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

Managing Director på Northridge Analytics

Most CDOs I work with start by asking the same question:
“Are we actually ready for the AI ambitions we’re pushing?”

I usually begin by sharing a 30-Day Data Governance Kickstart Kit a practical reference for how foundations, ownership, and governance tend to be established in real organizations.

It’s not a silver bullet or a DIY fix. It’s a way to ground the conversation in reality.

If you want to sanity-check how this would apply in your organization, I also run a short, senior-led Data Foundations & AI Readiness Reality Check.

No tooling. No hype. No transformation pitch.

Send me a message and I’ll point you in the right direction.

John Wernfeldt

Most companies think data governance is about rules.

It’s not.

It’s about who gets to decide what, when things change.

What actually happens in real organisations:
→ Data exists
→ Analytics gets built
→ AI gets layered on top
→ Decisions stall because no one owns the definition

This is the operating model that fixes that.

→ Data foundation creates shared facts
→ Data management makes them understandable
→ Decision authority assigns ownership, change rights, escalation
→ Analytics and AI finally have something stable to stand on

Miss the decision layer and everything above slowly degrades.
Dashboards drift.
Models lose trust.
Meetings multiply.

Governance isn’t control.
It’s clarity.

If decision authority is unclear, everything above it becomes opinion.

That’s the whole model.

CDOs & data leaders → from firefighting to strategic authority | Governance & Foundations executives trust

Every struggling CDO I've met has the same problem.

All the responsibility. None of the authority.

CEO: "Fix everything. Fast."
CFO: "No budget."
CTO: "Don't touch our systems."
CMO: "Our data is fine."
Sales VP: "We have our own process."
CHRO: "HR data stays with HR."
Legal: "Compliance first. Everything else second."

CDO: "Figure it out."

The most powerful title with the least authority.

I've watched this play out dozens of times.

The CDO gets hired to fix data quality, enable AI, and drive analytics transformation.

But nobody gives them the authority to actually do it.

They can't touch systems. Can't get budget. Can't override departmental definitions. Can't centralize anything.

So they spend years negotiating and building consensus while the CEO wonders why the data strategy isn't moving faster.

Here's what actually works:

The CDO needs executive authority over three things:

→ Data definitions: final say on what metrics mean
→ Change approval: who can modify core business logic
→ Escalation rights: authority to break ties when domains disagree

Without those, the CDO is a coordinator, not a decision-maker.

And coordination without authority just creates more meetings.

Which one is your CDO?

The board is not confused.

You are.

If you walk in talking about metadata, lineage and tooling, don’t be surprised when funding disappears.

Boards don’t fund “better data.”
They fund ↓
→ Risk reduction
→ Revenue protection
→ Cost efficiency
→ Competitive advantage

When teams say:
“We need better data quality”
“The platform doesn’t support this”

The board hears:
IT cost. More complexity. No business case.

So they kill it.

If you want board support for governance:

Do this instead ↓
→ Quantify one material risk in money
→ Tie one KPI to revenue or margin
→ Show what breaks without clear ownership
→ Commit to a visible, time-bound outcome

Governance is not a data initiative.
It’s a control system for the business.

And control systems get funded.

Frankly, I keep hearing leaders talk about layers of AI
while quietly assuming the layers underneath just exist.

They don’t.

On the left is what gets airtime in exec rooms.
On the right is what actually decides whether AI works or quietly embarrasses u.

Here’s the disconnect I see every week:

→ AI roadmaps assume clean, trusted inputs
→ The data stack underneath is still debated, patched, and owned by “Tom”
→ Models look smart
→ Decisions still get questioned

AI doesn’t fail because the model is bad.
It fails because no one agreed on the data before trusting the output.

What actually makes AI work isn’t another layer on top.

It’s boring, uncomfortable clarity underneath:

→ Clear ownership of data and metrics
→ Definitions that don’t change per meeting
→ Data quality enforced before usage
→ Governance that protects decisions, not documentation

Until that exists, AI just accelerates confusion.

Same arguments.
Faster answers.
Higher stakes.

If u want AI to scale, stop starting with AI.

Start with the layers that make someone confident enough to act on the result.

Most organizations confuse three roles that should never be confused.

Data Governance. Data Ownership. Data Stewardship.

They sound similar. They're not.

And when you mix them up, everything breaks.

Here's what each one actually does:

DATA GOVERNANCE = Decision authority
→ Defines who decides
→ Sets rules for change
→ Approves metric definitions
→ Establishes escalation paths
→ Designs control structure

Focus: Control over decisions.

DATA OWNERSHIP = Accountability
→ Accountable for a specific data domain
→ Ensures definitions are clear
→ Responsible for business meaning
→ Owns impact of changes

Focus: Who is responsible.

DATA STEWARDSHIP = Operational care
→ Maintains data quality
→ Documents definitions
→ Monitors issues
→ Coordinates fixes
→ Supports users

Focus: Day-to-day execution.

The formula is simple:

Governance = who decides.
Ownership = who is accountable.
Stewardship = who executes.

What happens when these get mixed up?

→ Owners without authority
→ Stewards expected to decide
→ Governance without accountability
→ Endless meetings

This creates friction.

The organizations that separate these roles clearly move faster, make better decisions, and spend less time in meetings arguing about who owns what.

The ones that don't spend years in governance where everyone talks and nobody decides.

Which one are you?

Every failed AI strategy I've seen had the same problem.

The people building the slide deck had never seen the data.

Inside the room:
• Chief AI Officer: read the Gartner report
• VP of Digital: saw it at Davos
• Head of Innovation: built a GPT wrapper
• Strategy Consultant: has the slide deck
• External Vendor: selling the solution

Outside the room:
• The Data Engineer, not invited, fixing what they just announced

This is how AI strategies get built.

Leadership reads about AI in a report, sees a demo at a conference, and decides to "move fast."

They bring in consultants who've never seen the data.

They invite vendors who promise everything works out of the box.

They build a strategy deck with growth arrows and timelines.

Then they announce it.

And the data engineer finds out the same way everyone else does.

Now they're expected to deliver on promises made by people who've never looked at the data quality, the pipeline architecture, or the definition conflicts that have existed for three years.

Here's what actually works:

Invite the people who know the system before you announce the strategy.

Not after.

The slide deck doesn't break when you deploy it.

The data does.

Sound familiar?

Most organizations treat data governance like a compliance project.

It's not.

It's the operating framework that makes everything else work.

Here's how data becomes trusted, usable, and scalable:

DATA FOUNDATION

This is where it starts. Not with dashboards or AI models.

→ Master data that's shared and neutral
→ Transaction data you can trace
→ Source systems you can rely on
→ Data products that deliver value
→ Event and IoT data that's structured

Make data understandable and reliable.

DATA MANAGEMENT

The layer most organizations confuse with governance.

→ Data quality monitoring
→ Metadata management
→ Lineage tracking
→ Cataloging

This operationalizes the rules. But it doesn't set them.

DECISION AUTHORITY

This is governance. The layer everyone skips.

→ Metric ownership assigned
→ Definition rights clarified
→ Change authority established
→ Escalation paths defined

This is what scales. Not the catalog. Decision clarity.

ANALYTICS & AI

Built on governed decisions.

→ Dashboards and reporting that people trust
→ Advanced analytics that stay accurate
→ RAG and GenAI that don't drift
→ AI models and agents that scale

BUSINESS OUTCOMES

→ Trusted metrics
→ Faster decisions
→ Scalable analytics
→ Safe AI adoption

The framework connects to:

→ Technical enablement (cloud, platforms, APIs, security)
→ Operating model (roles, governance cadence, stewardship)
→ Risk and control (regulatory compliance, auditability, ethics)

Here is how I see it:

If ownership is unclear, nothing above scales.

You can build the best data platform in the world. The cleanest pipelines. The most advanced AI.

But without clear ownership and decision authority, it all breaks when someone asks "who approved this definition?"

Start with the foundation. Build the governance layer. Then scale. Not the other way around.

Most teams think governance slows delivery.

What actually slows delivery is rework.

This is the lifecycle I keep seeing work in practice:

→ Design with intent
Clear metric. Clear owner. Clear why.

→ Build once
Pipelines, quality checks, lineage baked in. No hero fixes later.

→ Publish with rules
Access, certification, monitoring. Explicit, not implied.

→ Consume and learn
Dashboards, models, decisions. Feedback goes back to the source.

Governance doesn’t sit on top of delivery.
It sits inside it.

Fewer surprises.
Faster reuse.
Less cleanup disguised as “iteration”.

Governance isn’t a barrier.
It’s quality control for your data factory.

Most companies say they’re “AI-ready”.

Then you look closer.

→ Conflicting numbers
→ Manual reports
→ Excel doing unpaid overtime
→ Long discussions ending with “let’s double-check”

That’s not an AI problem.
That’s a maturity problem.

This ladder is the pattern I keep seeing:

Level 1 → Chaos and firefighting
You move fast, but trust is low.

Level 2 → Defined foundations
Things look better, but everything is still slow and fragile.

Level 3 → Governed and trusted
This is the tipping point. One number. Clear ownership. Fewer arguments.

Level 4 → Scalable and automated
Self-service actually works. Integration doesn’t break every week.

Level 5 → Strategic AI-ready
Models, agents, predictions. Now it compounds.

The uncomfortable truth:
You can’t skip Level 3.

No amount of GenAI, agents, or dashboards will compensate for data nobody trusts.

Don’t build Level 5 models on Level 1 data.
Fix the foundation first.

Curious where you’re actually standing on this ladder?

I help CDOs stop firefighting data problems and start leading with strategic authority | Ex-Gartner

Most data governance conversations die in the first 3 minutes.

Because we start with data.

Metadata gaps.
Lineage tooling.
Framework maturity.

Sounds operational. Business tunes out.

If you want the business to care, flip it ↓

Start here:
→ Who decides when this KPI changes?
→ What revenue does this protect?
→ What risk does this expose?
→ How do we avoid conflicting numbers?

That’s governance.

Clear decision authority → Controlled change → Reliable management → Trusted outputs.

Revenue growth, cost control and risk reduction sit at the top.
Decision authority sits in the middle.
Data management supports it.

Data governance is not about data.

It’s about who controls decisions.

Frankly, most data governance programs fail before they even start.

Not because the framework is wrong.
Not because people don’t care.
But because teams jump straight to “Step 5” and call it progress.

I’ve watched this play out for years:

→ Buy a tool
→ Create policies
→ Schedule a council
→ Still argue about the same numbers six months later

This board is the boring, unsexy version that actually works.

It starts where governance usually avoids going:

→ One concrete business problem
→ One metric that causes real pain
→ One owner who is allowed to decide

Then, and only then:

→ Define roles so “everyone” stops owning nothing
→ Lock definitions so debates end
→ Fix quality at the source, not in PowerPoint
→ Automate last, once behavior is stable
→ Measure trust, not documentation produced

The mistake is treating governance as a rollout.
It’s not.

It’s a habit.

Start small.
Make it visible.
Prove value fast.
Then scale what already works.

If governance feels heavy in your org, you probably skipped steps 1–4 and went straight to tooling.

That’s not governance.
That’s hope with a subscription fee.

Back to regular posting tomorrow. Probably about governance again.

Most business leaders think data governance is an IT project.

It's not.

It's the operating model that makes everything else work.

Here's how I explain it:

DATA PRODUCTS
Deliver trusted, reusable data for the business.

This is what everyone wants. Clean data. Reliable numbers. Consistent definitions.

But you can't build this without the layers around it.

DATA MANAGEMENT
Operationalize rules, standards, and controls.

→ Data quality monitoring
→ Lineage tracking
→ Metadata management
→ Master & reference data
→ Access rules

This is how you deliver the data products.

But management without governance is just firefighting.

DATA GOVERNANCE
Define decision rights, ownership, and accountability.

→ Data ownership model
→ Decision escalation paths
→ Domain accountability

This is where most organizations fail.

They build data management without governance.

So nobody owns the definitions. Nobody has authority to decide. Nobody is accountable when something breaks.

Management without governance creates activity without control.

DATA OPERATING MODEL
Orchestrate data accountability across the enterprise.

→ Stewardship at scale
→ Policy enforcement
→ Risk & compliance alignment

This connects everything to business outcomes:

→ Regulatory compliance
→ Financial reporting confidence
→ AI readiness
→ Board-level reporting
→ Enterprise KPIs
→ M&A integration
→ Vendor & partner data
→ Cross-domain decision-making
→ Trust in numbers
→ Issue management

The business doesn't care about your data catalog.

They care about whether the numbers on the dashboard are right.

Whether forecasts are accurate.

Whether compliance risk is managed.

Whether AI can be trusted.

Governance is how you connect the work to the outcomes.

Not an IT project. An operating model.

Want the full blueprint? I've documented the complete operating model framework, get it for free here:

https://lnkd.in/d68gxqRq

Every AI strategy I've seen stall had the same problem.

Not the models. Not the talent. Not the budget.

Legacy ETL jobs nobody documented.

The spaghetti remains.

I've seen this pattern in every organization rushing to adopt AI.

Leadership wants models in production. Data teams want to modernize the stack. Everyone agrees the future is clear.

But nobody wants to talk about the undocumented pipelines holding everything together.

The ones only one person understands. The intern who built it three years ago and left. The jobs that break every quarter and get "temporarily fixed" until next time.

You can't build AI on top of that.

AI doesn't fix technical debt. It inherits it.

Here's what actually works:

→ Document what's running — not perfectly, just enough to know what breaks what
→ Assign owners to critical pipelines — one person accountable per job
→ Define data lineage for model inputs — if you can't trace it, you can't trust it
→ Test the exit plan — can you turn off the legacy job without breaking production?

The catalog matters. The spaghetti stays until someone owns untangling it.

AI strategy roadmaps don't fail because of the models.

They fail because nobody fixed the foundation first.

The board does not fund governance.

It funds protection and growth.

If you're still selling "better metadata" and "data quality improvements," you're losing before you start.

Here's how to build a business case that actually works:

STEP 1: IDENTIFY A MATERIAL RISK

Pick one that costs real money:
→ Regulatory exposure
→ Incorrect reporting
→ Forecast errors
→ Incentive misalignment
→ AI instability

Quantify the exposure in money.

Not "our lineage is weak."
Real dollars at risk.

STEP 2: IDENTIFY A VALUE LEVER

Tie governance to business performance:
→ Revenue leakage
→ Pricing optimization
→ Working capital impact
→ Customer churn
→ Decision delays

Stop talking about maturity levels.

Start talking about margin.

STEP 3: CONNECT TO GOVERNANCE DESIGN

Show how structure reduces risk:
→ Named metric owner
→ Defined change control
→ Escalation path
→ Definition control
→ Approval structure

This is where most governance cases fall apart.

You show the risk. You show the value.

But you never connect how decision authority actually protects it.

STEP 4: DECISION AUTHORITY

Make it clear who decides:
→ Definition rights
→ Change approval
→ Conflict resolution
→ Documented decisions

This protects value.

Without this, governance is a cost center that slows things down.

With this, governance is the operating condition that makes speed safe.

EXPECTED IMPACT

When you tie it all together, the outcome is clear:
→ Revenue protected
→ Faster decisions
→ Reduced rework
→ Lower compliance exposure
→ Fewer reporting incidents
→ Controlled AI deployment

Protection + growth.

That's the business case.

The organizations that get funded are the ones that stop selling hygiene and start selling impact.

Which side are you on?

I've never seen an AI project fail because of the model.

It's always the data definitions nobody wanted to fix three years ago.

Everyone's moving to the penthouse. Nobody's been to B2.

I get it. Leadership wants AI agents. Teams are shipping dashboards. Analytics is self-service. Everything looks clean from the top floor.

But ask what "active customer" actually means and you get three different answers.

Ask who owns the definition of "revenue" and nobody raises their hand.

The foundation is broken. The penthouse doesn't notice until something breaks.

Here's what happens next:

Six months after launch, the AI agents start drifting. Dashboards show conflicting numbers. KPIs don't match across teams.

And nobody can explain why because nobody fixed the definitions.

Most organizations skip B2 because it's not exciting work.

Fixing data definitions doesn't get you promoted. Building AI agents does.

But the penthouse collapses when the basement floods.

What actually works:

→ Fix definitions before you build on top of them
→ Assign ownership at the data layer, not the dashboard layer
→ Test the foundation before you add floors

I've never seen an AI project succeed long-term without this.

Last year I walked into a boardroom that looked exactly like this picture.

The exec team had just approved a $2M AI initiative.

GenAI. Predictive models. AI agents. The works.

I asked one question:

"Who owns the customer data this will run on?"

Silence.

Not awkward silence. Terrified silence.

Nobody knew. Nobody had even thought about it.

They were pulling blocks from the bottom of the tower to build the top.

I've seen this pattern too many times now.

Organizations stacking AI Strategy, Automation, and Predictive Insights on top of a foundation where basic blocks are missing → Ownership, Lineage, Data Quality, Metadata.

And just like Jenga, it doesn't collapse right away.

It wobbles.

→ Dashboards nobody trusts
→ KPIs that contradict each other
→ Models that work in testing and fall apart in production

Then one day, someone asks a simple question. "Where does this number come from?"

And the whole tower comes down.

Here's what that experience taught me:

AI is not a technology problem.
It's a governance problem.

The companies I've seen win at AI aren't the ones with the biggest budgets or the flashiest tools.

They're the ones who did the boring work first.
→ They defined their data
→ They assigned ownership
→ They documented lineage
→ They enforced quality standards

Then, and only then, did they build up.

If your organization is racing to stack AI on top of a shaky foundation, take a step back.

Look at what's sitting on the table, untouched.

That's the block you need to put back first.

Dashboards don’t drift.

Decision authority does.

Here’s the pattern I see over and over:

Launch
→ Metrics agreed
→ Everyone aligned
→ “Looks good”

Then:

→ Small definition tweak
→ Minor logic change
→ Quick source update
→ Not documented
→ No clear owner

Repeat that a few times and suddenly:

→ “Which number is right?”
→ Meetings multiply
→ Teams build their own versions
→ Trust erodes

It’s rarely a data platform problem.

It’s this:

→ No named metric owner
→ No explicit definition rights
→ No formal change approval
→ No visible communication of updates

Silent KPI changes slowly destroy trust.

If a metric can change without:
– a clear owner
– explicit approval
– documented impact
– visible communication

Then it isn’t governed.

And over time, it won’t be trusted.

Dashboards don’t drift.
Decision authority does.

Lots of hype around Claude lately, I’m going back to ChatGPT, here is why:




I won’t.

"What if everyone could build software?"

That was Fabian Hedin's opening at the Light event yesterday.

Co-founder and CTO of Lovable. $1M to $200M ARR. Fastest in AI history.

His answer was sharp.

Work at a higher architectural level. Ship ideas in days not months.

He's right. That shift is real.

But sitting in that room I kept thinking about one question nobody asked the panel.

Who is doing the cleanup when everyone ships?

We have seen this before.

When businesses started buying their own applications without IT approval, everyone celebrated the speed. Until the sprawl hit. Conflicting systems. No ownership. IT left cleaning up decisions they were never part of.

This is the same pattern. Just faster.

I have seen companies automate without having the foundation in place. The result is always the same. Conflicting numbers. Everyone pointing at each other when something fails. Nobody accountable for the output.

Days not months means code not months.

Every idea shipped needs to be owned, maintained, and eventually cleaned up.

Do that without governance in place and you don't have a faster organization.

You have a faster mess.

The companies that get data ownership, decision rights, and accountability in place before the build-everything wave hits will save years of cleanup work.

The ones that don't will spend years untangling what they built in months.

Everyone can build software now.

The question is who governs what gets built.

That's the slide nobody showed yesterday.

"Can you pull a quick report on active users?"

I've seen this Slack thread play out hundreds of times.

It's never quick.

Sarah needs 3 numbers for an exec meeting at 2pm.

Dave asks which system to pull from. They have three.

They all show different numbers. Marketing includes free trials. Finance excludes them. Product uses 30-day rolling.

Sarah asks for "the real one."

The pipeline for the main source is broken. Dave's working on a workaround.

He sends it at 1:58pm with a note: "Finance will have different numbers. Different definition. I flagged it."

After the meeting: "Great meeting! They loved it. Quick question though, can we get this broken down by region?"

47 more replies.

No such thing as a quick report.

Not when nobody owns the definition.

Not when three systems calculate it differently.

Not when the source pipeline is held together with workarounds.

Here's what actually makes reports quick:

→ One definition, owned by one person
→ One source of truth
→ Pipelines that don't break every month

Until then, every "quick report" is a negotiation about what the number should mean.

Sound familiar?

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