6 Questions On Artificial Intelligence With Axiata Group’s Pedro Uria-Recio

We interviewed Pedro Uria-Recio, Vice President and Head of Axiata Analytics at Axiata Group. Axiata Group is a leading telecommunications and digital conglomerate with approximately 350 million customers in 11 countries across South and South-East Asia.

Pedro is a senior marketing leader with experience in data analytics, artificial intelligence (AI), product marketing and profit and loss (P&L) management. Pedro leads advanced analytics and AI across the operational companies of Axiata Group. With a cross-functional team of 50 marketing specialists, data scientists and engineers as well as digital professionals, Pedro focuses on marketing analytics, digital growth hacking and new revenue streams. He was previously a senior consultant at McKinsey & Company, and he has had roles at McGraw-Hill, Veolia Water China and Orange France. Pedro holds an MBA from the University of Chicago Booth School of Business. In this interview Pedro shares his personal views, which do not necessarily reflect those of his affiliations.

What are the most useful applications of AI in the business world today? And which do you believe are most promising for future use?

AI is the most general-purpose technology of our time. New products and processes are being developed thanks to better vision systems, speech recognition technologies or recommendation engines based on AI.

There are five main groups of business applications in AI, which correspond to the five kinds of tasks, typically associated with humans, that AI can perform. These business applications, from the least to the most sophisticated, are:

  • Robotic Process Automation (RPA) is a technology that can automate rule-based, repetitive, high volume processes. Many back- and front-office processes fit this description. Think of contact centers, where customer files need constant updating of address changes or service additions. Or banks, where a lost credit card means updating customer records across multiple systems and keeping customers informed during the process. Automating processes this way, RPA can potentially cut operating costs by 20-80%.
  • Machine vision enables capturing and understanding images and video, as well as other more complex datasets such as biometrics. Self-driving cars that perceive the road, traffic signals and other vehicles is a very clear application. Machine vision can also be applied to handwriting recognition; to medical image diagnostics and cancer detection; and to identification of faulty products in a production line, among other applications.
  • Language Processing handles language interactions with employees and clients (e.g. voice, chat, email). For example, a customer service bot can independently handle about 80% of queries, allowing customer service representatives to focus on the other 20% of tasks that require empathy or better contextual understanding. Customer service representatives can focus on higher-value activities that are often more rewarding.
  • Advanced Analytics is a support tool that enhances decision-making through predictions and analyses. Advanced Analytics can be applied to every function of the enterprise: In marketing, companies can target digital advertising to each individual customer or predict the products each customer is most likely to buy. In operations, infrastructure companies such as telecom operators, retailers or banks can analyze how to optimize their capital expenditure in very granular ways. In human resources, key attributes of leaders and managers can be assessed to better understand behaviors, develop career paths and plan successions. In back-office functions, machine learning can help detect fraud in real-time in credit card transactions or insurance claims.
  • Cognitive Agents are an autonomous intelligent workforce that can interact, execute, analyze and learn like human workers. This latest application is far less mature than the previous ones, but there are some commercial products already, like Amelia from IPSoft.

As we go down the above list of business applications from least to most sophisticated:

  • Applications are also less mature and less commercially ready to implement.
  • Additionally, costs and time to implement are higher and therefore short-term return on investment (ROI) to the business are lower because benefits are more qualitative.
  • However, applications have a higher long-term potential for future use.

Which industries do you expect to see more rapid vs. slower uptake of AI? And how might adoption vary across countries or geographic regions?

There is not a single industry that cannot benefit from AI. Let me choose, for illustration purposes, an example that is considered a highly qualitative and humanistic industry: marriage counselling. A well-designed questionnaire evaluated by Advanced Analytics, coupled with Machine Vision, to infer human feelings from face gestures and Language Processing tools to identify sentiments could help a human marriage counselor to give much better advice to his or her customers.

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