Machine First Delivery Model TM. Driving Business 4.0 TM, Intelligently

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1 Machine First Delivery Model TM Driving Business 4.0 TM, Intelligently 1

2 Introduction Table of Contents 1 Introduction 2 Machine First Philosophy 2 Machine First Delivery Model (MFDM ) Framework 4 MFDM delivering Superior Customer Experience 5 Institutionalizing MFDM 5 MFDM Maturity Model 7 MFDM Execution Model 7 Agile and MFDM We live in a world full of machines that are part of every part of our lives today, from mobile phones to smart appliances. Smartness is being added to every touchpoint humans have with the external world, in many ways traffic lights, self-driving cars, wearables and more. We use machines naturally as a first touchpoint today before we act We go to maps first before hitting the road We tend to browse for fashion before shopping In TCS we go to Fresco Play for digital learning before enrolling for a classroom training We prefer to do bank transaction on Mobile First than making a trip to bank And the list goes on Machines certainly support us by eliminating the mundane steps, providing us with unthinkable choices, and real time analytical information to speed up our actions and help us make informed decisions. That is why, we prefer this unique anytime, anywhere, any device experience with all real time information that helps us act or make decisions better. There begins the idea of extending this to enterprises, to let machines have the first right of refusal. We believe machine first as a philosophy, with all the ingredients required to realize it, would create incredible opportunities to enhance human creative abilities, which in turn would translate into exponential growth of an enterprise. This book covers the key ingredients and procedures to actualize the idea and philosophy to reality in the enterprise world. 1

3 Machine First Philosophy The machine first philosophy is about augmenting human capability using automation and artificial intelligence (AI) to enhance the potential of the enterprise. The pervasiveness of digital technologies poses a plethora of choices to enterprises and business leaders. Accelerating business growth, bringing operational efficiencies through automation and cloud, creating new business models through AI and machine learning, and deriving intelligence through analytics in a dynamic market place have become a part of the transformation agenda of digital enterprises. The machine first approach allows technology the first right of refusal to sense, understand, decide, and act in a robust networked environment equipped with analytics and AI, with the learning platform enabling superior quality information across the enterprise in real time, all the time. Shifting minds to machinefirst from human-first opens great opportunities to capitalize on the power of technology, free-up time for humans to be more creative, and unlock exponential value. Machine First Delivery Model (MFDM) Framework The MFDM Framework outlines the key components, stages, input, output, and stakeholders. This framework is the vehicle of execution for the machine first strategy. Driving this framework is: the Enterprise Intelligence Platform that comprehends inputs from multiple sources into a problem in IT or business process sense for the machine to decide and solve as well as the Enterprise Response Engine that fits in the rest of IT landscape to orchestrate the decided course of action and the Collaboration Platform that helps the machines to learn. The key stages of the framework are: Sense: Automated interpretation and assimilation of input data in structured, unstructured, and semi-structured form enabled by a plug and play ecosystem. Understand: AI techniques and analytics methods applied on available information and history to understand the data/ information available through multiple channels enabled by the Enterprise Intelligence Platform. Decide: Rational decisions on next course of action and required workflows for approvals (as needed) enabled by Enterprise Intelligence Platform. Respond: The Enterprise Response Engine executes the determined action by orchestrating the rest of enterprise landscape. Learn: Continuous learning of machines to perform Sense- Understand-Decide-Respond actions better from algorithms, data patterns, and human inputs on context and exceptions. 2

4 Collaboration platform Learning aspect of this framework is achieved by establishing a collaboration platform for leverage of resources and ecosystems with the following key components: TCS AI Collaboration platform containing a library of reusable atomic AI components, models, and frameworks to enable accelerated development of business solutions applying AI concepts Analytics and Insights on the actions performed over a period to create polymorphic dashboards to respective stakeholders of the enterprise Knowledge Database with a time series analysis of scenarios and actions taken by technology and solution for exceptions resolved with human intervention We discussed different components of MFDM that create an effective ecosystem for machines to perform at their best, so far. MFDM aligns with Business 4.0 when the following characteristics are incorporated into the framework: Business MFDM gives the first right of refusal to machines focusing on Business outcomes augmenting human contextual knowledge with technology to drive exponential value. MFDM incorporates a team of experts known as Reliability Engineering teams to handle exceptions with available real time information from the machine interface and learning platform applying contextual knowledge of the customer landscape. Polymorphic Driven by analytics and intelligence for personalized line of sight of the Enterprise to each stakeholder, as it relates to them from a single source of truth, MFDM provides personalized reporting and dashboards specific to each stakeholder and aligned to their respective goals. Inherently Secure MFDM is secure by design, deploying automation, and AI techniques to ensure compliance to regulatory requirements and security controls. Every use case is designed to be secure with all the required actions to be done for compliance in Operations. Security vulnerabilities are identified and eliminated by intelligent automation, applying contextual information, and best practices. MFDM also ensures explainable and ethical AI application. Every action or decision done in the Sense-Understand-Decide-Respond flow is logged for review and audit. The algorithms are refined for acting better based on these inputs via the algorithm design and development team. Ubiquitous MFDM is built around a robust, networked environment, leveraging both public and private ecosystems enabling superior quality information across the enterprise in real time. The key components of MFDM are: AI Collaboration Platform, Enterprise Intelligence Platform and Enterprise Response Engine leverage public, and private cloud solutions in addition to atomic AI solutions contributed by multiple groups coming together to achieve incredible tasks in almost no time. 3

5 MFDM Delivering Superior Customer Experience In the agile enterprises of today, the synergy with MFDM as a delivery model provides immense opportunities to deliver superior customer experience, as it applies to the industry and business model of the customer. A few examples of the level of influence MFDM can have in creating superior customer with experience: Improved Customer Satisfaction Order to activate process delivered for a Telecom customer in MFDM with transparency and accuracy can result in significant improvement in NPS with the following indicative use cases for machine first automation: Case creation and fallout management Order validation and fool proofing 360 degree view of order status Automated allocation, fallout management, and remediation Proactive appointment management Enhanced Productivity for Customer Experience In the banking and financial industry, 24 X 7 uptime of mission critical business applications is extremely important to meet the end user service s needs. Typically, manual checks are carried out on a daily basis by IT engineers to ensure that these business applications are consistently up as any error can lead to a highly dissatisfied end user segment. Automated daily health checks of the applications and infrastructure vitals assures near 100% uptime of applications, thus giving an edge for the enterprise in their customer experience journey. In the agile enterprises of today, the synergy with MFDM as a delivery model provides immense opportunities to deliver superior customer experience 4

6 Institutionalizing MFDM MFDM Maturity Model To ensure a seamless transformation of the current operations of companies and accelerate their growth journey, MFDM defines a maturity model to map the incremental stages of maturity towards autonomous operations. Starting off from establishing the fundamental elements in the Foundation level, companies can follow four levels of maturity stages in automation. Foundation 0 At the foundation level, following are the key elements that are required to be established for a seamless progression in automation maturity. Processes: Each stream of activity in operations need to have clearly laid out procedures for executing them, handling exceptions, and reporting progress and output. For example, IT operation is assessed for its level of compliance to ITIL processes and procedures. Technology: The underlying architecture and technology landscape has to be conducive to the automation and application of AI. The right set of tools have to be in place to be able to support the planned automation initiatives. Governance: Executive sponsorship and involvement is essential to enabling the scale of changes and iterations this journey would require. Clear definition of desired end state and outcomes as well as intermediate milestones have to be defined starting from foundation stage. People: Workforce knowledge assessment and transformation plans to the evolutionary stages of maturity is essential to success of this journey towards positive business outcomes. Knowledge assets: Digitization of knowledge and its continuous enhancement through collaboration is an essential factor for enabling automation. For example, availability of test scenarios for an application is required for automating them. Assisted 1 Automation is applied to assist isolated tasks based on defined processes and documented knowledge assets. These are point solutions that can result in elimination of repetitive mundane tasks or isolated activities. Regression automation in Application services and RPA for data entry in Business Process Services are examples of assisted automation. This level of maturity can yield productivity for a time period with additional efforts spent in maintaining the automation assets as well. Institutionalized 2 In this phase of maturity, automation is applied across services combined with organized relevant data/information enabling a cross-silo visibility and deeper influence to its purpose in the enterprise. 5

7 For example, extending regression automation to end to end functional test automation for biweekly agile releases can help accelerate the test cycles, and thereby the release timelines, and increase the throughput. There is a limit to what we can achieve by doing only automation, the next level of maturity would be to leverage the data in the right manner applying AI techniques and analytics methods. Intelligent 3 For automation to scale from repetitive task-doer to smart thinker, application of AI is essential. The stage at which two or more of MFDM defined stages (Sense-Understand- Decide-Respond, are done by machine) to execute context-inferred meaningful responsibilities is the stage of Intelligent Automation. 4 Autonomous A few examples of intelligent automation tasks: Predictive: The batch analytics and batch automation capabilities lend themselves to predict the possible job outcomes based of pattern analytics and proactively fixes the issue before it happens. This enables smooth and seamless completion of batch jobs, thus reducing application downtime and on-time generation of invoices. Prescriptive: Suggest the recommended price of a product to the product manager based on real time data on history of demand, current weather conditions, and seasonal events. Autonomous: This is the end state of continuous progression with self-learning and powering for perpetual transformation. A stage of maturity where human minds are applied only for art, innovation, and creativity for business and machines do all of what it takes to get the systems running for business. MFDM defines a maturity model to map the incremental stages of maturity towards autonomous operations 6

8 MFDM Execution Model Transform Discover Adapt The MFDM approach defines a set of processes, methods, and procedures to realize a machine first advantage. This model encompasses various dimensions of delivery execution including transition, transformation, organizational change management, and finally culminating in an autonomous approach to service delivery. This delivery model is technology agnostic and is primarily driven by the potential value that can be realized by the customer. MFDM factors in the existing ecosystem and investments, transformation plans, and organizational culture towards change, while taking a futuristic outlook in terms of business outcomes that can be achieved. Traditional service delivery models focus on FTE-associated productivity improvements, associated savings, and transactional service level agreements. The new avatar of MFDM focuses on business outcomes. For a successful service delivery using machine first, we propose the following life cycle phases. Discover: The current state of the enterprise IT landscape such as infrastructure environment, application architecture, current services operations, process maturity, and propensity of change management is studied in detail. The target state, the machine first roadmap, the solution design for Enterprise Intelligence Platform and Enterprise Response Engine, and exception management involving human intervention are defined. Adapt: The Enterprise Intelligence Platform and the Enterprise Response Engine are established and outof-the-box capabilities are installed in the test/ preprod environment. An intelligent command center is set up for real time monitoring of the IT landscape, the Enterprise Intelligence Platform, and the Enterprise Response Engine. A reliability engineering team is also formed to handle exceptions scenarios. Transform: A multitude of automation capabilities are identified, continuously developed, and deployed using cognitive capabilities. The reliability engineering team is also continuously cross-skilled to handle the ever-evolving use cases for transformation. Agile and MFDM As enterprises adopting agile are transforming their business at speed with the Fail-Fast Fail-often strategy, machine first is about IT transformation to iterate at scale, improve productivity. The combination of agile and MFDM can be highly synergetic in reaching a state of perpetual transformation in both the think and execute stages of creative innovation driven exponential growth. Going agile with MFDM in a Business 4.0 world can benefit the enterprises immensely to new orders of productivity, experience, and personalization to operate at scale and speed with actionable insights available real time all the time. 7

9 About Tata Consultancy Services (TCS) Tata Consultancy Services is an IT services, consulting and business solutions organization that partners with many of the world s largest businesses in their transformation journeys. TCS offers a consulting-led, Cognitive powered, integrated portfolio of IT, Business & Technology Services, and engineering. This is delivered through its unique Location Independent Agile delivery model, recognized as a benchmark of excellence in software development. For more information, visit us at IT Services Business Solutions Consulting All content/information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content/ information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content/ information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright 2018 Tata Consultancy Services Limited 8