By Ramya Nirmal, CEO, CI Global
Most of what the market gets wrong about AI for small and medium enterprises is this: it treats AI as a transformation problem. SMEs are told they must overhaul processes, adopt new platforms, and learn entirely new ways of working to see value from AI. After working closely with hundreds of business owners across retail, manufacturing, and services, I’ve come to believe the opposite. AI fails in SMEs not because they resist change, but because it is rarely built for how they actually run their businesses.
Most SME”s do not operate with textbook processes, clean data models, or dedicated IT teams. They run on a mix of ERP systems, other systems, spreadsheets, manual workarounds, and deeply ingrained operational habits. These “imperfect” systems are not signs of inefficiency. They are the result of years of practical adaptation to real-world challenges and constraints: limited budgets, limited staff, and the need to keep the business running every single day to survive.
When AI solutions demand that SME’s first become something they are not, adoption stalls. True democratisation of AI and analytics does not start with transformation. It starts with respecting reality.
AI must fit into existing workflows, speak the language of the business owner, and deliver decision intelligence – without requiring a wholesale process rewrite or data science degree.
For SME’s, the winning question is not “How do we transform business for AI?
It’s “How do we make AI work for the business we already have?”
The shift in mindset away from disruption and towards enablement is where real value begins.
Technology should speak your language
If you have run a business for twenty years, you already have the most important “data” in the world: your intuition. You know your customers. You know your products.
The problem is that as the world gets faster, your intuition is being drowned out by noise. When a client tells me, “Ramya, my sales are down,” they aren’t asking for a spreadsheet. They are expressing a deep, human frustration: they know something is wrong, but they don’t have the resources to go hunting for the “why” immediately.
A bakery client came to us with a similar problem. Overall sales hadn’t dropped enough to trigger alarm bells. But when we looked closer, we noticed a consistent dip in demand for a specific pastry. The reason wasn’t quality. It wasn’t pricing.
A competitor had opened nearby, selling a pastry with an almost identical name. Customers were confused. Some thought it was the same brand. Others assumed the original had changed. The business didn’t lose customers overnight. But it happened.
The owner didn’t know this was happening. Not because they weren’t paying attention, but because their systems weren’t built to show them what they didn’t know to look for.
This is the real challenge most small and medium enterprises face today.
The problem is not a lack of data.
The problem is that the right data doesn’t surface at the right time.
And by the time the issue becomes obvious in top-line numbers, the damage is already done.
Most small and mid-sized businesses are busy running their business. There is no time to sit with dashboards, analyze reports, or figure out trends. Non-IT teams usually discover problems only after they have already affected revenue, margins, or customers.
The question is not “Do I need better technology?”
The real question is “How do I get the right information, at the right time, to ensure decision intelligence?”
My philosophy is built on one simple rule: You should make technology work for you. You shouldn’t need an IT degree to know that a competitor down the street is siphoning off your regulars. You shouldn’t have to spend your Sunday night with a spreadsheet or dashboard full of numbers to realize one of your categories is quietly failing.
We start with the user, not the technology.
At CI Global, we don’t begin conversations with AI models, platforms, or tools.
We begin with a fundamental question:
Who will actually use this system?
In SME’s, the answer is rarely a data analyst or a power user. It’s more often a business owner, a store manager, a production supervisor, a stockist or a finance lead – people whose primary job is running the business, not learning new software.
If an AI or analytics solution assumes technical expertise, perfect data or time for experimentation, it’s already failed its most important test: real-world usability.
Designing for the user means understanding how decisions are actually made on the ground:
- What questions they ask at the end of a long day
- What data they trust and what they ignore
- How much time they can realistically spend on analysis
- What actions they need to take next, not just what insights they can see
Most SME’s already generate plenty of data. Sales live in POS systems. Operations live in ERP’s. Finance lives in Excel. The problem is not lack of data. The problem is lack of visibility.
So instead of asking SME’s to change everything, we ask:
- How can we combine what already exists?
- How can we show a single, clear view of what’s happening?
- How can we surface problems they don’t even know they have – before the problems become costly?
When visibility improves, behavior changes naturally. Decisions become faster. Conversations become factual. AI stops being an abstract concept and starts becoming a practical advantage.
Reports and dashboards are just a means
I want to be very clear about this. Reports and dashboards are not the value. They are just tools. The real value lies in what they enable:
- Increasing revenue
- Reducing costs
- Gaining a sustainable competitive advantage
Technology is only useful if it helps answer real business questions, not when it produces more charts.
Questions like
- Why did sales drop in one category but not another?
- Why is one store underperforming while another is growing?
- Are customers unhappy or just buying differently?
Often, the problem is not obvious. And that’s where smart use of technology helps. Not by overwhelming users, but by guiding them toward the right questions.
AI for small and medium enterprises
AI is no longer a “nice-to-have” tool that quietly improves efficiency in the background. It has become a strategic lever that determines who stays visible and who slowly fades from relevance.
The real risk for SMEs today isn’t adopting AI too early. It’s adopting it too late, or using it so superficially that it creates a false sense of control. When leaders rely only on high-level numbers and static reports, they miss early shifts in customer behavior, competitive moves happening next door, and small inefficiencies that compound into serious losses. By the time these signals appear in top-line revenue, the advantage has already moved elsewhere.
I’ve seen businesses lose ground not because they made bad decisions, but because they didn’t have the visibility to make timely ones; and in today’s market, delayed insight is often indistinguishable from a wrong decision.
SMEs don’t have unlimited budgets, teams, or time. So any solution must respect that reality.
That means:
- Low cost of entry
- Multi-language support
- Localization
- Minimal disruption
- No major process transformation
I don’t believe in ripping and replacing systems. The real opportunity lies in connecting what already exists and gently upgrading how businesses see and use that information.Yes, there is a learning curve.
Yes, processes evolve.
But it should feel like adapting, not transforming. That’s how AI becomes usable. That’s how analytics becomes trusted. And that’s how SME”s turn insight into sustained advantage – without losing focus on running the business.
Don’t fix what’s not broken
This is advice many established businesses live by, and for good reason. Processes that have worked for decades carry hard-earned wisdom. But what often goes unexamined are the small inefficiencies, blind spots, and assumptions that quietly accumulate over time. Left unaddressed, they don’t break the business overnight; they slowly narrow its field of vision.
What I’ve learned is that long-term resilience doesn’t come from constant reinvention, nor from standing still. It comes from being willing to examine what works and, just as importantly, what no longer reveals enough. The most future-ready SMEs aren’t chasing the latest technology. They are building the habit of seeing earlier, questioning sooner, and acting with greater clarity, year after year.
In the decade ahead, the gap won’t widen between businesses that adopt AI and those that don’t. It will widen between those who gain visibility early and use it consistently, and those who continue to rely on comfortingly familiar numbers. The future belongs to businesses that see ahead clearly, long before change becomes unavoidable, and make steady, thoughtful decisions that compound over time.
