Artificial Intelligence: Powering Business Outcomes

Sanjay Bakshi, Head Digital Strategy & Ventures, Shell India Lubricants

Sanjay Bakshi, Head Digital Strategy & Ventures, Shell India Lubricants

Introduction

AI is a collection of technologies that enable machines to sense, comprehend, act and learn—independently or with minimal human augmentation. Most of the AI examples that we hear today from chess-playing computers to self-driving cars, depend on deep learning and natural language processing. These technologies are programmed into computers to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data. From the Industry perspective, this helps in bringing analytics, analyzing Industrial IoT data from connected equipment’s, providing virtual shopping capabilities to the customer offering personalized recommendations, discuss purchase options etc.

Driving Intelligent revenue growth

Across industries such as consumer goods and retail, agility, flexibility, and responsiveness are the new key to drive revenue growth. With consumers constantly evaluating choices, business should be equipped to stay relevant to capture new opportunities to engage and grow. Enabling AI-powered solutions across customers, channels, and products help in gaining insights and providing a real-time, 360° view of the customer that help organization re-think on their strategies and capabilities for optimization across growth levers.

For example, maximize value per customer by understanding highly relevant products and offers for prospective and current buyers. Increasing customer reach across channels, driving higher levels of engagement, increasing the productivity of marketing media by continually optimizing with AI.

• 10-30 percent product profit increase

• 43 percent increase in campaign customer engagement

• 72 percent increase in revenue resulting from personalized support

Addressing operational challenges using AI

Thanks to AI and smart sensors, systems can now not only predict when and where machinery and equipment are going to need maintenance with a high degree of accuracy, but also act on the need. With reduced equipment downtime emerging, as a result, there’s huge potential for process optimization across product lines.

The integration of Business with AI will help unify functions and improve performance, discover new opportunities for growth, digitize operations to increase efficiency and unlock working capital and create 1:1 hyper-relevant experiences within an integrated ecosystem.

Companies will develop a powerful new capability to retain and expand their customer base, reduce costs, differentiate competitively and drive new growth. Furthermore, AI will help them set ever-increasing standards of performance by continuously optimizing interactions and transactions—creating a self-perpetuating path to growth.

The key to success of AI implementation is to understand what really fits the business goal. For example, Assisted Intelligence (process defined, rule based and repeatable) is preferred for upgrading existing processes, reducing costs, and improving productivity, whereas for companies looking for newer business and operating model, augmented intelligence with more complex AI applications is the solution. Hence, choosing the right adoption of AI in line with the business strategy and operating model is critical.

Develop AI strategy in line with Business Strategy

• Integrate AI into your existing Digital Strategy

• Decide on which business to disrupt and which one to enhance

• Develop new business model for improved productivity

Develop an Enterprise wide AI capability

• Redesign Product & services to incorporate ML/AI

• Use AI to upgrade the critical capabilities

• Automate existing processes or develop new ones

Institutionalize portfolio of AI Capabilities

• Embed AI in the business process

• Embrace cloud platforms and specialized hardware

• Foster a decision-making culture open to ideas from AI Support

Ensure Appropriate Governance

• Establish clear policies with respect to data privacy, decision rights, and transparency

• Set up governance structure to monitor possible errors and problems

• Set up communication practices to explain AI related decisions

• Consider the impact on employment and invest in developing the workforce that AI will complement

Adopting AI at an enterprise level

Key Challenges:

• Technology and data illiteracy continue to persist across businesses, and the talent gap within technology vendors developing AI

• Quality of Data - Do we have the right sensors in the right place? Does the stored data enough to perform predictive analytics?

• Make security foundational: To help safeguard organizations in a constantly changing threat landscape

Conclusion: Building Competitive Advantage

Whether a company is experiencing overall business growth, or seasonal or temporary growth, AI’s predictive and scalable platform allows businesses to respond quickly and smoothly to the needs of their customers and to changes in the market. AI is optimizing and modernizing companies and helping them rethink how they do business.

Although we spoke about companies implementing AI & reaping benefits, it is still too early to say which types of companies will be the most successful in this area. Current AI dominant players like Climate Corporation, Oscar W. Larson, Netflix etc. have become far more capable & competitive, in many relevant way, than they otherwise would ever be.

Luke O'Brien, CIO, ISS Facility Services Australia & New Zealand

David Kennedy, Group CIO, Transaction Services Group

Ani Paul, CIO, ING Australia

Christopher Zegers, CIO, Lowenstein Sandler LLP

Alexander Fleiss, CIO & CEO, Rebellion Research Partners LP

Gemma Dias, Head of Data Governance, Tyro Payments

Raul Macatangay, Chief Information Officer, Globe Telecom

Phiphat Khanonwet, Head of IT, Onyx Hospitality Group

Artificial Intelligence: Powering Business Outcomes

Predictive Analytics

IoT

Industrial IoT

Weekly Brief