Here is how I’m thinking about AI in contracting for 2026, speaking as an in-house GC who uses CLM in my day-to-day work.

I’ll group it into themes instead of predictions, because that’s how it shows up operationally.

1. From pilots to obligated performance

A 2025 Gartner survey of general counsel found that over a third are prioritizing AI adoption, skills, or AI risk management, with another chunk focused specifically on contract analytics to manage risk and cost, outpacing almost every other operational concern. You can see that direction in the Gartner survey on AI and contract analytics.

By 2026, that translates into a few practical expectations:

• AI in contracting will be assumed, not aspirational, especially for review, summarization, clause extraction, and portfolio analytics.
• Boards and audit committees will start asking GCs specific questions about how AI in contracts is governed and measured, not just whether it exists.
• Contract analytics will be treated as a primary risk and performance lens, not an add-on.

In other words, AI in contracting will move from “experimentation” to “you are on the hook for outcomes.”

2. Use cases settle into a predictable core

Gartner has already identified the top generative AI use cases for legal departments, including drafting and redlining, contract review and summarization, clause comparison, and data extraction for analytics and reporting, as described in the Gartner use cases for generative AI in legal departments.

In contracting, I expect 2026 to consolidate around:

• First-pass review and risk spotting on inbound paper.
• Summaries for business stakeholders and executives.
• Automated metadata extraction and clause tagging to keep the repository clean.
• Portfolio-level search and analytics for obligations, renewals, and risk themes.

In my own team, a lot of that runs through Concord as the system of record. The AI is only as useful as the contract data and workflows we maintain, so I treat CLM configuration as part of the AI program, not something separate.

3. WorldCC’s view: from untapped revolution to managed evolution

WorldCC’s work on AI in contracting is a good barometer for how the commercial side of the house feels. Their report AI in contracting: from untapped revolution to emerging evolution captures something important: adoption is growing, but nervousness remains high around accuracy, constraints, and governance.

A companion paper on AI and the contract management lifecycle explains how AI can shift contracting from transaction processing to a more data-rich, dynamic capability tied directly to business objectives, as laid out in AI and the contract management lifecycle.

For 2026, that implies:

• AI will be expected to make contracting more responsive and data-driven, not just faster.
• Contracting teams will need to show that AI usage is consistent with defined lifecycle stages and responsibilities.
• There will be more attention on human-AI collaboration rather than pure automation.

WorldCC’s language is a useful framing for conversations with procurement and commercial leadership about why we are investing in AI capabilities inside the CLM, rather than random tools on the side.

4. Governance and AI clauses move to the foreground

ISACA has been blunt that many organizations rushed into AI without governance, and is pushing COBIT and its AI toolkits as practical frameworks for getting control. Their blog on COBIT as an AI governance guide argues that skipping governance in the eagerness to deploy AI leaves organizations exposed and undermines long-term value, summarized well in COBIT: a practical guide for AI governance.

They are also building a certification pipeline around AI risk, such as the Advanced in AI Risk credential, and promoting audit-ready techniques via the ISACA AI audit toolkit.

Combine that with recent survey data showing that a large percentage of businesses have no comprehensive AI policy despite heavy use, as highlighted in TechRadar’s coverage of ISACA’s AI policy survey, and the 2026 picture looks like this:

• Legal will be expected to co-own AI governance, not just advise on it.
• Standard contract templates will start including explicit AI clauses: data use, training rights, audit, output ownership, and acceptable use.
• Vendor and customer negotiations will increasingly focus on how each side uses AI around shared data and deliverables.

From a contracting perspective, that means our template set and playbooks need to be ready for AI-specific terms, and our CLM (again, Concord in my environment) needs fields and workflows that track those obligations, not just store the language.

5. Quality, “workslop,” and the need for professional-grade tools

There is a growing backlash against low-quality AI output in professional settings. Research covered in an Axios summary of a Harvard Business Review study on “workslop” describes how AI-generated but shallow content is undermining productivity and perceived competence at work, as discussed in Axios’s report on AI workslop.

At the same time, judges are starting to see AI-generated briefs with fabricated citations and misleading references, as reported in AP’s coverage of AI mistakes in legal briefs.

For contracting in 2026, that pushes us toward:

• Treating consumer-grade AI tools as out of bounds for confidential or production-level contract work.
• Using professional-grade tools that have traceability, guardrails, and auditability built in.
• Training lawyers and contract managers to treat AI outputs as drafts that require verification, not conclusions.

The Harvard Law School Center on the Legal Profession’s issue on generative AI in legal work lays out this theme clearly, emphasizing that the profession needs to rethink workflows, training, and professional responsibility in light of AI, as seen in Generative AI in the legal profession.

6. Agentic AI and autonomous contracting will be selectively adopted

Gartner has started talking about “agentic AI” and semi-autonomous systems that can act across workflows. Independent coverage of their forecasts suggests that while agentic AI could handle a meaningful slice of routine business decisions by 2028, a large fraction of current projects will be scrapped by 2027 due to cost and unclear value, as summarized in Reuters’ report on agentic AI projects.

Translated into a contracting context for 2026:

• We will see more “autonomous” features: auto-suggested redlines, default negotiation moves, rule-based renewals.
• Very few mature organizations will fully delegate decisions on high-risk contracts to AI agents.
• The GC will increasingly act as “AI governor” for contracting systems: approving use cases, setting guardrails, and monitoring outcomes.

So 2026 is probably the year of semi-autonomous contracting under human governance, not fully autonomous deal-making.

7. Litigation and dispute risk rise as AI lowers barriers

A piece in the Harvard Business Review argues that generative AI will make it cheaper and easier for customers, employees, competitors, and regulators to initiate legal action. That is consistent with how I expect 2026 to feel on the ground:

• More demand letters and complaints generated with AI assistance.
• Higher volume of contract-related disputes because claimants can explore theories faster.
• Greater need for disciplined contract drafting and record-keeping to withstand AI-assisted scrutiny.

For contracting, that means AI is not just a drafting aid. It is also part of the threat model. Our contracts and our CLM data need to be robust enough to support us when someone on the other side uses AI to attack weaknesses in our documentation.

8. Talent, structure, and the GC’s role

Finally, AI in contracting will change what “good” looks like in a legal department by 2026. Analysts and academics point out that legal roles are shifting toward system design, data literacy, and governance, not just black-letter law. The Harvard Law School Center’s work on generative AI in legal practice makes this explicit in Generative AI in the legal profession.

I expect:

• New roles around contract data, AI configuration, and legal operations product management.
• Upskilling for lawyers and contract managers on how to supervise AI and interpret AI-driven analytics.
• GCs becoming accountable for the AI footprint in their contracting stack, not just for the doctrines in their contracts.

In my own shop, that means treating Concord not just as “the contract system,” but as a platform where AI, data, and workflow design intersect. The decisions we make in 2025 and 2026 about templates, fields, and guardrails are going to determine whether AI in contracting becomes an asset or a liability over the next few years.