94% of Surveyed Indian Learners Use AI, But Only 20% Have Built With It: What Students Should Learn Next

A new upGrad study of 2,075 learners across India found that 94% of respondents already use artificial intelligence in some form, but only 20% have built an AI-powered automation, AI agent or application.

The findings highlight an important shift in India’s AI learning journey. The next challenge may not simply be getting people to use generative AI, but helping learners move from basic AI usage to solving real problems, connecting tools, creating workflows and building practical AI-powered solutions.

For students and early-career professionals, the takeaway is important: knowing how to ask an AI chatbot questions is increasingly only the starting point.

Quick Answer: The study shows a clear gap between AI adoption and practical AI-building skills. While 94% of surveyed learners use AI, only 26% have connected AI with another tool or API, and only 20% have built an AI-powered automation, agent or application.

Facts

Finding	Reported Figure
Respondents who use AI	94%
Respondents who use AI daily	53%
Connected AI with other tools or used an API	26%
Built an AI automation, agent or app	20%
Enrolled in, exploring or planning AI upskilling	87%
Say knowing where to start is a major challenge	56%
Struggle with too many learning options or credibility	43%
Struggle to keep up with AI developments	43%
Report a career outcome they associate with AI	54%
Say AI helped them do work previously beyond their capabilities	49%

These figures come from Skill Shift – upGrad’s Annual Report on India’s AI Skilling. The study covered 2,075 respondents across different career stages and cities in India.

AI Usage Is High, But AI Building Is Still Limited

Artificial intelligence has quickly become part of everyday work and learning for the people surveyed in the report.

According to the upGrad study, 94% of surveyed learners said they use AI, while 53% said they use it daily.

But the numbers change sharply when the survey moves from AI usage to AI implementation.

Only 26% of respondents said they had connected AI with another tool or used an API. Even fewer — 20% — said they had built an AI-powered automation, agent or application.

What Is the Difference Between an AI User and an AI Builder?

An AI user might use an AI tool to:

• write or improve an email;
• summarise a PDF;
• generate social media captions;
• brainstorm ideas;
• rewrite content;
• create an image;
• research a topic.

An AI builder might go a step further and:

• connect an AI model with other software;
• create an automated marketing workflow;
• build an AI agent;
• connect AI through an API;
• automate lead qualification;
• build a research or reporting workflow;
• create a knowledge-based assistant;
• automate repetitive business processes.

Importantly, building with AI does not always mean becoming a software engineer. No-code and low-code tools can help marketers, business owners, analysts and other professionals create practical workflows. Platforms covered in areas such as n8n workflow automation are one example of how AI can be connected with other tools and processes.

The Real AI Skills Gap May Be Between Using and Applying

The report highlights a distinction that students should pay close attention to.

AI access is no longer necessarily the biggest barrier. A learner can open an AI assistant and begin experimenting within minutes.

The harder question is:

Can you use AI to solve a real-world problem?

There is a significant difference between asking:

“Write an Instagram caption for this product.”

and designing a workflow that:

Collects product information → researches the audience → generates variations → checks brand guidelines → prepares content → sends it for human review → tracks campaign performance.

The first demonstrates tool usage. The second demonstrates workflow thinking, automation, integration and business application.

56% Say They Don’t Know Where to Start With AI

Another important finding is the uncertainty learners face when trying to build deeper AI skills.

56% of respondents identified knowing where to start as their biggest challenge.

Another 43% cited too many learning options and uncertainty about what is credible, while 43% also identified keeping up with rapid AI developments as a day-to-day challenge.

That challenge is understandable. Someone searching for AI skills today may encounter:

• generative AI;
• prompt engineering;
• machine learning;
• AI agents;
• Python;
• APIs;
• workflow automation;
• ChatGPT, Gemini and Claude;
• data analysis;
• AI-assisted coding;
• AI image and video generation;
• retrieval systems;
• AI marketing.

Trying to learn everything at once can become counterproductive. A better approach is to learn AI in stages and connect it with a clear career or business use case.

What Should Students Learn Next?

Students do not necessarily need to begin with advanced machine learning or complex programming. For many career paths, a practical learning progression can look like this.

Stage 1: Understand AI Fundamentals

Before chasing individual tools, learners should understand:

• what artificial intelligence and generative AI are;
• what large language models do;
• the difference between AI, machine learning and generative AI;
• what AI can and cannot reliably do;
• hallucinations and incorrect outputs;
• privacy and responsible AI use;
• why human verification remains important.

This foundation helps learners understand why an AI tool may produce a particular answer rather than automatically trusting every output.

Stage 2: Learn Effective Prompting

Prompt engineering should not mean memorising hundreds of so-called “secret prompts.”

A better approach is learning how to provide:

Context + Objective + Input + Constraints + Expected Output

For example, instead of asking an AI system simply to “write SEO content,” a stronger instruction explains the business, target customer, search intent, topic, supporting entities, required evidence, tone and expected structure.

The important skill is not writing the longest possible prompt. It is learning how to communicate a problem clearly, provide useful context and evaluate the response critically.

Stage 3: Learn AI for Your Actual Career

Different career paths require different AI capabilities.

Digital Marketing

Students learning digital marketing can explore AI for customer research, keyword analysis, content briefs, advertising research, campaign ideation, competitor analysis, audience understanding and reporting.

Content and Social Media

AI can support research, scripting, creative ideation, repurposing, editing, content planning, image generation and AI-assisted video creation.

Business and Operations

Businesses can explore AI for lead management, customer support, reporting, document processing, internal knowledge, research and AI-powered business automation.

Development

Developers can go deeper into APIs, AI-assisted coding, model integration, retrieval, agents, testing, evaluation and AI website development.

The key lesson is simple: AI plus relevant domain knowledge is generally more useful than learning AI tools without understanding where they should be applied.

Stage 4: Move From Prompts to Workflows

This is where learners can begin moving from being AI users towards becoming AI builders.

Consider a simple enquiry process. A team might manually:

1. read a new lead;
2. understand what the person wants;
3. enter the information into a spreadsheet or CRM;
4. categorise the lead;
5. write a response;
6. inform the relevant team member.

An automated workflow could potentially connect:

Website Form → Automation Platform → AI → CRM/Sheet → Email or Messaging → Human Review

The value of such a project is not just automation. Students also learn how data moves between systems, where AI adds value and where human approval should remain part of the process.

Stage 5: Understand APIs and Integrations

The report found that only 26% of respondents had connected AI to another tool or used an API.

An API is a way for different software systems to communicate with one another.

A simple example could look like:

Form Submission → API → AI Model → Structured Response → CRM

Students do not necessarily need to become advanced programmers before understanding this concept. Knowing how systems connect can open the door to far more practical applications than using an AI chatbot in isolation.

Stage 6: Understand AI Agents

AI agents are becoming another important part of the AI ecosystem.

A basic chatbot normally responds to a request. An AI agent can be designed to work towards a goal by using available information and tools.

• receive a goal;
• decide on intermediate steps;
• use approved tools;
• retrieve information;
• perform defined actions;
• evaluate results;
• continue until a defined outcome or stopping condition is reached.

The upGrad report found an interesting relationship between structured learning and agent-building. Among learners enrolled in an AI programme, 27% reported having built an AI agent, compared with 10% among those who were neither enrolled nor exploring AI upskilling.

The report described enrolled learners as 2.7 times as likely to have built an agent. However, this is an association in the survey data and should not be interpreted as proof that enrolment itself caused the difference.

Stage 7: Build Real Projects

Watching tutorials can create familiarity. Building projects creates practical experience.

• AI Research Assistant: Collect and summarise information from approved sources.
• AI Content Workflow: Research → brief → draft → human review → optimisation.
• AI Lead Qualification Workflow: Form → AI classification → CRM → sales notification.
• AI Reporting Assistant: Marketing data → analysis → summary → recommendations.
• Knowledge-Based AI Assistant: Company documents → retrieval → question answering.
• AI Marketing Automation: Campaign information → audience insights → creative ideas → reporting workflow.

Students interested in working with data can also combine AI skills with practical data analytics skills to improve reporting and decision-making.

Instead of only saying, “I know how to use ChatGPT,” a project portfolio can demonstrate, “I can use AI to solve a defined problem.”

Experienced Professionals Are Also Learning AI

AI learning is not limited to college students or freshers.

The upGrad study found that daily AI use increased with professional experience in its surveyed population.

Among early-stage learners, 44% reported using AI daily, compared with 62% of professionals with eight or more years of experience.

Experienced professionals also reported spending more time learning AI. Around 31% of respondents with eight or more years of experience spent at least three hours per week learning AI, compared with 15% of early-stage learners.

For students, this is an important signal: people already established in their careers are also adapting to AI-driven changes in the workplace.

Are Learners Seeing Career Benefits From AI?

The survey also asked respondents about career outcomes they associated with AI.

According to the report, 54% said they had experienced a concrete career result that they associated with AI. Reported outcomes included increased confidence at work, a new role, a new job, a promotion or freelance income.

Another 49% said AI had enabled them to perform work that was previously beyond their capabilities.

These figures need to be interpreted carefully. The report notes that these are reported associations rather than evidence that AI training directly caused those career outcomes.

AI Skills Are Spreading Beyond India’s Biggest Cities

The study included learners across Tier 1, Tier 2 and Tier 3 cities. The reported pattern of AI adoption and upskilling was therefore not limited only to India’s largest metropolitan areas.

This is relevant for learners in cities such as Jaipur. Access to cloud software, AI assistants, online learning resources and automation platforms means practical AI skills can be developed from many locations.

The differentiator increasingly becomes not only whether someone has access to an AI tool, but what they can actually do with it.

AI Users vs AI Builders: What’s the Difference?

For students, this comparison is one of the simplest ways to understand the skills gap highlighted by the report.

AI User	AI Builder
Generates text	Creates a repeatable content workflow
Asks AI questions	Connects AI with data or tools
Uses one AI application	Integrates multiple systems
Creates individual outputs	Automates repeatable processes
Uses an existing prompt	Designs a workflow around a problem
Accepts an answer	Tests and evaluates output
Uses AI manually	Creates reusable AI-enabled processes

Using AI effectively is already a valuable skill. The next step is learning when to move from:

Prompt → Output

towards:

Problem → Process → AI → Tools → Human Review → Outcome

What This Means for Digital Marketing Students

The shift from AI usage to AI application is particularly relevant in digital marketing.

AI can already assist marketers with search research, competitor analysis, content planning, advertising ideas, social media content, audience research, analytics interpretation and reporting.

But future marketers will need more than content-generation skills.

AI + SEO

Students should understand how AI can support research, content analysis and optimisation while still respecting search intent, evidence, experience and human verification. This is also where newer areas such as AI SEO, AEO and GEO become relevant.

AI + Advertising

AI can assist with audience research, creative variations and analysis, but marketers still need to understand campaign objectives, conversion tracking, bidding, landing pages and business outcomes.

AI + Analytics

The important skill is not simply generating a report. It is converting campaign and customer data into useful marketing decisions.

AI + Automation

Students can learn how repetitive marketing processes such as lead handling, reporting, research and content preparation can be connected into workflows.

AI + Human Judgement

Knowing when an AI output is useful — and when it should be questioned, corrected or rejected — remains an essential capability.

The real advantage is not using the maximum number of AI tools. It is knowing which tool to use, when to use it, how to evaluate its output and how to convert that output into a useful decision or action.

Parth Skills Takeaway

The upGrad report highlights an increasingly important gap:

AI adoption is moving faster than practical AI-building capability among the learners surveyed.

For students, this means the learning journey should not finish with basic chatbot prompts.

A stronger progression is:

Understand AI → Use AI → Evaluate AI → Connect AI → Automate With AI → Build Projects → Solve Real Problems

Students do not necessarily need to become AI engineers. But whether they choose digital marketing, content, business, analytics, development or freelancing, learning how to apply AI inside their chosen field can help them build more practical and demonstrable skills.

At Parth Skills, the focus is on understanding tools through practical application rather than learning tools in isolation. Explore our AI, digital marketing and career-focused courses to see the different learning paths available.

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