Why today’s AI ignores MSMEs and how synthetic data levels the playing field

Imagine Arjun, an entrepreneur in Surat who recently moved his family’s textile business into the digital-first world. He launches an ad campaign with cautious optimism, his modest budget representing months of hard work. Weeks later, the dashboard tells a familiar story: money spent, impressions delivered, but no clarity on what worked. There are no meaningful insights to guide his next move, only the realisation that his budget for “experimentation” is already gone. Meanwhile, global fast-fashion giants refine their targeting effortlessly, powered by AI that gets smarter with every click.

This gap isn’t about ambition; it’s about how AI is built. While AI has transformed digital advertising, it hasn’t done so evenly. In India’s vibrant ecosystem, smaller brands often remain invisible to the very systems meant to optimise performance. The reason isn’t a lack of innovation; it’s a chronic lack of data.

The invisible bias in AI systems

Arjun’s struggle is a symptom of a structural flaw in modern AI. We often speak of AI as a neutral “brain”, but in reality, these systems are mirrors reflecting the data they consume. Large enterprises generate a relentless stream of “first-party data”, millions of customer interactions and feedback loops, that act as high-octane fuel for AI models. For these giants, AI identifies winning patterns effortlessly because it has the sample size to reach “statistical confidence”.

Small businesses, however, operate in what I’ve seen repeatedly in my work as a “data desert”. When a brand has only a few hundred customers, the AI sees their sales not as a “signal”, but as “noise”.

Consequently, algorithms naturally optimise toward data-rich players where they can predict outcomes with the highest certainty. This creates a silent, systemic bias – not against the entrepreneur’s intent, but against their scale. Over time, AI doesn’t fail small businesses loudly, it simply learns to ignore them.

Why this matters

This disparity carries significant weight in the Indian context. According to the Ministry of Micro Small Medium Enterprises (MSME), India is home to over 63 million MSMEs, contributing roughly 30% to the country’s GDP. They are the backbone of local livelihoods. However, when AI-driven systems disproportionately favor data-rich organizations, the consequences extend beyond marketing efficiency. Visibility for homegrown brands declines, and Customer Acquisition Cost (CAC) skyrockets, often becoming prohibitive for a self-funded startup.

If the tools of the future only serve those who already have the most resources, AI, which should be a great equalizer, risks becoming a multiplier of inequality. For India to achieve its “Viksit Bharat” vision, innovation must be inclusive, ensuring a boutique in Jaipur has the same digital “eyes” as a multinational corporation.

Why “just use AI” isn’t a real solution

The standard advice to “just adopt AI tools” ignores structural realities. Most advertising platforms are built on an assumption of abundance of data and financial cushion to absorb failed tests. A multinational can afford to burn cash on a “learning phase”, but for an SMB, trial-and-error is an existential risk.

Furthermore, with the introduction of India’s Digital Personal Data Protection (DPDP) Act, the bar for collecting and reusing customer data has risen sharply. For a small business, navigating these privacy

hurdles while trying to feed a data-hungry AI is a catch-22. The challenge isn't technical willingness; it’s that the current AI ecosystem was never designed for small-scale learning.

Enter synthetic data

This is where synthetic data changes the equation. Think of it not as “fake” data, but as “simulated” intelligence. It is artificially generated data that mirrors the statistical patterns of the real world without exposing sensitive customer information. It creates a “digital twin” of market behaviors, capturing how audiences react and why campaigns perform.

For an MSME, this is a gamechanger. It allows an AI model to “train” in a virtual environment far richer than the brand's actual customer base. Using a limited set of real transactions as a seed, the AI can generate a vast, privacy-compliant simulation. It enables experimentation without violating privacy or exhausting limited budgets on failed live tests. Synthetic data doesn’t replace the human touch of a small business; it amplifies its digital reach.

Impact and the road ahead

For entrepreneurs like Arjun, this technology translates into actionable power. He can “pre-test” creative variations and budget allocations in a virtual environment before spending a single real rupee. By balancing underrepresented patterns, synthetic data also improves fairness, ensuring ads reach inclusive audiences that data-starved algorithms might have ignored.

The rise of synthetic data points to a larger shift, the future of AI isn’t just about bigger models, but about accessibility and trust. Responsible AI means building systems that work under real-world constraints like the DPDP Act. When India’s MSMEs gain this visibility and confidence, the entire economy thrives. The next wave of innovation won’t be defined by who has the most data, but by who makes AI work for everyone, starting with the entrepreneur in Surat, and extending to millions like him across India.