Silicon Valley is no stranger to buzzwords, and artificial intelligence (AI) and machine learning are merely the latest to join the likes of the cloud, Big Data, and synergy. Gartner’s Hype Cycle graph demonstrates how expectations and interest surge then crash as reality sets in. Once expectations adjust, better ideas emerge, and infrastructures mature so new technologies can leave the road map and enter reality. Currently, most AI technologies fall between the “Innovation Trigger” and “Peak of Inflated Expectations,” with autonomous driving and virtual assistants falling into the “Trough of Despondency.” Despite AI’s relative immaturity, it is increasingly seen as the future for areas like contract lifecycle management, where automation could significantly improve security, reduce risk, and streamline workflows.
Exciting possibilities aside, here are three things to keep in mind when AI is casually thrown out during your next demo.
1) What do they mean by AI? Is it machine learning, a voice-activated AI assistant, or merely business automation? On the spectrum of AI technology, voice-activated AI automation (like Alexa) is comparably simpler for most companies to implement. And it is coming to the office soon. Imagine simply asking Alexa on a Sunday morning how many contracts have been approved since Friday rather than having to log into your account.
2) Machine learning takes a massive amount data to facilitate training. Most software vendors and business leaders are talking about AI as if it has already reached the “Slope of Enlightenment” or even the “Plateau of Productivity”. In reality, there is no such thing as an out-of-the-box AI solution- yet. True AI requires extensive data to train machine learning algorithms in order to provide client value. Insight from one partner indicates that 100-200 contracts per specific use case are needed to develop effective results. A customer with 30 use cases may require 3,000-6,000 contracts to sufficiently train its AI. However, with most companies regularly tracking hundreds or thousands of documents, AI could eventually be a feasible option provided there is thorough preparation and the proper infrastructure.
3) Is Your Company Ready for AI? Speaking of thorough preparation and proper infrastructure, they will be essential for any company looking to effectively implement an AI-based solution. One reason why Gartner observes a trough of despondency is because firms have been tempted to use new technology to overcome broken or poorly implemented processes. Like paving a path vs. building a new road, trying to automate bad processes proves to be disappointing. Organizations that haven’t put in the work to update and upkeep their CLM processes and infrastructure would be hard pressed to obtain full value from any AI based solutions for CLM.
Final Thoughts
The future for AI and machine automation in CLM is bright but also a work in progress. Agiloft customers clearly understand the value of optimized processes across the contract lifecycle and should be more prepared than others to use emerging AI capabilities to their full advantage. By taking advantage of Agiloft’s deeply configurable no-code platform, customers can start optimizing, organizing, and customizing CLM today so they are ready for the AI technology of tomorrow.
