Are AI Ambitions Outpacing CLM Readiness?

AI adoption is accelerating across industry, and procurement is no different. But while ambition is high when it comes to AI, operational readiness in contract lifecycle management (CLM) is low, reports Conga. 

Latest research from the team reveals that while 95% report using AI to some degree within their CLM processes, only 38% describe CLM maturity as "integrated." 

Nearly half of respondents, 49%, said they were highly confident using AI with minimal oversight, even though far fewer viewed their wider contract management set-up as fully integrated.   

Jason Smith, Global Director, CLM Product Launch at Conga, says: “With AI adoption accelerating, many organisations are moving faster in deployment than in discipline.

"What we’re seeing is not a technology gap, but a readiness gap.

"AI is exposing weaknesses in process, data quality and governance that CLM programs can no longer ignore."

Stuck in experimentation, instead of transformation

The research covered leaders in healthcare, financial services, technology and manufacturing, sectors where contract controls and auditability often carry significant weight.

That makes the absence of a formal AI policy in so many organisations particularly notable, especially where legal and compliance teams are expected to oversee growing use of automated tools.

Despite this, 67% of organisations still do not have a formal AI policy for its use. 

 Logan Maley, General Counsel at Conga, adds: “AI is accelerating the evolution of the legal function in ways we couldn't have predicted even two years ago.

"CLM sits at the centre of that shift, connecting legal, finance, sales and procurement around a shared source of contractual truth.

"Organisations that mature their CLM programmes, and close the gap between AI readiness and adoption, will be the ones positioned to move faster and with less risk."

Where is AI delivering in CLM?

When it comes to the adoption of AI in CLM, the industry's most common use cases are in search and reporting (69%), risk assessment (68%) and contract reviewing (59%).

This suggests organisations are yet to unlock the deeper, end-to-end value that more mature CLM programmes could deliver.

In particular, AI is showing the greatest improvement in reporting quality (59%) and risk identification (51%). 

However, some of the biggest barriers include a lack of staff training for early adopters (40%), as well as troubleshooting support gaps (31%) and unclear use cases (27%). 

The barriers to scaling AI in CLM point as much to people as to platforms. The emphasis on training points to a workforce issue as much as a technical one. If staff do not understand when to trust outputs, when to escalate concerns and how to apply internal controls, high confidence in using AI may not translate into best practice.