Point of View

The HfS Triple A Trifecta: Automation, Analytics, and Artificial Intelligence

August 31, 2017

The pace of change in today’s world of business and IT operations is astounding. “Digital,” “Platform,” and “Automation” are most frequently used words that clients hear from their service providers and advisors but there is no common understanding or definition behind these. While our industry continues to accelerate the build-out of these capabilities, client adoption and satisfaction[1] is still lagging. One of the primary reasons is the lack of a common understanding or a unifying framework.


The “Triple A Trifecta” (See Exhibit 1) of Robotic Automation, Smart Analytics, and Artificial Intelligence aims to provide a clear and crisp articulation of the emerging change agents for clients to optimize, renovate, or transform their business operations. We also focused on developing the Trifecta based on client-context versus describing technology capability.


This PoV discusses each element of the Trifecta to understand the value proposition from a service delivery perspective and required human governance. The PoV also highlights the different flavors and building blocks associated with each Trifecta element.


Exhibit 1: The HfS “Triple A Trifecta”: Automation, Analytics, and AI



Source: HfS Research


Before we describe each of the Trifecta elements, it is important to highlight two distinguishing characteristics of the framework:


– The Trifecta elements intersect with each other. While each element of the Trifecta has a distinct value proposition (RPA drives efficiency, Smart Analytics improves decision-making, and AI can solve business problems), there is increasing convergence between the three elements. For instance, smart analytics are increasingly reliant on AI tools such as NLP to conduct search-driven analytics, neural networks to do data exploration, and learning algorithms to build predictive models. In fact, the Holy Grail of service delivery transformation is at the intersection of automation, analytics, and AI

The Trifecta is nonlinear without a definite starting point. Transformation is not a linear progression. Enterprises can start anywhere across the Trifecta. It is not necessary to start with basic automation and then advance to AI-based automation. However, it is critical to understand the business problem that you are trying to solve and then apply the relevant value lever or a combination of value levers.

Robotic Process Automation (RPA)

IEEE defines RPA as “Preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.”


The primary value proposition of RPA is to increase efficiency /productivity through manual labor reduction by automating transaction-intensive activities. RPA implementations require human intervention for judgment-intensive tasks and to make changes/improvements. RPA tools can typically handle structured data and are user interface based (code-free), application agnostic, nondisruptive to legacy IT, and business user-friendly.


There is another flavor of RPA that often creates confusion – Robotic Desktop Automation (RDA). The agents pass on tasks to robots in RDA, while in RPA the robot passes on tasks to agents. Table 1 summarizes the differences and similarities between RDA and RPA. Since both RDA and RPA ultimately end up with a value proposition around efficiency, we treat RDA as a flavor of RPA in the Trifecta versus a completely different value lever.


Table 1: Differences and Similarities between RDA and RPA


RPA in Action 

NPower (a gas and electricity supply company in UK) needed a solution that could handle a low volume of highly complex processes characterized by compound steps, multiple variables, and access to fragmented systems. It leveraged Blue Prism (RPA Software provider) to automate the processes in a challenging regulatory and operational environment where enhancing and upgrading legacy infrastructure would not have been possible. Using RPA, NPower increased the speed and agility of process completion dramatically and to leverage more lifetime value from legacy systems without capital investment.

Smart Analytics

Analytics aim to improve human decision-making either by reducing the decision-making elapsed time and by improving the effectiveness of business choices with automated insights. With smart analytics, humans are only required to take the final decisions basis insights and recommendations that improve/learn over time.


Smart analytics can handle both structured and unstructured data through their ability to sense (collect data), comprehend (understand and analyze), adapt (learn and improve), and recommend (predict and prescribe). The term “cognitive analytics” primarily refers to the learnability and adaptability of the underlying analytics solution and is subsumed under smart analytics in the Trifecta.


Smart Analytics in Action 

Clinicians in the intensive care unit (ICU) often work with critically ill patients in a limited space, and need to make quick decisions at that point of care. Virgen Del Rocio University Hospital in Spain is teaming its doctors and technologists with NTT DATA’s data scientists to create use cases and build analytic solutions that use machine learning and real-time processing to create actionable recommendations in real-time. Doctors and nurses can also access all the data entered about a patient, at their fingertips, as needed, from any data capture point in the hospital, such as biomedical devices, the pharmacy, or labs.


Artificial Intelligence (AI)

AI is many things: It is hyped, it is not defined, it is starting to become pervasive, and it is fostering emotional, and at times heated discussions. However, many of those discussions are more focused on more consumer-facing issues, such as self-driving cars, drones delivering Amazon purchases or robotic home helpers. But the broader market is not yet recognizing the nearer-term impact of AI on B2B and Enterprise operations.


AI aims to automate intelligent activities that humans associate with other human minds through a combination of reasoning, knowledge, planning, learning, natural language processing (communication), and perception (aka cognitive). Exhibit 2 lists some of the major building blocks for AI. Note that Cognitive Computing is essentially a subset of AI but is marketed as a separate solution.   


Exhibit 2: Building blocks for AI (illustrative, not comprehensive)



AI in Action 

Electronic Arts (US headquartered video game company) is using Amelia (IPsoft’s cognitive agent) to authenticate a customer’s identity and to weed out phishers aimed at committing identity fraud before passing the customer to an agent, which ultimately reduces the overall handle time as well as instances of fraud. Amelia is also programmed to understand concepts and learn from experience, rather than just learn and memorize words. Fundamentally, Amelia opens the journey to much broader notions of self-learning and self-remediation.


Table 2 summarizes the three elements of the Trifecta and describes the value propositions, typical use cases and a set of characteristics around data, capabilities, and governance.


Table 2: Summary of the Trifecta elements




Bottom-line: Focus on services, not technology


Behind every successful transformation, lies a deep understanding and appreciation of the core business problem. It is true that technology has developed leaps and bounds over the last decade and will continue to take rapid strides forward. However, the true genius lies in understanding what to use when and how. Just like we realized that throwing bodies at a problem does not solve the problem, we need to realize that simply hurling software at services will not drive transformation. We need to view everything from a business perspective, and that is what the Trifecta aims to do.


[1] Our recent study on “State of Automation” based on interviews with 400+ enterprises found that only a little over 50% of clients are satisfied with their automation initiatives

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