January 28 by Kingman Tang
Vendors, pundits, and mainstream media are talking up Artificial Intelligence (AI) – it’s the new, in vogue buzzword that, as a set of technologies, is at the height of the hype cycle. Despite the promise of radical improvements and competitive advantage, enterprise prospects and customers are baffled; these savvy enterprise leaders want to know how they can apply AI to improve business outcomes for their organization.
I previously discussed how to get started with AI, but I will now take a step back to define AI, what technologies are used, and how AI is applied to sales operations and legal operations.
What is Artificial Intelligence (AI)?
Coined in the 1950s, AI is a broad reference that includes a set of methods, algorithms and technologies that make the underlying software seem capable of exhibiting behavior or intelligence which is indistinguishable from a human.
The most widely-known AI experiment is also one of the first: The Turing Test developed by Alan Turing in 1950 (the image below provides a brief AI development timeline) at the University of Manchester. The Turing Test contains criteria to determine whether a computer has human-like intelligence by convincing a human questioner that s/he is speaking to a human, not a computer.
Since this time, the concept of AI has ballooned to include machine learning (deep learning), computer vision, natural language processing, robotics and more. But remember, all these AI technologies go to serve the goal of AI – to exhibit human-like behavior and intelligence.
Artificial Intelligence Technologies
Through the decades, AI has gone through periods of euphoria and deep depression, but the advent of readily available, organized, and abundant data, open source AI algorithms, and the means of distribution (cheap and performant cloud computing and storage) have created the perfect storm for AI to be mainstream in the enterprise today.
Here are four key AI technologies used in the enterprise:
1. Computer Vision acquires, analyzes, and understands a digital image to function as, or make decisions like our human eyes. Identifying objects on the road for an autonomous vehicle, searching through a digital photo library, or allowing a frictionless store experience by forgoing the checkout line (see this Amazon Go video) are some examples of computer vision in action. In the enterprise, Optical Character Recognition (OCR), a branch of computer vision, is used to digitize text from a hard copy document. By digitizing legacy contracts, AI can perform analysis such as contract risk scoring or alternative clause recommendations. Augmented Reality (AR) technology, leveraging computer vision, like Microsoft’s HoloLens is being used to interact with parts on a factory floor, configure complex products, or help field technicians to fix a complex machine.
2. Intelligent Agents like Apple’s Siri or Amazon’s Alexa provide a conversational user interface that utilizes AI to simplify and accelerate business processes. You can interact with your business applications through text, touch, or voice, giving a more natural, engaging, and easy to use experience. These intelligent agents are available as a resource, whenever needed (always on) – when driving to a client, in the office, at home, or on the go, whenever logging onto an app is inconvenient. And they are rewriting the rules for modern enterprise software by transcending individual application silos and accessing these applications to form new business workflows. There is a potential in the future for Intelligent Agents to supplant the modern graphical user interface (GUI) in our work and home lives. Caveat: There are corner cases where the GUI will remain (e.g. performing complex modelling via spreadsheets.)
3. Machine Learning enriches and powers insights and recommendations. By finding patterns in customer and sales data, these insights are used to make predictions and prescriptive paths that increase revenue or accelerate revenue making.
4. Natural Language Processing (NLP) understands and interprets human language. A common use case is sentiment analysis where NLP understands key phrases or words that reveals the feeling of the author. By parsing human speech or written text, NLP can make sense of a request(s) or a command(s) and responds back accordingly like in the case of an intelligent agent. In contracting, NLP is used to scan through a repository of legal contracts to identify risky contracts, suggest alternative clauses, and more.
For the rest of this blog, I’ll talk about how some of these technology components are applied in the real world.
Applying Artificial Intelligence (AI) to Sales Operations
Innovative sales trailblazers have reinvented the selling experience for their teams by applying AI to sales processes. They capitalize on the power of natural language processing, machine learning and an intelligent agent, to streamlining their global organizations’ practices, thus increasing sales efficiency, margins and revenue.
By employing AI, every seller is empowered to perform like their company’s best sellers with the ability to:
• Uncover and create new opportunities – Surfacing and creating new sales and white space opportunities with machine learning analysis. Simplifying data entry and other CRM processes via an intelligent agent.
• Optimize the quote process – Applying machine learning and an intelligent agent to capture and transfer best practices, like cross-sell / up-sell recommendations, from leading sellers to drive faster deal conversion.
• Maximize deal / renewal revenue – Leveraging the power of machine learning to fuel revenue and margin growth. Insights such as recommended discount level can be surfaced via an intelligent or in the browser as prescriptions to sellers at the moment of action.
Applying AI to Legal Operations
Law departments are doing more with less. As budgets shrink and demands grow, Chief Legal Officers and Legal Operations directors seek ways to manage risk and make the best use of their teams, while also supporting the objectives of the business.
AI enables legal teams to automate routine tasks that frees lost time and resources for high-value activities. Harnessing the power of NLP and machine learning to extract valuable information from massive volumes of contract data, enabling more informed decision making and decision analysis that was not previously possible. The Chief Legal Officer benefits from a more informed and agile legal team that can devote more time to strategy and managing risk, while individual legal professionals benefit from efficiencies that allow them to focus on their expertise. An intelligent agent, NLP and machine learning technologies work together to apply AI for:
• Agreement Risk Identification – Identify your business exposure to revenue-related risk in negotiated agreements. Locate key terms and topics and compare negotiated wording with your agreement templates automatically.
• Contract Cycle Time Reduction – Avoid bottlenecks and reduce the time required to reach contract signature through intelligent cycle time prediction based on your company’s approval process history for agreements with similar characteristics.
• Third-Party Paper Automated On-boarding – Automatically map business terms and clauses in third-party documents to the legal playbook, enabling you to swap language, reconcile drafts and send for approval just as if your team had originally authored the contract.
• Contracting Recommendations – Reduce risk exposure, streamline negotiations, and increase Legal Ops productivity with alternate word choice and terms suggestions based on analysis of your legal repository.