The digitisation overdrive across the globe–especially post-pandemic–has widened the demand-supply gap for IT talents to a huge extent. According to a combined survey from Capgemini and Linkedin, almost 54 percent of the organisations said the talent gap is stymieing their digital transformation. The report said close to 50 percent of organisations are on the lookout for the right digital talents.
Three out of four C-suite executives believe not scaling analytics and AI capabilities in the next five years will put their businesses at risk. Though the industry has matured in terms of data availability, compute power, and toolsets, the talent gap remains a pressing challenge.
Here, we look at how hiring people with a growth mindset and upskilling them can address the demand and supply gap for the right digital talents.
Things to keep in mind
Below, we look at the external and internal challenges enterprises face while hiring analytics and AI talent.
External challenges include:
- Limited talent pool and stiff competition among enterprises to hire the best talent.
- Bad ROI in terms of offer withdrawals and no-shows late into the hiring process.
Internal challenges include:
- Lack of clarity around the hiring needs.
- Lack of focus in identifying the right candidate.
- Lack of contextual upskilling strategy.
Since external factors are beyond an organisation’s control, they should focus on tackling the internal challenges to finetune their hiring strategy.
India’s AI talent crunch
India’s tech industry talent pool stands at 3.8 million in FY 2021. The Nasscom report said India is ranked second globally, next to China, with a 2.14 million annual STEM graduate supply. Interestingly, 60-70 percent of digital talent gained by India in FY21 was through reskilling.
India currently has about 2.32 million digital/future skills jobs, of which 27% are unfulfilled. By 2024, out of 4.7 million jobs, 1.2 million will be unfulfilled.
Currently, there are 300,000 jobs In AI and Big Data Analytics, of which 88,000 (~30%) are unfulfilled. By 2024, the job pool will grow to 710,000, of which 180,000 (~25%) will remain unfulfilled.
Out of an annual pool of 6 million UG, PG, and PhD holders, 2 million belong to STEM. Only 220,000, out of that 2 million, meet the basic eligibility criteria for the IT-BPM sector, and only 45,000 out of the total number will transition into a digital role. The share of AI and BDA is even smaller: 12,000-13,000 per year.
Hiring strategy
Research shows as the complexity of a job goes up, the productivity gap between a high performer and an average performer exponentially increases.
Since analytics and AI jobs come under complex job categories, finding and hiring the right talent becomes a significant challenge.
Here’s a checklist to keep in mind while hiring analytics and AI talent:
- Increase the pool of entry-level talent by relaxing the eligibility criteria
- Look beyond STEM
- Hire from related job pools (IT and ITES, quantitative research, etc.) and upskill them.
- Hire the best talent for the key roles and chalk out an effective and contextual skilling plan for the existing workforce
- Deploy experienced, and accomplished professionals at complex roles needing wider impact, and, at junior levels, hire for achievers in adjacent fields or STEM entry-level (look for learning aptitude or the journey that shows ability and mindset to skill themselves) and formulate a robust skilling process
Holistic approach
Here are three approaches to consider when hiring talent for analytics and AI roles:
Know what you are hiring for:
- Plan and assess your company’s actual analytics & AI needs
- Define the short-term and long-term business use cases
- Do not go for hiring in parallel with the anticipation of ‘we will figure out what to do after we have the talent in place’
Source: Dataiku
Look at the right places to hire:
- Make sure to look for candidates across experience levels and skills.
- Understand their motivation to learn and the attitude towards continuous skilling
- Check if the candidates are keen on solving real-world problems or have participated in machine learning hackathons
Formulate and execute contextual skilling:
- Create an upskilling path – A skilling framework that is contextual, hands-on and maps to the outlined need/path of the organisation
- Consider the needs of the business, along with which types of data profiles would add the most value, and proceed accordingly
Upskilling path for your organisation (Source: Dataiku)
A well-structured plan for upskilling talent, alongside hiring the right people for the specific need of the business, becomes critical for organisations, particularly when hiring for analytics and AI roles. Remember, invest in the best talent for a few key roles and have a solid plan in place for contextual skilling.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
