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IVA market growth will accelerate post-pandemic as enterprises strive to overcome recession with focus on automation, customer experience
The global Intelligent Virtual Agent (IVA) market stood at US$300 million-US$350 million in 2019, exhibiting about 42% growth year on year, according to Everest Group. The firm projects a dip in demand in 2020 due to the COVID-19 pandemic but expects the IVA market to post strong growth going forward, achieving as much as a 70% compound annual growth rate (CAGR) through 2022. In fact, Everest Group has boosted this estimate by 13-22%, anticipating that enterprises will place greater emphasis on cost reduction and improving business continuity in the post-pandemic period.
IVA solutions are a key enabler of automation in the front office, currently being used primarily for customer support as well as IT and help desk functions due to their large volumes of repetitive queries. These functions account for more than 80% of the IVA market today. Banking, insurance, and telecom industries account for the highest adoption of IVA and continue to exhibit impressive growth, particularly given the maturity of contact centers within these industries.
Increasing sophistication and collaboration with complimentary artificial intelligence (AI) based technologies are driving IVA popularity in the market. Enterprises across industries and geographies are leveraging or plan to leverage IVA solutions for different use cases to reduce human involvement and improve customer experience (CX).
“IVA is still in the realm of early adoption today, but that is rapidly changing as enterprises realize what a tremendous opportunity they have to leverage this technology,” said Anil Vijayan, vice president of Everest Group. “IVA technology is continuously advancing and growing in sophistication well beyond rule-based chatbots. Today we see a higher level of maturity in intelligent IVA applications, which are being used for a variety of use cases including payment services account resolutions and employee onboarding, for instance. We’re also beginning to see IVA playing a key role in conversational AI ecosystems, where a collaborative set of tools—including IVA, AI, robotic process automation, learning and listening engines, analytics and more—is used to seamlessly integrate front and back office systems. Here, IVA supports more advanced use cases such as cross-selling and upselling, customer retention, and making personalized recommendations. We expect this evolution to continue, leading to reliable and delightful customer experiences while reducing human effort through automation.”
These findings are discussed in more detail in Everest Group’s recently published report “Conversing with AI – Intelligent Virtual Agents (IVA) State of the Market Report 2020.” The report includes a detailed analysis of the IVA market, including a market overview and adoption trends, solution characteristics, vendor landscape, barriers to IVA adoption and best practices, and the outlook for 2020-2021.
Evolution of the IVA Market
About Everest Group
Everest Group is a consulting and research firm focused on strategic IT, business services, engineering services, and sourcing. Our clients include leading global enterprises, service providers, and investors. Through our research-informed insights and deep experience, we guide clients in their journeys to achieve heightened operational and financial performance, accelerated value delivery, and high-impact business outcomes. Details and in-depth content are available at http://www.everestgrp.com/.
Only a small percentage of enterprise call-centers use Robotic Process Automation or Intelligent Virtual Agents at scale in the front or back office. Everest Group’s Skand Bhargava join Jacada’s Scott Merritt, Global Head of Automation, to discuss how the current pandemic will change the way organizations will engage with BPO partners. Key learnings you can expect include:
June 16, 2020; 11:00 AM EST
Global Head of Automation
4 ways intelligent virtual agents and humans can work together: a contact center example