Tag: artificial intelligence

Using AI to Build, Test, and Fight AI: It’s Disturbing BUT Essential | Sherpas in Blue Shirts

Experts and enterprises around the world have talked a lot about the disturbing concept of AI being used to build and test AI systems, and challenge decisions made by those systems. I wrote a blog on this topic a while back.

Disquieting as it is, our AI research makes it clear that AI for AI with increasingly minimal human intervention has moved from concept to reality.

Here are four key reasons this is the case.

Software is Becoming Non-deterministic and Intelligent

Before AI emerged, organizations focused on production support to optimize the environment after the software was released. But those days are going to be over soon, if they aren’t already. The reality is that today’s increasingly dynamic software and Agile/DevOps-oriented environments require tremendous automation and feedback loops from the trenches. Developers and operations teams simply cannot capture and analyze the enormous volume of needed insights. They must leverage AI intelligence to do so, and to enable an ongoing interaction channel with the operating environment.

Testing AI Biases and Outcomes is not Easy

Unlike traditional software with defined boundary conditions, AI systems have very different edge scenarios. And AI systems need to negate/test all edge scenarios to make sense of their environment. But, as there can be millions of permutations and combinations, it’s extremely difficult to manually assure or use traditional automation to test AI systems for data biases and outcomes. Uncomfortable as it may be, AI-layered systems must be used to test AI systems.

The Autonomous Vehicle Framework is Being Mirrored in Technology Systems

The L0-L5 autonomous vehicle framework proposed by SAE International is becoming an inspiration for technology developers. Not surprisingly, they want to leverage AI to build intelligent applications that can have autonomous environments and release. Some are even pushing AI to build the software itself. While this is still in its infancy, our research suggests that developers’ productivity will improve by 40 percent if AI systems are meaningfully leveraged to build software.

The Open Source Ecosystem is Becoming Indispensable

Although enterprises used to take pride in building boundary walls to protect their IP and using best of breed tools, open source changed all that. Most enterprises realize that their developers cannot build an AI system on their own, and now rely on open source repositories. And our research shows that 20-30 percent of an AI system can be developed by leveraging already available code. However, scanning these repositories and zeroing in on the needed pieces of code aren’t tasks for the faint hearted given their massive size. Indeed, even the smartest developers need help from an AI intelligent system.

There’s little question that using AI systems to build, test, and fight AI systems is disconcerting. That’s one of the key reasons that enterprises that have already adopted AI systems haven’t yet adopted AI to build, test, and secure them. But it’s an inevitability that’s already knocking at their doors. And they will quickly realize that reliance on a “human in the loop” model, though well intentioned, has severe limitations not only around the cost of governance, but also around the sheer intelligence, bandwidth, and foresight required by humans to govern AI systems.

Rather than debating its merit or becoming overwhelmed with the associated risks, enterprises need to build a governing framework for this new reality. They must work closely with technology vendors, cloud providers, and AI companies to ensure their business does not suffer in this new, albeit uncomfortable, environment.

Has your enterprise started leveraging AI to build, test, or fight AI systems? If so, please share your experiences with me at [email protected].

Artificial Intelligence without the Hype: The Real Role of AI in Business Today | In the News

Artificial Intelligence (AI) has been the stuff of science fiction for decades and more recently has become a rampant buzzword in business media headlines. But CIOs need to know if there are realities amid the hype. Is AI actually delivering value and not just Proofs of Concept? In other words, are the business bona fides showing up yet?

Click to read the full article

Also available online at Applications Europe Magazine

Artificial Intelligence-led Alert Correlation | Webinar

Wednesday, September 12, 2018 | 11 a.m. EST | Hosted by GAVS Technologies with featured speaker, Ashwin Venkatesan, Practice Director, Everest Group

Register Now

AIOps is poised to become the next big thing in IT management. By maintaining the fidelity of data and generating insights, it has ability to influence business decisions. In the era of digital transformation, the adoption of AIOps is imperative for businesses with dynamic and complex IT environments.

Join GAVS for a webinar on “Artificial Intelligence-led Alert Correlation – Enabling the Journey towards Zero Incidents” featuring expert guest speaker Ashwin Venkatesan.

Why should you attend?

  • To know about the increasing complexities within the current enterprise IT operations model
  • To understand how AI-led alert correlation can deliver a step change in IT operations and business performance
  • To learn about the best practices for enterprises while adopting AI-led alert correlation solutions
  • To explore GAVel – GAVS’ AIOps platform for intelligent alert correlation

Guest Speaker

Ashwin Venkatesan, Practice Director, Everest Group

Register to attend

IEEE WIE International Leadership Summit 2018 — August 13-15 | Event

Research EVP and distinguished analyst Sarah Burnett will be a key speaker at the IEEE WIE International Leadership Summit held on August 13-15 in Southhampton. During her August 14 session, Sarah will unpack findings from industry-leading research into the capabilities of RPA and AI for enterprises, as well as how they can be combined for advanced automation of business processes.

Sarah’s presentation will highlight:

  • The capabilities of the two different types of technologies and how they differ in what they can offer
  • Evolution of RPA technology and its development into enterprise-grade software
  • How AI technologies are helping extend the scope of RPA, making it easier to apply, use and maintain
  • How the two combine to help enterprises automate more business processes
  • What the office of the future will look like with a mixed workforce of people, AI and robots
  • What the future holds in terms of technology and market development for real life applications of business process automation

About the event

The 2018 IEEE WIE International Leadership Summit (ILS) mission is to empower women to choose their own career path and facilitate the recruitment and retention of women in technical disciplines. Our summit is dedicated to promote women in Engineering, Science and Technology and inspire girls around the world to achieve rewarding careers in engineering.

When

August 13-15, 2018

Where

University of Southampton
Southampton SO17 1BJ

Speaker

Sarah Burnett, Research EVP and Distinguished Analyst, Everest Group

Learn more and register

How to Operationalize AI in Contact Centers | Sherpas in Blue Shirts

All types of artificial intelligence (AI) technology – from machine learning to natural language processing to cognitive computing – are being leveraged by enterprises to drive better customer experiences and process efficiency. Based on our market research, more than one-third of enterprises have prioritized adoption of AI-powered customer experience management (CXM) solutions in the next two to three years.

Contact Centers Looking for Value in AI Face Numerous Issues

  • Lack of stakeholder buy-in: Implementation of new technologies can be a barrier for firms that have made substantial investments into existing technologies and agent skill sets
  • Workforce resistance: Agents may be afraid that their jobs will be at risk, which in turn discourages use of AI technologies
  • Poor data management: Most data currently resides in siloes, which makes it very difficult for firms to leverage it to train AI systems, and results in suboptimal returns.

Despite these challenges, AI can be a key contributor to upping organizations’ competitive capabilities in the contact center space.

Three AI Benefits in Contact Centers

  • Enhanced customer experience: AI can help steer conversations in the right direction through real-time sentiment analysis, and deliver personalized recommendations. Consider, for example, a situation wherein AI informs the contact center agent that a telecom customer, who has called for billing clarification, can save money by opting for an international roaming pack as he/she travels abroad frequently.
  • Enable highly skilled talent: AI can be leveraged to monitor agent behavior and recommend training to enhance individual agent productivity. It can also ensure process compliance through regular prompts when agents are interacting with customers.
  • Drive process efficiency: AI-based bots deployed in the back-office can tap into the large volumes of data available for analysis to anticipate customer needs and smart route the request to the best fit agent.

But, to derive real, tangible, sustainable benefits from AI, we recommend enterprises carefully address the following considerations when attempting to operationalize their AI deployments.

Key considerations to operationalize AI in contact centers

There’s no question that AI is a key enabler in driving personalized, targeted customer service. But how enterprises embrace it will mean the difference between also-ran and game-changer status.

To hear more about how some of the leading brands are strengthening their customer experience delivery, the role of next-gen technologies, and how the Philippines’ contact center industry is matching the pace of the global industry-wide disruption, we invite you to join us at the Contact Center Association of the Philippines’ annual Contact Islands conference on July 25 and 26.

Everest Group is the knowledge partner for this annual event, and two of our executives – Eric Simonson and Karthik H – will be moderating plenary sessions.

53% of Insurers Are Opting to Develop AI Capabilities In-house— Everest Group | Press Release

A high skills gap in AI expertise is impeding adoption and acceleration of AI initiatives and compelling insurers to consider creative tech partnerships

As leading insurers transition toward becoming technology-focused firms, they are encountering a high skills gap in the area of Artificial Intelligence (AI), a significant barrier to their efforts to scale pilot projects and realize the expected value from AI initiatives. Everest Group reports that the majority (53 percent) of insurers are opting to build in-house AI capabilities through hiring, internal training, hackathons, acquisitions and InsurTech partnerships.

Conversely, 47 percent of insurers are turning to IT service providers to address the skills gap and accelerate time to market. Many of these service providers bring AI implementation expertise not only from their work with other insurers but also from their work in other industries that are further ahead on the AI adoption curve. Service providers such as Capgemini, Cognizant, HCL, IBM, Infosys, LTI, TCS and Wipro are building insurance domain-wrappers on top of their existing AI platforms to demonstrate early Proof of Value (POV) and accelerate the time to market for their clients.

“Global insurance executives correctly believe that adopting AI can catalyze the transformation of their business models and help their companies stay competitive in the market,” said Ronak Doshi, practice director with the IT Services research practice at Everest Group. “However, among all technologies being adopted by insurers, the skills gap is the highest for IoT and cognitive and AI-based technologies. So, insurers are exploring creative ways to address the skills gap, not the least of which is partnerships with InsurTechs and service providers who can bring AI expertise to the table.”

Everest Group studied 80 distinct AI-focused investments by global 100 insurers and recently released its findings in the report, “Artificial Intelligence (AI) in Insurance Moving From Pilots to Programs: Insurance IT Services Annual Report 2018.” In this report, Everest Group explores the adoption penetration of AI across the insurance value chain and provides snapshots of nearly 20 successful applications of AI by leading insurers.

***Download a Complimentary Abstract of the Report***

The key business objectives and leading use cases for AI in the insurance industry fall into these three categories:

  • Customer Experience (58 percent). Improving front-end customer experience remains the top priority and accounts for 58 percent of all the analyzed use cases. Insurers are trying to provide personalized and instant services to customers using chatbots and mobile applications. Leading use cases include validating insurance cases against business rules and using speech analytics solutions for sales and operational efficiency.
  • Process Improvement (43 percent): AI is helping insurers optimize processes, both internally and externally. Claims management remains a priority for the insurer, helping customers to fast-track their claims process and reduce the time taken for payments. Insurers are also using AI to improve efficiency in documentation and call center operations. Leading use cases include mobile applications and web portals to answer customer queries and give policyholders one-stop access to their documents.
  • Product Innovation (19 percent): Leveraging AI for product innovation is in the nascent stage of development. Insurers are using IoT devices such as those for telematics, connected homes and connected self, to develop more usage-based insurance products for customers. Leading use cases include leveraging data from connected vehicles and using AI-powered wearable devices and mobile applications to help customers with personalized advice.

AI projects in Insurance are Moving from Pilots to Business Programs | Sherpas in Blue Shirts

Insurers are rethinking their business ethos to become protectors instead of payers. The insurer of the future is aiming to develop a customer-centric value proposition. Carriers are looking at developing innovative products that are contextualized to meet evolving customer needs. And the insurance distribution strategy is shifting to adapt to new product offerings, client needs, and digital technology-led disruption in the ecosystem.

Not surprisingly, insurers are adopting AI and related technologies to drive these capabilities. According to our just released Insurance IT Services – Annual Report, the top three business objectives insurers are trying to achieve with AI projects are customer experience, process optimization, and product innovation.

AI Ins BlogAI Trends in the Insurance Industry

Our annual report studied 80 unique AI initiatives by global insurers to unearth AI trends in the insurance industry. Here are the top ones we identified.

Capabilities

Approximately 53 percent of insurers are developing in-house capabilities for their AI initiatives. But many have large skills gaps that will inhibit their ability to scale pilot projects and realize the expected value from AI initiatives.

Embedded intelligence

Insurers have accelerated their focus on embedding intelligence across the value chain, with higher adoption of AI for sales & distribution and underwriting processes.

Self-service

Insurers are adopting intelligent self-service AI tools to enhance the customer experience.

Mid- and back-office process value

The value delivered through front-office AI initiatives such as chatbots is limited. But real value can be unlocked when AI is applied to optimize mid- and back-office processes such as agent support and claims management.

Data

While structured enterprise data remains the major source of data for insurers (52 percent, per our research), the connected ecosystem – i.e., data from IoT-based devices – is gradually gaining traction, at approximately 35 percent. As insurers evolve in their AI journey, deploying AI and machine learning (ML) to leverage unstructured data from third-party sources and connected ecosystems is likely to increase. But as of today, enterprise data silos, legacy systems, and lack of interoperability standards to tap into the connected ecosystem and third-party data are slowing down insurers’ AI initiatives.

Some Standout Examples

Many insurers have made progress in deploying AI and ML to their data and are starting to see quantifiable results. For example:

  • Zurich Insurance deployed AI in its personal injury claims process. The company claims that AI has helped it save 40,000 work hours, and reduced claim processing time from 58 minutes to five seconds per medical report
  • ICICI Lombard launched a chatbot called MyRA to underwrite two-wheeler, fire, and burglary insurance for SMEs. Since its launch, MyRA has been engaged in 65,000 customer interactions, and has sold more than 750 policies without any human intervention.

AI has the potential to deliver significant value to insurers and their customers. To learn more about how it can impact your business, our recent Insurance IT Services – Annual Report is packed with data and our take-away insights from 80 unique insurance firm AI projects. In it, we outline how AI implementation is impacting the insurance industry, and present various AI use cases across the insurance value chain.

Please write to Ronak and Priyanka to discuss how you’re adopting AI in your insurance business processes.

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