Reskilling employees in AI: A vital step for tech companies

By Dharmarajan Sankara Subrahmanian

The success of Chandrayaan 3 Mission has aptly demonstrated that powered by a rich reservoir of scientific acumen, engineering talent, programming & software expertise, youthful vigour, and intellectual prowess, India is rightly positioned globally as a technological and scientific powerhouse.

India with its talent resource can play a greater role in technological skilling. The country has the potential to become a world leader in Artificial Intelligence (AI), Machine Learning (ML)and Analytics. According to a recent Nasscom report, India boasts the world’s second-largest AI talent reservoir and holds the top position in both AI skill adoption and AI talent concentration. However, India still faces a substantial 51% deficit in meeting the demand for AI/ML and big data analytics tech talent in the country.

As a first step to bridge this talent gap and for tech companies to harness AI’s full potential, they must go beyond having AI tools. They must equip their workforce with AI skills. Reskilling employees in AI and ML is not just a choice it is the need of the hour. With several simple steps or measures tech companies should undertake to navigate this transition effectively.

1. Evaluate existing skill sets: Prior to diving into reskilling, it is essential to gauge your employees’ current AI knowledge. Conduct a thorough assessment to pinpoint skill gaps. This could involve surveys, interviews, or even AI proficiency tests. The objective is to identify specific areas that require improvement.

2. Design Tailored Training Initiatives: Reskilling is not one-size-fits-all. Different roles within an organisation may necessitate different AI skill sets. Customise your training programmes accordingly. For instance, your sales team may require AI knowledge for customer analytics, while your developers may need skills in AI model development. Develop courses that cater to these specific needs.

3. Leverage online learning platforms: AI is fortunate that it offers a plethora of online resources. Make use of these platforms for training. Encourage your employees to enrol in these courses to acquire fundamental knowledge at their own pace.

4. Organise in-house training workshops: While online resources are good for individual learning, consider organising inhouse training workshops. These workshops provide hands-on experience, foster teamwork and enable employees to learn from each other. You can bring in AI experts or collaborate with AI training organisations for these workshops.

5. Implement mentorship programmes: Pair employees with strong AI, ML or Analytic skills with those who are still acquiring them. Mentorship programme provides practical insights and guidance. This cultivates a culture of learning within the organisation and offers a supportive environment for skill development.

6. Define clear goals and metrics: Establish clear objectives for your skilling efforts. What are you aiming to achieve? It could be increasing the number of employees with AI certification or enhancing AI-related project outcomes. Monitor progress using measurable metrics, such as the number of employees completing the courses or the successful application of AI in projects.

7. Create AI projects and Sandbox environments: Learning by doing is the most effective method. Encourage employees to stay updated with the latest AI trends and technologies. Support attendance at AI conferences, webinars and seminars.

8. Foster a learning culture: Reskilling should be an ongoing journey and not a one-time event. Nurture a culture of continuous learning. Encourage employees to stay updated with the latest AI trends and technologies.

9. Recognise and reward progress: Acknowledge and celebrate the achievements of employees who successfully acquire AI skills. Recognition can serve as a powerful motivator. Consider introducing incentives or bonuses tied to AI skill development milestones.

10. Seek feedback and Iterate: Regularly solicit feedback from employees regarding the reskilling programmes. What worked well, what did not? Use this feedback to continually refine and improve your training initiatives. Adapt to the evolving AI/ML landscape.

11. Measure Return on Investment: Ultimately reskilling constitutes an investment. Measure the ROI by assessing how the AI skills have influenced your company’s operations. Has it boosted efficiency, reduced costs or enhanced innovation?

12. Commit to diversity and inclusion: Ensure that your reskilling efforts are inclusive. Make AI education accessible to all employees irrespective of their background. Diverse perspectives in AI can lead to more innovative solutions and enhanced problem solving.

In conclusion, reskilling employees in AI and Analytics is not just about maintaining competitiveness; it’s about future-proofing your organisation. The tech industry is in a perpetual state of flux and companies that adapt and equip their workforce with AI skills will thrive. Remember, it is a journey and not a destination. Embrace AI as an opportunity for growth and innovation, and employees will follow suit.

Artificial Intelligence (AI)machine learning (ML)reskilling
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