NEW REPORT: The State of CLM and AI-Powered Contract Intelligence

What Does the Future Hold for Enterprise Contract Management? A Conversation with Forrester Research

By Bernadette Bulacan

A few weeks ago, Icertis hosted an in-depth conversation about Contract Lifecycle Management (CLM) between Forrester Research's Andrew Bartels, one of the leading analysts covering CLM, and our very own Samir Bodas, one of the most passionate CEOs in the CLM tech universe.

The two met at Microsoft Studios for the latest installment of our "Meeting of the Minds" series to explore what has changed in the CLM space in the last year and what 2020 has in store.

The term "meeting of the minds" is a legal phrase used to describe mutual assent among contracting parties, and we've developed the webinar series as a way to bring together the most active minds in CLM to confer on where this exciting technology is taking us.

This recent episode of the "Meeting of the Minds" was chock full of moments when Andrew and Samir reached "mutual assent." And, to be expected, there were plenty of moments when they respectfully departed in opinion.

Here is a recap of some of the highlights of their chat.

Digital Transformation: Hype or reality?

Samir noted that more organizations are undertaking digital transformation initiatives and that the availability of CLM technology is fueling this revolution. Digitizing traditional contracting workflows ─ which have long relied on paper and manual processes ─ is fertile ground for these digital transformation initiatives.

Andrew shared a different opinion, suggesting that digital transformation is oversold and overstated. Andrew argued that digital transformation is "evolutionary," not "revolutionary." He agreed, though, that there is a "broadening awareness of CLM among C-level executives" and the value digitally transforming core processes can deliver.

During the conversation, Samir added that digital transformation should not be confused with mere automation, and that automating current processes is table stakes for vendor offerings. He challenged enterprises considering CLM to ask vendors tough questions about business outcomes: "Show me, vendor, how I can increase cash flow with your system. How can I increase revenue or reduce cost? How can I reduce risk and improve compliance in specific cases that apply to my company?"

Enterprise-wide coalition building and deployment for CLM

Andrew astutely observed that contracting is threaded across entire enterprises and that "CLM serves many stakeholders." Throughout the conversation, both Samir and Andrew took inventory of the many stakeholders who touch contracting: procurement, sales, legal, HR, IT, and others.

However, Andrew noted that too often, companies skip the hard work of getting cross-functional buy-in in the C-suite when choosing a CLM. Instead, one member of the C-suite makes a CLM buying decision, and the rest get dragged along.

But Samir observed that companies are getting better at this. "We have seen an expansion of an interest and input into the evaluation─irrespective of what type of contract is involved─from across the enterprise," he said. Both agreed that this approach introduces complexity into the evaluation stage, but as Samir advised, "When you put this whole thing together, what buyers and enterprises are realizing is that this is a complex area that needs focus─focus from the company, focus from a vendor. An enterprise contract management system just makes so much more sense."

The Impact of Artificial Intelligence (AI) in CLM

Samir and Andrew then turned their conversation to the impact of AI and other innovative technologies, like blockchain, in CLM. Andrew provided a framework to discuss AI, stating, "Artificial intelligence is a combination of algorithms and data. And, the third element is training. It is often underestimated how much training must go into an AI system."

Andrew noted the AI training limitations inherent in traditional, on-prem systems wherein the data from each customer is siloed. Cloud-based vendors, on the other hand, can train on multiple customer datasets (with customer permission, of course) and unleash the power of AI by giving it access to a robust and rich training ground.

"There's an underpinning of the cloud that really differentiates a vendor if it has that access to create a much richer, smarter, better trained AI-engine for the task being performed," Andrew said.

Samir largely concurred with Andrew, adding another critical factor to train AI systems: diversity of data in the training sets. "If the diversity doesn't exist, in addition to quantity and of course quality, you are only half-way there," Samir said. "And half-way isn't good enough." Moreover, both observed that vendors that have been training their data for a longer time, on a larger data set, have a critical advantage.

Watch the Demo Now

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