Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

As an AI engineer, LLM Twin is an important architecture to understand.

An LLM Twin refers to a system where a language model is designed to represent the knowledge, writing patterns, and decision logic of a specific person, team, or organization. The goal is consistency and reliability, not novelty.

At its core, an LLM Twin is built around how information flows. Think of it as 4 steps:

1️⃣ First, content is collected from real sources: blogs, internal documents, posts, code repositories, or knowledge bases. This data reflects how the person or organization actually thinks and communicates.

2️⃣ Next, the data is structured for two purposes.
One path prepares instruction-style examples that help the model learn how to respond.
Another path prepares retrieval data so the system can pull relevant context at inference time. This ensures responses stay grounded in real material.

3️⃣ Then comes training and evaluation. Models are fine-tuned where appropriate, tested against clear criteria, and versioned before deployment. This step looks very similar to traditional ML workflows, just adapted for language models.

4️⃣ Finally, at inference time, user requests are combined with retrieved context and passed through the deployed model. Monitoring is critical here: quality, latency, and cost all need to be observed continuously.

Where is this used in practice?

✦ Personal or creator assistants that help scale writing and communication
✦ Company-wide assistants that maintain a consistent product or brand voice
✦ Internal tools that answer questions based on code, docs, or prior decisions
✦ Support systems that reflect how experienced engineers respond to issues

Why should AI Engineers understand this?
This architecture reflects how GenAI systems are actually built and maintained. It forces you to think beyond prompts and focus on data quality, retrieval design, evaluation, and long-term system behavior.

My 2 cents 🤌
This is a GREAT topic to build a portfolio project around!! 

Credit: LLM Engineer’s Handbook by Paul Iusztin  and Maxime Labonne. I really like how this book breaks down AI engineering clearly and goes deep into different GenAI system architectures without hand-waving.

Over the past few months, Arvind and I've delivered seven deep technical workshops across agentic AI, RAG, context engineering, AI evals, MCP, vibe coding, and more.

One theme kept coming up: you want something more structured. Not just standalone sessions, but a clear end-to-end roadmap from A to Z.

So that’s what we’re building.

A structured, comprehensive course for:
- Hands-on builders: software, AI, and ML engineers
- AI product managers
- Tech leaders and CXOs looking to upskill meaningfully

Before we finalize it, I’d love your input. I’ve created a short survey to understand where you are, what you want to learn, and your career goals.

It takes under 10 minutes.

As a thank you, you’ll receive an additional 10% discount when we launch the course soon 🫶

Survey: https://lnkd.in/gQYeBCRP

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Here’s my honest pattern with almost every AI tool I try.

I end up using a lot of tools, usually early access. After repeating this cycle enough times, I realized my reactions are almost predictable.

👉First hour
There’s real excitement.
I’m clicking around, testing odd prompts, trying things that used to fail. No strategy yet, just my curiosity and momentum.

👉Next few hours
I start comparing.
Where would this actually fit in my work?
What would I stop using if I kept this?
Is this clearly better, or just new?

👉Later that day
I experiment without guidance.
I skip the docs and examples on purpose. I try incomplete ideas, messy workflows, prompts I would never put in a demo. This tells me more than any tutorial ever could.

👉Day two
Then I look at the examples the team shared.
They usually work smoothly. They’re polished. They show the tool at its best, which is exactly what they’re meant to do.

👉After a few days
This is where the real understanding kicks in.
I start tweaking those examples. Changing assumptions. Using it in ways that feel realistic, not ideal. That’s when the boundaries show up. Not frustration. Just clarity.

Here’s the takeaway I’ve learned the hard way:
If you haven’t noticed limitations yet, you probably haven’t pushed the tool very far.

And honestly, if I use something consistently for three days and still can’t find a meaningful flaw, that’s rare. That earns real respect.
I haven’t experienced that yet.

Curious how others see this.
Are there tools you’ve used deeply where the limits still haven’t shown up?

Just a couple of weeks ago, Arvind and I hosted an in-person AI meetup with The Hub Bengaluru.

Over 300 people showed up in person.

Every single one of them was either already working in AI or actively trying to break into the space. The energy in the room was unreal. It genuinely felt like a room full of builders, practitioners, and people who are curious enough to go deep.

During the event, we rolled out a small challenge. We asked attendees to share a recap of what they learned, and we said we would pick one winner.

Really excited to share that Suraj V is that lucky winner 🎉
Suraj gets free access to all the The Gen Academy workshops we have hosted so far.

Huge congratulations, Suraj V!! 

Building The Gen Academy has always come from a very real observation that Arvind and I see every single day in our work. The adoption of generative AI and LLMs is moving incredibly fast, but the depth of AI engineering skills needed to build reliable systems is not keeping up.

There is a very real skill gap in AI engineering right now! There are so many curious souls who want to become AI engineers- but they aren't able to get those opportunities; and there are so many companies out there who  want to hire, but aren't finding the right talent. 

Our mission is to bridge that skill-gap!

As people who are deeply involved in this space, we felt a responsibility to help bridge that gap. Not just for people who are already experienced, but also for entry-level folks who want to build strong foundations the right way.

That is why our workshops are intentionally designed to be both intuitive and practical. We focus on theory that actually helps you reason about systems, and we pair that with hands-on demos where you build real things using modern toolkits and frameworks.

So far, we have hosted 6 workshops, and the response has been incredibly encouraging. We have trained 1500+ people already ❤️ 

Our next workshop is coming up on February 15th, and it will be focused on AI evaluations (link in comments)

Do follow The Gen Academy to stay updated on all our upcoming workshops.

Six months ago, Arvind and I started The Gen Academy with one simple goal- to create workshops that are deeply-technical, practical, cuts through the ton of noise out there, and that provides people clarity on building productive-grade AI systems.

So far we have trained 2100+ people. We’ve hosted 7 workshops, and consistently we’ve had 300–400 builders join us live.

The audience has been incredible.
- Software engineers moving into AI.
- AI engineers building LLM systems in production.
- Product leads thinking about reliability and evaluation.
- Directors and VPs from top tech and Fortune 500 companies who want to truly understand what’s under the hood.
- Even people from non-tech background who wanted to strengthen their technical fundamentals 

Recently, we hosted a workshop on AI Evaluations.

And this one felt especially important.

Because evaluating non-deterministic generative systems is fundamentally different from traditional ML evaluation.

We covered:

✦ How to evaluate non-deterministic generative AI systems
✦ How to think about benchmarks and what they actually tell you
✦ How to design your own evaluation dataset
✦ How to choose the right metrics for qualitative and system-level evaluation
✦ How to combine human evals, code-based evals, and LLM-as-a-judge
✦ A hands-on walkthrough so this isn’t just theory

If you’re building LLM applications, RAG pipelines, or agentic systems, evaluation is not optional. It is the difference between a demo and a reliable product.

If you missed the live session, we’ve shared the recording here: https://lnkd.in/gzMdbfWH

We’ll continue building these workshops for engineers who want to go beyond experimentation and into production.

More coming soon, so do follow us & The Gen Academy to get updates on our next workshops.

5 days to go! 
Arvind and I are super excited to be hosting an AI evals hands-on workshop on February 15.

It’s a 90-minute session where we’ll cover the fundamentals of AI evaluations and then actually do it together. You’ll walk away having built and reasoned through real evals yourself. 

If you’re aspiring to be an AI engineer or planning to upskill for AI roles in 2026, understanding how to evaluate LLM applications is critical.

Building an LLM app is often the easy part. Knowing whether it truly works in production is where most teams struggle.

LLM evaluation is not just about correctness anymore. You are judging usefulness, safety, faithfulness to sources, consistency across runs, cost, latency, and real user outcomes across prompts, RAG pipelines, tools, and agentic workflows.

Because this is hard, many teams fall back to vibe-evals. If a few outputs look good, they ship. That approach breaks quickly when models change, prompts regress, retrieval fails, or user behavior shifts. Without evals, problems surface only after users complain.

Why learning evals matters:
✦ Evals turn LLM development from guesswork into engineering
✦ They help catch regressions as models, prompts, or tools evolve
✦ They are essential for scaling RAG systems and agentic workflows
✦ In 2026, strong teams use hybrid evals combining automated metrics, LLM-as-a-judge, and targeted human review

Evals are no longer optional. They are a core AI skill.

In this workshop, Arvind and I will walk through how AI evals are done in real systems, with hands-on exercises so you build intuition.

This is designed for AI engineers and tech leads building systems, and even business leaders who want intuition for what “good” really means in production. Even if you are a beginner, don't worry, we will give you the foundations for you to understand everything comprehensively, and enough resources that you can review at your own pace.

If you want to level up your AI skills for 2026, register here:
https://lnkd.in/dQSR7yhU

Evaluation Resources

Fine-tuning is going to be one of the biggest focus areas for AI engineering teams this year.

We’re past the stage where calling an API is enough. The teams creating real leverage are the ones adapting models to their own data, workflows, and edge cases. Out-of-the-box models are powerful, but they are generic. Fine-tuning is where enterprises unlock meaningful efficiency gains and build real differentiation.

If you want to upskill in AI engineering, this is a topic you cannot skip.
Arvind and I are hosting a deep, hands-on workshop on Fine-Tuning LLMs.

In 90 minutes, you’ll learn:
✦ When fine-tuning makes sense and when it does not
✦ How SFT, RLHF, and DPO fit into real-world pipelines
✦ An end-to-end, code-based demo from raw data to a specialized model
✦ How to evaluate true performance improvements and avoid production pitfalls

This session is designed for software engineers, AI engineers, and data scientists, and it is equally valuable for product managers, solutions architects, tech leads, and executives working closely with model ecosystems.

If you want to move from using LLMs to actually optimizing them, this workshop will give you a practical framework to do it: https://lnkd.in/g-5gHrsY

In 2026, AI isn’t judged by a wow factor anymore.
It’s judged by whether it actually improves your workflows.

One of the most common questions I get from founders and teams I advise is how to automate go-to-market and sales properly. Real GTM automation needs deep ICP research, clear definitions, personalized messaging, and credible sources. This isn’t a one-prompt problem.

Normally, building a solid GTM or sales report would take me several hours, even with existing AI tools helping with research.

I recently got early access to Airtable’s new Superagent, and what impressed me was the approach. It didn’t treat my request as a single task. It broke it into ~16 steps, ran multiple agents in parallel, did deep research per subtask, cited sources, and stitched everything together into one structured output called a Super Report.

I used it to build a technical, informational report on AI for GTM/Sales in minutes. No fluff. No hallucinations. Just deeply researched content I could actually use with business leaders.

If your work involves building reports, decks, or MVP websites that need accuracy and depth, this feels less like “AI that makes things pretty” and more like AI that does real work.

Do check it out here: https://lnkd.in/gDkfSSG2

PS: You can get 2 months free for @Superagent, using code: AISH2FREE

#SuperagentPartner, #ad, #SuperagentbyAirtable

I help organizations in finding solutions to current Culture, Processes, and Technology issues through Digital Transformation by transforming the business to become more Agile and centered on the Customer (data-informed)

CDO, CIO & Digital Transformation Director focused on helping organizations navigate the digital age. I partner with leaders to develop and execute strategies that resolve critical business issues related to culture, processes, and technology, ensuring a human-centric approach to innovation and growth.

Dynamic and results-driven Executive with 25+ years of experience leading strategic change in global organizations and SMBs. Proven expertise in consulting, designing, and implementing business-transformative initiatives that drive cultural, process, and technological change. Skilled in optimizing business processes through innovative technology solutions. Strong track record of building high-performance teams and ensuring organizational effectiveness. Experienced in business units, IT, operations, and professional services/consulting areas. Adept at delivering results, driving efficiency, and leveraging cutting-edge technologies. Excels in strategic planning and execution, collaborating with stakeholders across the organization to drive Digital Transformation. Effective communicator and relationship builder, fostering collaboration and innovation. Demonstrated ability to establish data and information strategies, governance, and policy development to create business value. A human-centric leader committed to driving organizational effectiveness and success in the digital age.

You can reach me at https://dtstrategist.com

Manuel Barragan

What the new Italian law on AI education teaches organizations about workforce strategy

I have seen many organizations completely baffled. They bought expensive AI licenses expecting a productivity boom, but instead, morale tanked and data leaked. Teams feared the software was there to replace them, so they either sabotaged it or used it recklessly. Italy just offered a masterclass on how to fix this: don't just install software; build a fence around it.

In the article, you will learn how to:
1️⃣ Prioritize the People Pillar by mandating real training on ethics and prompts. If a student in Rome needs a license, your directors do too.
2️⃣ Codify the Human-in-the-Loop to lower resistance by explicitly stating that AI recommends while humans decide.
3️⃣ Centralize Governance to stop "shadow AI" tools from compromising your network.
4️⃣ Protect Data with Granularity, ensuring sensitive financials stay off public servers through tier-based classification.

The corporate sector is a playground without fences. We have powerful toys and no supervision. We risk injury, data loss, and revolt. The Italian government showed us that regulation does not stifle progress; it creates the safety required for real adoption.

If your organization lacks a governance framework for its digital tools, Digital Transformation Strategist can help. We design policies, training programs, and data strategies to keep your business safe as you grow. Contact us to build your fence.

View newsletter: Digital Transformation Stories

I have known that many organizations are baffled. They had bought expensive AI licenses for their teams. They expected productivity to double. Instead

Open article: What the new Italian law on AI education teaches organizations about workforce strategy by Manuel Barragan • 3 min read

What the new Italian law on AI education teaches organizations about workforce strategy

The talent war is over.

You lost.

There are not enough experts to fill your open roles.

Here are the 4 rules to develop a "Build from Within" engine.

1. Hunt for Curiosity, Not Credentials
 ❌ Old way: Screen for degrees.
 ✔️ New way: Screen for the employee who hacked their own workflow.

2. The 20% Rule
 ❌ Old way: Training happens after hours.
 ✔️ New way: Allocate 20% of the work week to learning new tech. Paid.

3. Kill the "Fear of Firing"
 ❌ Old way: Mistakes = Performance Improvement Plan.
 ✔️ New way: Celebrate the pilot that failed because it taught you something.

4. Cross-Pollinate
 ❌ Old way: Marketing stays in Marketing.
 ✔️ New way: Move the math wiz from Ops to Data Science.

❌ Stop waiting for a unicorn to apply. ❌

✔️ Build the horse you have into a unicorn. ✔️

Which of these rules is most missing from your culture?
At Digital Transformation Strategist, we guide organizations through this shift.

Stop Buying Tech Toys Until You Build a Fence

Companies throw cash at Tech Toys and expect magic. It didn't happen. Instead, your teams panicked. They saw the new tools as a replacement rather than a helper. Morale dropped, and someone probably pasted confidential financial data into a public chatbot.

This is a failure of organizational culture, not technology. Organizations must build a perimeter. How?

✅ Centralize Your Governance
 Stop the shadow IT purchases immediately. Marketing does not need a credit card to buy an unvetted writing bot. Form a council with IT, HR, compliance, and legal (among other areas). You must validate every tool against security standards before it touches your network. If the tool fails the test, it stays out.

✅ Protect Data with Granularity
 Treat your data like a tiered security system. Classify everything. Public marketing copy goes into the open model. Q4 financial projections stay on your private/secure server. Clear rules prevent expensive leaks.

The corporate sector currently looks like a playground without supervision. We have powerful toys but no fences. We risk injury and data loss daily. Regulation does not stop progress. It creates the safety your team needs to trust the tools.

Is your Tech strategy chaotic or without control? Let Digital Transformation Strategist audit your governance structure today.

The Productivity Paradox: Why Latin American Business Leaders Must Act Now

I sat in a boardroom in Mexico City some month’s ago. The coffee was strong. The strategy was bold. But the CIO looked exhausted. He had a team of brilliant developers, yet they spent their days patching ten-year-old code. They were running fast just to stay in the same place.

This is the Latin American paradox: we have the talent and the hunger, but we lack the structural plumbing to make it flow. Buying tech isn't a transformation; it’s often just an expensive digitization of chaos.

In the article, you will learn how to:
▶️ Rethink your strategy to stop "paving cow paths" and start pivoting toward real value creation.
▶️ Fix your foundation because you can't build an AI skyscraper on a swamp of messy, disconnected data.
▶️ Build talent from within to win the digital war by fostering a culture where it's safe to experiment.
▶️ Stop working alone and embrace partnerships with startups to gain the agility you lack.

Potential does not pay the bills. Execution does. Are you ready to turn your potential into actual results? Let Digital Transformation Strategist look at your current roadmap and find the friction points. Send me a message, and we can start a conversation about real change.

I sat in a "boardroom" in Mexico City some months ago. The coffee was strong. The strategy was bold. But the CIO looked exhausted. He had a team of br

Open article: The Productivity Paradox: Why Latin American Business Leaders Must Act Now by Manuel Barragan • 3 min read

The Productivity Paradox: Why Latin American Business Leaders Must Act Now

The best salesperson blew a massive deal.

He had taken the negotiation course three months prior.

But he forgot the most important rule when it mattered.

He froze on the call.
He caved on price.
Later, he explained himself, "I knew we covered this, but I couldn't recall the specific tactic".

Everybody wanted to blame him.
But it was the organization’s fault.
They had forced him to memorize a textbook in a classroom.
They expected him to recall page 42 while under pressure.
That is not how the human brain works.

They changed their entire approach in just weeks.
They stopped doing "just in case" training.
They started doing "just in time" support.

They built a simple sidebar for his screen.
Now, when he is on a call, he has the key questions right in front of him.
He doesn't need to memorize anything.
He just needs to read.

He closed the next three deals.
He didn't get smarter overnight.
We just stopped making him rely on his memory.

Are you setting your team up to fail by relying on their memory? At Digital Transformation Strategist, we help you fix it.

Corporate Learning Is Broken: Why Your Team Ignores Your New Platform
 
I sat across from a CEO who was fuming. He’d spent a small fortune on a top-tier Learning Management System (LMS), yet completion rates were in the single digits. 📉 He thought the software was broken; I told him the truth: his approach to human curiosity was the problem. In 2026, corporate learning can’t be a separate destination. If it isn't part of the workflow, it’s just noise.

In this article, you will learn how to:
- Shift from compliance to capability by treating learning as a habit rather than a mandated event.
- Foster psychological safety, creating a culture where "I don't know" is the celebrated starting point for innovation.
- Apply Lean and Agile principles to education, breaking down monolithic workshops into high-impact, five-minute micro-learning chunks.
- Leverage AI as a personal tutor to move past generic content and deliver personalized learning paths based on real performance data.
- Move the needle on Data and Risk Management by measuring business outcomes (like code quality or security) instead of useless "hours completed".
 
Most companies react to the rapid expiration of skills with panic. They buy content libraries and mandate "Digital Basics" courses that busy managers click through while muted on a conference call. This is a process failure. When you treat learning as a supply chain for talent, you realize that an untrained workforce isn't just a productivity drain; it’s a security risk.
 
The tools of 2026 (AI coaches, VR, instant analytics) are only as powerful as the strategy behind them. You can't buy a learning culture; you build it by respecting your people's time and solving their immediate problems. Stop tracking completion rates and start tracking growth.

Are you struggling to get your team to adopt new digital skills? Let’s stop wasting your budget on ghost-town platforms and start driving results. Digital Transformation Strategist can help you turn your learning strategy into a competitive advantage.

I sat across from a CEO some time ago. He looked tired. His company had just spent a small fortune on a top-tier learning management system. He slamme

Open article: Corporate Learning Is Broken: Why Your Team Ignores Your New Platform by Manuel Barragan • 4 min read

Corporate Learning Is Broken: Why Your Team Ignores Your New Platform

AI will not save your broken business.

It will just make you make bad decisions faster.

Here is the 3-step foundation you need before you buy the hype.

1. The "Swamp" Check (Data Hygiene)
 ◾ Most companies sit in a swamp of dirty data.
 ◾ AI needs fuel. That fuel is accurate history.
 ◾ If your customer records are a mess, your insights will be worthless.
 Rule: Fix the data at the source, not in the report.

2. The Speed Test (Cloud Strategy)
 ◾ Stop treating the cloud like a hard drive in the sky.
 ◾ It is not about storage. It is about speed.
 ◾ Can you deploy a new tool in 20 minutes?
 ◾ If it takes 3 months to provision a server, you have already lost.
 Rule: value agility over ownership.

3. The Silo Breaker (Integration)
 ◾ Sales sells what Operations cannot deliver.
 ◾ Operations builds what Sales cannot sell.
 ◾ Why? Because their systems do not talk.
 ◾ Connect the pipes.
 Rule: If data has to be emailed, the process is broken.

You cannot apply a fresh coat of paint to a crumbling wall.
Fix the structure first.

Which part of your foundation is cracking right now? 
At Digital Transformation Strategist, we help you arrange it.

At one organization, they looked at their learning logs and saw a massive failure.

They assigned "Advanced Python" to the entire engineering team.

The senior architect finished it in 4 minutes.

He was insulted.
"I wrote a book on this", he told everyone.
"Why are you making me click through these slides?"

He was right.
They were treating their experts like novices.
They were wasting their time satisfying a compliance box.

So they turned on the AI engine.
Now, the system looks at his code commits.
It "sees" that he is flawless in Python but struggles with documentation.
It only sends him resources on technical writing.

It sees the junior dev struggling with syntax.
She gets the Python course.

The complaints stopped.
Engagement went up.
People learn what they need, not what HR thinks they need.

Are you wasting your experts' time with generic content? At Digital Transformation Strategist, we guide organizations through this shift.

Corporate Learning Is Broken

This is a sharp look at how habits drive Digital Transformation more than software, Digital Transformation Strategist. A true learning culture starts with trust and curiosity. Focus on psychological safety, reward questions, and measure problem-solving for real growth.

High-performance teams thrive when leaders trade tracking logins for measuring actual problem-solving. These human-centric strategies are the best way to ensure organizational effectiveness in any business unit.

Which of these habits is missing in your organization? DM me to talk more about it.

View company: Digital Transformation Strategist

Companies waste billions on learning platforms.

Most employees rarely log in after the first week.

Stop buying software and start building these 5 habits.

1️⃣ Safety Over Software
 - You cannot force someone to learn.
 - You must invite them.
 - Fear kills curiosity faster than bad UI.

2️⃣ The "I Don't Know" Rule
 - Make ignorance acceptable.
 - When a leader admits they don't know an answer, the team relaxes.
 - It signals that learning is part of the job.

3️⃣ Reward the Question
 - Most companies reward the expert.
 - You should reward the student.
 - Praise the person who asks the "dumb" question that everyone else was thinking.

4️⃣ Schedule the Time
 - Tools do not create time.
 - Calendars do.
 - Block out 30 minutes a week for the team to learn something new.
 - No meetings allowed during this block.

5️⃣ Measure Growth, Not Logins
 - Stop tracking how many hours people spend on the platform.
 - Track how many new problems they solve.
- Fix the human side first.
 - The technology is the easy part.

Which of these habits is missing in your organization? Contact Manuel Barragan to talk more about it.

Most companies burn cash automating waste.

They see a bottleneck and immediately buy software to clear it.

The use of this 4-step audit shared by Digital Transformation Strategist highlights a vital truth about why many projects fail.

This is disciplined thinking. I have seen many leaders buy tools to speed up processes that should not exist. 

Digital Transformation must start with a whiteboard and a red pen to find real value. Fix the process first, test it in one team, then bring in tech. Automation should multiply value, not hide broken flow.

What process in your company needs to be reviewed today? 
DM me to start talking about it.

Most companies burn cash automating waste.

They see a bottleneck and immediately buy software to clear it.
 
Use this 4-step audit before you spend a dime on technology.

We use this with every client before we talk about Digital Transformation.
 
Step 1: The Value Stream Map
 - Draw the process on a whiteboard.
 - Include every hand-off, approval, and waiting period.
 - Circle every point where work stops.
 - If a step does not add value to the customer, mark it in red.
 
Step 2: The Elimination Round
 - Do not ask "How do we make this faster?"
 - Ask "Why does this exist?"
 - If the answer is "compliance", check the actual rule.
 - Usually, the rule is a myth.
 - Delete the red steps.
 
Step 3: The Revenue Test
 - Look at what is left.
 - Will digitizing this create new revenue?
 - Or will it just save pennies?
 - If it is only about cost, be careful.
 - Cost-cutting projects often cost more than they save.
 - Revenue projects pay for themselves.
 
Step 4: The Tiny Pilot
 - Do not roll out globally.
 - Pick one team.
 - Change the process manually for them.
 - See if it breaks.
 - If it works, then bring in the IT team.
 - Fix the flow.
 - Then buy the tool.

What process in your company needs to be reviewed today? 
Contact Manuel Barragan to start talking about it.

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Aishwarya Srinivasan's LinkedIn Performance

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