(BPT) - Personal New Year's resolutions usually come with gym membership and ambitious habits. For companies, they show up in spreadsheets, a mix of optimism, logic and recalculated risk. As 2026 approaches, one critical business workflow deserves more attention than annual forecasts usually give it.
Contracts.
Contracts are a medium of trust between a company and its key stakeholders, representing the business' risk, revenue and reputation. This means managing them is as important as it is complex. In technical terms, contract lifecycle management (CLM) governs how every commercial agreement is created, negotiated, signed, tracked and fulfilled.
However, managing contracts from start to finish is a time-consuming and complex process, if not managed properly.
According to the report, "The Race is On: Navigating Uncertainty Through CCM Resilience" by World Commerce & Contracting (WorldCC) in collaboration with Sirion, a leader in the CLM space, and produced by the Commerce & Contract Management Institute (CCM Institute), 70%-80% of organizations lack clear accountability for the quality and integrity of the contracting process. Without clear accountability or ownership of the contracting process, enterprises leave themselves vulnerable to risk, compromising transparency and ultimately trust.
Future-focused enterprises that are seeking to upgrade their existing CLM platforms and keep pace with a changing industry in 2026 would do well to embrace intelligent contracting run by next-generation technology: Agentic AI.
The Gap in Enterprise Contracting
CLM was meant to bring structure to commercial agreements. Instead, it became a process where teams endured long approval cycles, scattered ownership, repetitive redlining and more routing than reasoning.
While many companies know that contract management is a critical aspect of business success, there is a significant gap between knowing and doing. The Sirion-WCC research also highlights that even though 88% of executives understand that Contract and Commercial Management (CCM) excellence matters, it's often deferred because of its complexity. These aren't edge cases. This is the baseline experience of global contracting.
AI automation has helped contracts move along, but it has not always helped the documents get sharper. That's because the real bottleneck in contracting isn't motion — it's in output quality.
Output is where intelligent contracting powered by governed, agentic AI really shines. This isn't simply AI that drafts. It's an agent that, through the clauses, maps them to policy, and explains the risk like a reviewer would, minus the swivel-chair chaos.
Sirion's founder and CEO Ajay Agrawal brings home the point. "Most of what is sold today as 'AI for contracts' still starts and ends with a text generator." He adds, "It can draft, summarize or spot keywords, but it doesn't reason. It can't tell you why a clause is risky, or how that risk maps to a company's playbook."
Knowing how to spot risks is critical when contracts and clauses are at play, especially when external factors, like mergers and acquisitions or political forces such as tariffs, create an impact on agreements across the globe.
To truly lead a business into the future, the need of the hour is AI that is trained by lawyers and engineers to understand an enterprise's needs; helping with better reasoning and working with higher efficiency and accuracy, where every clause, negotiation and decision can be traced, explained and trusted.
The Future of Contracting Is Not Just Autonomous. It's Accountable.
Accountability is the name of the game for contract management in 2026. CLM platforms must deliver more than just contracting efficiency solely based on saving time through automation. Governable AI for CLM must offer risk management and ownership over the contracting process.
For enterprises seeking to improve their contracting, their CLM resolutions list would look similar to these goals below:
1. Set a real target to shorten contract timelines without increasing rework.
2. Evaluate AI by the quality of its logic, not just speed.
3. Lock core risk positions into governed playbooks.
4. Surface revenue, compliance and clause collisions before negotiation begins.
5. Shift to issue-based review, not serial document edits.
6. Create one place for commitments across legal, procurement, sales and finance.
7. Ensure that every change has a reasoning and playbook reference.
8. Prioritize CLM platforms that pause when confidence is low.
9. Make contracts a common, enterprise-wide language for risk and revenue.
10. Track defensibility and insight, not just completion.
"2026 will be the year enterprises start asking harder questions, primary among them, 'Can I trust this AI to act on my behalf?' The answer will not come from bigger models or faster demos, but from systems that know their limits," said Agrawal. "Governed agents will become the standard layer between human judgment and machine action: They'll understand policy, explain every recommendation and decline to operate when the data isn't defensible."
To learn more about agentic AI's potential for CLM in 2026, visit Sirion.AI.
