Companies currently invest a lot of money in target markets to generate potential customers’ interest in products and services. But after they achieve a sale, they often frustrate customers by not providing effective customer service support. A poor customer experience can erode the company’s brand and reputation and destroy the company’s opportunities to increase revenue through new purchases by those existing customers. Obviously, these are significant problems, especially in today’s highly competitive environment with customers’ quick pace in buying decisions. Let us now explore the solution.
A sustained focus on digital, agility, and advanced technologies is likely to prepare enterprises for the future, especially following COVID-19. Many enterprise leaders consider IT infrastructure to be the bedrock of business transformation at a time when the service delivery model has become more virtual and cloud based. This reality presents an opportunity for GBS organizations that deliver IT infrastructure services to rethink their long-term strategies to enhance their capabilities, thereby strengthening their value propositions for their enterprises.
GBS setups with strong IT infra capabilities can lead enterprise transformation
Over the past few years, several GBS organizations have built and strengthened capabilities across a wide range of IT infrastructure services. Best-in-class GBS setups have achieved significant scale and penetration for IT infrastructure delivery and now support a wide range of functions – such as cloud migration and transformation, desktop support and virtualization, and service desk – with high maturity. In fact, some centers have scaled as high as 250-300 Full Time Equivalents (FTEs) and 35-45% penetration.
At the same time, these organizations are fraught with legacy issues that need to be addressed to unlock full value. Our research reveals that most enterprises believe that their GBS’ current IT infrastructure services model is not ready to cater to the digital capabilities necessary for targeted transformation. Only GBS organizations that evolve and strengthen their IT infrastructure capabilities will be well positioned to extend their support to newer or more enhanced IT infrastructure services delivery.
The need for an IT infrastructure revolution and what it will take
The push to transform IT infrastructure in GBS setups should be driven by a business-centric approach to global business services. To enable this shift, GBS organizations should consider a new model for IT infrastructure that focuses on improving business metrics instead of pre-defined IT Service Line Agreements (SLA) and Total Cost of Operations (TCO) management. IT infrastructure must be able to support changes ushered in by rapid device proliferation, technology disruptions, business expansions, and escalating cost pressures post-COVID-19 to showcase sustained value.
To transition to this IT infrastructure state, GBS organizations must proactively start to identify skills that have a high likelihood of being replaced / becoming obsolete, as well as emerging skills. They must also prioritize emerging skills that have a higher reskilling/upskilling potential. These goals can be achieved through a comprehensive program that proactively builds capabilities in IT services delivery.
In the exhibit below, we highlight the shelf life of basic IT services skills by comparing the upskilling/reskilling potential of IT services skills with their expected extent of replacement.
Exhibit: Analysis of the shelf life of basic IT services skills
In the near future, GBS organizations should leverage Artificial Intelligence (AI), analytics, and automation to further revolutionize their IT capabilities. The end goal is to transition to a self-healing, self-configuring system that can dynamically and autonomously adapt to changing business needs, thereby creating an invisible IT infrastructure model. This invisible IT infrastructure will be highly secure, require minimal oversight, function across stacks, and continuously evolve with changing business needs. By leveraging an automation-, analytics-, and AI-led delivery of infrastructure, operations, and services management, GBS organizations can truly enable enterprises to make decisions based on business imperatives.
IVA market growth will accelerate post-pandemic as enterprises strive to overcome recession with focus on automation, customer experience
The global Intelligent Virtual Agent (IVA) market stood at US$300 million-US$350 million in 2019, exhibiting about 42% growth year on year, according to Everest Group. The firm projects a dip in demand in 2020 due to the COVID-19 pandemic but expects the IVA market to post strong growth going forward, achieving as much as a 70% compound annual growth rate (CAGR) through 2022. In fact, Everest Group has boosted this estimate by 13-22%, anticipating that enterprises will place greater emphasis on cost reduction and improving business continuity in the post-pandemic period.
IVA solutions are a key enabler of automation in the front office, currently being used primarily for customer support as well as IT and help desk functions due to their large volumes of repetitive queries. These functions account for more than 80% of the IVA market today. Banking, insurance, and telecom industries account for the highest adoption of IVA and continue to exhibit impressive growth, particularly given the maturity of contact centers within these industries.
Increasing sophistication and collaboration with complimentary artificial intelligence (AI) based technologies are driving IVA popularity in the market. Enterprises across industries and geographies are leveraging or plan to leverage IVA solutions for different use cases to reduce human involvement and improve customer experience (CX).
“IVA is still in the realm of early adoption today, but that is rapidly changing as enterprises realize what a tremendous opportunity they have to leverage this technology,” said Anil Vijayan, vice president of Everest Group. “IVA technology is continuously advancing and growing in sophistication well beyond rule-based chatbots. Today we see a higher level of maturity in intelligent IVA applications, which are being used for a variety of use cases including payment services account resolutions and employee onboarding, for instance. We’re also beginning to see IVA playing a key role in conversational AI ecosystems, where a collaborative set of tools—including IVA, AI, robotic process automation, learning and listening engines, analytics and more—is used to seamlessly integrate front and back office systems. Here, IVA supports more advanced use cases such as cross-selling and upselling, customer retention, and making personalized recommendations. We expect this evolution to continue, leading to reliable and delightful customer experiences while reducing human effort through automation.”
About Everest Group
Everest Group is a consulting and research firm focused on strategic IT, business services, engineering services, and sourcing. Our clients include leading global enterprises, service providers, and investors. Through our research-informed insights and deep experience, we guide clients in their journeys to achieve heightened operational and financial performance, accelerated value delivery, and high-impact business outcomes. Details and in-depth content are available at http://www.everestgrp.com/.
Consider what’s now happening at companies that made investments in automation and moving work to the cloud. They’re doing better than others in the COVID-19 pandemic. They’re more flexible under trying conditions. They’re more resilient to challenges. They are a bright spot in this awful crisis. The pandemic showed what companies invested in as preparation for challenges. Unfortunately, it also exposed companies that were less prepared. As I mentioned in my prior blog, the pandemic was like what Warren Buffet described as the tide going out, exposing naked swimmers. One fact that the COVID-19 crisis exposed is that automation matters.
Read my blog on Forbes
The increasing popularity and uptake of Artificial Intelligence (AI) is giving rise to concerns about its risks, explainability, and fairness in the decisions that it makes. One big area of concern is bias in the algorithms that are used in AI for decision making. Another risk is the probabilistic approach to handling decisions and the potential for unpredictable outcomes based on AI self-learning. These concerns are justified, given the implicit ethical and business risks, for example, impact on people’s lives and livelihood, or bad business decisions based on AI recommendations that were founded on partial data.
The good news is that the software industry is starting to address these concerns. For example, last year, vendors including Google, IBM, and Microsoft announced tools (either released or in development) for detecting bias in AI, and recently, there were more announcements.
Last year IBM brought out:
Adversarial Robustness 360 Toolbox (ART), a Python library available on GitHub, to make machine learning models more robust against adversarial threats such as inputs that are manipulated to derive desired outputs
AI Fairness 360, an open-source toolkit with metrics that identify bias in datasets and machine learning models, and algorithms to mitigate them
Last month, IBM further augmented its offerings with the release of AI Explainability 360, an open source toolkit of algorithms to support the understanding and explainability of machine learning models. It is a companion to the other toolkits.
Cognitive Scale recently unveiled the beta of Cortex Certifai, software that automatically detects and scores vulnerabilities in black box AI models without having access to the internals of the model. Certifai is a Kubernetes application and runs as a native cloud service on Amazon, Azure, Google, and Redhat clouds. Cognitive Scale also unveiled the AI Trust Index. Developed in collaboration with AI Global, it will provide composite risk scores for automated black-box decision making models. This is an interesting development that could grow to become a badge of honour for AI software, and a differentiator for those with the most trusted rating.
The Reality of Bias
While these announcements and those made last year are good news, there are aspects of AI training that will be difficult to address because bias is all around us in real life. For example, public data would show AI that there are many more male CEOs and board members than female ones, leading it to possibly conclude that male candidates are more suitable for shortlisting for a non-executive director vacancy than women. Or public data could lead AI to increase mortgage or auto loan risk factors for individuals living in a particular zip code or postcode to unreasonably high levels.
It is the encoding and application of these kinds of biases automatically at scale that is worrying. Regulations in some countries address some of the issues, but not all countries do. Besides, the potential for new threats and risks is high.
There is still a lot more for us to understand when it comes to making AI fair and explainable. This is a complex and growing field. As demand for AI grows, we will see more demand for solutions to check AI as well.
In a previous blog post, we explored the evolution of enterprise IT infrastructures from a cost-center positioning to one that enables digital transformation through a concept known as aware automation — a combination of intelligent automation and cognitive/Artificial Intelligence (AI)-driven automation. In this post, we’ll explore some potential use cases and best practices for aware automation within the enterprise.
Read more in our blog on IPSoft