Showing posts with label AI. Show all posts
Showing posts with label AI. Show all posts

Saturday, July 25, 2026

Technology Was Never the Lesson: What Foresight Taught Me About Leading Transformation

The most valuable thing foresight ever taught me wasn't how to predict the future—it was how to help organizations prepare for it.





Ironically, I didn't learn that by studying the future. I learned it by asking better questions in the present.

In 2021, during one of our Horizon Z workshops, we posed a question that sounded almost absurd at the time.

What if one day purchasing decisions weren't made by people?

Not because humans disappeared.

But because intelligent systems had become sophisticated enough to evaluate options, compare products and make recommendations—or even decisions—on behalf of customers.

At the time, our discussions weren't focused on generative AI. ChatGPT didn't exist yet.

We were exploring emerging technologies like digital twins, robotics, automation, machine learning and other technologies that could fundamentally reshape industries over the next decade.

The question wasn't whether one specific technology would make this happen.

The question was:

If technology changes how decisions are made, what does that mean for businesses?

Within minutes, the conversation shifted.

We quickly realized the technology wasn't the most interesting part of the conversation. Its implications were.

If intelligent systems were evaluating products...

Would emotional marketing still matter?

Would brand reputation influence the decision in the same way?

Or would purchasing become increasingly based on technical performance, interoperability, lifecycle cost and measurable outcomes?

Then the discussion became even more interesting.

Where would those systems get their information?

Would they trust manufacturers describing their own products?

Would independent third-party testing become the most trusted source?

Would peer recommendations matter more than advertising?

Would structured product data become a competitive advantage?

Would superior engineering matter even more than superior messaging?

By the end of the workshop, we weren't trying to predict whether this exact future would happen.

We were asking a much more important question.

If this future became reality, how would we need to think differently today?

That conversation fundamentally changed how I thought about foresight.

Not because we found the answer.

But because we discovered better questions.


Horizon Z wasn't really about technology

Several years ago, I had the opportunity to establish and co-lead Horizon Z, an initiative created to help our organization explore emerging technologies and prepare for the future of business.

On paper, it looked like an innovation program.

We explored artificial intelligence, robotics, automation, digital twins, quantum computing, gamification, and future ways of working.

Initially, I thought our job was to understand these technologies.

It didn't take long before I realized something.

The technology was never the destination.

It was the catalyst.

Our goal wasn't to become experts in emerging technologies. It was to use those technologies to challenge assumptions, expand thinking, and prepare our organization for transformational change.

Each new technology became an opportunity to challenge long-held assumptions about customers, markets, products and business models.

Over time, I realized the hardest part wasn't understanding the technology.

It was helping people become comfortable questioning the way things had always been done.



The questions that mattered were different

Every emerging technology generated the same initial questions.

What does it do?

Should we invest?

What's the business case?

How quickly should we adopt it?

Those are important questions.

But they rarely led to transformation.

The conversations that ultimately changed our thinking sounded different.

  • What assumptions does this technology challenge?
  • If this became mainstream, how would customer expectations change?
  • What new sources of competitive advantage might emerge?
  • If we were building our business today, would we design it the same way?
  • What capabilities should we begin developing before the market demands them?

Those questions weren't about predicting the future.

They were about preparing for multiple possible futures. In foresight and scenario planning, we often explore possible, plausible, probable, and preferred futures—not to predict which one will happen, but to help organizations become more resilient regardless of which future unfolds.

That realization fundamentally changed how I viewed foresight.


Foresight isn't about predicting the future

One of the biggest misconceptions about foresight is that its purpose is to predict the future correctly.

In my experience, it's something much more practical.

Foresight builds an organization's ability to navigate uncertainty.

It creates space to challenge assumptions before they become constraints.

It encourages leaders to ask better questions before rushing toward better answers.

Most importantly, it helps organizations prepare before change becomes unavoidable.

Because transformational technologies don't arrive with certainty.

They arrive with ambiguity.


When Generative AI went Mainstream..

When ChatGPT was released in November 2022, something interesting happened.

A technology that had once felt distant suddenly became accessible to everyone.

Almost overnight, organizations began asking,

"What AI use cases should we build?"

The pattern felt remarkably familiar.

It was the same instinct we had seen with every transformational technology before it.

Jump to implementation.

Find quick wins.

Start building.

A few months later, our executive leadership asked the Horizon Z team to help the organization understand what generative AI could mean for the business as the technology matured.

We deliberately chose a different starting point.

Before building use cases, we built understanding.

We invited external experts to explain what large language models actually were—and just as importantly, what they weren't.

We created opportunities for people to experiment, learn, share ideas, and explore possibilities together.

The goal wasn't simply to teach people how to use a new tool. It was to help them think differently about how work itself could change.

The conversations evolved.

People stopped asking,

"Can AI do this?"

They started asking,

"What becomes possible if we redesign the way we work?"

That shift in thinking was far more valuable than any single AI use case.

Looking back, Horizon Z taught me that technology was never the lesson.

It was simply the catalyst that challenged us to think differently.

Because foresight isn't about predicting the future.

Because the future doesn't reward the organizations that predict it perfectly. It rewards the ones that are prepared to adapt when it arrives.

Sunday, March 2, 2025

The Future of LLMs: A Commodity, a Platform, or a Locked Ecosystem?

 There was a time when hardware was everything. The processor, the storage, the power—it was all about the physical components. But over time, hardware became a commodity, and the real intelligence moved to software—the brain that made everything work.

I believe LLMs (Large Language Models) are heading in the same direction.

Today, LLMs are impressive, but above-average performance is no longer enough—everyone is doing that. Soon, just like hardware in smart buildings, LLMs will be necessary but not the differentiator. The real power will lie not in the model itself, but in how we use and move our data between models.


Imagine This: Seamless LLM Portability

What if, instead of training a new LLM from scratch every time you switch, you could export all your training, memory, and preferences from one model and load it into another?

  • Think of it like exporting browser bookmarks from Chrome to Edge—simple, quick, and frictionless.
  • Or how smart building automation isn’t about having the best sensors, but the best software to control them.

If someone solves this seamless transfer problem, it would be a gamechanger. Suddenly, I as the user would have full control over which LLM I want to use. It would be a free market of AI models, where I decide which one serves me best—not the other way around.

But here’s the challenge: Why would companies allow this?


The Moat Problem: Will AI Stay Closed or Go Open?

Right now, the biggest LLM companies—OpenAI, Anthropic, DeepSeek—are building moats, just like Apple did with its App Store and music ecosystem. They don’t want you to leave. They don’t want you to take your trained data elsewhere.

Why? Because data is the real moat. If I allow my users to transfer their AI memories and training to a competitor, I lose my stickiness. I lose my power. I lose my users.

So, will AI follow the Apple model (closed ecosystem) or the Android model (open, interchangeable, user-centric)?

Right now, it’s looking more like Apple—companies are keeping their data walled off, because why would they give up control?

But here’s the twist—eventually, they may not have a choice.


A Future Where AI Models Become Interchangeable?

Once the market gets saturated, and there are too many competing LLMs, the biggest differentiator will no longer be the models themselves. It will be how well they work together.

At that point, the pivot to interoperability will be inevitable. Some LLM company—or maybe an entirely new startup—could rise up as the AI platform that enables seamless training across all models.

  • Think of what Android did to Apple. Instead of one locked system, they enabled an open ecosystem where users had freedom of choice.
  • Think of what API-first companies did to traditional software. They created layers that made everything compatible, and suddenly, closed ecosystems lost their advantage.

Could this happen with AI? Maybe not yet—but in a few years, as competition increases, it might be the only way forward.


What About AI Agents? Are They Moving Towards Openness or Isolation?

I’ll admit—I don’t know enough about AI agents to say for sure. But I do wonder:

  • Are they being developed to connect models and enable interoperability?
  • Or are they reinforcing more isolation, making it even harder to move from one LLM to another?

POE (Quora’s AI marketplace) kind of does this by offering multiple models under one roof. But as far as I know, it doesn’t allow seamless data exchange between them—it just acts as a layer function that gives memory to different LLMs.

So, are we headed towards more walled gardens or an open AI economy?


Dreaming of the Wild Possibilities

Maybe I’m naïve. Maybe a smart AI engineer would think I’m nuts for even suggesting this.

But if we don’t imagine new possibilities, we will never disrupt.

The future is always built by those who dare to think differently. And if seamless, user-controlled AI memory becomes reality, hopefully, you’re the one who builds it before your competition does.

What do you think—are we moving towards LLM portability, or will the walled gardens stay? 🚀



Wednesday, May 10, 2023

Can't believe I wrote about this almost 10 years ago!!

 While reviving my old blogs, I came across it and was amazed that I was thinking and so much into it, 10 years ago!!

http://lifeisbeautifultalks.blogspot.com/2012/08/

Well atleast now with all the developments in AI, and not to mention atleast some of Level3 and Level4 autonomous driving, maybe Level 5 is just 10 years away?

Check out this interesting article/interview from WSJ!