How can AI help in Remote Recruiting during COVID-19

4 minutes, 42 seconds read

Published on May 4, 2020

The outbreak of Coronavirus has set off a chain reaction across industries taking the world economy into probably what can be called the worst recession so far. Various sectors like travel, hospitality, BFSI, supply chain, and logistics are getting hit due to social distancing and lockdowns. The effect of 2008 depression can be felt again and possibly more this time. This will have an adverse effect on employment as organizations will now re-evaluate their current business position. The initial wave of unemployment has already hit due to a lack of demand and limited cash flow in the economy.

Due to the lockdown in India, the number of unemployed people increased from 32 million to 38 million in March, said a report released by Centre for Monitoring Indian Economy (CMIE). The unemployment rate crossed 23%. Many small and medium businesses have already started layoffs and furloughs. Organizations have become more cognizant of the money spent on human resources and deploying methods to simplify hiring through remote recruiting. Even though there’s less demand for workforce, this situation will prove to be an opportunity for jobs in some sectors.

Jobs in demand during this pandemic

The current crisis is difficult for businesses as they have to reassess crucial positions and develop new roles and responsibilities for its workforce. Organizations will look for multiple skills and capabilities within their workforce. In the post-pandemic world, once the crisis is under control, there will be an upsurge in the employment opportunities for people. The COVID-19 crisis will end one day but it has taught the world an important lesson about being prepared for any possible future pandemics.

Professions like virologists and epidemiologists which were neglected earlier will now be more in demand. Much is said about supply chains being disrupted but the essential products and services still need to reach the end consumer. Organizations still have money and inventory which needs to be delivered. Those working in the supply chain system will still be in high demand. The education sector has gone under a transformation due to social distancing. The rise of online education has led to a rise in teaching jobs.

Now that organizations have seen that ‘Work from Home’ actually works, there’ll be an increase in freelance job opportunities leading to a growth in the ‘gig economy’, which, in-turn will focus on efficient remote recruiting.

[Also read: Enterprises investing in Workplace Mobility Can Survive Pandemics]

Applications of AI in Recruitment

In the current situation, there is a need for people with multiple skill-sets for critical positions. Temporarily, the job scene might not look good but it will soon pick up the pace and when it does, recruiters will have a lot of work cut out for them to hire the right people. In both scenarios, AI will play an important role. Here are some applications of AI in remote recruitment-

Candidate screening

One of the most tedious and challenging tasks in remote recruiting is to screen candidate profiles for the relevant positions. AI-powered tools can investigate millions of profiles saving time and helping them to focus on other important tasks like building relations.

Skill-set matching

Every position needs a certain set of skills, talent, personality, and qualifications. AI can use data and match the job description with the applicant’s work experience, skill-sets, personality etc. This helps in improving the selection criteria of the potential candidates.

Recommendation for positions

Some AI-powered tools screen the pool of candidates and grade them in the ranks which are best suitable for the mentioned positions. This gives a much clearer picture to the recruiters enabling them in better decision making.

Identifying potential skills within the organization

In many organizations, there are internal job postings which employees can apply for. Now, during the on-going crisis, companies need employees who can take up additional responsibilities. AI here can screen the profiles within the organization and identify the potential candidates for the required positions.

[Details: AI in Recruitment & Discovering Talent]

Post-pandemic world: Role of AI in Remote Recruiting

Post this pandemic, once the economic graph picks up, the market will see a rise in employment opportunities as well. This will increase the pool of candidates applying for jobs. There will be huge pressure on recruiters to screen thousands of profiles and source the right candidates for required positions. AI here can help in automating time-consuming workflows. With automation, organizations can cut down on costs and save a lot of time for their recruiters.

AI-powered tools bring speed and accuracy in recruitment which helps improve the quality of hiring. Even after the pandemic, the world doesn’t seem likely to go back to normal. Many organizations will continue to have their employees work from home. In the recent news, TCS announced that around 75% of its workforce is likely to work from home by 2025. The entire recruitment process will will have to adapt to remote working.

Conclusion

AI has been around for quite some years and is strengthening its position in across industries. Organizations have understood the importance of AI in increasing operational efficiency. The success of AI-powered tools has shown that it would be a necessity for a recruiter soon. Yet, they hesitate to invest in AI for recruitment. Sure there might be budgetary concerns right now due to the slowdown but sooner or later organizations will have to integrate AI into their recruitment process. An organization’s strength lies in the quality of its workforce. Sure AI cannot replace the intelligence brought in by recruiters but in the coming years, quality hiring will depend on how well the recruiters automate their workflows which would be possible with the help of AI.

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

How can AI help in Remote Recruiting during COVID-19

Jobs in demand during this pandemic