How to Survive a Crisis with AI-Driven Operations

Responding to any crisis involves being able to predict the impact of disastrous events and being well prepared to handle challenges under pressure. The COVID-19 pandemic caught the whole world by surprise. As leaders quickly tried to find ways of using their resources judiciously, they realized the need to drive hyper efficiency and business resilience, turning to AI.

AI has the astute ability to both find and solve problems, enabling it to play a significant role in crisis management and response. Here are three recommendations for how organizations can leverage AI to be better prepared for the next crisis:

1. Aligning AI strategy to business goals

This is an essential first step for organizations to maximize AI investments. When crisis hits and enterprises are forced to quickly mobilize and respond to new, unexpected market pressures, a technology-led approach to AI can be extremely limiting. Mistakenly focusing on individual use cases for the technology will not drive organization-wide momentum for the company. Enterprises need to instead think of the bigger picture, and approach AI with a strategy rooted in meeting business goals and driving growth. This ensures that as AI investments scale up from pilot projects, they don’t lose sight of the target business outcome.

2. Employee upskilling

As an enterprise turns to AI during a crisis -- whether for predictive sales modelling or automating customer-center operations -- leaders must prioritize developing employees’ core competencies around AI. Employees skilled in AI will be of course be needed to develop and operate the new automation advancements, but the benefit extends beyond this. AI-skilled employees can be tapped to create a roadmap on how to best leverage the technology to drive business value in times of crisis. Organizations should consider developing internal reskilling and upskilling programs or using third party learning platforms to help employees develop AI specializations. Employees can also be instrumental in galvanizing coworkers to readily adopt new AI technology, accelerating adoption rates as an organization looks to quickly scale up the technology across the business to adjust operations in response to a crisis.

3. Clear data strategy and execution framework

Enterprises need a clear data strategy around data governance in order to scale up AI quickly and successfully. Ensuring they have a clear set of repeatable protocols and methodologies in place to help them execute that strategy effectively is critical, so leaders don’t have to worry about compliance as they scale up AI in the face of a crisis.  

For instance, one of our clients, a major global energy player created a data marketplace to make data more easily accessible for non-technical users. Given the explosion in data, it was important to minimize access time and maximize value delivery time. The marketplace enables data-search across multiple big data platforms with a review and rating system for data thereby allowing the best data to self-emerge.

COVID-19 has no doubt revealed flaws in businesses where AI could have been deployed to prevent these flaws from being exposed or created to begin with. If strategically implemented, enterprises can leverage AI to build resilience that’s not only critical in a time of immediate need -- but vital in preparing for the next crisis.

Sanchit Mullick is associate vice president and global head of sales for AI and Automation Services at Infosys. In this role, he leads worldwide sales, marketing and alliances for AI and Automation Services and partners with customers to help them chart their roadmap across the Automation spectrum, leveraging everything from robotic automation to cognitive services. Sanchit has worked across the US, Australia, the UK and India, having played roles spanning sales, consulting and delivery.