How AI/DS leaders have an edge over others at the times of COVID

Artificial Intelligence, Machine learning, and Data science have played a vital role in the fight against the recent worldwide deadly pandemic caused by the COVID virus since February 2020. Most of the countries have been using some form of the DS/AI domain to see the growth rate of the infected patients, death rate, and hospital beds needed to better prepare each day. Apart from the healthcare industry, the entire finance and corporate sector have used advanced AI/ML models to base their business decisions. For e.g., since the outbreak, most of the companies in India (at least in the IT and software sector) adopted work from a home mode wherein most of the employees were asked to work from home. There were apprehensions on this mode to continue longer as higher management was skeptical on how much productivity would be achieved. Most of the companies (especially financial domain companies) used AI/ML frameworks to choose the right metrics and parameters for the work from home (WFH) model. 

Let us look at some ways in which leaders who implemented AI have an edge over the others during the COVID-19 pandemic.

  1. New Job Opportunities
  2. Upskilling 
  3. Work From Home Model
  4. Cost-Benefits 

New Job Opportunities

Another indirect effect of turbulent time during the last 1.5 years is a sustained push for the adoption of Data analytics in the decision-making at the operational and strategic level. As per a recent survey done by Analytics India Magazine, it has been noted that 92% of leadership believe that the demand for analytics would gain traction in the post-COVID world. This immediately translates to new job opportunities in data analytics. AI/DS leaders already saw this coming last year and have already hired a well-competent team for future work. Most AI/DS managers figured out from their experience in the covid era that when the world is moving back to normalcy, there would be cutthroat competition among firms to get the best AI/ML talent. Remote working and virtual hiring, irrespective of work location, has now been a new norm. This automatically creates more competition for freshers and amateur data scientists who are starting their careers amid this crisis because they have to compete with data scientists/engineers from their region or country and with professionals from other parts of the world. Indians would benefit more since they would have more opportunities worldwide. But that would also mean increased rounds of highly in-depth interviews. AI/DS managers equipped themselves with the necessary knowledge and were ready to leverage this in 2021.


Another factor where AI/DS managers scored over traditional managers in defining their team’s upskilling/cross-skilling. Upskilling is essential for the data science professional who is just starting their career amid this crisis. With the pandemic disrupting the way businesses work, many look to employ professionals with advanced skill sets. In the non-covid era, the traditional way is to conduct a series of workshops in the office training room, pair a new engineer with an experienced engineer, and give the new engineer some time to self-study. But Covid-era changed the rules of the game. With WFH mode one, the whole upskilling and learning process for these young data scientists becomes isolated. AI/DS managers created a curated set of online training having premium content from reputed sources such as Great Learning. This was designed as per role and technology. It was usually followed by a project digging across all relevant technologies so that an engineer gets a theoretical foundation for skill and practical exposure. Many managers created technology-specific online societies using tools such as yammer and conducted weekly sessions on advanced topics/doubt clearing sessions to supplement online learning.

The astute AI/DS managers also figured out that working post-Covid would require more effort than before, particularly in the Data Science domain. The DS/AI/ML solution is stitched together by an interdisciplinary team, and it requires immense collaboration among team members to achieve an acceptable solution. Effective collaboration among engineers would not only allow the companies to make informed decisions but also enhance business operations and build better products. However, in WFH mode, with no physical face-to-face meetings, achieving a high degree of collaboration has become difficult.

Work From Home 

AI/ML leaders have sensed that early and created a framework for working during this pandemic. Many leaders mandated short daily calls among team members working on a specific project and weekly or bi-weekly video calls between higher management and team leads. They have also defined minimum collaboration time in a day, i.e., if two engineers are working on a problem, they must set a time frame where both will be online to take each other’s support at the same time. Leaders have also given liberty to team members to take care of their personal lives and strike a balance between home and office. The leader always believed that online collaborations would bring many challenges for new engineers who would need in-person training to understand the business and its problems better. This mode can diminish the productivity and efficiency of the team in general and can also create a significant communication gap between the higher management and the team. Therefore, the AI/DS leader implemented this framework early on and gave the team sufficient time to adjust to it. All this fostered cross-functional communication sufficient enough to sustain in the times of online collaboration. 

Also Read: How to Increase Productivity while Working from Home

A good leader must always have his plan B ready. Smart AI/DS leaders sensed that working post-Covid would change altogether, and hiring and right-sizing would also change drastically. Now, there is a greater thrust on contract-hiring or outsourcing work to freelancers. It is driven at both ends. On the one hand, many professionals leave a routine software job and opt for freelancing.

On the other hand, companies want the right size, reduce slack, and outsource any work that is less confidential and can be done outside easily at a low cost. Post pandemic, businesses would rely on massive cost-cutting measures to save their top line. Therefore, they would turn towards hiring freelancers, gig workers, and contract-based hiring to allow them to avail data science capabilities for specific projects and keep their tenure for a limited time. This also allows companies under a financial crunch to hire highly paid data scientists for particular projects and avail themselves of the analytical benefits. 


Contract-based workers or freelancers always have a competitive edge on full-time employees because they have cost benefits (no retiral benefits, leaves, social welfare, etc.) and provide much-needed flexibility in the organization. Pragmatic AI/DS/ML leaders saw this trend coming and organized their team as per the role and technology. For areas that are less significant or for which there is no expertise in the team, they generally outsource work to contractors or freelancers who have credible credentials and have had a working relationship with the organization in the past. The key mantra for them is to reduce the operating cost of their group by hiring more generalists than specialists and outsource niche work to contractors for the short term. E.g., most organizations tend to outsource UI designing work to freelance designers. However, in the long term, most such leaders have a plan to build missing expertise in-house. 

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