What is machine learning?

What's the difference between AI and machine learning?

Artificial intelligence (AI) is when computers are designed to think like humans. This includes doing things like solving problems, understanding language or making decisions.

Machine learning is a part of AI. It's how computers learn from data to get better at a task without being told exactly what to do every time. Think of AI as the umbrella concept and machine learning as one of the methods used to achieve intelligent behavior in machines.

How machine learning works

Machine learning starts with data. A model is trained using a large set of examples. The model uses algorithms to find patterns in that data and then applies what it's learned to new information. The more data it processes, the more accurate and useful the predictions become.

When the model makes predictions, those predictions can be compared to real-world outcomes. If the model gets it wrong, the algorithm adjusts to improve future performance. This continuous learning is what makes machine learning so powerful: it evolves and improves based on experience, just like humans do.

A machine learning system used in recruitment might learn from previous hiring outcomes to better identify candidates likely to succeed in certain roles. If some predictions turn out to be inaccurate, the system can be fine-tuned to improve its selection process over time.

Types of machine learning

• Supervised learning: Involves training the model on a labelled dataset. This means the data includes both the input (like a CV) and the correct output (such as whether the applicant was hired). The algorithm learns the relationship between the two and applies this to future data.
• Unsupervised learning: Used when the data doesn't come with predefined labels. The algorithm explores the data to find hidden patterns or groupings. For instance, it might cluster employees based on engagement levels or work habits, helping HR teams identify trends.
• Reinforcement learning: Learns through trial and error. The model receives feedback in the form of rewards or penalties based on its actions. This type is often used in areas like robotics or gaming, but also in dynamic environments like personalized marketing or workforce scheduling.

What are the benefits of machine learning?

Machine learning offers a range of benefits for organizations, with one of the most significant being improved accuracy across complex operations. By moving toward next-generation AI, businesses can go beyond simple data processing to gain deeper insights into customer preferences or predict internal trends like employee turnover. This shift allows leaders to make more informed decisions based on real-time data rather than historical assumptions.

One of the primary advantages of these advanced systems is the ability to free up teams to focus on strategic work by automating routine, high-volume tasks. Whether it is screening large volumes of CVs or processing customer support tickets, next-gen AI handles the administrative heavy lifting. This not only leads to more efficient service delivery over time but also ensures that human expertise is applied where it is needed most—on complex problem-solving and innovation.

In the context of HR and workforce management, machine learning helps organizations build a more nuanced understanding of their people and their potential. By leveraging an AI-first approach to development through Workday Learning, powered by Sana, businesses can identify critical gaps in skills and provide hyper-personalized growth paths for every employee. By using these insights into workforce trends and talent management, organizations can create a more resilient employee experience that evolves alongside the business.

What are the challenges of machine learning?

Despite its advantages, machine learning isn't without challenges. One major concern is data privacy. Models are only as good as the data they are trained on, and that often includes sensitive personal or organizational information. Ensuring data is collected, stored and used responsibly is essential, particularly given India's requirements under the Digital Personal Data Protection Act, 2023 (DPDP Act).

Another challenge is integration. Many organizations struggle to embed machine learning into their existing systems and processes. It can be complex and resource-intensive to get up and running, especially without the right tools or expertise.

Machine learning also requires ongoing data management. The quality of predictions depends on access to fresh, accurate data. Outdated or biased data can lead to flawed outcomes. Ethical issues are also a concern, especially when models unintentionally reflect human biases. For example, if past hiring data is skewed, the machine learning model may repeat those patterns, leading to unfair disparities in recruitment or performance evaluation.

To make the most of machine learning, businesses need to combine strong technical tools with thoughtful governance and routine monitoring whilst ensuring compliance with India's data protection framework and NITI Aayog's Responsible AI guidelines.

What are some use cases of machine learning?

Machine learning is already used in HR to improve how organizations hire, support and retain their people. One common use case is recruitment. Machine learning models can scan thousands of CVs and applications, identifying candidates who closely match the requirements of a job.

These models also use past hiring data to help HR teams make faster and more informed decisions about who to shortlist, particularly valuable in India's fast-growing business environment where talent acquisition is highly competitive.

Another powerful use case is employee sentiment analysis. Machine learning can process feedback from surveys, performance reviews and internal communication channels to identify patterns in employee engagement or wellbeing. This helps HR teams act early to address concerns, reduce turnover and improve workplace culture, essential for retaining talent in India's competitive job market.

Outside of HR, machine learning is also transforming operations. Many organizations use machine learning to forecast demand and manage inventory. By analyzing past sales trends, seasonal patterns and real-time data, machine learning models help businesses ensure they have the right products available at the right time, reducing waste, improving customer satisfaction and increasing efficiency. This is particularly relevant for Indian businesses managing supply chains across pan-India and South Asian markets.

Sana from Workday is the superintelligence that puts AI agents to work across your organization. It automatically handles the most common Workday tasks and completes requests before they turn into tickets. With access to your enterprise apps, it runs complex workflows end-to-end and provides a single place to build, orchestrate, and manage all of your agentic teammates securely and at scale.

Frequently asked questions

How is machine learning being adopted by Indian businesses?

Indian businesses across sectors are rapidly embracing machine learning to drive efficiency and innovation. The IT and BPO industry uses ML for automation and customer service chatbots. Banks and financial institutions deploy it for fraud detection, credit scoring and personalized financial products. E-commerce platforms leverage ML for recommendation engines and demand forecasting. The Indian government has also been proactive through initiatives like the National Strategy for Artificial Intelligence by NITI Aayog, which encourages responsible AI adoption with the theme of "AI for All". Startups and enterprises alike are investing in ML capabilities to remain competitive in both domestic and global markets.

What regulations should Indian companies consider when implementing machine learning?

Indian companies implementing machine learning should be aware of several regulatory considerations. The Digital Personal Data Protection Act, 2023 governs how personal data can be collected, processed and stored, which directly impacts ML systems that use employee or customer data. For financial institutions, the Reserve Bank of India has guidelines on the use of AI/ML in areas like lending decisions and fraud detection. Additionally, NITI Aayog's Responsible AI framework provides principles around fairness, transparency and accountability that organizations should follow. Companies must also ensure their ML systems do not discriminate against individuals based on caste, religion, gender or other protected characteristics under the Indian Constitution. Regular audits and impact assessments are recommended to ensure ongoing compliance.

Can machine learning help address India's unique workforce challenges?

Yes, machine learning is particularly well-suited to address several workforce challenges unique to the Indian context. With a young, large and diverse workforce spread across multiple states and languages, ML can help organizations personalize learning and development programs at scale. It can analyze attrition patterns to identify flight risks in high-turnover industries like IT services and retail. ML-powered tools can also help bridge skill gaps by recommending targeted upskilling programs aligned with industry demands, supporting initiatives like Skill India. For companies with distributed teams across metros and tier-2 cities, machine learning can optimize workforce scheduling, performance management and employee engagement strategies to suit regional variations and preferences.