Generative AI isn’t just a cool tool — it’s the engine that’s creating every other AI use case businesses will ever need.
Recently, there has been a buzz of discussion on the future of AI. It has been widely written that this is an AI bubble and can burst any time soon. In my opinion, AI is not a bubble, but a reality that will stay with humankind forever.
So, whenever I’m asked about the “right” use cases for AI for the enterprises, the general expectation is a list of function-specific examples: resume screening, predictive maintenance, sales forecasting, fraud detection and so on.
All of those are valid, proven applications of AI. But if you ask me what the most transformative use case is, my answer is simple: Generative AI.
And I don’t mean GenAI as “a chatbot that writes text” or “a tool for summarization.” I mean Generative AI as a foundational capability — one that will eventually lay the foundation and seed all other use cases inside an enterprise.
How we experience it as individuals
Like many, I use ChatGPT, Gemini and Perplexity almost every day. For me, these tools are no longer experiments. They’re extensions of my work and thinking.
Whether it’s summarizing a dense research paper, drafting an outline for an article, generating ideas for clients or checking perspectives I may have overlooked — Generative AI has become an analyst, assistant and coach rolled into one.
That’s the first power of GenAI: democratization. Anybody with an internet connection now has access to a digital knowledge worker.
When technology becomes this accessible, there’s no going back. Just as search engines transformed how we consume information, GenAI is already reshaping how we create, evaluate and decide.
The enterprise shift
In enterprises, the story is unfolding differently. Many companies are experimenting with ChatGPT Enterprise, Microsoft Copilot or Gemini for Workspace. These are useful, but they’re still surface-level.
The real transformation begins when companies adopt open-source large language models (LLMs) like Llama or Mistral and train them on their proprietary knowledge bases.
Imagine a GenAI platform that:
- Knows your company’s processes better than any consultant.
- Has read every policy, manual, compliance document and technical specification.
- Can instantly answer employee questions — from HR to engineering.
- Generates new workflows or even designs models tuned for specific functions.
That’s not just one use case. That’s a factory of use cases.
We’re already seeing this shift. JPMorgan Chase, for instance, has built an internal “LLM Suite” that supports over 50,000 employees with summarization, research and productivity assistance — built in-house to meet regulatory and security requirements. This is not a chatbot experiment. It’s a productivity engine integrated into day-to-day work.
MIT Sloan has also highlighted how businesses are moving from simply “prompting” generic LLMs to using techniques like retrieval augmented generation (RAG), where internal proprietary data is layered into the model to deliver answers that are contextual and enterprise-specific. That’s when GenAI stops being “another SaaS tool” and becomes part of the organization’s brain.
From function-specific AI use cases to a use case factory
Most AI projects in enterprises so far have been narrow in scope. One model for fraud detection. Another for demand forecasting. A third for resume screening. Each of these is a single use case — valuable, but limited.
Generative AI changes that paradigm. Instead of building separate models for every problem, enterprises can build one GenAI foundation that adapts across functions.
Think of the contrast this way:
- Traditional AI is like buying a specialized machine built to cut only one shape of metal.
- Generative AI is like setting up a workshop where the same tool can be reprogrammed to cut, shape or design almost anything you need, or the same person can be trained to do multiple jobs based on the need of the day.
Once enterprises bring GenAI inside the firewall — trained, aligned and governed — it doesn’t just assist with isolated tasks. It creates entirely new applications across the organization:
- An analytics assistant for the CEO that delivers direct answers instead of static dashboards.
- An AI trainer for field staff, guiding them through complex tasks step by step.
- An auditor that reviews thousands of compliance points in hours instead of weeks.
- A recruitment engine that interprets candidate fit beyond keywords, combining resumes with digital footprints.
These are not separate, disconnected projects. They’re all byproducts of the same GenAI core, shaped by context and data.
That’s why I call Generative AI not just another use case, but a use-case factory.
Why enterprises should build in-house
The big question is: why not just stick to ChatGPT Enterprise or Microsoft Copilot? The answer lies in control, security, performance and adaptability.
As Elisheba Anderson wrote on Medium, more enterprises are turning to local or self-hosted LLMs because they can’t risk sensitive data leaving their environment, and they need more predictable performance for mission-critical processes.
In other words: if AI is going to sit at the core of your business operations, you need to own it, not rent it.
What enterprises need to get right
Let’s be clear — this isn’t “plug and play.” For GenAI to work as a use-case factory, enterprises must get three things right:
- Data readiness. GenAI is only as smart as the data it’s fed. Clean, accessible, structured knowledge bases are critical. Garbage in, garbage out applies more here than anywhere else.
- Governance & security. Open-source LLMs trained on enterprise data create new responsibilities. Guardrails around privacy, compliance, intellectual property and model bias aren’t optional.
- Leadership & vision. This is the biggest gap I see. Many enterprises are rushing to deploy GenAI because “everyone else is doing it.” But without a clear strategy — why are we doing this, for whom and with what outcome? — they risk chaos instead of transformation.
Here’s how I think about it: bringing AI into your enterprise is like hiring a brand-new team.
- If you bring them in without clarity on roles, they’ll flounder.
- If you don’t give them the right training, they’ll make mistakes.
- If they don’t align with leadership vision, they’ll pull in different directions.
The same is true for GenAI. Treat it like an internal team member rather than an external consultant. Train it on the right data, in the right context, with the right governance and aligned to leadership vision. Do that, and it will perform like a high-value in-house team. Fail to do that, and it will remain an outsider — promising, but never fully delivering.
My takeaway
Generative AI is unique because it is both:
- A use case — the co-pilot, the assistant, the analyst.
- A use-case factory — the foundation from which countless other applications emerge.
Enterprises that treat GenAI as “just another project” will underutilize it. Enterprises that treat it as foundational — and invest in leadership, governance and data — will unlock its true potential.
Closing thoughts
We often ask: “What’s the killer app of AI?”
I believe we’re already looking at it. Generative AI is the killer app. Not because of what it does today, but because of what it enables tomorrow.
The question is no longer “Should we adopt it?” but “How do we adopt it with clarity, purpose and control?”
For individuals, that may mean experimenting with ChatGPT or Gemini.
For enterprises, it means building their own GenAI platforms — responsibly, strategically and with vision.
And this is not a one-time project; this is something that will evolve as the organizations evolve. So, looking for a vendor to do the AI implementation in 6 months will not work.
When any organization takes any new initiative, like new product development or new market access, this is not a one-time activity. One cannot put a fixed budget for the same. However, if one has the right vision and leadership, the organization should define a yearly budget to keep building and evolving enterprise AI on a year-on-year basis.
Those who get this right won’t just ride the wave. They’ll shape the future.
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