Amid strong contention on whether AI will deprive people of jobs or liberate them from mindless tasks, a better question would explore the possibility of using AI to maximize gains for workers, seeing as humans are what direct the flow of AI innovation in the first place.
So how can Artificial Intelligence (AI) be harnessed to both take over mundane tasks and hone the skills of workers? The answer to this comes in the form of the Coaching Cloud.
This is an AI machine that is learning to guide workers towards more effectively doing their jobs. The secret to its method is how its software gathers data from a distributed network of workers and pinpoints the best techniques for getting things done. It functions as a real-time on-the-job coach by directing people towards successful outcomes, and then gathering new data that is fed back into the system.
The Cloud is wired to do two things: it learns the best practices that have been proven to be effective across a variety of situations, and it also identifies outlier cases where a creative person finds a new and better solution, and then adds both those techniques to its coaching. In this way, it uses humans as ‘mutation engines’; generators of new ideas that it then distributes to help other people.
Coaching Cloud has the potential to rise in every sector of the economy, but will be prominent in sales and service-related jobs where human interaction is essential. Interpersonal communication itself can only be fully understood by humans, but machines can assist them in making better decisions to enrich their communication. Some companies are already making their way forward, and below are some core principles that have set them apart from the rest of the pack
Go deep, not big with data
Successful Coaching Cloud companies focus on a specific set of problems within a domain or field. This is in contrast to applying AI to everything - a common strategy among startups today.
To succeed, these companies need to get access to colossal amounts of user behavior data that is nuanced to the problem they are working to solve. A network is needed to generate this data - one that is highly focused on the specific jobs at hand - and this is where ‘deep’ data trumps ‘big’ data.
Textio is a startup that has quickly grown to help major Fortune 500 companies, such as Cisco and Johnson & Johnson in recruiting better talent. It employs machine learning techniques to help businesses write better job postings so that attracting qualified candidates is likelier. Their innovation can predict the likelihoods of their post getting the attention of the right applicants. This makes a difference in how companies hire - one of their clients Expedia saw a 25% increase in qualified applicants as well as a 20% increase in women applying for technical and management jobs. It works alongside the person writing the post, helping them craft effectively, while learning from unseen words and phrases of outlier writers. Textio calls this augmented writing, and its potential in other areas of business writing is evident. However, because they chose to start by focusing solely on job postings, it has amassed the world’s largest data set in this domain. Thus, it offers the best coaching.
Sweat data, not algorithm
The best Coaching Cloud companies will use opensource tools like Google’s TensorFlow, Microsoft’s Cognitive Toolkit, and Amazon Machine Learning. There is no need to create a unique algorithm when more time can be invested on proprietary user behavior data.
Chorus.ai has created an intelligent engine that listens in on every call sales reps conduct with a prospect. While the call is happening, it works to offer coaching on what phrases and words to say and when to say them. It is capable of suggesting certain questions to ask, or recommending a tone of voice depending on the type of call, or even flagging a question the customer asked earlier that the rep forgot to respond. It then looks at whether the outcome of customer interaction is a closed deal or not, adds relevant data to a body of knowledge that forms the Coaching Network, and uses all this knowledge to help other sales reps in future calls.
What Chorus.ai is doing is difficult to do well - what helps them provide high-quality coaching is all determined by the quality and quantity of proprietary data it gathers. One of their customers, Everstring, saw a nearly immediate 30% increase in the rate of closings over its starting point.
Useful, not visible
It is imperative to invest in building a user experience that encourages further use. If it is annoying or too intrusive, it will discourage people from regularly using it. The lack of use means that there will be a lack of fresh data - and this is deadly for new networked software.
A UI that lets users know that what they’re doing is helping others like them is also worth working on, because it has been proven to encourage further contribution. It’s one of the reasons that Waze works well.
Guru has created a Google extension that links workers to the institutional knowledge they need to complete certain tasks. Every company has some tasks that require a unique workflow, like handling a product return or addressing customer objections in a sales process. This knowledge tends to get lost in miscellaneous documents on a corporate intranet or file storage system, but mostly lives in the minds of employees. When Guru notices someone working on a task in Gmail, Salesforce, Slack, or other application, it automatically pulls up related information in context and in real time.
Employees, especially new ones, like how it saves them the hassle of looking for the information they need. This means that they will continue to use it, and the high rate of usage creates more data on what works best, which in turn helps Guru formulate better suggestions over time. Shopify, since deploying Guru, has seen a five times increase in knowledge base usage, which speeds up critical processes. Intercom has seen a 60% reduction in the time it takes its support team to respond to customers.
As Coaching Cloud establishes itself in large enterprises, there may soon be an enterprise software company that will ride the network effect with their data to achieve significant scale. One company on its way is Salesforce.com, whose market capitalization just recently passed the $70 billion mark. As successful as it is, it is considerably smaller than Facebook ($500 billion market cap) and Google/Alphabet ($640 billion). The reason for this disparity is that no enterprise company - except Linkedin, perhaps - has fully harnessed this network effect with their data the same way the consumer internet giants have. It’s highly likely that the first enterprise company to do this will be from the Coaching Cloud.