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
Nice article. Keep it up brother!
ReplyDeleteThanks Bhai :)
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