Forem: saurabh tripathi

He left Tata Projects with no job lined up. Six months later: VP offer, MOU with a university, and teaching AI to 400 professors.

saurabh tripathi — Tue, 28 Apr 2026 18:29:35 +0000

"Pankaj has 25 years in construction and project management. Mega projects. Multi-project coordination. Ground supervision all the way to senior leadership. He worked his way through Stewards and Lloyds (a Tata subsidiary) to Tata Projects itself.

When his division stagnated and opportunities dried up, he tried to create change internally. The company told him the steel sector was struggling and they could not fund new initiatives.

He waited three months. Nothing changed. In March, he left.

No job lined up. No immediate plan. Just a decision that staying in a stagnant situation was worse than the uncertainty of starting over.

What followed over the next six months is one of the most striking professional pivots I have come across.

WHAT HE FOCUSED ON:

He made an honest assessment. His domain expertise — project management, infrastructure, multi-stakeholder coordination — was deep and current. What was missing was the technology layer now embedded in how senior transformation roles are scoped, led, and delivered.

He spent focused time learning AI tools:
→ Prompt engineering — how to get precise, useful outputs
→ Data synthesis — processing large amounts of information quickly
→ AI-assisted documentation and reporting
→ Presentation automation
→ Digital project architecture and planning frameworks

He applied everything immediately to real work — not exercises, not theory. Real proposals. Real programme designs. Real architecture documents for digital projects.

The shift in output quality was immediate. Documents that used to take days came together in hours. Presentations that required multiple revision rounds were cleaner from the first draft.

WHAT HAPPENED NEXT:

He was invited to deliver an AI session at Jain Institute of Technology.

400 to 500 faculty members attended. Assistant professors. Associate professors. Full professors. PhD holders from universities across the region.

He was transparent with the room. He told them he was a new learner himself — sharing what he was practicing, not claiming mastery he did not have.

They were not bothered. They were engaged.

The session led to a signed MOU for a four-year, fully government-sponsored continuous learning programme.

He is now in active negotiation for a VP of Digital Transformation and VP of Operations role at an educational institution.

THE LINE THAT EXPLAINS EVERYTHING:

""Whatever I have learned — maybe 1% — that 1% has made me at least 80 to 90% more effective.""

This is the asymmetry that most people underestimate. You do not need to master AI to benefit from it significantly. A small investment in learning produces outsized returns — because the baseline for most professionals using AI well is still remarkably low.

Domain expertise plus AI fluency is a rare combination right now. Most people with deep domain knowledge have not yet built the technology layer. Most people building AI skills are early in their careers.

If you are experienced and willing to learn the tools, you are in a genuinely differentiated position.

The question is whether you act on that before the window closes.

▶️ Watch Pankaj's full story: https://youtu.be/caznIFK-fxg"

10 years as a .NET full-stack developer. Here are the specific AI tools and workflows that cut 3-4 hours from my daily workload.

saurabh tripathi — Sat, 25 Apr 2026 17:53:35 +0000

"Hari Krishna has been a .NET full-stack developer for 10 years. He knows what a long day looks like.

Until recently, 10 PM was a regular finish time. Email alone — crafting careful, tonally appropriate messages for UK and US clients where every word choice matters — consumed 3 to 4 hours of his working day.

He now logs off at 6:30 PM.

That is not a minor productivity improvement. That is 3 to 4 hours returned to his life, every single day. Let that compound over a year.

Here is exactly what changed, tool by tool — because vague productivity advice is useless and he was specific enough to be worth sharing precisely.

TOOL 1 — EMAIL AND COMMUNICATION: ChatGPT

UK and US clients parse language differently from Indian communication norms. Meanings shift in ways that are hard to anticipate if you did not grow up in that cultural context. A sentence that feels polite in one register can land as passive-aggressive in another.

He uses ChatGPT with detailed prompts: who is the recipient, what is the context, what tone is needed, what is the specific outcome he wants from the email. The result is messages that land correctly the first time, eliminating the back-and-forth of misunderstanding.

3 to 4 hours → 30 minutes. Every day.

TOOL 2 — CODE: GitHub Copilot

Boilerplate generation. Unit test case creation. Exception handling suggestions. SQL query optimisation — asking Copilot to review queries and suggest improvements rather than writing from scratch.

He also cross-references results: ask one AI model a coding question, then ask a second model the same question. Cross-referencing two outputs pushes accuracy significantly higher than relying on either one alone.

TOOL 3 — PRESENTATIONS: Canva + AI content structure

His standard for a good presentation: not lengthy, not boring, slides that support the presenter rather than replace them. The content should be precise and the speaker should be able to elaborate from the points — not read from the screen.

AI helps him get to that structure faster. Canva handles the visual execution.

TOOL 4 — RESEARCH AND SYNTHESIS: NotebookLM + Napkin

For processing long documents and generating visual summaries. Particularly useful when pulling insights from technical documentation or meeting notes across multiple sources.

ON WHETHER AI WILL REPLACE DEVELOPERS:

His view is clear and worth repeating:

""AI means a team of 5 can now do what 15 used to do in the same timeframe. That is not a job-replacement story. It is a team-composition story. The developers who understand AI will be building and leading those smaller, more efficient teams. The ones who don't will find themselves increasingly on the outside.""

Evolution is not optional. It never has been. The developers who resist it are not protecting their jobs — they are just making the transition harder for themselves.

▶️ Watch Hari Krishna's full story: https://youtu.be/RjNABAf3foY"

5 years in a non-technical BA role. 2 months of AI upskilling. Then Big 4 companies started calling.

saurabh tripathi — Fri, 24 Apr 2026 18:18:21 +0000

"Anushree is a Senior Business Analyst. Five years of experience — stakeholder management, task coordination, reporting, cross-functional communication.

Her background is not technical. For most of her career she described herself as task-oriented: waiting for tickets, following up, executing. Over time she grew into taking ownership, managing stakeholder relationships, running her own workstreams.

Then layoffs started happening around her at MNCs across the industry. Not at her company specifically — but close enough to feel real. People she knew, at companies she respected, losing roles suddenly.

She felt the anxiety of someone who knows things are shifting but is not sure what to do about it.

She enrolled in an AI masterclass. Two months later, she was getting interview calls from Big 4 firms.

WHAT SPECIFICALLY CHANGED:

She rebuilt her LinkedIn profile and CV using AI — substantively, not cosmetically. She restructured how she described her work to reflect the language and priorities of senior BA and transformation roles. She made her ongoing AI learning explicitly visible on her profile — not claiming to be an AI expert, but demonstrating active, current engagement with a field that matters.

That distinction is important. Hiring managers at Big 4 firms are not looking for BAs who have mastered AI engineering. They are looking for BAs who are thinking clearly about the future of the work — who can navigate AI-adjacent projects, digital transformation programmes, process improvement workstreams. A profile that shows deliberate upskilling with specifics stands out.

She also started using AI daily in ways she could speak to concretely in interviews:
→ ChatGPT for drafting complex stakeholder communications
→ AI tools for structuring difficult team conversations
→ Better, faster preparation for pitches and presentations
→ Research synthesis for project inputs

She was not just saying she used AI. She had actual examples. That is the entire difference.

THE CONFIDENCE SHIFT:

Beyond the practical tools, something else changed. She described going from genuine anxiety about being replaced — to feeling equipped. Able to pitch clients. Able to handle senior conversations with preparation she had not had before.

Before the course, when recession hit her company, she was afraid. She thought she might be one of the people let go. That fear was real and it was affecting how she showed up.

Now she says: ""I'm 100% confident that whatever situations come, I'm going in the right direction with the right mentors.""

She has a salary hike. She is applying to bigger companies than she ever imagined approaching. She is getting calls back.

The tools did not do that alone. The confidence that came from genuinely knowing how to use them — that did it.

▶️ Watch Anushree's full story: https://youtu.be/U6KAEYyUNCY"

She had 1.5 years of experience, rebuilt her resume with AI, spent one month job searching, and got a 30% salary hike.

saurabh tripathi — Thu, 23 Apr 2026 18:27:08 +0000

"Priyanka is based in Bangalore. Electronics and communication engineering background, MBA in marketing management. She had both technical and management exposure — but like many people, she was slow to start with AI tools.

She described herself as apprehensive. Not opposed. Just unsure where to begin, cautious about committing to something new.

She enrolled in a workshop and masterclass on AI tools for professional use.

One month of active job searching later, she had a new role and a 30% salary increase.

Here is what actually happened — and why the numbers make sense when you look closely.

WHAT SHE CHANGED:

The first concrete output from her learning was a rebuilt resume. Not a cosmetically improved one — a genuinely restructured document written for the MIS executive roles she was targeting.

MIS — Management Information Systems — involves managing how information flows through an organisation. A core competency is handling large, messy datasets and turning them into usable summaries and reports for decision-makers.

This is exactly the kind of work where AI tools create a measurable difference. And Priyanka had learned how to do it.

She could speak credibly in interviews about consolidating reports from spreadsheets with 10,000 to 20,000 rows. She could describe specific AI workflows for data summarisation. She had actual examples — not vague claims.

In interviews, specificity is credibility. ""I use AI tools"" is easy to say. Being able to describe a concrete workflow is different entirely.

THE RESULT:

Placed at Embassy Services (Vivanta India) — a respected conglomerate — as an MIS Executive. Salary increase from previous role: 30%. Just 20 days from her interview to joining.

And the 30% hike was not the end. It was the beginning. She now uses AI daily in her new role — for dataset consolidation, summary generation, PPT updates, quick research tasks. Each of those small time savings compounds across a full working year.

HER MESSAGE TO OTHERS:

She addressed freshers and career-changers directly: AI is not just for engineers or data scientists. It is useful in every domain, every vertical, every industry. You do not need to be technical to start. You need to start.

The platform just has to teach you how to use it. The rest follows from practice.

If you are hesitating because you do not feel ready — that is exactly why this is the right moment.

▶️ Watch Priyanka's full story: https://youtu.be/r3Ga4yONivg?feature=shared"

Rejected by UK. Rejected by Germany. How building real AI skills helped me get a student visa to Ireland.

saurabh tripathi — Wed, 22 Apr 2026 17:44:14 +0000

"Arsh finished his BCom from Delhi University expecting that a degree would lead to a job. It did not.

He tried for the UK. Not financially viable at the time.

He applied to Germany. Rejected — insufficient technical skills to qualify for the programme he wanted.

He enrolled in a distance Masters in Commerce through the School of Open Learning, Delhi University, trying to keep moving forward. But he described feeling like he was not reaching his potential — not getting a platform to genuinely learn and grow.

Then in January, scrolling through Instagram, he saw an ad for an AI masterclass.

WHAT HE LEARNED:

He enrolled in a 6-month programme covering:
→ Power BI (data visualisation)
→ Python (programming and automation)
→ SQL (database querying)
→ Prompt Engineering (how to actually use AI tools effectively)
→ AI tools across multiple domains

He had zero background in any of these. He had always assumed they were for ""technical"" people — not for a commerce graduate.

The masterclass proved that assumption wrong. He found himself learning Power BI in a session and realising — he could actually do this.

HOW IT CHANGED HIS VISA APPLICATION:

He applied for a Masters in Business Analytics at University College Cork in Ireland.

His counsellor gave him a critical piece of advice: visa officers do not just check your grades and your bank balance. They check for relevance. They want evidence that you are genuinely building toward the course you say you want to study — not just seeking migration through education.

Arsh mentioned the ongoing 6-month masterclass explicitly in his Statement of Purpose. He described the specific skills he was developing. He framed his enrolment as deliberate, active preparation for a postgraduate programme in business analytics and AI.

He got his offer letter.

He believes the SOP framing — backed by something concrete, verifiable, and currently in progress — played a significant role in the decision. Immigration has become strict. Showing real upskilling with specifics is now a meaningful differentiator.

THE MINDSET SHIFT THAT MATTERS MOST:

Before the masterclass, Arsh was afraid of AI. He thought it would take jobs — specifically jobs that people like him might hold.

After it, his understanding completely changed.

""AI will not take jobs. AI will take the jobs of those people who do not know how to use AI. And people who know how to use AI — they are going to take the jobs.""

That is not a motivational quote. That is an accurate description of what is actually happening in the labour market right now.

Understanding it changes what you do next. Instead of fearing the tool, you learn it.

▶️ Watch Arsh's full story: https://youtu.be/nfw5xePQfWQ"

What a CA with 30 years of experience learned when she used AI for legal drafting — including where it completely lied to her.

saurabh tripathi — Tue, 21 Apr 2026 17:15:23 +0000

"Manisha has been a Chartered Accountant for 30 years. She runs her own practice in litigation, traditional CA work, auditing, and systems reviews. She is a Certified Information Systems Auditor and a Certified Insolvency Resolution Professional.

She is not someone who adopts new tools carelessly. She approached AI with curiosity AND caution. And because of that, her account of using it is one of the most honest and useful I have encountered.

Here is both sides of her experience.

WHERE IT GENUINELY HELPED:

Income tax litigation involves serious, time-intensive research. When a client receives an assessment order and wants to appeal, the CA must:

✦ Understand the assessment order in depth
✦ Identify legally defensible counter-arguments
✦ Find applicable case laws with accurate citations
✦ Draft the grounds of appeal in correct legal format
✦ Ensure everything is factually and legally defensible

Before AI, this took Manisha 10 to 15 days per case. That is not inefficiency — that is the thoroughness that good legal work demands.

After she learned to use AI properly — specifically how to upload documents, give context, and build responses through iterative conversation — the process changed dramatically.

She would upload the assessment order as a PDF. Ask for a summary. Identify key points to contest. Ask the AI to help draft grounds of appeal. Refine through further questions.

Over time, the AI began anticipating her needs — suggesting related provisions, offering to draft in specific formats, flagging arguments she had not considered.

The same process now takes 2 to 3 days.

That is a 5x to 7x productivity improvement on some of the most complex, high-stakes work in her practice.

WHERE IT COMPLETELY FAILED — AND WHY THIS MATTERS MORE:

AI halluccinates case laws. This happened to Manisha directly.

The AI cited cases with legitimate-sounding names, courts, and citation numbers. She cross-checked on actual legal databases. Those cases did not exist. They were invented.

Around the same time, she read a newspaper report about a legal professional who submitted AI-generated case citations in a court filing. The judgment noted that the cited cases had never existed. The professional consequences were severe.

Manisha's response was not to stop using AI. It was to build verification into every step of her workflow. She now treats AI output as a starting point for research — never an endpoint. She cross-checks every citation. She reads actual case summaries. She applies 30 years of domain knowledge to assess whether a case is genuinely applicable.

HER SUMMARY — IN HER OWN WORDS:

""Unless you know the subject — if I don't know what 1+1 is and AI gives me 3 — I should also know whether it is correct.""

AI amplifies expertise. It does not replace it.

In fields with real professional consequences — law, medicine, finance, accounting — the human with domain knowledge is not optional. They are the essential check on a tool that is powerful but not infallible.

Use AI to accelerate. Use your expertise to verify. The combination is formidable. Either one alone is incomplete.

▶️ Watch Manisha's full story: https://youtu.be/9uIr9kNoKCk"

Unemployed for a year in software testing. Sliding into depression. Here's how talking about AI in interviews finally got me hired — as a lead.

saurabh tripathi — Mon, 20 Apr 2026 17:35:34 +0000

"Karthik has been in software testing since 2014. Java, automation, Python, Robot Framework. A decade of hands-on experience.

He lost his job during a restructuring.

For one full year, he applied for roles. Different positions. Different companies. Different approaches. Nothing worked. Not a single offer.

He described what happened to his confidence during that year: it became very low. He started to feel like the field had closed to him permanently. He was thinking about applying for a loan. Starting a business. Doing anything else. Because the thought of continuing to try and fail in testing was becoming unbearable.

He said he was starting to feel depression.

A person with a decade of professional experience — genuinely afraid that his career was over.

WHAT CHANGED:

He came across an AI and skills workshop online. Initially skeptical. But he had time and nothing to lose, so he attended.

The session showed him something he had not considered: how AI tools could be integrated directly into software testing workflows. Not as a replacement for testers — but as a force multiplier for them.

Test plan creation. Documentation. Report generation. Structured analysis. Things that take hours, done in minutes with the right prompts and tools.

He started learning. He rebuilt his confidence slowly as he saw what was possible.

THE INTERVIEW THAT CHANGED EVERYTHING:

When he finally got an interview for a test lead position at a major tech company, he approached it differently.

He did not just present his background. He walked the interviewer through exactly how he would use AI in the role — as a lead:

→ Using AI to write and refine test plans for complex features
→ Generating documentation that previously took days, in hours
→ Creating structured weekly reports automatically
→ Teaching junior team members to use these tools so the whole team becomes more efficient

He was interviewing for a leadership role. The interviewer was evaluating whether he could think at a systems level — not just execute tasks himself, but elevate an entire team.

Demonstrating a clear, specific vision for how AI would make his team better answered that question directly.

The interviewer was very impressed.

He got the offer. Lead position. A step UP from where he was before losing his job.

THE BIGGER PICTURE:

Karthik is 35. He described looking at the next 20 to 25 years of his career and feeling, for the first time in over a year, that it was navigable.

That shift — from fear and depression to genuine orientation — might be the most valuable thing he gained from the entire experience.

The tools are real. The results are real. And sometimes, what looks like a career crisis is actually the moment just before things turn around.

▶️ Watch Karthik's full story: https://youtu.be/A054oeWnDEw?si=_vKG6Yg0txTuWBrA"

My bank was about to lose a ₹252 crore government deposit bid. I had 10 minutes and an AI tool. Here's what happened.

saurabh tripathi — Sun, 19 Apr 2026 17:42:49 +0000

"There is a moment in competitive bidding when the room goes quiet. Usually it means someone has lost.

Deepak works in a public sector bank in Navi Mumbai. His bank was pitching for a government deposit worth ₹252 crore. A rival bank — Union Bank of India — suddenly quoted 17 paise more than them in the middle of the bidding.

Everyone in the room assumed it was over. Other banks conceded.

Deepak asked for 10 minutes.

WHAT HAPPENED IN THOSE 10 MINUTES:

Deepak had recently learned how to use AI tools properly — not just asking generic questions, but feeding real data and getting real analysis.

Here is exactly what he did:

Step 1: He pulled publicly available deposit rate data from peer bank websites. These websites are open to anyone. The data was always there. What Deepak now had was the ability to process it quickly.

Step 2: He fed that data into the AI tool and asked it to compare the rates, identify the structure of the rival bank's offer (non-callable deposits with cumulative quarterly reinvestment — which artificially inflates the headline rate), and generate a clear comparison chart.

Step 3: Ten minutes later, he had a visual analysis showing that his bank was offering 9 to 10 paise MORE than Union Bank on a genuine like-for-like basis.

He presented the chart to the panel.

His bank won the bid.

The difference in returns to the government of India: approximately ₹40 to ₹50 lakhs.

WHAT THE AI DID AND DID NOT DO:

The AI did not understand banking. It did not know what a non-callable deposit was. It did not know why cumulative reinvestment inflates a headline rate.

Deepak knew all of that. He is a banker with years of domain experience.

What he did not have before — was the ability to process and present that understanding fast enough to matter in a live, time-pressured bidding room.

The AI compressed what would have been two hours of manual spreadsheet work into ten minutes. Deepak's expertise directed what the analysis should look at and what the result meant.

That is the partnership. Domain knowledge tells the AI what to look for. The AI does the work at a speed human effort alone cannot match.

HOW DEEPAK LEARNED THIS:

He saw an ad on Instagram while scrolling. Registered for a ₹99 introductory session. Listened to classes on his phone during his daily commute on the Mumbai local train.

A public sector banker. Commuting through Mumbai. Learning on a phone. Applying that knowledge in a live bidding room. Winning ₹50 lakhs for the government.

The barrier to learning these tools is not high. The returns to learning them can be extraordinary.

You cannot beat that ROI.

▶️ Watch Deepak's full story: https://youtu.be/AEgotMPXQ-0?feature=shared"

I attended 15 interviews over 5 years and never made it past the first round. Here's what finally changed.

saurabh tripathi — Sat, 18 Apr 2026 16:52:15 +0000

"For 5 years, Ragendra — a mechanical engineer with 13 years of experience in product design and manufacturing — could not crack a single second round interview.

Not once. Out of 10 to 15 attempts.

Think about that for a moment. 13 years of real, hands-on experience. Product development from concept to customer. Design, manufacturing engineering, vendor coordination, process capability improvement. A bachelor's degree in mechanical engineering. A master's degree in progress.

And yet — rejection after rejection after rejection.

Here is the thing nobody tells you: the problem was never his experience. It was how that experience was being communicated.

WHAT HE CHANGED:

He started using AI tools — primarily ChatGPT and Gemini — to rebuild his resume from scratch. Not to fabricate anything. Not to exaggerate. But to translate his real experience into the language that hiring managers in his target roles were actually looking for.

The process was simple but powerful:
→ Take the actual job description
→ Feed his real responsibilities and achievements into the AI
→ Ask it to reframe and restructure the content to match

The result was a resume that felt targeted, current, and specific — rather than the same generic document he had been carrying around for years.

He did the same for his LinkedIn profile. Summary, headline, project descriptions — all rewritten with AI assistance to speak directly to the roles he wanted.

Within TWO MONTHS of applying this approach consistently, he got selected.

Same experience. Same qualifications. Completely different outcome.

WHAT MAKES THIS STORY POWERFUL:

Ragendra said something that stuck with me: he estimates he is using maybe 15 to 20 percent of what AI can actually do. That number represents his current ceiling — and yet that 15 to 20 percent was enough to end a five-year losing streak.

He also started using AI for his medical reports. It suggested a diet plan and morning walk routine. He changed his routine. He started waking up earlier. He has followed the plan for weeks.

Same tool. Completely different domain. Same principle: give it good context, ask a precise question, act on the output.

THE LESSON:

Most career advice focuses on what to do — update your resume, network more, practice interviews. All correct. None of it explains why smart, experienced people still fail to get through first rounds.

The real answer is simpler and harder to hear: the way you are describing yourself is not connecting with the people reading about you.

It is not a skills problem. It is a translation problem.

AI is very good at translation.

Five years is a long time to wait before discovering that.

▶️ Watch Ragendra's full story here: https://youtu.be/ntkF2tImt4Y?si=moe_IoXQW320zg8B"