AI Archives - Page 8 Of 18

Everest Group Reports Demand for IVA Technology Grew 42% in 2019, Projects 70% Growth Through 2022 After Momentary Dip Due to COVID-19 Pandemic | Press Release

July 20, 2020

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

The IVA market is witnessing a significant shift from rule-based solutions to AI-driven IVA solutions, propelled or aided by the following:
- Increase in consumer demand for self-service
- Integration of IVA solutions with critical enterprise back-end systems
- Innovation of voice-based conversational capabilities
- Shift from multi-channel to omnichannel delivery by software vendors
In order to meet the evolving CX needs, next-generation intelligent automation technologies are expected to witness high growth as they play a key role in transforming service delivery.
The conversational AI ecosystem in contact centers will enable seamless collaboration between front and back office and empower faster, more reliable, and lower cost operations.

*** Download a complimentary abstract of the report ***

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/.

Global Business Centers Leading Set-up of New Digital Service Delivery Programs | Market Insights™

May 29, 2020

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COVID-19 Business Crisis Proves Automation Matters | Blog

May 4, 2020

Consider what’s now happening at companies that made investments in automation and moving work to the cloud. They’re doing better than others in the COVID-19 pandemic. They’re more flexible under trying conditions. They’re more resilient to challenges. They are a bright spot in this awful crisis. The pandemic showed what companies invested in as preparation for challenges. Unfortunately, it also exposed companies that were less prepared. As I mentioned in my prior blog, the pandemic was like what Warren Buffet described as the tide going out, exposing naked swimmers. One fact that the COVID-19 crisis exposed is that automation matters.

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4Es Artificial Intelligence Business Impact Framework | Market Insights™

December 18, 2019

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Types of Artificial Intelligence Use Cases | Market Insights™

December 18, 2019

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Common Artificial Intelligence Challenges | Market Insights™

December 18, 2019

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In AI We Trust, Thanks to AI Checking Software | Blog

October 9, 2019

The increasing popularity and uptake of Artificial Intelligence (AI) is giving rise to concerns about its risks, explainability, and fairness in the decisions that it makes. One big area of concern is bias in the algorithms that are used in AI for decision making. Another risk is the probabilistic approach to handling decisions and the potential for unpredictable outcomes based on AI self-learning. These concerns are justified, given the implicit ethical and business risks, for example, impact on people’s lives and livelihood, or bad business decisions based on AI recommendations that were founded on partial data.

The good news is that the software industry is starting to address these concerns. For example, last year, vendors including Google, IBM, and Microsoft announced tools (either released or in development) for detecting bias in AI, and recently, there were more announcements.

IBM

Last year IBM brought out:

Adversarial Robustness 360 Toolbox (ART), a Python library available on GitHub, to make machine learning models more robust against adversarial threats such as inputs that are manipulated to derive desired outputs
AI Fairness 360, an open-source toolkit with metrics that identify bias in datasets and machine learning models, and algorithms to mitigate them

Last month, IBM further augmented its offerings with the release of AI Explainability 360, an open source toolkit of algorithms to support the understanding and explainability of machine learning models. It is a companion to the other toolkits.

Cognitive Scale

Cognitive Scale recently unveiled the beta of Cortex Certifai, software that automatically detects and scores vulnerabilities in black box AI models without having access to the internals of the model. Certifai is a Kubernetes application and runs as a native cloud service on Amazon, Azure, Google, and Redhat clouds. Cognitive Scale also unveiled the AI Trust Index. Developed in collaboration with AI Global, it will provide composite risk scores for automated black-box decision making models. This is an interesting development that could grow to become a badge of honour for AI software, and a differentiator for those with the most trusted rating.

The Reality of Bias

While these announcements and those made last year are good news, there are aspects of AI training that will be difficult to address because bias is all around us in real life. For example, public data would show AI that there are many more male CEOs and board members than female ones, leading it to possibly conclude that male candidates are more suitable for shortlisting for a non-executive director vacancy than women. Or public data could lead AI to increase mortgage or auto loan risk factors for individuals living in a particular zip code or postcode to unreasonably high levels.

It is the encoding and application of these kinds of biases automatically at scale that is worrying. Regulations in some countries address some of the issues, but not all countries do. Besides, the potential for new threats and risks is high.

There is still a lot more for us to understand when it comes to making AI fair and explainable. This is a complex and growing field. As demand for AI grows, we will see more demand for solutions to check AI as well.

Artificial Intelligence: Why It’s Essential For Digital Platforms | Blog

September 10, 2019

Companies widely recognize the potential power of artificial intelligence (AI). They instinctively understand that it feels like we’re on the cusp of something that will change our lives and our businesses in a profound way. Yet, many struggle with where to apply it. Executives can’t shake the feeling that they should have use cases for AI and use it productively today, even recognizing that AI is not mature yet and will be far more powerful tomorrow and in the future. If you’re looking for how and where your company should use AI, let me give you a perspective on a great application of AI today: your digital platforms.

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Aware Automation: How Enterprises Can Capture Value | Blog

September 10, 2019

In a previous blog post, we explored the evolution of enterprise IT infrastructures from a cost-center positioning to one that enables digital transformation through a concept known as aware automation — a combination of intelligent automation and cognitive/Artificial Intelligence (AI)-driven automation. In this post, we’ll explore some potential use cases and best practices for aware automation within the enterprise.