It Was Never About the Models

Seriously, if only model quality was preventing companies from transforming with AI, then the last couple of years should have been enough to change almost every organization. Every few months we got a better model. GPT-4, Gemini, Claude, Llama, reasoning models... the pace has been unbelievable. Yet when I look around, most organizations are still figuring out how to move beyond pilots and isolated use cases. They have a chatbot, maybe an internal copilot, some proof of concepts, but very few can confidently say AI has fundamentally changed the way they work. That makes me wonder... maybe it was never only about the models.

Now before someone jumps in, I am not saying models don't matter. They absolutely do. The leap we have seen in the last couple of years is remarkable and has unlocked possibilities that simply didn't exist before. But if the models have improved so much, why are so many organizations still struggling to create measurable business value? According to McKinsey's latest State of AI survey, AI adoption is widespread today, yet nearly two-thirds of organizations are still experimenting or running pilots, and only around 39% report measurable enterprise-level EBIT impact from AI. That statistic stayed with me because it tells me that getting access to a better model was only one part of the puzzle. The harder part was, and perhaps still is, everything around it.

For years we have spoken about data quality, governance, infrastructure, integration between systems, master data, data lakes, pipelines... frankly none of those topics were glamorous. If someone spoke about data governance five years back, not many people got excited. Today everyone wants Agentic AI, reasoning models and autonomous workflows, but if the underlying data is fragmented, scattered across SharePoint folders, PDFs, PowerPoint presentations and Excel sheets, then even the smartest model eventually runs into the same wall. In a way, GenAI didn't create these problems. It simply exposed them.

Another thing I have been noticing, and maybe it's just me, is that nowadays when we say AI, most of the conversations automatically become GenAI conversations. If someone says, "We are using AI," chances are they mean they have built a chatbot or some assistant. Go back five or six years and AI meant something very different. We were talking about anomaly detection, forecasting, recommendation systems, optimization, predictive maintenance, computer vision, classification, regression and dozens of other machine learning applications that quietly solved business problems. There wasn't this level of excitement around them, there weren't billions of dollars flowing into foundation models, and there definitely wasn't this fear of "If we don't do AI today we'll be left behind tomorrow."

Sometimes I wonder whether that narrative itself has changed enterprise priorities. Billions, perhaps even trillions if yu include the entire ecosystem, have been invested in developing and serving these large language models. Naturally, a very strong narrative has followed. Boards are asking about GenAI strategy, companies are hiring for Agentic AI, budgets are being allocated much faster than they ever were for traditional ML initiatives or even digital twins. None of this is necessarily bad. In fact, it has accelerated AI adoption more than anything we have seen before. But it also makes me think that organizations which quietly invested in data platforms, analytics and ML over the last ten or fifteen years are probably in a much better position today because GenAI is building on foundations they already created.

Think about coding for a minute. We see tremendous productivity gains there, and honestly I am not surprised. Code was always written for machines. It has syntax, structure, version control, tests, documentation and well-defined rules. LLMs naturally excel in that environment. Now think about enterprise knowledge. PPTs were created for people. Word documents were written for people. Excel sheets evolved over years with custom formatting and assumptions known only to the author. Meeting notes, PDFs, scanned reports, emails... these were never designed so that machines could understand them. We are now asking AI to reason over decades of human communication. Maybe expecting a better language model alone to solve that was optimistic from the beginning.

Okay... a lot of huff and puff. So where do I think organizations should actually start?

Personally, I feel the first layer should be a knowledge assistant. Not an autonomous agent that promises to do everything, but something much simpler and probably much more valuable. Help people find the right reports, engineering documents, standards, previous project learnings or design decisions from the company's own knowledge base. Especially all the unstructured information that nobody remembers exists. Let AI help people reach the right information in minutes instead of hours, but let humans still apply their judgement. I still believe judgement should stay with people.

The second layer, once yu are reasonably confident about your data maturity, is an analytics assistant. Imagine a finance organization where all mutual fund data, market data and risk metrics are already cleaned, governed and available. Instead of writing SQL queries or navigating ten different dashboards, analysts could simply ask questions, run scenarios, validate hypotheses or compare portfolios in natural language. AI isn't replacing analytics there. It is making analytics accessible to more people.

Coding... well there is already so much written about it that I don't think I have anything unique to add. 😄

On a personal level too, I still use these tools as assistants rather than replacements. If I have an important presentation coming up, I might ask an LLM to behave like my toughest stakeholder. "What questions would yu ask? Which assumptions am I have missed? Are there gaps in my argument?" That is a fantastic use case. Similarly, if I am building an assistant for battery charging systems, I will happily use an LLM to understand the overall process and clear some of my doubts. But if my goal is to become a battery expert, then I would still spend time with domain experts, read standards, understand the physics and learn from people who have spent years in that field. AI can definitely accelerate the journey, but I wouldn't outsource the journey itself.

Another interesting observation came from some of the recent industry reports. McKinsey's AI survey shows widespread adoption, but limited enterprise-scale impact in many organizations. BCG also talks about how the organizations creating the most value are not just deploying models but redesigning business processes around them. That actually resonated with me. Maybe we have spent the last two years asking questions like GPT or Gemini? Claude or Llama? Closed source or open source? Those are interesting discussions and they matter, but perhaps the more important enterprise questions today are much simpler.

Do we trust our data?

Can our systems actually talk to each other?

Can employees find the information they need without spending half a day searching?

Are we redesigning workflows, or simply adding another chatbot on top of an existing process?

Those questions don't sound as exciting as discussing the latest reasoning model, but I have a feeling they will decide who creates real business value over the next five years.

Maybe the title of this article isn't completely true after all.

It was about the models.

The progress has been phenomenal and there is no denying that.

But enterprise transformation was never only about the models. It has always been about the data, the engineering, the workflows and ultimately the people using them. Perhaps what GenAI really did was force all of us to finally look at those foundations again.

References

McKinsey – The State of AI

McKinsey – The State of AI: How Organizations Are Rewiring to Capture Value

BCG – Closing the AI Impact Gap

Doing the Right Thing

Doing the Right Thing - that is the final chapter of the book -- Designing Data-Intensive Applications - 2nd ed by Martin Kleppmann and Chris Riccomini and boy am I hooked !
I actually jumped straight to the last chapter, and I genuinely feel like writing my reflections after every chapter I read. This is the second one. (Yu can check out the first reflection here - Encodings)

This chapter isn't really about databases or distributed systems. It's about something much bigger, it is about doing the right thing. More specifically, collecting data responsibly, using it ethically, and understanding the trade-offs that come with it. One example the author gives is of someone suffering from a rare/critical disease. The more researchers who have access to that person's data, the better the chances of finding a treatment that could help not only that individual but many future patients as well. But now imagine the other side of that equation. What if sharing the same data affects that person's ability to get insurance, a loan, or even a job? Suddenly, the same data that could save lives can also become a liability. That's the thing with these problems they rarely have a perfect answer.

Another interesting point the author makes is that, especially in research, we often don't know upfront how data will eventually be used. New questions emerge, new discoveries happen, and entirely different research directions appear. So it's not always about organizations being unwilling to explain everything. Sometimes they genuinely don't have all the answers when the data is first collected.

Then I came across this paragraph, and honestly, I had to stop reading for a few minutes -- 

Surveillance:

As a thought experiment, try replacing the word data with surveillance, and observe whether common phrases still sound so good [23]. How about this: “In our surveillance-driven organization we collect real-time surveillance streams and store them in our surveillance warehouse. Our surveillance scientists use advanced analytics and surveillance processing in order to derive new insights.”

That one paragraph completely changed how I looked at many of the things we casually accept today.

It also made me reflect on my own work, but even more on the amount of data that is already floating around us. I still receive countless calls for loans and credit cards, and honestly, I have no idea where my phone number came from or how it reached those companies. Once that information is out there, there is practically no mechanism to pull it back or even know who has access to it.

The chapter then walks through several fascinating examples. One that genuinely surprised me was about smartwatches. Researchers have shown that motion sensor data from wearables can, under certain conditions, be used to infer the keys someone types and thus crack the password. Whether that is easy or difficult isn't really the point. The point is that something we mostly think of as a harmless fitness tracker can reveal much more than we imagine. It was one of those moments where I paused and thought, "I never looked at it that way."

The book also discusses something we all know at some level—that many of the "free" services we use are funded through data collection, primarily for advertising and marketing. That itself wasn't a new revelation for me. What I appreciated was how the author connected all these seemingly unrelated examples into one larger conversation about ethics, responsibility, and power.

Another section that really changed my perspective was around consent.

Until now, my thinking had always been fairly simple. If I don't like the terms and conditions, I simply don't use the service.

This chapter challenged that belief.

The author argues that many of these services are no longer just products—they have become part of our social infrastructure. If participation in society increasingly depends on using a platform, then saying "just don't use it" isn't always a practical choice anymore.

Take WhatsApp as an example. Friends', Family and even unOffical Office groups are there. If someone decides not to use WhatsApp, they aren't simply opting out of an app—they are slowly disconnecting themselves from an important part of modern communication. WhatsApp is just one example, and I'm sure yu can think of many others.

The same thought came to me while thinking about ride-hailing apps. Could I live without quick-commerce apps? Probably yes. But in cities like Mumbai or Bangalore, could I realistically avoid Uber or Ola altogether? That's becoming much harder. Many taxi and auto drivers now prefer accepting rides through those apps, especially for airport trips. These platforms have quietly become part of everyday infrastructure.

Again, there isn't a perfect answer here.

This chapter didn't try to provide one either.

What it did do was change the way I think about data. It made me realize that privacy isn't simply about hiding information. Consent isn't simply clicking "I Agree." And doing the right thing isn't always obvious when technology, research, business, and society all intersect.

Sometimes the most valuable thing a book can do isn't answer your questions.

It changes the questions yu ask.

These are just a few of my reflections after reading the chapter, and as always, I'd love to hear yours. 🙂

Are We Losing the Ability to Listen to Our Body?

This one is going to be a highly opinionated and biased post. :P

A few months back, one of my friends went to a dietitian, and one of the recommendations was to have exactly 25 grams of chutney with a meal. I remember thinking.. WTF. Not because nutrition doesn't matter, but suppose yu end up eating 30 grams instead of 25. The stress of constantly thinking "Oops, I overdid it" might end up doing more harm than those extra five grams ever would.

That got me thinking. Are we overdoing tracking and measurements? Wearables, fitness trackers, sleep scores, food labels, calorie counters... it almost feels like we are heading towards paralysis by over-analysis. These days our watch tells us how well we slept, how stressed we are, whether we should train, whether we should rest, and sometimes it almost feels like it knows us better than we know ourselves. I even came across a LinkedIn post mentioning "Protein Chai." Not sure how true that example was, but it perfectly captures where we seem to be heading—we take something useful and somehow find a way to overdo it.

Don't get me wrong. I absolutely believe trackers have their place. Take someone suffering from insomnia, for example. If they are trying different treatments, medicines, meditation, music, or changes in routine, then tracking their sleep for a few weeks makes complete sense. It helps identify what actually works. But once things stabilize, maybe stop measuring every single night and let your body take over again. The goal of technology should be to teach us something, not become something we can't function without.

Listening to our body—that's really what I wanted to talk about.

I'll use running as an example because I've lived through that grind. For many years, including the two full marathons I completed, I used nothing more than a simple digital watch with a stopwatch. I knew my running routes, tracked the overall time, and that was about it. I never chased a particular cadence with a metronome, never worried about staying in Zone 2 all the time, and never kept staring at my wrist every few minutes. Instead, I tried listening to my body. How were my muscles feeling? Was my breathing deep or shallow? Was I landing too hard? Could I push a little more today, or was it one of those days where I should simply back off? Those became my metrics.

Interestingly, I later heard David Goggins talk about something very similar. Early in his SEAL training and running journey, he relied much more on effort and feel than on technology. The heart-rate monitors and advanced gadgets came much later. Forget wearables—I am guilty of this too—but I have also seen people run entire marathons with headphones on from start to finish. Personally, I'm completely against that for my longer runs. For long runs - 10 miles and above, I prefer no music at all. I want to hear my breathing, feel my stride, notice if I'm getting tired, or if my form is slowly falling apart. Music, for me at least, drowns out that inner conversation. Of course, that's just my preference, and everyone is different.

The same thought applies beyond running. Want to eat healthier? Maybe the answer isn't another tracking app. Maybe it's simply eating less packaged food, cooking more meals at home, and including the ingredients yu actually want. Sure, get a health check-up every six months or once a year to make sure your vitals are within range. That's sensible. But we also need to trust that our bodies are remarkably good at adapting. Mindful eating is wonderful. Obsessing over every gram... maybe not so much.

I'll end with a completely different example.

Formula 1 cars have hundreds of sensors. Teams analyze thousands of data points every lap. They know tyre temperatures, brake temperatures, engine settings, wind conditions, fuel loads—almost everything. Yet if data alone decided races, every driver would perform exactly the same.

They don't.

The very best drivers constantly talk about feeling the car. Yu often hear commentators mention their internal gyroscope—their ability to sense grip, balance, weight transfer, and tiny changes that no dashboard can fully communicate. The data helps optimize the machine, but intuition still wins races.

Maybe it's the same with us.

In a world where social media has already taken over so much of our attention, let's not allow trackers and wearables to completely replace our ability to listen to our own body. Technology should improve our awareness, not replace it.

Sometimes, the most advanced sensor we have... is still ourselves.