# AI adoption outpaces operational readiness in contract lifecycle management

> 95% of companies use AI in contract processes, but only 38% have fully integrated systems. This leaves 67% without a formal AI policy, creating compliance risks.

- Type: AI news
- Page: https://completeaitraining.com/news/ai-adoption-outpaces-operational-readiness-in-contract/
- Published: 2026-06-15
- Updated: 2026-06-15
- Author: Jeroen Erne
- Jobs: Operations
- Reading time: 1 min

Organizations are deploying artificial intelligence in contract lifecycle management faster than they are establishing the operational discipline to support it, according to new research from [Conga](https://www.conga.com/?ref=completeaitraining.com). While 95% of respondents report using AI in their contract processes, only 38% describe their systems as fully integrated, creating significant compliance and oversight risks for legal and operations teams.

#### The readiness gap

The disparity between adoption and maturity is creating systemic friction across departments. "With AI adoption accelerating, many organizations are moving faster in deployment than in discipline," says Jason Smith, global director, CLM product launch at Conga. He noted that companies are facing a **readiness gap** rather than a technology gap. AI is exposing weaknesses in process, data quality and governance that CLM programs can no longer ignore.

#### Policy and training deficits

Despite heavy reliance on automated tools, 67% of surveyed professionals report their company lacks a **formal AI policy**. This leaves legal teams to manage contractual risk without clear guardrails, underscoring the need for structured [AI for Operations](https://completeaitraining.com/tag/operations/) frameworks and better [AI for Legal](https://completeaitraining.com/tag/legal/) governance. Early adopters point to a lack of staff training as their primary obstacle, cited by 40% of respondents. Troubleshooting support gaps and unclear use cases follow closely behind as major barriers to effective implementation.

#### Measured improvements in risk and reporting

When implemented correctly, the technology delivers measurable gains in specific workflows. Search and reporting functions represent the most common application at 69%, followed closely by risk assessment at 68%. Professionals using these tools report the greatest improvements in reporting quality and risk identification. However, realizing these benefits requires moving past the current phase of isolated experimentation.

#### Why this matters for operations professionals

Operations leaders must audit their contract workflows before scaling AI deployments. Investing in staff training and formalizing AI usage policies will prevent compliance failures and reduce the troubleshooting burden on your teams. Without this foundational work, automation will only amplify existing process flaws.

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