Category

Blog

Have RPA Vendors been MARVELous? | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog

The relationship between RPA vendors and their clients isn’t so different from the relationship between Marvel Studios and its fans.

Since the movie Iron Man hit the big screen in 2008, fans’ expectations of superhero films have skyrocketed. Despite the rising and evolving expectations, Marvel has satisfied its audience and has made a little pocket change in the process.

In a similar way, RPA buyers are expecting increasingly more from their RPA vendors. So, have RPA technology vendors been MARVELous in their customers’ eyes?

The Drivers

Our recent research study among 50 enterprise RPA buyers makes it clear that vendors have excelled in addressing their primary drivers, which are cost reduction and process optimization.

However, vendors didn’t score as high on secondary drivers such as improved customer experience, governance, and top-line growth. With increasing awareness about the potential impact of RPA beyond immediate cost and efficiency benefits, enterprises have started to view RPA as a primary contributor to their digital strategy, rather than a tactical measure.

Consequently, technology vendors should focus on continuously evolving their RPA solutions with a host of capabilities to help enterprise buyers achieve their strategic business outcomes.

The Capabilities

As to be expected, the buyers in our research study found their RPA vendors excelled in certain areas and had work to do in others.

The key strengths for those vendors who were identified as the Leaders as per our PEAK Matrix™ assessment on RPA included:

  • Customer support and service
  • Ease of use and robot development
  • Vision and strategy

Key improvement areas for Leaders included:

  • Responsiveness
  • Product vision and strategy
  • Product training and support

The X Factors

As there are so many RPA tools available in the market, each with its own strengths and weaknesses, it can be daunting for enterprises to select the right vendor for their unique needs. One critical part of the decision-making process is to focus on the X factors that are most important to their strategic agendas.

Our study found that factors including “ease of use and robot maintenance” and “scalability” highly correlate to buyers’ overall satisfaction levels. This is not surprising, as these are factors that buyers typically face issues with during RPA adoption. “Product vision and strategy” – and in some cases vendor expertise in a specific vertical industry or function – are also important buyer X factors.

While it’s clear that RPA vendors can do more to satisfy the needs of their customers – and that they’ll need to continually evolve their solutions – they have indeed been relatively MARVELous in delivering value and overall satisfaction to their buyers.

To learn more, please read our report “Buyer Satisfaction with RPA – How Far or Close is Reality From Hype.”

 

 

AI for Experience: From Customers to Stakeholders | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog, Customer Experience

Everest Group’s digital services research indicates that 89 percent of enterprises consider customer experience (CX) to be their prime digital adoption driver. But we believe the digital experience needs to address all stakeholders an enterprise touches, not just its customers. We touched on this topic in our Digital Services – Annual Report 2018, which focuses on digital operating models.

Indeed, SAP’s recent acquisition of Qualtrics and LinkedIn’s acquisition of Glint indicates the growing importance of managing not only CX, but also the digital experience of employees, partners, and the society at large.

AI Will Usher in the New Era of the Digital Experience Economy

Given the deluge of data from all these stakeholders and the number of parameters that must be addressed to deliver a superior experience, AI will have to be the core engine powering this digital experience economy. It will allow enterprises to build engaging ecosystems that evolve, learn, implement continuous feedback, and make real time decisions.

 

AI’s Potential in Transforming CX is Vast

Today, most enterprises narrowly view the role of AI in CX as implementing chatbots for customer query resolution or building ML algorithms on top of existing applications to enable a basic level of intelligence. However, AI can be leveraged to deliver very powerful experiences including: predictive analytics to pre-empt behaviors; virtual agents that can respond to emotions; advanced conversational systems to drive human-like interactions with machines; and even to deliver completely new experiences by using AI in conjunction with other technologies such as AR/VR, IoT, etc.

Digital natives are already demonstrating these capabilities. Netflix delivers hyper personalization by providing seemingly as many versions as its number of users. Amazon Go retail stores use AI, computer vision, and cameras to deliver a checkout free experience. And the start-up ecosystem is rampant with examples of cutting-edge innovations. For instance, HyperSurfaces is designing next-gen user experiences by using AI to transform any object to user interfaces.

But focusing just on the customer experience is missing the point, and the opportunity.

 AI in the Employee Experience

AI can, and should, play a central role in reimagining the employee journey to promote engagement, productivity, and safety. For example, software company Workday analyzes 60 data points to predict attrition risk. Humanyze enables enterprises to ascertain if a particular office layout supports teamwork. If meticulously designed and tested, AI algorithms can assist in employee hiring and performance management. With video analytics and advanced algorithms, AI systems can ensure worker safety; combined with automation, they can free up humans to work on more strategic tasks.

AI in the Supplier and Partner Experience

Enterprises also need to include suppliers and other partners in their experience management strategy. Using predictive analytics to automate inventory replenishment, gauge supplier performance, and build channels for two-way feedback are just a few examples. AI will play a key role in designing systems that not only pre-empt behaviors/performance but also ensure automated course correction.

AI in the Society Experience

Last but not least, enterprises cannot consider themselves islands in the environment in which they operate. They must realize that experience is as much about reality as about perception. Someone who has never engaged with an enterprise may have an “experience” perception about that organization. Some organizations’ use of AI is clearly for “social good.” Think smart cities, health monitoring, and disaster management systems. But even organizations that don’t have products or services that are “good” for society must view the general public as an important stakeholder. For example, employees at Google vetoed the company’s decision to engage with the Pentagon for use of ML algorithms for military applications. Similarly, employees at Microsoft raised concerns over the company’s involvement with Immigration and Customs Enforcement in the U.S.  AI can be leveraged to predict any such moves by pre-empting the impact that a company’s initiatives might have on society at large.

Moving from Customer to Stakeholder Experience

As organizations make the transition to an AI-enabled stakeholder experience, they must bear in mind that a piecemeal approach will not work. This futuristic vision will have to be supported by an enterprise-wide commitment, rigorous and meticulous preparation of data, ongoing monitoring of algorithms, and significant investment. They will have to cover a lot of ground in reimagining the application and infrastructure architecture to make this vision a distinctive reality.

What has been your experience leveraging AI for different stakeholders’ experiences? Please share with us at [email protected] and [email protected].

 

Upskilling and Reskilling: Is It Just Good L&D or Something Different? | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog, Shared Services/Global In-house Centers, Talent

Is upskilling and reskilling little more than a thinly disguised attempt by HR departments to rebrand Learning and Development (L&D)? The answer, as one practitioner pointed out at a conference in Poland, is “no.”

I recently presented to the Association of Business Services Leaders (ABSL) Chapter in Krakow, Poland about the talent acquisition challenges that digitization poses to Shared Services Centers (SSCs.) The argument runs roughly like this:

  • Robotic Process Automation (RPA) is replacing human agents in transactional roles, freeing up capacity in the workforce. This can mean lay-offs at worst, or unqualified internal candidates reapplying for roles at best
  • There is greater demand for people with new skills both technological (design thinking, robotics, autonomics, analytics) and soft (pattern-recognition, complex problem solving, leadership, intuition) than can be met by simply recruiting externally
  • As automation takes care of transactional processes, organizations can enhance the value of their brands and the service they provide by having more people in roles which emphasize first contact resolution, emotional intelligence, listening, etc.
  • This new value chain focuses on outcomes: people are measured against quality of outcome rather than throughput (for instance, a shift from average handling time to CSat), which in turn requires new management thinking around staff incentives, culture, and business model.

The data in the presentation was based on the Everest Group survey of 81 SSC leaders in Poland, the Philippines, and India, published earlier this year (see “Building a Workforce of the Future – Upskilling/Reskilling in Global In-house Centers.”)

So obvious was the message that emerged from the survey that one or two skeptics in the audience questioned why retraining that part of the workforce most affected by the trend of automation was even worthy of discussion. Is it not just good L&D practice? And surely survey respondents would not admit to anything other than good practice when asked the question?

Not quite true: there were survey respondents, albeit no more than 10 percent of them, who said that they were not planning to undertake upskilling and reskilling as a means of addressing talent shortages. A small majority, 58 percent, said upskilling/reskilling was the highest priority in addressing this same problem, while 10 percent, possibly the same nagging 10 percent, said it was a low priority.

The discussion continued after the presentation. Without experience as a practitioner, I wrestled with an explanation as to why this 10 percent stubbornly refused to fit the theory. Thankfully, the HR head of a Krakow-based SSC rode to my rescue and gave the answer.

This is the group, she said, which understands that reskilling and upskilling is indeed good L&D practice but remains wedded to external hiring of permanent and temporary staff. It is the group that fails to see that existing employees must be recognized as the key pool to meet scarce talent requirements in SSCs.

Her explanation, thankfully, echoed our contention that successful application of reskilling/upskilling to talent acquisition needs:

  • Senior leadership backing to ensure adequate resource and profile within the organization
  • Implementation of a skill-specific talent acquisition strategy to identify both critical areas of shortage and those most suitable for reskilling/upskilling
  • Quick roll-out of pilots in critical areas of shortage to build confidence and to learn
  • Breaking down of functional barriers and giving employees wider exposure to functional roles
  • A combination of effective duration and appropriate method (job rotation, on-the-job training, mentoring, peer-to-peer learning, and specialist external providers)
  • Clear communication of career paths, internal opportunity, incentive, and compensation
  • Patience and persistence!

She explained further. In her experience, the real difference between reskilling/upskilling as good L&D practice and reskilling/upskilling as a talent acquisition solution is simple. The talent acquisition solution approach is not considered aspirational, “something that HR does,” or nice to have. Rather, it is a strategic imperative.

How nice to have somebody who really knows what they are talking about answer a difficult question on my behalf!

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!

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].

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

Why TCS Entering Digital Marketing Space is Significant in Digital Transformation | Sherpas in Blue Shirts

By | Blog, Digital Transformation

Two champions have emerged among service providers in digital transformation: Accenture and TCS. Accenture is driving business transformation, and TCS is doing a marvelous job of driving IT modernization. TCS’ recent acquisition of W12 Studios, a London-based digital design agency, is worth noting for its implications in the digital marketing space.

Motivation

 W12 will be part of TCS Interactive, TCS’ digital design division. Digital marketing is an attractive, high-impact growth area in digital transformation. It is pivoting toward greater and greater use of technology, clearly calling out for technology companies such as TCS to participate in it more fully. Accenture is building big business in this space quickly. Even so, this acquisition is surprising.

Unlike Accenture, TCS has not driven its success by acquiring companies. But the digital marketing space is growing very quickly, so TCS felt it needed to break its mold and gain a foothold in the space by acquisition. The increasing need for sophisticated technology such as AI and automation to execute well in this space makes it more attractive for TCS. This technology sophistication is well beyond the capabilities of customers for third-party services.

Cautions

Two factors may be growth hindrances that affect TCS’ strategy for entering the digital marketing space.

First, TCS is late to this party. Companies such as Accenture, Capgemini, Infosys and others already created very large, formidable businesses in this space. Accenture is the prime example and has a big lead. TCS historically proved effective at closing market gaps once it established a foothold. But the firm has a big gap to close in digital marketing. It seems unlikely that TCS will succeed in closing this gap purely without further acquisitions.

Overall, the Indian services firms are late entries and are losing share to Accenture and the domestic players. For the Indian players to challenge for leadership, they will need to invest heavily and continue to grow inorganically.

The second possible growth hindrance involves the delivery model. It seems reasonable that much of the support of digital marketing technology can be delivered from an offshore model. But it’s not clear that the creative aspects are best delivered from a remote location. However, given that the technology and technology support is growing in importance it makes sense that TCS distributed model will work well for this part of the equation. Despite a growing and rich source of creative talent in India, I am skeptical that customers will move their creative work offshore. Why? Because proximity to the business and cultural emersion are critical aspects of the delivery.

Alternative View

I think it’s important to recognize that TCS’ goal may not be to enter digital marketing in a big way. At this point, there is such a fundamental disruption happening in the space that even capturing a small part of this marketplace might be a welcome and lucrative component to the broad portfolio that TCS offers. Even with a small market share, TCS can create a nice book of business given the growing market and secular trend toward technology.

Impacts From Changing Administrative Rule For H-1B And L1 Work Visas | Sherpas in Blue Shirts

By | Blog, Talent

The US Labor Department’s recently announced new regulations for H-1B and L1 work visas focus on the Trump Administration’s ongoing effort to tighten regulations and increase administrative hurdles. Viewed individually no new regulation is a show-stopper; but it’s clear that, collectively, they will have a more material effect. Here’s my take on the significant issues for service providers and their customers.

Read more in my blog on Forbes