AFFORDABLE AI FOR INDIAN MANUFACTURERS

Across industries – from auto components to chemicals and pharmaceuticals to machinery – manufacturers have one thing in common: they are already sitting on a goldmine of data. And it does not take a fortune to start using it.

AI adoption does not have to begin with a massive cloud migration, expensive integrations, or hiring a team of data scientists. In fact, the smarter move is often the simplest one: to look inward. One’s existing production logs, Excel files, maintenance reports, invoices, and SCADA exports can be the foundation for meaningful AI-led growth.

The Power of Structured and Unstructured Data

Factories already generate vast volumes of data. Structured data comes from Enterprise Resource Planning (ERP) systems (inventory levels, production schedules, financial records); Manufacturing Execution Systems (MES) (machine performance, quality control logs); and Supervisory Control and Data Acquisition (SCADA) systems (sensor readings, historical trends).

Additionally, it is crucial to also take into account the unstructured data: maintenance technicians’ handwritten notes, quality inspection images, customer feedback emails, or voice recordings from site inspections. Modern AI and Machine Learning (ML) algorithms are exceptionally good at processing this diverse data.

KBy applying basic ML models to this existing data, it is possible to immediately generate actionable recommendations. For instance:

• Predictive Maintenance: Analyzing SCADA sensor data and maintenance logs to predict equipment failure before it happens, drastically cutting downtime.
• Quality Control: Using images of finished goods combined with rejection rates to identify subtle patterns causing defects.
• Demand Forecasting: Combining ERP sales data with unstructured customer feedback to create more accurate production schedules.

The key message is clear: manufacturers already possess the raw material for AI — their own data. The first step lies in analysis, not acquisition.

Utilizing Before Accumulating

Before sinking funds into creating sophisticated data lakes, buying expensive new IoT sensors, or building complex data pipelines, a critical question must be asked: Have we already utilized the existing data to its full potential? For most small to medium-sized enterprises (SMEs) in the Indian Manufacturing sector, the answer is usually no. Many companies are still using their data primarily for reporting what has already happened, not for predicting what will happen or prescribing the best course of action. To bridge this gap, it helps to start by asking these three simple questions:

• Does the organization already have access to historical logs and operational files?
• Has it attempted to extract insights or patterns from this existing data?
• Are decisions being made based on facts or intuition?

The truth is: most manufacturing decisions – about what to procure, how to schedule production, and where losses occur – can be dramatically improved using just what’s already being captured.

The myth suggests that companies must first collect two years of high-resolution data before adopting AI. The reality, however, is that even two months of machine logs, Excel-based downtime reports, or purchase data can provide valuable insights. Even partial or imperfect data can deliver meaningful results — what matters most is direction, not perfection.

The key lies in starting small — by solving one real business problem. Companies must begin their AI journey not with a vague goal like: ‘We want to use AI somewhere’, as it often leads to wasted pilots and poor ROI. Instead, they should anchor their efforts around real, high-priority business problems such as:

• Addressing too many quality defects by using AI to find contributing factors.
• Reducing frequent breakdowns by analyzing maintenance logs for predictive patterns.
• Preventing overordering raw materials by forecasting run demand based on just past Excel sales data.

The problem should guide the solution—not the other way around. Companies must avoid force-fitting AI where it doesn’t belong. The goal is not to ‘adopt AI’ but to reduce downtime, improve margins, cut costs, or increase order win rates. AI is just a tool to get there.

Avoid Letting Integration Become a Bottleneck

Many companies delay AI adoption, waiting for the perfect system integration, which is akin to postponing a doctor visit until one has built a hospital. Modern AI platforms (like file-first decision agents) require no integration to get started. One can:

• Upload files directly (CSV, Excel, PDF, SQL dumps)
• Run analysis on isolated data sets
• Generate insights, rootcause analysis, and forecasts in plain English.

Integration can come later only when it starts to add value. This means one’s existing workflows need not be disrupted. Teams can continue using their familiar formats and simply get better intelligence layered on top.

From Pilot to Powerhouse: Scaling Smartly

The AI journey for Indian manufacturing is not a single giant leap; it’s a series of strategic small steps. By first optimizing existing data, committing only to solving specific, high-value problems, leveraging accessible low-code tools, and, crucially, applying a skeptical, first-principles mindset to cut through the industry hype, companies can transform their operations. The real growth is not only about faster machines; it’s about smarter decisions, and the data to fuel those decisions is already captured within one’s factory walls. By embracing this frugal, problem-first approach, Indian manufacturers can quickly unlock growth, secure a competitive edge, and pave the way for a data-driven future without draining their capital reserves. AI is no longer a luxury for industrial giants – it’s an accessible, strategic tool for every ambitious Indian manufacturer.

RAHUL KHARAT Co-founder & CTO RESET AI