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Next-generation HR: Key Considerations for Successful Adoption | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

HR has certainly come a long way in being perceived as a strategic function with significant impact on business outcome. Yet, despite workforce and technology investments, multiple challenges – including the growing talent deficit, problems with skilling and retaining niche talent, and the increasing flexibility and better experience demands of Millennials and Generation Z – are inhibiting HR departments from attaining their full strategic potential on behalf of the enterprises they serve.

The solution is moving to a next-generation HR model with digital transformation at the core.

Next-gen HR Model

The inefficiencies of the traditional model – siloed HR systems, a large number of touchpoints, and a disjointed employee experience – are clearly exposed by the challenges cited above. The next-generation HR model addresses these issues with a cloud-based platform at the center, augmented by technologies such as advanced analytics and automation. This results in an intuitive and integrated model that has the ability to provide an enhanced employee experience.

To successfully adopt the next-generation HR model, enterprises should take a structured approach that considers several important factors.

Employee Experience Should be the Focal Point

While the importance of operational cost reduction and process standardization can’t be disparaged, enterprises should prioritize the employee experience when they plan for a digital HR transformation. Be it HR service delivery or technology modernization, the end goal should be to provide an integrated, intuitive, and seamless employee experience to better attract, engage, and retain talent.

In our recently published report, “The Key Ingredients for a Digital-First HR Transformation,” we identified two critical components of the best employee experience:

Empowerment: HR should offer employees integrated, accessible, and disintermediated workflows and systems that empower them to serve themselves. Methods include employee self-service tools, omnichannel experiences, chatbots, and analytical tools, all of which enable employees to have more control over the decisions they make.

Engagement: Millennials and subsequent generations exhibit different behavioral patterns, are digital natives, and expect seamless employee experiences. Enterprises should adopt solutions that enable HR to engage and retain this ever-evolving talent. Solutions that are integrated, user-friendly, and provide consistent experiences across sub-processes / third-party portals with optimized response times and accuracy should be the key focus areas.

Ensure Orchestration of Digital Technologies to Maximize Impact

Rather than implementing a handful of technologies haphazardly, enterprises must take an orchestrated approach to digital HR transformation that enables the technologies to feed off each other, find synergies, and maximize the impact.

The findings in our recently published report made it clear that while each individual technology lever (see chart below) is powerful, enterprises can realize the maximum transformative impact when all the levers are applied in cohesion.

Technology in HR

Why is this? Although the impact of technologies such as Robotics Process Automation (RPA) and BPaaS are focused on enhancing the efficiency of various processes, predictive and prescriptive analytics are capable of deriving net new insights.

On the other hand, cognitive/AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) can be bundled with other digital levers to significantly improve the stakeholders’ experience, in addition to increasing efficiency and providing net new gains.

Engage Service Providers for Help

To help support buyers’ growing demands and needs, service providers are increasingly offering HR and technology consulting services. Capabilities they offer include:

  • How to understand and plan for the impact of digital adoption on the enterprise’s workforce
  • How to adopt and derive value out of digital investments (i.e., third-party cloud solutions such as Workday, SuccessFactors, and ServiceNow, automation, and analytics solutions)
  • How to optimize HR processes

With technology changing so rapidly, organizations need to make sure that they fully embrace digital transformation, and buckle up to face and be ready for the changes. Many organizations are already working in this direction.

To learn more about this topic, our recent report titled “The Key Ingredients for a Digital-First HR Transformation” identifies and deep dives into five key levers (automation, analytics, cloud, advisory, and employee experience) that will help enterprises successfully transform their HR function.

Is your enterprise planning to undergo a digital HR transformation? Have you completed it? We’d love to hear from you about your experiences, questions, and concerns. Please write to us at: [email protected] or [email protected]

Understanding Differences In Results Of Implementing Digital Technologies | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

I believe it’s now apparent that all companies will go through one of two different forms of digital journeys over the next 20 years. Why? These journeys are inevitable because of the irresistible forces of competitive advantage and lower cost as outcomes. It’s not a question of “if;” it’s a question of when and to what results. However, the result or potential outcome is an aspect of digital that executives sometimes misunderstand and, in doing so, they end up with failed initiatives. So, let’s clear up the possible misunderstandings and look at what digital journeys are about, the types of results that companies can achieve and how digital platforms fit into that picture.

Read more in my blog on Forbes

Enterprises Must Bake “Contextualization” into Their IT Security Strategies | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

Given the rapid uptake of digital technologies, proliferation in digital touchpoints, and consumerization of IT, traditional enterprise security strategies have become obsolete. And challenges such as security technology proliferation, limited user/customer awareness, and lack of skills/talent are making the enterprise security journey increasingly complex.

Against that backdrop, the key thrust of our just released IT Security Services – Market Trends and Services PEAK Matrix™ Assessment 2019 is that the conventional, cookie cutter best practices prescribed by service providers no longer cut it. Indeed, we subtitled this new assessment “Enterprise Security Journeys and Snowflakes – Both Unique and Like No Other!” because the complexities of today’s technological and business landscape are forcing enterprises to use a much more guided and contextualized approach toward securing their IT estates.

What does this mean? To achieve success, enterprise IT security strategies must focus on three discrete, yet intertwined, levers.

Enterprise-specific Business Dynamics

In order to prioritize their investments in next-generation IT security, every enterprise needs to understand which assets it considers its crown jewels, how the business – and its security investments – will scale, and how to best mitigate risk within budgetary constraints. For example, a traditional BFS enterprise has far different endpoint security needs than does a digital-born bank.

Enterprises must also determine how delivery of superior customer and user experiences and exceptional security can co-exist. For example, a BFS enterprise’s introduction of an innovative new payments service backed by multi-factor authentication must operate without degrading the customer experience with delays.

Vertical Considerations

Enterprises need to take an industry-specific, value chain-led view of IT security that ensures optimal budget control without compromising the overall security posture.

For example, BFS firms must invest in security measures that protect their transaction processing and control/compliance capabilities. And building security controls for user access management, introducing behavioral biometrics into an integrated authentication process, and developing identity controls for anti-money laundering compliance are essential safeguards for sustainable competitive advantage.

Regional Considerations

Stringent regulatory environments (such as GDPR for customer data protection in Europe, PCI DSS for payments in the U.S., HL7 for international standards for transfer of clinical and administrative data between applications) and geography-specific nuances require a circumstantial approach to IT security. This means that geography-specific compliance around data protection, protectionist measures undertaken by the government, enterprises’ digital demand characteristics, and enterprises’ priorities in specific regions need to be taken into account. And global organizations must adhere to a well-defined strategic roadmap to address multiple variants of IT security standards across the globe.

For service providers, this essentially implies delivery of localized services in their focus geographies.

Taking a Phased Approach

While bolting-on IT security capabilities may lead to unnecessary – and valueless – sprawl, enterprises can avoid this challenge by investing in their IT security strategies in a phased manner, as outlined in the figure below.

To learn more about IT security contextualization, please see our latest report delves deeply into the important whys and hows of contextualizing IT security, and also provides assessments and detailed profiles of the 21 IT service providers featured in Everest Group’s IT Security Services PEAK Matrix™.

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

HCL Acquires IBM Products – Desperation or Aspiration? | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

On December 6, 2018, HCL announced it had acquired seven IBM products across security, commerce, and marketing for a record US$1.8 billion. To provide a financial context to this acquisition: HCL, India’s third largest IT services provider, invested about 22 percent of its annual revenue to bolster its products and platforms portfolio – what it refers to as its Mode 3 portfolio – which barely contributes to 10 percent of its annual revenue.

Demystifying the Why

What strategic outcomes could HCL potentially derive from this deal?

  • Cross-sell opportunities: Access to the more than 5,000 enterprises currently using the acquired IBM products
  • Superior value proposition around as-a-service offerings: Integration of these products with HCL’s ADM, infrastructure, and digital services
  • Top-line growth due to recurring revenue streams and expanded EBIDTA margins
  • Fewer dependencies on external vendors: Improved capabilities to bundle internal IP with services can enable HCL to have greater control over outcomes, thereby enhancing its ability to deliver value at speed

 Sounds good…Right?

At first glance, the acquisition may seem to be a strategic fit for HCL. But when we dug deeper, we observed that while some of the IP plugs gaps in HCL’s portfolio, others don’t necessarily enhance the company’s overall capabilities.

HCL acquisitions

This analysis raises meaningful questions that indicate there are potential potholes that challenge its success:

  • Confusion around strategic choices: The product investments point to a strong proclivity towards IT modernization, rather than digital transformation. This acquisition of on-premise products comes at a time when inorganic investments by peers’ (recent examples include Infosys’ acquisition of Fluido and Cognizant’s acquisition of SaaSFocus) and enterprises’ preference are geared towards cloud-based products
  • Capability to drive innovation at speed on the tool stack: To address the digital needs of new and existing clients, as well as to deliver on the promise of as-a-service offerings, HCL needs to repurpose the products and make significant investments in modernizing legacy IP
  • Financial momentum sustenance: With an increasing number of clients moving away from on-premise environments to cloud, it remains to be seen if HCL can sustain the US$650 million annual revenue projection from these products
  • Customer apprehensions: Customers that have bundled these products as part of large outsourcing contracts built on the foundation of their relationships with IBM will likely be apprehensive about the products’ strategic direction, ongoing management, and integration challenges as their IT environments evolve
  • The illusion of cross-sell: It remains to be seen if HCL can succeed in cross-selling digital services for these legacy products, especially in the beginning of its relationship with the 5,000+ clients currently using the in-scope IBM products.

 The Way Forward

The acquisition definitely is a bold move by HCL, which may seem meaningful from an overall financial investment and ROI perspective. However, the subdued investor confidence reflects poor market sentiment, at least at the start. Although this could be considered a short-term consequence, HCL’s investments in these legacy products is in stark contrast to the way the rest of industry is moving forward.

On the day of the acquisition, HCL’s stock price fell 7.8 percent, signaling negative market sentiments and thumbs down from analysts. In contrast, the market behaved differently in response to  acquisitions by HCL’s peers in the recent past.

To prove the market wrong, HCL needs to focus its efforts on developing and innovating on top of these products; developing synergies with its ADM, infrastructure, and digital services; alleviating client apprehensions; and providing a well-defined roadmap on how it plans to sustain momentum leveraging these products over the long term.

What is your take on HCL’s acquisition of these IBM products? We would love to hear from you at [email protected] and [email protected].

Have RPA Vendors been MARVELous? | Sherpas in Blue Shirts

By | Sherpas in Blue Shirts

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 | Sherpas in Blue Shirts

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 | Sherpas in Blue Shirts

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 | Sherpas in Blue Shirts

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 | 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].

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

By | Sherpas in Blue Shirts

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.

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