Presented by Inference Solutions


Demand for AI-based solutions that automate processes and assist human workers is skyrocketing — and for good reason. AI is poised to drive enormous economic value to the businesses that are able to effectively implement it. Realizing this, businesses are jockeying for a “first mover advantage” as they compete to be the first to leverage its’ economic potential.

In the research report, Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017 2025, Gartner studied the impact of AI in terms of the industry gross output (IGO) that will be generated by the adoption of AI.  IGO is similar to gross domestic product (GDP) but captures all transactions in a supply chain rather than just the value of the final, consumed product.

In the research, Gartner segmented the AI market into four types:

  1. Decision Support/Augmentation
  2. Agents
  3. Decision Automation
  4. Smart Products

The “Agent” segment includes virtual agents that “allow corporate organizations to reduce labor costs as they take over simple requests and tasks from call center, help desk and other service human agents, while handing over the more complex questions to their human counterparts.”

According to Gartner, “Agents account for 43 percent of the global AI-derived business value in 2017 and 24 percent by 2030, as other AI types mature and contribute to business value.” That translated into business value of $300 billion in 2017 and is expected to increase to $1.2 trillion by 2030.

Today, many routine tasks are being automated.  We can expect the complexity of these tasks to increase as the AI matures. This should also enable expansion in the number of organizations using virtual agents.

Contrary to popular belief, more automation will not necessarily result in fewer jobs. Gartner predicts that “In 2020, AI becomes a positive net job motivator, creating 2.3 million jobs, while eliminating only 1.8 million jobs.”

Gartner included “three different sources of AI business value”:

  1. “Customer experience: The positive or negative effects on indirect cost. Customer experience is a necessary precondition for widespread adoption of AI technology to both unlock its full potential and enable value.
  2. New revenue: Increasing sales of existing products and services, and/or creating new product or service opportunity beyond the existing situation.
  3. Cost reduction: Reduced costs incurred in producing and delivering those new or existing products and services.”

$1.2 Trillion is an enormous number but, after a bit of inspection, may not be completely unrealistic.  Let’s look at a sample case and drill into one of the three areas of business value — cost reduction.

Let’s assume the call center uses 1,000 live agents to handle largely routine tasks. In the U.S., a live agent makes an average of $28,000 with the fully burdened costs reaching more like $40,000. So, 1,000 agents can cost $40 million per year. Virtual agents by contrast cost on average 1/10 the cost of a live agent. That results in savings of $36 million per year. DMG consulting recently forecast that the leading virtual agent vendors had just under 2,000 customers last year. That equates to costs saving of around $72 billion in 2018. Gartner forecast that cost savings were one of three sources of the total business value. If we multiple that by three, we get to $216 billion in total economic value — that’s still a pretty impressive number.

This economic value is driving a massive wave of investment. Industry leaders like Google, Amazon, Microsoft and IBM are all investing heavily in cloud-based conversational AI to bring speech recognition, text-to-speech, and natural language processing into their respective cloud offerings.  Nearly every major UC and Contact Center software company is integrating AI and Intelligent virtual agents into their platforms. Leading telecommunication service providers are reselling virtual agent solutions and at Inference, we are now partnered with over 40, including AT&T, Telstra, Vonage and Nextiva. Furthermore, there are now hundreds of venture-backed startups that are set to bring innovative solutions to market.

Adoption is accelerating even faster with intelligent virtual agents now offering natural language processing to improve the user experience and automate tasks that were too difficult to automate using touch tone IVR or even directed dialog speech recognition applications.  Virtual agents can now be configured to ask open- ended questions like, “How can I help you today?” or “Please tell me the reason for your call.” They then access advanced NLP services like Google’s Dialogflow to determine the caller’s intent and solve their problem.

They can now use machine learning to understand the various ways your customers ask for things. You simply provide a set of examples of things a customer might say, and the virtual agents learn to understand other ways that your customers may ask for the same thing. For example, you might teach the virtual agent to understand the phrase “I’d like to get my car fixed,” and over time our virtual agent will understand that “Can you fix my car?” means the same thing. These kinds of efficiencies produce more correct answers to a wider variety of inquiries.

These advances in conversational AI technologies are enabling virtual agents to do more things, more effectively giving consumers the type of natural interactions that they now get from their Google Home or Amazon Alexa devices.

Conclusion

Virtual agents are poised to drive an enormous amount of business value. That business value will be derived from improved customer experience, new revenue generated, and reduced costs. The growth curve is being driven by a combination of technology advancement, consumer acceptance, and the need for businesses to keep up with growing demands for customer support.

To learn more about Intelligent Virtual Agents, you can download Inference’s Solution Guide.

See Callan Schebella in a session where we’ll learn how to build a virtual agent that provides real business value on July 11th at 4:10 at the Conversational AI Summit, part of VB’s Transform 2019. 


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