Transform SME Workflows into Growth Engines with AI

In 1992, Nokia introduced one of the first GSM mobile phones. It was clunky by today’s standards, but it marked the start of something extraordinary — the mobile era. Yet, the real transformation didn’t begin until 2007, when Apple launched the iPhone and opened the App Store a year later.

That’s when mobility shifted from hardware to ecosystem — from selling devices to enabling experiences. Suddenly, software, connectivity, and cloud infrastructure converged to create entirely new industries. Uber, Airbnb, WhatsApp, Instagram, and Zomato didn’t just use technology — they were technology.

Today, we’re standing at a similar inflection point.
Generative AI is in its 1992-Nokia moment — promising, experimental, and not yet fully understood. But the pace of change suggests that the iPhone moment for AI is not years away; it’s already unfolding.

# From CAPEX to OPEX — And Beyond

When Amazon launched AWS in 2006, it quietly rewrote the rules of technology investment. Businesses that once spent millions on data centers could now rent computing power by the hour.

That shift — from CAPEX to OPEX — changed everything.
It democratized access to computing, fueled innovation, and created the foundation for the platform economy.

Something similar will happen with AI.
Today, most organizations treat GenAI as an experiment or a tool, but soon it will evolve into an operational model — embedded in workflows, running quietly in the background, turning text, data, and conversation into productivity.

We don’t yet know what the AWS of AI will look like — but we know it’s coming. And those who start experimenting early will recognize the opportunities first.

# The Arrow of Tech Only Moves Forward

In 2022, when I asked an image-generation model to create a simple photo of an Indian family walking down a street, the result looked like a distorted sketch — faces misshaped, lighting awkward.

When I ran the same prompt this year, the image came back shockingly realistic — complete with natural shadows, proportional faces, and subtle details like wall textures and reflections.

That’s how fast AI is improving.
The arrow of technology only moves forward — never backward.
Each generation of models builds upon the previous one, compounding capability.

So while it’s easy to wait for the “perfect version,” the truth is: it will never stop evolving. The smart move isn’t to wait — it’s to start learning.

# Why SMEs Can’t Wait

ChatGPT reached 100 million users faster than any other technology in history. That’s not a vanity milestone — it’s a signal.

Technology cycles are shrinking. The window between “new” and “mainstream” has collapsed.

For small and medium enterprises, that means two things:

1. You don’t have the luxury to “wait and see.”
2. You don’t need a massive budget to begin.

GenAI tools are already helping SMEs:

Function	Example Workflow	AI Action
HR	Recruitment	Generate job descriptions and interview questions
Finance	Operations	Automate invoice processing
Sales	Enablement	Draft proposals and client follow-ups
Operations	Reporting	Summarize field data and daily reports

Anything that touches text, numbers, or decisions is fair game for AI augmentation.

# A Framework to Identify AI-Ready Workflows

Not every process is ready for AI, but many are closer than you think.
A simple framework to evaluate your workflows:

1. Repetitive: Happens frequently and follows a predictable pattern.
2. Language- or Data-Heavy: Involves text, communication, or structured data.
3. Rule-Based: Can be described through clear if-then logic.
4. Impactful: Saves measurable time, cost, or effort.

If a workflow ticks at least two of these, it’s AI-ready.

In our workshop, each leader identified one such workflow — and began designing their first AI pilot. It might be something small: automating follow-up emails, summarizing reports, or preparing client decks. But every big transformation begins with one small experiment.

# Privacy and the Future of Local AI

As we discussed during the session, AI adoption isn’t just about capability — it’s also about control and privacy.

New tools like Ollama and LM Studio now allow organizations to run powerful language models locally, right on HP hardware — keeping sensitive data within their walls.

And the next step? Agentic AI — systems that can perform multi-step tasks automatically, not just respond to prompts. The good news is that even these can be deployed locally, bringing both autonomy and assurance.

# Your First Step

When you return to your office:

1. Pick one workflow that fits the AI-readiness framework.
2. Identify its biggest pain point.
3. Collaborate across teams.
4. Take one small step to test AI in it.

You don’t need to transform your entire business overnight — just start with one workflow, one problem, one win.

# Closing Thought

The future won’t wait for perfect timing.
The companies that experiment early — even imperfectly — will define how AI reshapes business.

As we step into Diwali, a festival of light and renewal, perhaps this is the right moment to light the first lamp of experimentation in your business.

Because transformation doesn’t begin with technology — it begins with curiosity.