Senior VP of Product Management and Product Strategy at Mitratech, Justin Silverman, writes on the ways in which AI and automation complement each other so that they can benefit business processes.
While the idea of Artificial Intelligence (AI) may drive fear and uncertainty in many of us, AIs already all around us in today’s corporate world, helping organizations reduce time spent on repetitive tasks, increase efficiency, and enhance the human experience. In fact, 90% of leading businesses have ongoing investment in AI. And there are important reasons for this adoption – AI can drive tremendous efficiency and quality improvements for a business and its customers!
We’ve previously discussed how automation software and artificial intelligence are highly complementary and can be used in tandem to drive quick time-to-value and ROI. Now, let’s look at some specific ways AI and automation fit together to accelerate business processes. We will use the review of documents, specifically contracts, to bring these benefits to life.
1. Accelerating Review and Approval Processes
Organizations can automate the document creation process and close agreements much faster by relying on a combination of AI and automation. In the back-and-forth of onboarding paperwork, contract negotiation, and vendor partnerships, for example, automation can push the paper trail forward based on a human-structured workflow: the correct people are brought in automatically and have complete visibility through the stages of the agreement.
By “learning” which sections of a document are more likely to cause delays or difficulties, however, AI can enter into the equation to make predictions even without a human gatekeeper. In other words, it can analyze patterns in the document and identify specific clauses that will bring a deal to close much faster— with fewer headaches— and act on that intelligence to streamline the process. With AI and automation, the engagement between your team and a third party becomes easier, faster, and more efficient for everyone involved.
2. Find what isn’t there: document omissions
AI is not just useful for moving documents through a system quickly, but also for flagging when a document is not quite right or when it could be improved. For example, AI is great for detecting anomalies in documents that humans might otherwise overlook through “unusual language or undue omissions or additions.”
When AI does flag unusual omissions in documents, an escalation may be needed: the “path” that the document takes to get to a signature may become more complicated. By using automation and AI-driven intelligence in conjunction, organizations can build in variable paths to handle unexpected changes and redirect a document without the need for human intervention. Not only does this save your team time, but it also ensures the integrity of the document by accounting for all necessary clauses.
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3. Drafting and Editing Contracts
The drafting of legal contracts can be a tedious and time consuming task. Often when a new contract needs to be created, attorneys at a corporation or a law firm seek previous contracts they have worked on to serve as the starting point for the new contract. Unfortunately, the attorneys often pick a poor starting point when much better options are available. Through automation, document templates can be created that can serve as the basis for new contracts. Contract requesters answer a series of questions which are then used to select the ideal combination of contract clauses.
With AI this drafting and review process can improve even more. On initial drafting, AI can be used to help refine those contract templates. Tracking what changes the attorneys are making or what requests the other party is negotiating, can help optimize those template options. Further, new AI-driven service providers are training their systems not only to draft contracts, but to automatically respond to contracts from the opposing parties. Providers, such as LawGeex and Lexcheck, offer AI-driven contract redlining.
4. AI and Automation for Analytics
After you’ve automated different processes for your team or organization, you should be able to access all of the data around those processes. How long does it take to do each task, and where are the hold-ups? Are your average costs higher than anticipated? Whereas manual data collection can be time-consuming and prone to information gaps, AI and automation lay the groundwork for incredibly specific data analytics. For example, when contract approvals are pulled into automated workstreams, each approval becomes a data point that you can leverage to drive better business decisions.
While reporting tools don’t always leverage AI, it is becoming increasingly common for them to do so. The reason? AI can sort through enormous data sets easily, answering questions that simple drag-and-drop graphs don’t always offer. AI makes it possible to look for patterns before you know which patterns are emerging: by pulling together and analyzing these large sets of data. Further AI allows companies to move from looking at historical data to more predictive insights – AI-tools can create forecasts of what the data is likely to show in the future which enables even more informed human decision-making.
5. Driving future improvements
Once you’ve leveraged automation and AI to speed up processes and sort through business analytics, the intelligence you’ve collected will empower you with vast amounts of reliable, actionable data that will allow you to ever-improve:
- What elements of the software are working for your team?
- Where can processes be further streamlined?
- Is there a way to achieve broader implementation?
Artificial intelligence and workflow automation are highly-configurable tools that can often be implemented with no code at all; your citizen engineers who are closest to the pain points can build upon the tech solutions to improve organizational adoption and find creative new ways to solve your company’s most complex business concerns.
While automation and Artificial Intelligence are powerful standalone tools, their integration can optimize business processes, maximize output, and offer organizations a competitive edge. In this article, we’ve used examples from contract generation, review and analytics. But these principles can be applied across numerous business use cases. And though the future of technology remains largely unexplored, we can expect to see these tools blended and applied in more tangible ways in 2022 and beyond.