A CIO Plan to Become a Leader in AI Transformation

Abhijit Akerkar, Head of AI Business Integration, Lloyds Banking Group
Abhijit Akerkar, Head of AI Business Integration, Lloyds Banking Group

Abhijit Akerkar, Head of AI Business Integration, Lloyds Banking Group

Do CIOs have a seat at the AI transformation table of their companies? Yes, if you are in parts of the world such as the Middle East where CIOs have been tasked to lead on AI. But in most of the Western World especially in large organisations, a tri-headed coalition among Chief AI Officer, Chief Data Officer, and Business Leaders is occupying the driver’s seat.

I am uber optimistic though about the critical role CIOs could play in helping their companies accelerate AI adoption and generate sustainable value at scale. The path to creating value from AI models goes through CIO’s estate. CIOs could also play an outsized role in scaling AI adoption by deploying the right infrastructure. And nothing is stopping CIOs from implementing AI and automation within their own divisions to demonstrate their expertise.

I see four distinct roles CIOs could play to make the mark:

1. Early adopter

By automating IT operations early on, CIOs can demonstrate the competency needed to lead the Process Automation for their companies using robotics and machine learning. There are various use cases such as automating password requests, mining log data for root cause analysis, and optimising user notification e-mails that will improve operational efficiency, process quality, and service delivery time. CIOs could also build predictive models to optimise infrastructure usage and enhance equipment uptime and application performance through predictive maintenance. The key is to prioritise the high-value use cases best suited for implementation. Equally important is overcoming the cultural resistance within their own teams for automation.

2. Accelerator

Access to the public cloud is a must for scaling AI across the organisation. On the one hand, elastic cloud services provide specialist processing power with state-of-the-art GPUs or FPGAs for accelerating heavy computational workloads. On the other hand, Clouds Service Providers are providing extensive portfolios of AI development platforms and pre-trained models for voice, text, image, and translation processing.

As responsible executives for cloud migration, CIOs could accelerate the access to the cloud which in turn will clear the pathway for many new AI use cases within their companies. For example, access to Speech-to- Text API will allow millions of calls landing into the call center to be transcribed and mined for finding reasons for customer calls. Fixing the root causes will boost customer satisfaction and reduce the load on call centres.

  ​By automating IT operations early on, CIOs can demonstrate the competency needed to lead the Process Automation for their companies using robotics and machine learning. 

CIOs are best placed to champion the self-serve consumption of infrastructure and managed services as well as to shift their organisations from ‘perpetual license’ to ‘pay as you go’ models. Having these best practices in place will boost the scaling of AI driven use cases. CIOs could also deploy multi-cloud strategies which will widen the market choice for Chief AI Officers.

3. Integrator

A critical step in bringing machine learning models to life is the integration of models into existing IT systems. This could be as simple as passing on the call centre demand forecasts into existing call center management system or as complicated as overhauling the lead distribution system to ensure that the right leads go to the right salesperson at the right time. This scope falls squarely under CIO’s remit. Also, CIOs are in a unique position to drive consistency of approach for integration because CIO unit typically provides services across all parts of the organisation.

4. Visionary

Scaling the machine learning across organisation will require building the tech stack that will allow data science teams to seamlessly build, deploy, operate, and monitor a variety of machine learning models covering the end-to-end data science workflow. Sitting on top of the data lake and associated data ingestion infrastructure, the tech stack will need multiple layers including the ones for data management, model development, publishing APIs, and model performance monitoring. The stack will need to support risk and governance controls and DevOps requirements such as model repository. CIOs could play an essential role along with Chief AI Officers in designing, building, and running such a tech stack. Likewise, dedicated infrastructure will also need to be built and run for virtual assistants and robotic process automation.

In summary, AI is rapidly widening its web. CIOs who understand how to draw on their strengths and capabilities to help their companies generate sustainable value at scale from AI can become leaders in their companies’ AI transformation.

Read Also

Identifying Technological Requirements Prior to AI Implementation

Identifying Technological Requirements Prior to AI Implementation

Kumar Srivastava, VP, Product and Strategy, Machine Learning, Artificial Intelligence, BNY Mellon
AI: Accelerating Decision-Making

AI: Accelerating Decision-Making

Nigel Duffy, Global Innovation AI Leader, Ernst & Young LLP
Machine Learning and Artificial Intelligence: Revolutionizing the Physician/Patient Relationship

Machine Learning and Artificial Intelligence: Revolutionizing the Physician/Patient Relationship

Kali Durgampudi, VP of Innovation, Mobile Architecture, R&D, Healthcare Solutions, Nuance Communications