Why an AI and Data led Value Creation Framework Is Critical For Accelerating DX

Image credit: iStockphoto/Butsaya

Businesses across all sectors were on a digital transformation journey. They were working on their multi-year roadmap till COVID-19 forced companies to pivot quickly. It became critical for organizations to swiftly accelerate the digital transformation; change how we work, and more importantly, rapidly create a new set of skillsets for jobs of the future

Stepping back to earlier in the year when COVID-19 started to impact, many businesses and organizations were caught off guard. The best way to look at the problem then was to create a set of ‘Digital Transformation’ time horizons that industries and businesses were facing — Now, Soon, and Later

The immediate focus at that time was on stabilizing operations (0-60 days), prioritize critical activities and digital projects (2-6 months), and then implement structural transformational change (after 6-9 months).

Data and AI have become even more critical, and instead of waiting till tomorrow, clients are proceeding with making business decisions today. Organizations that have a good set of data and AI best practices have started on the journey of making transformational changes. Employees who have the “Digital Technology” skills have been placed in critical positions to help the companies lead the transformational agenda

The creation of resilient customer care is an excellent example of a use case to demonstrate the acceleration of digital transformational journeys and how clients dynamically pivoted to handle various customer categories. With most of the world in lockdown and call centers shut down, agile businesses rapidly moved to use digital virtual assistants to help their clients with ‘easy to use’ self-serve queries. Virtual assistants took on standard queries that could not be answered because agents were not available.

Examples include Healthcare sectors providing citizen stakeholders with the latest updates on the virus and local policies and telecommunication businesses running large call centers using virtual assistants to help customers on different topics. This included helping with simple functions such as queries on bills, free data packages, or payment exemptions. In addition, businesses using call centers internally for HR functions expanded digital assistants to help employees with questions about their company’s policy and daily updates under COVID-19. AI-built tools helped the HR department face a surge of requests and used data to perform many HR functions

Initial support in the digital transformation journey revolves around intents with short tail queries. Clients gradually expand these functions by performing back-end integration to answer intents with long-tail queries. These can perform complex functions like paying bills by integrating with back-end payment systems, providing detailed drill-down of complex billing functions, and processing new orders. It is important to note that as clients link it with their internal systems, good data best practices and AI tools come in handy. Since the output is only as good as the data that is fed to the system.

Value Creation Framework

While building a digital transformation “value creation” framework, it is essential to create an omnichannel experience across all the multiple channels being used. A customer-driven strategy with a primary focus on providing a seamless customer experience is critical irrespective of the consumer's device and channel. Whether the customer logs in online from a mobile device or laptop, the experience through all channels (SMS, Voice, Facebook Messenger, WhatsApp) should be consistent and harmonious leveraging customer insights. Data shows that customer loyalty and conversion rates increase dramatically when the customer uses multiple channels and receives the same experience across all platforms.

As clients prioritize use cases, it is crucial to identify the use cases that can have the most significant impact across a pre-established time frame. This is followed by performing an initial Proof of Concept (PoC) to ensure that the use case is successful in a controlled environment with initial data sets. Definition of success entails meeting the pre-defined Key Performance Indicator (KPI) metrics established for a use case. After the successful outcome of PoC, the use case is then expanded within the Line of Business (LoB) and, in many cases, across multiple LoB’s of an organization

Expansion of use cases can be categorized into vertical and horizontal scaling. Vertical growth refers to expanding the use case by moving to the following stages of the maturity curve. An example to demonstrate vertical scaling includes taking the digital assistant to the next phase by expanding intents, followed by moving from text to voice over AI features and then performing integration by adding related use cases such as Active listening and Cognitive email to provide a 360-degree view of the customer for proactive, targeted interactions. Horizontal scaling refers to functional expansion to other LoBs in enabling the organization to eventually become an AI-enabled enterprise. Assuming we started with a 360-degree customer experience use case, the horizontal expansion would entail moving to an AI-driven high-impact use case in new functional areas. Examples will include Procurement, HR, Finance, Marketing, and Supply Chain functions

Each step of the AI-led roadmap should be clearly measured and tracked by using KPIs. While the broader metrics around cost savings, revenue growth, customer satisfaction, and net promoter score are very relevant, individual use cases should be measured. For example, measurement of AI-infused use cases at the contact center can be measured by call deflections, average handling time reduction, and cross-sell improvements

We will focus on providing the next level of details in the “AI journey in our next series.” We will show how organizations that have taken the initial steps in their digital journey can take advantage of the digital transformation framework. We will also look at how organizations can leverage the best practices in building new and existing AI/ML models.

Utpal Mangla, vice president and senior partner at IBM, Mathews Thomas, distinguished engineer at IBM, Shikhar Kwatra, AI and data architect at IBM, and Vinod Bijlani, a thought leader in data science, AI and IoT at HPE, wrote this article.

Mangla leads the digital innovation agenda for IBM's Telco, Media, Entertainment clients globally, while Thomas leads the architecture design and framework for digital transformation of its clients. Kwatra leads the AI / ML architecture design and build for digital transformation of IBM clients globally. Bijlani currently leads the AI and IoT Practice at Hewlett Packard Enterprise in Singapore.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Butsaya; Illustration: IBM