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October 31, 2018

The Future of Work Expo's Arnold Discusses Near-Term AI, ML Expectations

Technology Marketing Corp. early next year launches The Future of Work Expo. It will focus on how artificial intelligence and machine learning are challenging and enhancing business communications and collaboration, customer service, human capital management, marketing, and sales.

TMC’s (News - Alert) old friend Jon Arnold will moderate The Future of Work Expo. I thought Jon would be perfect to help me lead the charge on this event, because he has a strong foundation in the business communications and collaboration space. And he brings an analytical eye to new trends and strategies in communications and knowledge worker productivity arena.

Here’s part one of my conversation with Jon about The Future of Work.

You’re been in the industry for a good long time. How long has it been exactly? And what have you focused on and seen over the years?

Jon: I’ve been an independent tech analyst since 2005, and was initially focused on VoIP. At the time, VoIP was pretty disruptive, so there was plenty of ground to cover, but as it became mainstream, the trends shifted to integration with other applications like chat, video, conferencing, video, etc.

That gave rise to unified communications, so my analyst focus has broadened to include all of these modes instead of just VoIP. Likewise, as UC has matured, the use cases have gone beyond everyday communications to encompass the larger sphere of collaboration and team work.

That’s where much of my analyst work is now, and from there, it’s not a big leap to future of work themes that are being driven by AI and ML.

What’s the difference between artificial intelligence and machine learning?

Jon: This ground will be covered during the conference, but basically machine learning is one branch of the artificial intelligence tree. It’s important to understand that one is a subset of the other, as these terms – and others like natural language processing – often get used interchangeably as if they’re equivalents.

In short, AI is an umbrella term for several technologies and branches of computer science that focus on using computers to emulate human behavior and interact with humans in a natural way. ML is just one of those technologies that leverages the computational power of computers to identify patterns that map human behavior. The accuracy of ML improves over time, and makes AI attractive by being able to learn with minimal human interaction or additional programming.

What’s the low-hanging fruit for using AI and/or ML to improve business communications and collaboration, customer service, etc.?

Jon: Workplace AI applications are still at a basic level, so the initial use cases will be for simple tasks that can be managed with plain language commands. These would generally be closed-ended questions with unambiguous answers, so the applications would be logic-driven, much like an IVR decision tree.

In terms of everyday communications, typical tasks would be calendaring or planning meetings. The key here is that the AI apps aren’t engaging directly with humans, so there is little risk of ambiguity. As these apps build a track record, they can take on more challenging tasks such as organizing and managing meetings.

Messaging-based apps will be the safest to deploy, but as employees come to trust them, they’ll be more comfortable using voice-based AI, and that opens up another set of use cases.

To what extent are workable solutions available today to support these improvements?

Jon: We’re seeing this now for both messaging and voice-based applications. Digital assistants like Amazon Echo are finding their way into businesses now, and while easy to use, their capabilities are quite limited. Most applications for now will be search-based, but with ML, there will be deeper integrations with business software, and then AI will have a bigger role to play to support collaboration.

AI-driven chatbots are also being used in the contact center, but only cautiously, as customers are wary of their limited capabilities and shaky accuracy. That said, all the leading vendors have major AI initiatives, so they are aggressively touting the virtues of AI. While the hype level is high, there should be little doubt that in time, these applications will bring a lot of new value to the workplace.

To what extent have businesses actually deployed such solutions?

Jon: Contact centers have certainly ramped up their use chatbots to help automate customer service and shift routine inquiries away from live agents. The rationale is sound, but it’s difficult to say just how much of this is based on conventional webchat that uses little or no AI, as opposed to chatbots that are fully AI-driven.

Currently, it’s more likely the former, but ML is quickly improving chatbot capabilities, as is NLP – natural language processing. These technologies are the key for developing conversational interfaces that can make the chatbot experience almost as good as interacting with another person.

The same applies inside the enterprise, where various forms of digital assistants are gaining utility for managing personal productivity or team-based workflows. Whether using Microsoft (News - Alert) Cortana or IBM Watson on the desktop, or Alexa for Business in the conferencing room, workers are just starting to become familiar with this person-to-machine interface, and as their comfort level grows, so will the use cases.




Edited by Maurice Nagle
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