Yes. 75% of Indian leaders won’t hire without AI skills, but managers need data judgement, not coding. Here’s what to learn and what the evidence can’t prove.
Yes, managers need to learn AI and analytics in 2026, but they do not need to learn to code. According to the Microsoft and LinkedIn 2024 Work Trend Index India findings, 75% of leaders in India say they would not hire someone without AI skills, against a global average of 66%.
What managers need is data judgement: the ability to read data, question it and direct AI tools towards better decisions. The rest of this guide covers what the evidence shows, what to learn first, and what the evidence does not yet prove.
Indian Employers Now Rank AI Skills Above Experience
Indian leaders value AI skills more highly than the global average. According to the same Microsoft and LinkedIn report, 80% of leaders in India would hire a less experienced candidate with AI skills over a more experienced candidate without them.
The same report found that 92% of knowledge workers in India use AI at work. A knowledge worker is someone who works mainly at a desk with information, such as a manager, analyst or consultant.
The training gap is the problem. According to Microsoft’s global summary of the report, only 39% of AI users had received AI training from their company, so most professionals are teaching themselves.
AI and Big Data Top the World Economic Forum’s Skills Growth List to 2030
AI and big data is the fastest-growing skill area to 2030. According to the World Economic Forum’s Future of Jobs Report 2025, it ranks first, followed by networks and cybersecurity and technological literacy.
Analytics still matters most today. The full Future of Jobs Report 2025 names analytical thinking as the core skill employers seek most, which means the ability to break a problem down and reason from evidence.
The report also expects 39% of workers’ core skills to change by 2030, according to the World Economic Forum’s summary. For a manager with 15 or more working years ahead, that is a direct signal to update skills now.
Managers Need Data Judgement, Not Data Science
A manager’s job is to make decisions with data, not to build the models that produce it. Data literacy, the ability to read, interpret and question data, is the foundation; coding is optional.
The table below separates what most managers need from what they can leave to specialists.
| Skill | What it looks like for a manager |
| Data literacy | Reading a dashboard and spotting a misleading chart |
| Asking sharp questions of data | Turning “sales are down” into “which region, product and month?” |
| Basic statistics | Knowing averages, ranges, and that correlation is not causation |
| Generative AI tools | Using ChatGPT, Copilot or Gemini to draft, summarise and analyse, then checking the output |
| Visualisation tools | Building simple charts in Excel, Tableau or Power BI |
| Judging AI vendors and risk | Asking where training data comes from, how errors are caught, and who is accountable |
| Coding (Python, R) and building machine learning models | Writing code that trains predictive models |
Generative AI means AI that creates new text, images or code from a prompt, as ChatGPT does. Machine learning is the wider technique of software learning patterns from past data to make predictions.
Most Companies Use AI, but Few Have Made It Work at Scale
AI adoption is now almost universal, but impact is not. According to McKinsey’s State of AI 2025 survey of 1,993 respondents across 105 countries, 88% say their organisations regularly use AI in at least one business function, up from 78% a year earlier.
Yet according to McKinsey’s summary of the same survey, only 7% of respondents say AI is fully scaled across their organisation. Scaling means moving AI from small pilots into everyday operations across the business.
This gap is where managers matter. Choosing which processes to change, which outputs to trust and how to measure results is management work, and it is the work most organisations have not yet done.
What the Evidence Does Not Prove
The hiring figures measure what leaders say, not what they do. The Microsoft and LinkedIn data is a 2024 survey of stated preferences, and it does not show that AI-skilled candidates were actually hired or promoted faster.
The World Economic Forum figures are employer forecasts to 2030, not observed outcomes. Forecasts about skills have been wrong before, so treat them as direction rather than certainty.
A certificate does not equal capability. The McKinsey gap between 88% adoption and 7% full scaling suggests that access to AI tools alone does not create value; applying them to real problems does.
5 Steps to Build AI and Analytics Skills as a Manager
Start with your own decisions, not with a tool. These 5 steps move from low effort to structured learning.
- List 3 decisions you currently make on instinct. Pricing, hiring, stock levels or campaign spend are common examples. Write down which data could have informed each one.
- Use 1 generative AI tool every working day for 30 days. Start with low-risk tasks such as summarising reports or drafting emails, and check every output for errors before using it.
- Learn to read data before you learn to build it. Focus on averages, trends, outliers (results far from the norm) and the difference between correlation and causation.
- Learn 1 visualisation tool. Start with Excel charts and pivot tables, then move to Tableau or Power BI if your organisation uses them.
- Run 1 small data project and present it. Analyse attrition in your team, segment your customers or compare supplier performance, then share the finding and the decision it supports with your leadership.
Which Welingkar Analytics Programme Fits Your Starting Point
Welingkar Bengaluru (WeSchool) offers analytics programmes at three depths for working professionals. Choose based on how much time you have and how deeply analytics features in your role.
| Your situation | Programme |
| You want a fast, practical introduction | Leveraging Analytics in Business (3 Days) |
| You lead people and want HR-specific skills | Leveraging Analytics in HR |
| You want deep, credentialled capability | Leveraging Analytics in Business, Joint Programme with IBM |
Descriptive analytics means summarising past data to explain what happened. IBM SPSS is statistical software used to analyse survey and business data without heavy coding.
Frequently Asked Questions
No. Most managers need to read data, question it and use AI tools well, not build models. Coding languages such as Python matter for analysts and data scientists, and managers who understand the basics can brief those specialists better.
Analytics means examining data to find patterns and answer business questions, such as why sales fell in one region. AI (artificial intelligence) is software that learns from data to predict outcomes, generate content or automate decisions. Analytics tells you what happened and why; AI often acts on that pattern at scale.
The evidence does not suggest it. The World Economic Forum’s Future of Jobs Report 2025 says leadership, analytical thinking and resilience will remain critical core skills alongside AI and big data. What is changing is that managers who can direct AI and judge its output are being preferred over those who cannot.
It depends on the depth you need. A short workshop can cover data visualisation and descriptive analytics in a few days, while a structured programme that builds decision-making capability across functions can run for around 10 months.
The Bottom Line
Managers in 2026 need AI and analytics skills, and Indian employers are already hiring for them. The skill that matters is judgement: asking the right question of data, using AI tools daily and checking what they produce. Start with one real decision this week, then build structured learning around it.