Monthly Archives

November 2018

Working with an Outdated Pricing Model? Discover the Latest in Outsourcing Pricing | Webinar

By | Uncategorized, Webinars

Complimentary 60-minute webinar held on Thursday, November 29, 2018

 

Intriguing research findings we’ll address:

  • Enterprises are moving away from input-based pricing in both BPO and ITO contracts … but how, what, why, when and where? We’ll share the details
  • One of the key drivers of positive business outcomes is who initiated the move to outcomes- and outputs-based pricing, but we’ll share which initiators drive the most improvement (by a 2:1 margin)
  • The reasons for the pricing model transition are not surprising (cost savings and customer satisfaction), but the variation in results for high-impact and low-impact may surprise you

Automation truly is changing literally everything about service delivery, including pricing models. If your pricing metrics haven’t been updated recently, you’re working with outdated pricing models and not getting the optimum value from your outsourcing contracts.

Who should attend, and why?

This webinar will offer results from our recent market survey on outsourcing pricing trends coupled with our insights into what pricing model trends mean for you and how to take advantage of them to drive business impact.
The content is geared to senior executives –
Enterprises: Executives responsible for pricing strategy, strategic outsourcing, vendor management, and business executives

Presenters:

Michel Janssen
Chief Research Guru
Everest Group

Shirley Hung
Vice President
Everest Group

Abhishek Sharma
Partner
Everest Group

5 Types of Outsourcing Providers — and How to Get the Most from Them | In the News

By | In The News

As corporate technology leaders pursue their digital transformation strategies, many are looking to IT service providers as potential partners in those change efforts. However, a one-size-fits-all approach to outsourcing providers is not likely to serve CIOs well in meeting innovation goals. In fact, bigger doesn’t necessarily mean better in the digital change era.

“Traditionally, size was a good proxy for capability, especially when technology was viewed fundamentally as an enabler of efficiency,” says Jimit Arora, partner in Everest Group’s IT Services practice.

Read more in CIO

APIs a Key Component of Insurance Digital Transformation | In the News

By | In The News

Fun fact: Many long-established insurance companies still rely on legacy mainframes computers running applications in an outdated programming language from the 1960s. That’s not ideal, because today’s tech-savvy, smartphone-using customers are more mobile and expect real-time information whenever and wherever they are, without the assistance of an agent or a call center representative. Not good, because the modern agent requires new tools and applications to maintain productivity in a mobile environment, including instant policy quotes. And especially not good, because the “insurtechs” – new, innovative, and rapidly growing companies with social savvy marketing and modern technologies – are just waiting in the wings, ready to grab your market share.

You have to adapt and evolve in record time to create a seamless customer experience while improving employee productivity to remain competitive and increase customer retention. By implementing APIs and microservices, you can extend those insurance backend systems to mobile, web, and cloud rapidly, and cost-efficiently, all with low risk. By using the “art” of the API, you succeed where past transformation options failed.
According to an article in Forbes by Peter Bendor-Samuel, “The record of studies on digital transformation indicate a high failure rate, with a notable 2013 McKinsey study finding that 70% fail. That is a lot of wasted time, money and unmet expectations.”

Read more in Digital Insurance

Enterprises Should Jump – Carefully – on the Cloud Native Bandwagon | Sherpas in Blue Shirts

By | Blog, Cloud & Infrastructure

With enterprise cloud becoming mainstream, the business case and drivers for adoption have also evolved. The initial phase of adoption focused on operational cost reduction and simplicity – what we call the “Cloud for Efficiency” paradigm. We have now entered Wave 2 of enterprise cloud adoption, where the cloud’s potential to play a critical role in influencing and driving business outcomes is being realized. We call this the “Cloud for Digital” paradigm. Indeed, cloud is now truly the bedrock for digital businesses, as we wrote about earlier.

This is good and powerful news for enterprises. However, to successfully leverage cloud as a business value enabler, the services stack needs to be designed to take advantage of all the inherent benefits “native” to the cloud model – scalability, agility, resilience, and extendibility.

Cloud Native – What Does it Mean Anyway?

Cloud native is not just selective use of cloud infrastructure and platform-based models to reduce costs. Neither is it just about building and deploying applications at pace. And it is definitely not just about adopting new age themes such as PaaS or microservices or serverless. Cloud native includes all of these, and more.

We see cloud native as a philosophy to establish a tightly integrated, scalable, agile, and resilient IT services stack that can:

  • Enable rapid build, iteration, and delivery of, or access to, service features/functionalities based on business dynamics
  • Autonomously and seamlessly adapt to any or all changes in business operation volumes
  • Offer a superior and consistent service experience, irrespective of the point, mode, or scale of services consumption.

Achieving a true cloud native design requires the underlying philosophy to be embedded within the design of both the application and infrastructure stacks. This is key for business value creation, as lack of autonomy and agility within either layer hinders the necessary straight-through processing across the integrated stack.

In this regard, there are salient features that define an ideal cloud native IT stack:

Cloud native applications – key tenets

  • Extendable architecture: Applications should be designed for minimal complexity around adding/modifying features, through build or API connections. While microservices inherently enable this, not all monolithic applications need to be ruled out from becoming components of a cloud native environment
  • Operational awareness and resilience: The application should be designed to track its own health and operational performance, rather than shifting the entire onus on to the infrastructure teams. Fail-safe measures should be built in the applications to maximize service continuity
  • Declarative by design: Applications should be built to trust the resilience of underlying communications and operations, based on declarative programming. This can help simplify applications by leveraging functionalities across different contexts and driving interoperability among applications.

 Cloud native infrastructure – key tenets

  • Services abstraction: Infrastructure services should be delivered via a unified platform that seamlessly pools discrete cloud resources and makes them available through APIs (enabling the same programs to be used in different contexts, and applications to easily consume infrastructure services)
  • Infrastructure as software: IT infrastructure resources should be built, provisioned/deprovisioned, managed, and pooled/scaled based on individual application requirements. This should be completely executed using software with minimal/no human intervention
  • Embedded security as code: Security for infrastructure should be codified to enable autonomous enforcement of policies across individual deploy and run scenarios. Policy changes should be tracked and managed based on version control principles as leveraged in “Infrastructure as Code” designs.

Exponential Value Comes with Increased Complexity

While cloud native has, understandably, garnered significant enterprise interest, the transition to a cloud native model is far from simple. It requires designing and managing complex architectures, and making meaningful upfront investments in people, processes, and technologies/service delivery themes.

Everest Group’s SMART enterprise framework encapsulates the comprehensive and complex set of requirements to enable a cloud native environment in its true sense.

Smart Cloud blog image

Adopting Cloud Native? Think before You Leap

Cloud native environments are inherently complex to design and take time to scale. Consequently, the concept is not (currently) meant for all organizations, functions, or applications. Enterprises need to carefully gauge their readiness through a thorough examination of multiple organizational and technical considerations.

Cloud Key Questions blog image

Our latest report titled Cloud Enablement Services – Market Trends and Services PEAK Matrix™ Assessment 2019: An Enterprise Primer for Adopting (or Intelligently Ignoring!) Cloud Native delves further into the cloud native concept. The report also provides the assessment and detailed profiles of the 24 IT service providers featured on Everest Group’s Cloud Enablement Services PEAK MatrixTM.

Feel free to reach out us to explore the cloud native concept further. We will be happy to hear your story, questions, concerns, and successes!

Investments in Healthcare AI Will Quadruple by 2020, According to Everest Group | Press Release

By | Press Releases

New research predicts US$6 billion investment will drive innovations in patient identity verification, opioid abuse detection and individually tailored healthcare.

Healthcare organizations are pouring billions into embedded AI across the value chain, driving an estimated quadrupling of AI investments in the next three years, according to Everest Group. The firm predicts that healthcare AI investments will grow from US$1.5 billion in 2017 to exceed US$6 billion by 2020, representing a compound annual growth rate of 34 percent.

While AI is a relatively new area in the healthcare space and its adoption is in the nascent stage, digitalization of healthcare is accelerating healthcare enterprises’ interest in AI. AI has the potential to transform healthcare processes and dramatically reduce costs and improve efficiencies.

For example, healthcare payers are leveraging AI for product development, policy servicing, network management and claims management. Examples include:

  • Use of fingerprints, eye texture, voice, hand patterns and facial recognition to reduce the time taken for customer verification
  • Leveraging of machine learning with integrated claims data and analytics to detect opioid use patterns that suggest misuse
  • AI-powered wearable devices and mobile applications to help customers with personalized advice
  • Chatbots and virtual assistants to predict the right answer to standard customer inquiries and assist customers in navigating through the insurance plan selection process.

Currently, the area where payers are adopting AI to the greatest extent is in care management.

Likewise, the highest adoption of AI by healthcare providers is for care and case management. Providers also are employing AI tools to:

  • collaborate more effectively with patients
  • reduce the time required for aggregating, storing, and analyzing patients’ data
  • streamline workflows
  • monitor patients remotely
  • detect diseases faster and more accurately
  • come up with better treatments.

These findings and more are discussed in Everest Group’s recently published report, “Dr. Robot Will See You Now: Unpacking the State of Artificial Intelligence in Healthcare – 2019.” The firm has analyzed the market from the vantage point of 27 leading healthcare enterprises and closely examined the distinctive attributes of the leaders, who are far ahead of the other industry participants in terms of AI capability maturity. The report identifies best practices, illustrates the impact generated, and offers proposed a roadmap for market stakeholders.

***Download a complimentary abstract of this report here. ***

“While healthcare enterprises are still in the nascent stages of AI adoption, the scale of opportunity in AI demands C-level vision,” said Abhishek Singh, vice president of Information Technology Services at Everest Group. “AI presents unique opportunities for healthcare enterprises – allowing them to improve customer experience, achieve operational efficiency, enhance employee productivity, cut costs, accelerate speed-to-market, and develop more personalized products. In the case of the leading healthcare organizations, their CEOs and CIOs are acknowledging the transformative power of AI, rapidly building appropriate AI strategies, and building a robust, overarching business plan to harness its benefits.”

Additional key findings:

  1. Nearly two-thirds of spending on AI in healthcare is driven by North America. The North American market is also expected to be the fastest growing during the next five years, driven by regulatory mandates for use of electronic health records, increasing focus on precision medicine and a strong presence of service providers engaged in developing AI solutions for healthcare.
  2. Around 75 percent of all AI initiatives in healthcare are still driven by large enterprises as most mid- and small-sized firms are taking a wait-and-see approach.
  3. With a boom in enterprise AI, talent scarcity has become one of the biggest leadership challenges in implementing and evolving AI capabilities.
  4. Application of machine learning (ML) for structured data and natural language processing (NLP) for unstructured information have become mainstream in the healthcare industry.
  5. Cognitive technologies are expected to play an important part in health plans’ technology strategies going forward. Also, providers are looking to increasingly leverage deep learning to explore more complex, non-linear patterns in data, such as that found in research papers, doctors’ notes, textbooks, clinical reports, health histories, X-rays and CT and MRI scans.

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

By | Automation/RPA/AI, Blog

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].

Are You Effectively Leveraging Provider Relationships to Drive Better Customer Experience? | Webinar

By | Webinars

Complimentary 60-minute webinar held on Thursday, November 15, 2018 | 9 a.m. CST, 10 a.m. EST, 3 p.m. GMT, 8:30 p.m. IST

Download-View Presentation

Questions we’ll address:

  • How do Pinnacle Enterprises leverage Contact Center Outsourcing (CCO) to drive a better customer experience, and what uptick in business impact do they realize?
  • What key practices and capabilities do Pinnacle Enterprises deploy to realize higher business impact?
  • How do Pinnacle Enterprises’ expectations of, and engagement with, CCO providers differ from other enterprises, and how is that improving results?

Pinnacle Enterprises™ have achieved 2.4X improvement in customer satisfaction, through their CCO provider engagements, compared to other enterprises. In the webinar, we will discuss Pinnacle – or best-in-class – strategies for leveraging their CCO relationships to drive better business outcomes. By expanding scope of traditional CCO relationship into broader Customer Experience Management (CXM) services, Pinnacle Enterprises™ have transformed it into a strategic differentiator rather than a cost center. This webinar provides insights into the leading CXM models, practices, and capabilities that contribute most to business impact, based on our Pinnacle Model peer research.

Who should attend, and why?
This webinar will offer market-tested insights into how organizations can effectively leverage COO to deliver a better customer experience … and ultimately business impact.
The content is geared to senior executives –
Enterprises: Heads of CXM, Heads of Contact Center, Global Sourcing Managers, Heads of Outsourcing, IT/BPO Strategy Heads, Vendor Managers
Service Providers: CXM/CCO practice head, Senior BPO and CXM/CCO sales executives

Presenters
Michel Janssen
Chief Research Guru
Everest Group

Shirley Hung
Vice President
Everest Group

Skand Bhargava
Practice Director
Everest Group

 

SAP Accelerates Experience Pivot with a $8 billion Bet on Qualtrics | Sherpas in Blue Shirts

By | Blog, Cloud & Infrastructure, Customer Experience, Mergers & Acquisitions

Just days before 16-year old Qualtrics was due to launch its IPO, SAP announced its acquisition of the customer experience management company in an attempt to bolster its CRM portfolio. Qualtrics, one of the most anticipated tech IPOs of the year, and oversubscribed 13 times due to investor demand, adds to SAP’s arsenal of cloud-based software vendor acquisitions.

Delving into SAP’s Strategic Intent

Seeking transformational opportunities, the acquisition will allow SAP to sit atop the experience economy through the leverage of “X-data” (experience data) and “O-data” (operational data). Moreover, the acquisition will enable SAP to cash in on a rather untapped area that brings together customer, employee, product, and brand feedback to deliver a holistic and seamless customer experience.

SAP had multiple reasons to acquire Qualtrics:

  • First, it combines Qualtrics’ experience data collection system with SAP’s expertise in slicing and dicing operational data
  • Second, it sits conveniently within SAP’s overarching strategy to push C/4 HANA, its cloud-based sales and marketing suite.

SAP’s acquisition history makes it clear it seeks to achieve transformative growth by bolting in capabilities from the companies it acquires. It has garnered a fine reputation when it comes to onboarding acquired companies and realizing increasing gains out of the existing mutual synergies. Its unrelenting focuses on product portfolio/roadmap alignment, cultural integration, and GTM with acquired companies have been commendable.

Here is a look at its past cloud-based software company acquisitions:

SAP has taken a debt to finance the Qualtrics acquisition, making it imperative to show business gains from the move. With Qualtrics on board, it seems SAP’s ambitious cloud growth target (€8.2-8.7 billion by 2020) will receive a shot in the arm. However, the acquisition is expected to close by H1 2019, implying that the investors will have to wait to see returns. Moreover, SAP’s stock price in the past 12 months has dropped by 10.6 percent versus the S&P 500 Index rise of 3.4 percent. While SAP has seen revenue growth, its bottom-line results have been disappointing with a contraction in operating margins (cloud revenues have grown but tend to have a lower margin profile in the beginning.) This is likely to be further exacerbated given the enterprise multiple for this deal.

Fighting the Age-old Enterprise Challenge

Having said that, SAP sits in a solid location to win the war against the age-old enterprise conundrum of integrating back-, middle-, and front-office operations and recognize the operational linkages between the functions. Qualtrics’ experience management platform, known for its predictive modeling capabilities, generating real-time insights, and decentralizing the decision-making process, will certainly augment SAP’s value proposition and messaging for its C/4 HANA sales and marketing cloud. In fact, the mutual synergies between the two companies might put SAP at an equal footing with Salesforce in the CRM space.

While it may seem that SAP has arrived a bit early to the party, given that customer experience management is still a niche area, the market’s expected growth rate and SAP’s timely acquisition decision may allow it to leap-frog IBM and CA Technologies (now acquired by Broadcom), the current leaders in the space. Indeed, over the last couple of years, Qualtrics has pivoted beyond survey and other banal customer sentiment analysis methods to create a SaaS suite capable of:

  • Analyzing experience data to derive insights about employees, business partners, and end-customers
  • Democratizing and unifying analytics across the back-, middle-, and front-office operations
  • Delivering more proactive and predictive insights to alleviate experience inadequacy.

Cognitive Meets Customer Experience Management – The Road Ahead

SAP’s Intelligent Enterprise strategic tenet, enabled by its intelligent cloud suite (S/4 HANA, Fiori), digital platform (SAP HANA, SAP Data Hub, SAP Cloud Platform), and intelligent systems (SAP Leonardo, SAP Analytics Cloud), has allowed customers to embed cutting edge technologies – conversational AI, ML foundation, and cloud platform for blockchain. SAP is already working towards the combination of machine learning and natural language query (NLQ) technology to augment human intelligence, with a vision to drive business agility. Embedding the experience management suite within next-generation Intelligent Enterprise tenet will play a key role in achieving the exponential growth targets by 2020.

Please share your thoughts on this acquisition with us at: [email protected] and [email protected].

The Big Four Accounting And Auditing Firms Are Becoming Challengers In Digital Transformation Services | Sherpas in Blue Shirts

By | Blog, Digital Transformation, Outsourcing

The pivot of third-party services firms to digital is disrupting the entire services industry. Times of disruption always give rise to new competitors, and challengers among service providers can shift share. This is clearly happening now in the demand for digital transformation services. The Big 4 accounting and auditing firms – Deloitte E&Y, KPMG and PwC – are emerging as formidable challengers to Accenture, IBM and the Indian service providers. Here’s what’s happening and what it means for competitors and enterprise customers.

Read more in my blog on Forbes

Telematics in Insurance – A Big Opportunity yet to be Fully Explored | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog, Healthcare & Life Sciences

Price competition used to define the competitive dynamics of the P&C insurance industry. However, as margins started squeezing with low interest rates and rising claims costs, it became imperative for insurers to focus on product differentiation in order to attract new customers and drive premium growth.

This is when usage-based insurance (UBI), an insurance product model where the premium varies according to the risk of claims that the insured’s policy-related behavior poses, started gaining traction. UBI is noteworthy as it offers a remarkable opportunity for insurers to deliver hyper-personalization and evolve from a product-centric to a customer-centric business mindset.

To date, the auto insurance segment has been the most aggressive adopter of the UBI model, which is enabled by the underlying telematics infrastructure. Telematics technology enables insurers to capture each customer’s driving data, which is then used to continually update the customer’s risk profile and compute the payable premium. Data collection devices have evolved from black-box to OBD-II dongles to in-built telematics units in automobiles and smartphones.

UBI’s Business Case is Strong; however, Sourcing Gets Complicated for Insurers

We expect the market for UBI to grow substantially at a CAGR of ~40 percent during 2018-2020, with an estimated 35-40 million UBI policies in force by the end of 2020. This is certainly impressive growth.

However, to launch UBI products, insurers must make substantial investments in connected devices and data infrastructure. Moreover, not all insurers have the scale, risk-appetite, investable capital, or technology expertise to make significant inroads into UBI. Thus, insurers are leveraging third-party vendors to support their telematics journey.

Yet, the vendor ecosystem is fragmented, making it challenging for insurers to determine what organization to partner with.

Here’s the breakdown of the three major categories of telematics vendors:

Telematics Service Providers (TSPs)

These have the capability to manage the entire value-chain, from telematics device sourcing to device deployment and maintenance to end-customer engagement to telematics data management. However, as a single TSP might not be able to provide access to all the underlying connected devices, insurers must pre-strategize their requirements for data depth and breadth. There have been cases where insurers have entered into partnerships with multiple vendors with varying competency to leverage connected devices and technology maturity.

Data exchanges

The core value proposition of this class of vendors lies in their access to huge volume of data and their data handling capabilities, which reduces the burden of data management at the insurer’s end. Players that have entered this market also have developed a modest understanding of the insurance sector, which enables them to provide risk assessment support to insurers. However, while data exchanges typically can augment insurers’ telematics journey, they cannot provide end-to-end support.

OEMs

OEMs have emerged as significant competitors to the other classes of vendors due to their direct control of the point-of-sale. As the telematics unit is prebuilt into the automobile, insurers do not have to worry about the entire infrastructure management of telematics devices. However, partnering with an OEM could also mean loss of revenue from value-added services.

Telematics in Insurance – A Big Opportunity yet to be Fully Explored - potential impact

Service Providers as the Orchestrator – Big Opportunity Waiting to be Capitalized

With each of the categories of vendors specializing in specific parts of the telematics value-chain, insurers face a big challenge in connecting with different parties for different values, and in managing the multi-vendor ecosystem.

This is where IT/BP service providers can enter the picture. To date, they have failed to establish a competitive differentiation for themselves in this market. However, considering they have a sound understanding of insurers’ businesses, operations, and IT systems, they could provide significant value as the orchestrator of this branched ecosystem.

They could look to source the best value from different classes of vendors by tying partnerships with select technology vendors across the ecosystem. Then, they could serve as a specialist to help insurer wrap their operations around telematics technology to drive product differentiation.

In this model, service providers could – potentially – offer an integrated value proposition that would involve: owning the implementation risk; providing value-added services such as risk assessment and customer management support; managing the complexity involved in coordinating with multiple classes of vendors; and assuming responsibility for the risks (e.g., business risk, technology lock-in, etc.) associated with engaging with niche firms.

This could be a win-win-win scenario, for insurers, end-customers, and providers.

How service providers ultimately decide to capitalize on the telematics opportunity remains to be seen. However, they should be cognizant of not frivolously trying to compete where their expertise does not lie, and instead leverage their strengths to make themselves most relev