Alisha Mittal, Author At Everest Group

The Future of Digital Transformation May Hinge on a Simpler Development Approach: Low Code | Blog

May 28, 2021

In today’s high-tech world, low-code software development is emerging as a lever to accelerate digital transformation. With strong activity and broad capabilities by players in this space, who are the leaders to watch, and what are the obstacles to adoption? Read on for more on the state of low-code application platforms, real-world use cases, and our outlook.

Compelled by COVID-19, many enterprises are now looking beyond their traditional development approaches for ways to deliver faster, more agile applications and processes. Low-code platforms, requiring little or no programming to build, are surging in adoption.

These platforms combine declarative tooling with pay-as-you-grow business models, enabling enterprises to accelerate application development and delivery, and align it with their businesses.

Low-code application platforms: state of the market

Market participants in the low-code space are focusing on new products, partnerships, and acquisitions to drive growth. Here’s a look at the flurry of activities by leading players.

Product launches and expansion: In April 2020, Microsoft released a beta version of Power Apps on mobile for no-code application development allowing anyone with knowledge of programming languages to assemble business applications easily and quickly. A month later, Quickbase announced the addition of drag-and-drop integration and new workflow automation functions enabling business users to build and execute workflows connected to third-party apps. Pegasystems also launched Pega Express, a new low-code software development methodology.

Partnerships and collaboration: As part of recent collaborations, Appian signed a technology partnership and integration initiatives with Celonis, the market leader in AI-enhanced process mining and process excellence software. Appian also entered a strategic alliance with Deloitte Consulting, LLP to help modernize mission-critical systems for their clients within the commercial, federal civilian, defense, state, and local government agencies.

Mergers and acquisitions: In February of 2021, SAP acquired AppGyver Oy, a pioneer in no-code development platforms. Then months later, Siemens acquired TimeSeries to expand its portfolio through the development of industry-specific apps built on the Mendix platform. Google’s acquisition of AppSheet last year to bring no-code development to Google Cloud has added heat to the market as it demonstrates hyperscalers’ interest in seizing market share.

Low-code platform types

As we attempt to simplify the highly fragmented low-code platform market, three broad categories emerge, with each offering unique strengths as shown below:

Our research identifies automation of process applications as the highest priority for low-code applications. It can help enterprises shape next-generation workflows to enhance customer experience and spur the development of innovative apps in areas such as service delivery/management, human resource management, field services management, supply chain transformation, etc.

The below exhibit provides an overview of opportunities that low-code platforms can deliver and the key players focusing on each of these areas.

In terms of industry adoption, we see banking, financial services, and insurance (BFS&I) leading the pack, followed by healthcare, public sector, education, and manufacturing.

Let’s take a look at one use case example of how low-code technology platforms are enabling transformation. The exhibit below illustrates how a bank is using low code to enhance each step in the customer experience lifecycle and realize value faster.

Pain points to adopting low code

To better understand why low code is gaining such quick popularity, we looked beyond what is being portrayed by the platform providers and identified three challenges that enterprises will need to overcome to adopt low-code development.

Talent availability: Despite the low technical barrier to usage, most low-code systems are proprietary and require at least a modest amount of system-specific training. Highly skilled traditional developers often view low-code with skepticism and defensiveness – partly due to the slow devaluation of the traditional skillset
Licensing cost: The pay-as-you-grow model makes the licensing costs more opaque and somewhat higher than those of traditional opensource development environments and tools
Lack of proof points: Although low-code makes it possible to quickly create a working application, low-code tools can stop just shy of enabling the development of enterprise-wide apps. Enterprises need to analyze the scalability of these platforms for integrations and security as they expand to the enterprise level before committing to a buy decision

Positive outlook

The adoption of low-code platforms is still in its infancy, but the COVID-19 pandemic has compelled enterprises to look beyond their traditional way of IT application development and resort to much faster and agile application delivery models materialized through these low-code platforms. Undoubtedly, these platforms will gain more prominence in the very near future. A line of business way of looking at these platforms is needed to enable enterprises to gauge their capabilities accurately and ultimately lead to more enterprise-level adoption.

To share your thoughts and discuss our research related to low-code platforms, please reach out to [email protected] and [email protected].

Internet of Things Will Connect the Supply Chain in the “Next Normal” | Blog

May 18, 2021

Imagine a utopia where minimum human intervention is needed to run an entire shop floor. In this world, manufacturers have total control and visibility of all products, machines predict equipment failures and correct them, shelves count inventory, and customers check themselves out. While such a supply chain model seems improbable and far into the future, the likes of Amazon, Walmart, and Toyota, are already on their way to achieving this vision. At the center of their supply chain initiatives making this possible is the Internet of Things (IoT.)

The supply chain is considered the backbone of a successful enterprise. However, firms find it increasingly challenging to establish a robust supply chain model. The disruptions caused by COVID-19 have further made matters worse as ‘disconnected enterprises’ struggle to gain complete supply chain visibility. The pandemic has established that supply chain disruptions and uncertainties will become more frequent going forward.

Supply chain challenges

The current supply chain landscape faces numerous challenges that need to be addressed. These issues are illustrated below:

Future-proofing the supply chain using IoT

As enterprises strive to develop a resilient supply chain, IoT will occupy the center stage. An interconnected supply chain will bring together suppliers/vendors, logistic providers, manufacturers, wholesalers and retailers, and customers dispersed by geography. The technology ensures improved efficiency, better risk management, end-to-end visibility, and enhanced stakeholder experience.

A seamlessly connected supply chain provides advantages at every stage of the value chain for each of the stakeholders. The exhibit below showcases a connected supply chain ecosystem:

Let’s look at how some companies are capturing the benefits IoT:

Real-time location tracking

Using real-time data (captured from GPS coordinates) tracking the movement of raw materials/finished goods, IoT technology aids firms in determining where and when products get delayed. This helps managers ensure route optimization and better plan the delivery schedule. IoT, in combination with blockchain, helps secure the products against fraud. For example, Novo Surgical leverages IoT for optimally tracking and tracing its ‘smart surgical instruments.’ This has reduced errors, decreased surgical instrument loss, increased visibility and efficiency, and improved forecasting of demand for the firm.

Equipment monitoring

Sensors on machines constantly collect information around the functioning of the machine, enabling managers to monitor them in real time. By analyzing parameters such as machine temperature, vibration, etc., manufacturers can better predict machine downtime and take necessary actions to mitigate this. For instance, Toyota partnered with Hitachi to leverage the vendor’s IoT platform and use the data collected to reduce unexpected machine failures and improve the reliability and efficiency of equipment.

Smart inventory management

IoT sensors in the warehouse assist in tracking the movement of individual items, providing an efficient way to monitor inventory levels and prevent pilferage. Smart shelves contain weight sensors that monitor the product weight to determine when products are out of stock. Walmart has been leveraging smart shelves in its retail stores to manage its products more efficiently and improve the shopping experience.

Warehouse management

IoT technology uses sensors that can monitor and adjust warehouse parameters such as humidity, temperature, pressure, and avoiding spoiling of items. Leading e-commerce players like Amazon and Alibaba have been pioneers in leveraging IoT to optimize warehouse management.

Charting the journey for a connected supply chain

As enterprises aim to future-proof their supply chain, they will need a structured path following these five steps below:

Develop a business case: Enterprises need to determine the current gaps in their supply chain and identify the extent of digitization of their supply chain to develop the business case for a connected supply chain.
Secure buy-in from supply partners: Successful implementation of IoT in the supply chain requires the various partners to collaborate and adopt the technology together. Securing a buy-in from each member of the value chain – vendors/suppliers, OEM players, logistics operators, and retailers – is imperative for firms to realize the complete benefits. Compatibility of the technology platforms leveraged by the various supply partners is essential to develop a seamless supply chain.
Invest in security: Invest in security and data protection initiatives early on to avoid supply chain breaches. Performing regular security and vulnerability assessments across the value chain and investing in next-generation technology-based security solutions is essential.
Leverage other technologies: While IoT has a plethora of benefits across the supply chain, consider leveraging next-generation technologies such as blockchain, artificial intelligence, and edge computing in confluence with IoT to further enhance the capabilities of the use cases.
Partner for implementation: To overcome concerns around skills and address data reconciliation challenges, consider partnering with IoT providers with expertise in the supply chain arena. Service/solution providers also are instrumental in bringing a security layer that can aid in addressing data security concerns and governance issues.

Since IoT is an interplay of multiple devices and machines, a successful IoT implementation requires firms to invest in sensors, cloud/edge infrastructure, IoT connectivity networks, data management and analytics solutions, and application development and management. Enterprises can accelerate their IoT supply chain journeys by partnering with solution providers with strong expertise in IoT products and services capabilities in the supply chain arena.

Are you embarking on your connected supply chain journey? Please share your thoughts and experiences with us at [email protected] and [email protected].

The Emergence of Distributed Agile Software Development: An Old Weapon with New Firepower | Blog

April 9, 2021

Imagine brainstorming your customers’ journey with digital holograms of globally located developers, user experience (UX) designers, and business leaders, all collaborating organically with each other’s 3D replicas – something straight out of Star Wars and other sci-fi films! Such virtual interactions may be coming closer to reality with application software development kits (SDK) like Microsoft Mesh, a photo-realistic AR/VR application SDK for creating holograms, recently released at Microsoft Ignite 2021.

The advent of tools like Mesh makes it clear that the second wave of digital transformations will empower creators and communities and expand economic opportunities for global workforces. This comes as no surprise since the COVID-19 pandemic has driven lasting changes in the enterprise IT operating model as technology further becomes the foundation of doing business.

Enterprise IT functions are now expected to deliver enhanced employee experiences, rapidly adapt to business requirements, and mitigate operational risks to drive sustainable growth. These coincided with the pre-pandemic focus on personalized customer experiences and the pandemic-induced need for work-from-anywhere models.

The evolution of a new Agile

Agile software development has come a long way from its conception in the Agile Manifesto near the turn of the century. Since then, the traditional and widely accepted model with offshore and onshore delivery has helped many enterprises derive value out of their software and operations.

The pandemic threw the Agile model into chaos and tested its limits as the lockdowns bound team members to their homes. As enterprises rapidly transitioned into an Agile++ model, they replicated the processes, governance, and workflows of traditional Agile development. The pace of change also pushed the importance of remote collaboration and productivity technologies to connect teams to the forefront.

The increasing need for continuous value delivery alongside risk-efficient, employee-centric operations is driving enterprises to adopt the Distributed Agile methodology. In fact, 40 percent of the enterprises that participated in Everest Group’s 2021 Key Issues in Global Sourcing study are looking to adopt Distributed Agile as their de facto software development model.

True Distributed Agile

The next generation of Agile embraces a natively distributed nature. In a Distributed Agile model, communication, processes, and workflows are optimized for remote delivery. This is achieved by divesting focus from a location-based team model and building virtually proximate global feature pods. The operating philosophy rests on product teams structured as core teams comprised of architects and Agile feature pods. Each feature pod is laser-focused on end-to-end product features with no notions of an offshore-onshore construct. The Distributed Agile model will have a flexible location mesh covering:

Hub (key delivery location)
Spoke (secondary delivery location)
Satellite (tertiary location)
Work from home

Benefits of Distributed Agile

Significant cost savings: Contrary to typical apprehensions about the cost implications of Distributed Agile development, a feature pod approach is expected to cut operational costs by as much as 13 percent compared to a traditional Agile model.

Improved talent models: The location-agnostic nature of Distributed Agile helps improve access to quality talent by two to five times compared to traditional models. The overall increase in talent quality will overpower concerns around running virtually and an associated drop in productivity. A wider pool of candidates will make it easier to hire for niche skills in both older and emerging areas.

Enhanced BCP / Resiliency: In the Distributed Agile methodology, the risk of environmental disruptions is apportioned across various regions creating a more resilient business continuity model. The fundamental overlap of skills in a feature pod allows teams to manage short-term disruptions with ease.

Improved delivery model flexibility: The Distributed Agile model helps source talent from multiple locations, creating flexibility in the traditional pyramid. With virtual interaction as its foundation, this model allows firms to shift out of onshore/offshore delivery into location agnostic application delivery.

Societal and environmental benefits: Increased location flexibility allows employees to allocate more time for personal endeavors, thereby improving overall work satisfaction. Spoke and Satellite offices for a distributed workforce can rejuvenate smaller cities.

Setting the foundation for Distributed Agile

Because a Distributed Agile model fundamentally rethinks enterprises’ IT organization, substantial change in processes, people, and structure will accompany the technological shift. It will require a cultural and mindset shift at all levels of the IT organization that prioritize non-invasive governance and autonomy. Such a cultural shift can be built on what we named a foundation of TRUST.

Transparency: A change effort as extensive as this will compel enterprises to focus on measuring the productivity of adopting teams. Having holistic metrics that track the efficiency, efficacy, and timeliness of the team will equip enterprises with the information necessary to ensure lasting impact. This can be done by adopting practices such as using virtual whiteboard repositories, creating healthy backlogs with well-refined stories, clearly separating work duties, and optimizing the use of overlapping hours across the team locations.

Resilience: A Distributed Agile model creates a new set of vulnerabilities that need to be mitigated through a thorough leftward shift of security in the development process. Inclusion of security early in the development process relies on educating developers and testers on security aspects, including security as a key criterion in user stories, periodic reviews of security practices, and automated security across the CI/CD pipeline.

Understanding: This is about a stronger emphasis on softer work aspects that build psychological bonding between team members. Success in a Distributed Agile model will rely heavily on catering to employees’ self-actualization with critical focus on independent ownership, familiarity with members, and empathy for the individual members’ motivations and challenges.

Self-reliance: Driving self-reliance in a distributed agile setup will require following a ”Team of Teams” construct that provides autonomy to feature pods. Emphasis will also be placed on non-invasive governance through Scrum Masters for each team with a centralized scrum of scrums approach.

Tech bedrock: Technology will need to be seeded as the enabler of the shift to the Distributed Agile model. The IT backbone needs to be supplemented with a wide array of tools to foster collaboration, drive productivity, improve knowledge management, and enable continuous improvement.

As businesses emerge from the pandemic, we expect enterprises to consider shifting gradually to a Distributed Agile model. And we expect initiatives with high people complexity to be prime candidates for the first wave of adoption, followed by those with high project complexity. Enterprises can accelerate their adoption of Distributed Agile by engaging IT service providers to simplify and guide them through the change.

If you’d like to learn more about the Distributed Agile landscape, please reach out to us at [email protected], [email protected], and [email protected].

Advancing from Artificial Intelligence to Humane Intelligence | Blog

January 19, 2021

I recently came across a news article that said doctors will NOT be held responsible for a wrong decision or recommendation made based on the recommendations of an artificial intelligence (AI) system. That’s shocking and disturbing at so many levels! Think of the multitude of AI-based decision making possible in banking and financial services, the public sector, and many other industries and the worrying implications wrong decisions could have on the lives of people and society.

One of the never-ending debates for AI adoption continues to be the ethicality and explainability concerns with the systems’ black box decision making. There are multiple dimensions to this issue:

Definitional ambiguity – Trustworthy, fair and ethical, and repeatable – these are the different characteristics of AI systems in the context of explainability. Most enterprises cite explainability as a concern, but most don’t really know what it means or the degree to which it is required.
Misplaced ownership – While they can be trained, re-trained, tested, and course corrected, no developer can guarantee bias-free or accurate decision making. So, in case of a conflict, who should be held responsible? The enterprise, the technology providers, the solution developers, or another group?
Rising expectations – AI systems are being increasingly trusted with highly complex, multi-stakeholder decision-making scenarios which are contextual, subjective, open to interpretation, and require emotional intelligence.

Enterprises, particularly the highly regulated ones, have hit a roadblock in their AI adoption journey and scalability plans considering the consequence of wrong decisions with AI. In fact, one in every three AI use cases fail to reach a substantial scalable level due to explainability concerns.

While the issue may not be a concern for all AI-based use cases, it is usually a roadblock for scenarios with high complexity and high criticality, which lead to irrevocable decisions.

In fact, Hanna Wallach, a senior principal researcher at Microsoft Research in New York City, stated, “We cannot treat these systems as infallible and impartial black boxes. We need to understand what is going on inside of them and how they are being used.”

Progress so far

Last year, Singapore released its Model AI Governance Framework, which provides readily implementable guidance to private sector organizations seeking to deploy AI responsibly. More recently, Google released an end-to-end framework for an internal audit of AI systems. There are many other similar efforts by opponents and proponents of AI alike; however, a feasible solution is still out of sight.

Technology majors and service providers have also made meaningful investments to address the issue, including Accenture (AI fairness Toolkit), HCL (Enterprise XAI Framework), PwC (Responsible AI), and Wipro (ETHICA). Many XAI-centric niche firms that focus only on addressing the explainability conundrum, particularly for the highly regulated industries like healthcare and public sector, also exist. Ayasdi, Darwin AI, KenSci, and Kyndi deserve a mention.

The solution focus varies from enabling enterprises to compare the fairness and performance of multiple models to enabling users to set their ethicality bars. It’s interesting to note that all of these offer bolt-on solutions that enable an explanation of the decision in a human interpretable format, but they’re not embedded explainability-based AI products.

The missing link

Considering this is an artificial form of intelligence, let’s take a step back and analyze how humans make such complex decisions:

Bias-free does not exist in the real world: The first thing to appreciate is that humans are not free from biases, and biases by their nature are subjective and open to interpretation.
Progressive decision-making approach: A key difference between humans and the machines making such decisions is the fact that even with all processes in place, humans seek help, pursue guidance in case of confusion, and discuss edge cases that are more prone to wrong decision making. Complex decision making is seldom left to one individual alone; rather, it’s a hierarchy of decision makers in play, adding knowledge on top of previous insights to build a decision tree.
Emotional Quotient (EQ): Humans have emotions, and even though most decisions require pragmatism, it’s the EQ in human decisions that explains the outcomes in many situations.

These are behaviors that today’s AI systems are not trained to adopt. A disproportionate focus on speed and cost has led to neglecting the human element that ensures accuracy and acceptance. And instead of addressing accuracy as a characteristic, we add another layer of complexity in the AI systems with explainability.

And even if the AI system is able to explain how and why it made a wrong decision, what good does that do anyway? Who is willing to put money in an AI system that makes wrong decisions but explains them really well? What we need is an AI system that makes the right decisions, so it does not need to explain them.

AI systems of the future need to be designed with these humane elements embedded in their nature and functionality. This may include, pointing out edge cases, “discussing” and “debating” complex cases with other experts (humans or other AI systems), embedding the element of EQ in decision making, and at times even handing a decision back to humans when it encounters a new scenario where the probability of wrong decision making is higher.

But until we get there, a practical way for organizations to address these explainability challenges is to adopt a hybrid human-in-the-loop approach. Such an approach relies on subject matter experts (SMEs), such as ethicists, data scientists, regulators, domain experts, etc. to

Improve learning models’ outcomes over time
Check for biases and discrepancies
Ensure compliance

In this approach, instead of relying on a large training data set to build the model, the machine learning system is built iteratively with regular inputs from experts.

In the long run, enterprises need to build a comprehensive governance structure for AI adoption and data leverage. Such a structure will have to institute explainability norms that factor in criticality of machine decisions, required expertise, and checks throughout the lifecycle of any AI implementation. Humane intelligence and not artificial intelligence systems are required in the world of the future.

We would be happy to hear your thoughts on approaches to AI and XAI. Please reach out to [email protected] for a discussion.

Scale Up IoT, but Not without Securing It | Blog

November 2, 2020

How often does security or privacy cross your mind when you install a smart bulb or purchase a smart fridge? What about when you hear about examples of Alexa eavesdropping on private conversations? And these are examples from consumer IoT; we can only imagine the scale of IoT’s impact – and its lapses – in an enterprise setting!

The accelerating IoT security threat

Every device that is added to the IoT network without proper security measures in place increases the risk of a cyberattack. Given the exponential increase in smart devices, IoT has become an invisible omnipresent layer, in both the digital and physical worlds. This combined ubiquitousness and lack of visibility makes us oblivious to the plethora of connected devices around us at all times that capture minute details in real time, leaving one exposed to risk. Examples of breaches are abundant and often surprising, such as the time hackers accessed data about a casino’s high-paying customers via the casino’s aquarium’s smart thermostat.

Because of these challenges, enterprises, big tech firms, and even governments have prioritized IoT security.

Gaps to address

Security, in terms of network and device security and data privacy, is a big concern given the significant risk it poses to an organization and its clients. One of the biggest challenges that organizations face in establishing IoT security practices is the lack of universal standards and specifications for devices, which leaves devices with substandard compliance and undiscovered vulnerabilities.

Exhibit 1 highlights the key challenges organizations face when securing their IoT ecosystems.

Some industries are more vulnerable than others

IoT security is a major concern across all industries, particularly manufacturing, energy & utilities, and healthcare, which have more convergence of IT and OT than other industries. For them, security is crucial for several reasons:

Operationally, IT and OT are different domains, and, thus, replicating the security practices and architecture of IT in OT is challenging
The IT-OT integration results in a significant amount of sensitive OT data on the network, which is vulnerable to security threats
The talent required to mitigate OT security challenges needs to be developed
The IT-OT convergence is exposed to several supply chain and third-party vendor risks

A closer look at healthcare shows that the COVID-19 pandemic has accelerated toward digital healthcare, with IoT playing a critical role in enabling remote patient data monitoring, smart sensing, and remote patient interactions. In this scenario:

The proliferation of IoT-based networks that capture health outcomes and the Internet of Medical Things (IoMT), which connects medical devices to the internet, exposes enterprises to phishing attacks
IoT devices that capture critical patient health data, including newer endpoints such as wearables, in-home devices, and smart implants, can lead to potential data breaches due to inadequate security measures
Hackers can exploit IoT networks to extract sensitive patient information and gain unwanted access to organizations’ data and devices, with severe consequences

Therefore, now more than ever, the need to resolve data security challenges to ensure all stakeholders’ safety and privacy is urgent.

Maneuvering the security challenge

While keeping pace with increasing IoT adoption, organizations need to rapidly ramp up their security measures, and, in so doing, ensure certain security and risk mitigation features, as illustrated in Exhibit 2.

Exhibit 2: Initiatives enterprises should undertake to ensure a secure IoT implementation

Let’s take a closer look at each of these components.

Ensure security by design: Organizations need to start factoring in security as a vital aspect of their implementations, not as an afterthought after scaling their IoT implementations. This means making parallel investments and setting up dedicated units to manage security and data protection
Invest in security literacy to create awareness: Enterprises need to create a security awareness program that educates employees about best practices to avoid breaches and reduce exposure, and it needs to a regular exercise to keep the employees updated on compliance and related policies
Perform regular vulnerability/risk assessments: Enterprises need to assess their risk profiles on a regular basis to identify any vulnerabilities and take preemptive action. They should continually test their systems and connected devices to ensure robustness
Invest in next-generation technology-based security solutions: Organizations should leverage unique and innovative solutions that leverage next-generation technologies such as blockchain, deep automation, and machine learning for connected devices to make them more secure
Engage in industry partnerships: Companies should forge partnerships with device manufacturers to build more robust devices with embedded security features that make them less vulnerable to attack. They should also form alliances with other companies and industry consortia to develop uniform specification standards and provide certification programs for devices

Only an all-round focus on security, along with partnerships with different stakeholders active in IoT, digital, and digital security, will help ward off security threats and thrive in a digital-first environment. As organizations take big strides in IoT implementation and maturity, they stand to gain if they equally emphasize security.

What has your experience been with IoT implementation and security? Please share your experiences and thoughts with us at [email protected] and [email protected].

Post-COVID-19 Recovery: A Technological Reform | Blog

May 6, 2020

COVID-19 has revealed some crippling inadequacies to enterprises across the globe. With employees locked down in their homes, manufacturing processes on hold, warehouse facilities inaccessible, and supply chains at a standstill, businesses are facing an undeniable economic crisis. This downtime has made them question their underlying business models, operating methodologies, and, most importantly, their tech investments. An overarching question that also looms large is: What will a post-pandemic business ecosystem look like, and how can enterprises and service partners be better prepared for it?

The 2008 financial crisis was a wake-up call for businesses around the world, and was accompanied by new financial governance structures, steps to ensure transparent and efficient decision-making, and large-scale legislative reforms. In contrast, COVID-19 is expected to usher in massive technological reforms. We already see early signs of enterprises preparing for the future by leveraging technologies not only to address current requirements but also to build a strong foundation for the post-pandemic ecosystem.

Let’s take a look at some areas where technology is being leveraged to respond in radically newer ways.

Next normal – virtual experiences

Imagine taking your kids to a safari, or attending a concert in London, or test driving a car, all of this while you are in self-quarantine at home. Virtual tours, virtual resource-sharing, and even virtual personalities and friends are likely to become routine, compelling enterprises to rethink their business models. Customer interactions and customers’ expectations from brands will also change dramatically, requiring enterprises to design innovative solutions and new experiences, and ensure their seamless delivery. Technologies such as Internet of Things (IoT), Augmented Reality (AR), and Mixed Reality (MR) offer enterprises exciting opportunities to address these needs. Service partners will play a critical role in helping enterprises understand customers’ evolved expectations, visualize future demand themes, re-design virtual customer journeys, and build the required technological assets.

Cross-industry collaborations

Amid the lockdown and uncertainty across the globe, technology platform-based businesses seem to fit like pieces in a puzzle. Think about food and grocery delivery businesses or content delivery platforms such as Amazon Prime and Netflix, or even social media platforms such as Facebook. As users get more comfortable with technology and use it increasingly to meet varying needs, the treasured data gathered across platforms will also pave the way for cross-industry collaborations. For instance, secured access to customers’ virtual profiles can enable insurance firms to design personalized products based on lifestyle and preferences. Similarly, travel and tourism businesses can identify target customers and offer them curated solutions based on their preferences identified by converging insights across these platforms. These changes will usher in a new wave of innovation, driving seamless interactions, deeper customer insights, personalized marketing, and new business opportunities. Service partners will be trusted with the critical role of catalyst, which can onboard various stakeholders, enable seamless integration of diverse systems, and facilitate value realization for all entities involved.

Business continuity planning

Many technology firms are already supporting enterprises with solutions to ensure business continuity. For instance, Oracle, Salesforce, SAP, and ServiceNow are enabling collaboration, file sharing, and data sharing with flexible payment options to support their enterprise customers. And Cisco, Dell, Nutanix, and many others are offering free-to-use solutions or adopting creative financial models such as deferred payments and lower initial payments to facilitate business continuity for customers. This is, in turn, helps their clients build brand equity, which is likely to pay off in the long term. These firms are “doing good by doing well,” essentially responding with much-needed solutions. Increasingly, service partners will be expected to bring about changes in their engagement, delivery, and commercial models to address the challenges confronting their clients.

While businesses are still trying to fully grasp what it will take to survive and thrive post-pandemic, we can say one thing for sure: an astute understanding of customer needs, coupled with the right technological assets, proper governance, and a structured investment approach will help prepare enterprises for the world we encounter on the other side of the pandemic.

If you are looking to understand the evolving business ecosystem and a roadmap for how you can prepare your organization better for growth post-pandemic, please write to me at: [email protected].

Real IoT Value Requires a System of Technologies | Sherpas in Blue Shirts

May 31, 2018

Our latest enterprise survey revealed that more than 40 percent of large enterprises have already implemented IoT and another 25 percent have piloted IoT use cases and will likely make meaningful investments in the technology in near future. All these enterprises have invariably invested in building an ecosystem of partners, made technological and business process changes, and trained their talent to sustain the investment.

However, 75 percent of these enterprises are realizing benefits limited to cost reduction, better visibility at the operational level, and access to more data for better decision making. This, unfortunately, is a massive underestimation and under-utilization of the potential of connected ecosystems.

On the other hand, the other 25 percent of the enterprises, we call them “Innovators,” are realizing IoT-driven outcomes with substantially greater business impact, such as identifying alternate revenue sources, offering new products and services, and increasing customer intimacy. These enterprises have undertaken a holistic approach to technology adoption, and have designed connected systems with in-built cognitive capabilities and next generation technologies such as machine learning, augmented reality, virtual reality, natural language processing, and blockchain.

The convergence of these technologies with IoT has created a multiplier effect by building further strength and adding new capabilities in the connected ecosystem, thereby, enabling these enterprises to re-visualize their businesses.

Achieving transformational impact with IoT

Leveraging next generation capabilities with IoT

A noteworthy 45% of these Innovators are exploring these next generation capabilities to derive higher value from their IoT investments. Some model transformational use cases by leading industries have paved the way for others to make higher bets in IoT. For example:

Blockchain, in combination with IoT, is being used extensively by the logistics industry to achieve a low power secure network for transaction recording and timestamping. The insurance industry is set to be disrupted once the supply chain network reaches its desired level of transparency with accountability with this application.

Augmented reality, in combination with IoT, is recognized as the crucial technology to attain the “factories of the future” objective. Remotely accessing a visual overlay of the connected asset enables real time issue identification, remote service provisioning, and an exponential increase in operational efficiency.

Machine learning has enormous applications, many of them yet to be identified by enterprises. Enabling continuous process improvement, customization of services or messages, and helping experts take the most suitable action are the known use cases being implemented today. ML with IoT can be the defining moment for personalized service provisioning for every enterprise in the services industry.

Natural language processing can help enterprises deliver on customers’ expectations for connected devices to be interactive and intuitive. The world would be a different place if this technology were used in parallel with connected devices for meaningful value delivery.

With macro-economic changes and massive financial pressure to ensure sustainability, the travel & transportation and manufacturing sectors have been at the forefront of conceiving new revenue streams and launching products in as-a-service mode with IoT adoption. Changing customer preferences and competition from players across industries have pushed retail enterprises to adopt IoT with AR/VR technology to enable delivery of interactive and personalized experiences to customers. Infusing data security with blockchain adoption has enabled healthcare & insurance and energy & utilities enterprises to bring about continuous process improvement and ensure business sustainability.

To sustainably implement these systems of technologies, leading IoT adopters are building technology adoption roadmaps with an enterprise-wide view aligned to a unified vision. Their clarity in their business objective/s has helped them extract more from their IoT investments. These innovators have also laid considerable focus on transforming their people in tandem with the technology transformation, and have placed organizational change management at the core of IoT adoption.

For more insights on the possibilities and value potential offered by IoT, read our latest report “IoT Services PEAK Matrix™ Assessment and Market Trends 2017: Have You Taken the Plunge in IoT Yet?”

Reality Check on the Top 5 IT Innovation Myths | Sherpas in Blue Shirts

April 17, 2017

How do Amazon, Apple, and Tesla keep innovating? What do they do differently than many others do not, or cannot, do? And how many industry leaders can say their organization is truly innovative?

To get answers to these and other pressing questions, we conducted a focused research study with more than 100 application service executives – approximately 50 percent of whom were CXOs – in North America-based enterprises engaged in IT outsourcing programs. The research revealed startling insights. For example, only 30 percent of study participants felt their companies were somewhat innovative, even though all of them realized the importance of innovation and had made strategic investments in it.

And from defining it and its objectives, to funding it, to defining and institutionalizing the process to drive it, innovation has remained an elusive concept both for enterprises and service providers.

The study also busted innumerable myths associated with IT innovation. Let’s look at the top five.

IT Innovation Myth 1: Innovation is abstract and cannot be measured

But, over 75 percent of the study participants already have a highly effective mechanism to measure the impact of innovation. Linking the investment made to measurable results and desired benefits has enabled them to devise a formal approach for impact assessment.

IT Innovation Myth 2: Innovation should result in a disruptive idea

In reality, this is the last priority for executives of best in class enterprises! A siloed disruptive idea that does not impact the business model or enhance customer experience is the least appreciated outcome, and does little to serve the purpose of innovation. Instead, transformation is the primary lever deployed by enterprises to identify disruptive innovation. Moreover, the overall approach to it and the returns derived from it are considered more significant for driving innovation than the idea itself.

IT Innovation Myth 3: Episodic initiatives such as “idea of the month” and “innovation events” can deliver innovative results

Unfortunately, such sporadic investments have a probability of less than 10 percent to deliver innovative outcomes. Though used by most service providers, these are the least preferred approach to innovation from the enterprise executive’s perspective. Continuous innovation with prototyping and demonstrations/MVPs are far more likely to deliver on customers’ expectations.

IT Innovation Myth 4: Large scale investment is required from the enterprise or service provider to fund innovation

Though investment is required, 65 percent of the study participants with high satisfaction with their innovation program believe in shared responsibility and co-funding. Their belief is that shared responsibility spreads the risk involved, and reduces the investment required, thereby attracting the best-in-class capabilities from both sides.

IT Innovation Myth 5: A dedicated centralized team/CoE should be set up to drive innovation

Rather, best-in-class innovative businesses embed a culture of innovation across their enterprises to encourage the concept of continuous and crowdsourced innovation.

To enable enterprises to adopt a systematized innovation approach and achieve their desired outcome, Everest Group designed a unique framework on which to base their innovation strategy. We also used the framework to identify the 14 most innovative service providers in the industry.

Application Services IT Innovation Maturity

For more information and insights on this research, please refer to our reports, “How to innovate – A Comprehensive Guide to Innovation in Application Services,” and “Cracking the IT Innovation Code.”

The Widening Gap between Customer Satisfaction Perception and Reality | Sherpas in Blue Shirts

March 23, 2017

Not surprisingly, every service provider claims to have exceptionally high customer satisfaction ratings from their enterprise clients. Yet, we see anti-incumbency rising and deal size dwindling.

To assess enterprises’ satisfaction levels in IT services engagements, we conducted a deep dive study of 30 service providers and 130+ of their clients. We largely focused our analysis lens on six vital parameters of service delivery – technical expertise, domain expertise, talent management, commercial models, client management, and strategic partnership.

Enterprise customers are dissatisfied with service providers

The results, presented in our recently published report entitled, “Customer (Dis)Satisfaction: Why Are Enterprises Unhappy with Their Service Providers?” were quite disturbing. They indicated that nearly 50 percent of IT service buyers are not satisfied with their providers, feeling that they fall short in many areas of service delivery.

We investigated the reasons behind the huge gap between buyer expectations and current service delivery and arrived at the following insights:

Early-stage differentiating factors have become table stakes: The value propositions of labor arbitrage and low-cost delivery are no longer compelling. Instead, enterprises want service providers that can create a positive impact on their core business functions.
Inability to meet the unspoken demands of customers: Enterprises expect their service providers to have evolved from “order takers” to “collaborators” capable of effectively partnering with them in strategic decision making. They want their providers to go beyond the project ask and demonstrate transformative skills, even though such expectations are largely unspoken.
Limited understanding of clients’ businesses narrows down business opportunities: Visibility into enterprises’ business dynamics and priorities are critical for service providers to align their offerings and strategy to client needs. Yet their margin obsession and hesitation to make new technology investments have precluded them from taking a futuristic approach to IT engagements.

How service providers can turn the tide

So how can service providers turn the tide to have a more positive impact on existing and future engagements? Here are Everest Group’s top three recommendations.

Shift from an operational to a strategic mindset: Service providers need to go the extra mile to proactively identify enterprises’ business drivers and must develop capabilities to offer innovative solutions. Just delivering on the agreed upon SLAs does not elevate service providers to the level of service partners.
Innovative engagement: With rising competition, it is imperative that service providers walk the talk. While they cannot avoid investing in new technologies, they can share the adoption risk with their enterprise clients. Newer engagement models like outcome-based, risk-reward sharing, and output-based give enterprises the necessary confidence to take the leap and engage service providers for a next generation technology adoption initiative.
Invest. Automate. Improve: Two-thirds of the enterprises are gearing up for large scale process digitalization, and they expect their service providers to be able to technologically support their objectives. Service providers must strategically invest in automation to improve efficiency, reduce costs, enable faster time-to-market, and deliver process improvements in order to offer a compelling solution.

With anti-incumbency risks, anti-offshoring rhetoric, and clients’ propensity to adopt a digital arbitrage model looming large, service providers cannot afford to lose customer confidence. They must, today, start looking through a clearer lens to evaluate where their relationships with their enterprise clients stand.

For details on the areas in which service providers must smooth their rough edges, polish their existing skills, and develop new skill sets, please read our report, “Customer (Dis)Satisfaction: Why Are Enterprises Unhappy with Their Service Providers?”

Seize the IoT Opportunity | Sherpas in Blue Shirts

January 13, 2017

A leading car manufacturer dispensed a spare part even before the customer knew it was needed. A doctor knew precisely when a patient took a vital medication. A metro city police department accelerated crime response time. A retailer designed its offerings based on dynamic in-store customer behavior.

Three in every four enterprises have a similar type of story to share about connecting to the “things” of interest and digital enablement of businesses. Recognized as the next big opportunity, the Internet of Things (IoT) is being embraced by enterprises to generate greater value and achieve their business objectives. Indeed, more than 50 percent have already piloted IoT, and the majority are highly optimistic about its returns.

Despite the high level of optimism, there exist numerous unanswered questions and concerns about IoT. Is it being used to the full potential, or are we just scratching the surface so far? Where is industry adoption headed? What risks should an enterprise take? What should an organization do to extract the most out of this investment?

Everest Group’s recently published PEAK Matrix™ report on IoT Services reveals intriguing industry trends, enterprise adoption patterns, probable future developments, and services expectations based on extensive discussions on all things IoT with 30+ enterprises.

IoT is no longer a buzz term
Currently, organizations are leveraging IoT to achieve agility, flexibility, customer centricity, and cost reduction. We identified four types of IoT adopters, based on the adopting organization’s desired outcomes: Optimizers, Engagers, Integrators, and Innovators. Most enterprises are categorized as Optimizers. That is, they focus on solving their operational issues and on infusing efficiency with IoT. Integrators and Innovators – which collectively equal less than 20 percent of IoT adopters – focus on enterprise growth or invest to seize larger benefits from the opportunity.

From an industry perspective, the leading beneficiary of IoT to date has been manufacturing, primarily focused on bringing efficiency to the shop floor. Customer-centric industries such as telecoms and retail are investing to improve ecosystem efficiencies and enhance end-user engagement. Other industries such as agriculture, BFS, and mining are expected to make considerable investments in IoT in the near future.

Substantial hurdles stall rapid adoption
Hype aside, the majority of the enterprises are taking cautious steps and embracing IoT in small, incremental stages only. A multitude of challenges such as data security and privacy, storage and rapid analysis of large volumes of data, and availability of a high-speed network at all locations are impeding large scale investments in IoT. Another major hindrance is change management that necessitates significant investment in talent, infrastructure, and processes.

Enterprises need to collaborate with a variety of partners from the vast IoT ecosystem to design, implement, and manage an IoT system. The service provider landscape itself is segregated at this stage, and players across the value chain are trying to capture a larger share of the pie by expanding their partner ecosystem and their internal delivery capabilities.

But you can’t afford to miss the bus!
Despite the challenges, IoT remains among the top three investment priorities for a majority of organizations. To be front runners in the race, they must strategize their IoT adoption in a phased process for enterprise-wide benefit. And they need a transformational vision, investments in innovation and R&D, and a good partner ecosystem to maximize ROI.

The action is equally intense in the service provider camp. While some have up to 20 partners to complete their portfolio, others have acquired up to as many. Players with expertise in operational technology, engineering capabilities, and industry partnerships are best positioned to define success in the IoT services market. We anticipate large-scale convergence and new partnerships to cater to the services demand, which is expected to double by 2020.

Interested in learning more about IoT? Our PEAK Matrix™ report on IoT Services provides deep insights on IoT market trends, expected service market size, implications for enterprise and the service providers, and a detailed evaluation of 16 major IoT services providers.

Author: AlishaMittal