leave legal teams with more time for decisions that require their judgment.

🔑 Key takeaways

• Legal AI expanded into multi-step work. Agents increasingly combine research, contract review, drafting, and follow-up, though their ability to act independently varies.
• Contract information became more useful across systems. Integrations allow AI assistants to draw on agreements, while vendors are developing ways to preserve negotiation history and organizational context.
• Adoption and investment grew faster than confidence. Funding and usage increased, but reliability, oversight costs, and measurable returns remain important questions.
• Regulatory changes put existing agreements back on the agenda. European requirements are giving teams new reasons to review data access, provider switching, and supplier responsibilities.