case study

2017 Guide for Deep Learning Business Applications

We are witnessing a historic moment for technology advancement. Today we can pull together the best hardware, affordable infrastructure and vast amounts of data to fundamentally transform the way we conduct business.

Mikhail Naumov

December 30, 2016

DigitalGenius - Human+AI™ Customer Service

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We are witnessing a historic moment for technology advancement. Today we can pull together the best hardware, affordable infrastructure and vast amounts of data to fundamentally transform the way we conduct business. Having spent the last three years in the trenches of this industry, as Co-founder & CSO of DigitalGenius, here is what we see coming next year. Artificial Intelligence & Deep Learning for Business Applications

The promise of artificial intelligence is ubiquitous and often portrayed in Hollywood as a calculating robo-nemesis, disguised as a friend or personal assistant (just see Her, exMachina, and Westworld). Yet, there are few areas better suited for an AI-powered transformation than enterprise & business functions.

Deep learning (a common method for developing AI applications) is exceptionally useful for training on very large and often unstructured historical datasets of inputs and outputs. Then, given a new input, predicting the most likely output. A simple intelligence formula — but one which can be applied across almost every function inside a business.

A simple intelligence formula — but one which can be applied across almost every function in a business.While applicable to an endless number of use-cases, this method follows some general principles in order to be practical and achievable in the near term:

  1. A company must have lots of historical data to train the deep learning algorithm
  2. A company should have a recurring need for predicting things that either:
    • Cut costs: for example reducing average handling time in a customer service conversation; or reducing the need for in-person insurance assessments
    • Create value: like up-selling the right product to the right customer at the right time; or helping marketers create engaging content which will lead to more sales

The requirements above point to a number of business use-cases, which are going to see major transformation over the next 12 months.

Business Use Cases for AI-powered Transformation:

  • Customer Service: Currently the largest market, and exceptionally well-positioned for disruption, due to availability of vast historical customer service data. Our company — DigitalGenius — operates here bringing meaningful results to companies like KLM Royal Dutch Airlines and partnering with leading platforms like Salesforce Service Cloud & Zendesk.
  • Sales: Another obvious use case. Just think of all the emails you get inside your inbox from people trying to sell you something. Very soon, those emails will be hyper-personalized, and will only land in your inbox during the 20-minute time slot when you’re statistically most likely to open them and respond positively. Salesforce Einstein, for example, makes it easy for sales professionals to focus their time on the most important leads, through predictive lead scoring.
  • Marketing: Marketers have one major pain point today – too many data elements to segment, organize and learn from. They are suffering from data overload thanks to an endless menu of analytics tools. In 2017, deep-learning algorithms will bring order to their marketing data and provide real-time recommendations for audience targeting, campaign timing and content marketing. For good examples check out Radius and Persado.
  • Operations: Companies like x.ai are already achieving near-perfect automation of meeting scheduling. And in 2017 will likely become household names inside medium and large enterprises. Similarly, recruitment chatbots like Mya will screen candidates and handle all communication with prospective talent, Saving companies time & valuable resources in the talent acquisition process. Tools like Clarke.ai will dial into our conference calls and send a summarized outline with action-points and to-do lists to all the participants afterwards.
  • Government Affairs: Notably, more sensitive areas like government affairs will finally become transparent and preemptively actionable. For great examples look at the way FiscalNote analyzes government data to predict outcomes of law-making processes around the country.

Thanks to “online learning”, the real time re-training of AI algorithms, the models which get trained first will grow faster and become stronger over time. This will propel early adopters towards producing more consistent results faster, enabling them to rapidly pull ahead of the pack.

Between large technology companies, hyper-focused startups, and massive investment into the space, deep learning and artificial intelligence will certainly become the most important driver for transformation of business functions in 2017. Finally, We will hear less “AI announcements”, and more success stories of companies using AI to win in their respective fields .

As appeared here.

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