The water reckoning: Why AI is the answer to industry's next crisis

The article was first published in Green Business World on 09 February 2026. Please click here to read the article.

The one question that must keep the CFOs and operations heads awake at night is, ‘what happens when your most critical input becomes your biggest liability?’ For India’s industrial sector, that moment has come. Water, which was once abundant and cheap, has become a strategic choke point that will determine the industry leaders in the next decade.

A fundamental shift is underway in India’s industrial sector driven by surging demand, water scarcity and stricter regulatory oversight. The data paints a striking picture for this: Oil refineries are staring down at an 80 per cent surge in water demand by 2030, followed by the paper and pulp industry at 71 per cent and cement at 22 per cent¹. These are not random projections but clear market indicators demanding immediate strategic action. In this scenario, traditional water management approaches, even digitised ones, cannot solve problems at this scale and complexity. The solution requires artificial intelligence (AI). In the coming years, the key differentiator amongst industries would be the gap between organisations that deploy AI-powered water systems and those that do not.

Most of the industrial plants today still operate on decade old practices like manual sampling, scheduled inspections, fixed chemical dosing, and reactive maintenance that kicks in only when the problems arise. This model was successful when water was inexpensive, and regulators exercised leniency. It is no longer viable in a landscape defined by tighter norms that require real-time compliance, where investors track ESG performance, and a single water-related failure can trigger losses worth millions.

We stand at a critical inflection point today because water operators cannot monitor thousands of parameters, detect anomalies in milliseconds, or forecast demand weeks in advance. The sheer complexity and the velocity of water decisions now exceed human cognitive capacity. AI does not just make the tasks faster; it makes them possible.

Artificial intelligence fundamentally changes what industrial water systems can achieve. It continuously analyses flow patterns and pressure variations and fine-tunes chemical dosing based on shifts in water quality and process needs. It thereby cuts chemical expenses while delivering better treatment results and more dependable compliance. Quality issues get flagged within milliseconds, well before any operator can spot a subtle change, thereby stopping violations before they can happen.

AI optimises pump operations, flow regulation, and treatment processes continuously without human intervention, therefore delivering near-autonomous operations that respond faster and more accurately than any manual system ever could. All these are not simple upgrades. What we are witnessing is a fundamentally different way of operating that builds lasting competitive advantages for industrial facilities.

The projections of the global market for AI in water management tell their own story. It is currently valued at USD 16.6 billion and is projected to reach USD 28.2 billion in 2028². This growth is not driven by the hype around AI but by proven operational and financial outcomes like lowered costs, real-time compliance, fewer expected shutdowns, smarter resource allocation and better climate preparedness. Industrial forecasts suggest that by 2027, more than 60 per cent of major industrial players and utilities might adopt digital technologies³. And by 2035 AI-driven systems are expected to the norm rather than the exception in advanced industrial operations. Hence companies that will adopt technologies and AI sooner might have the competitive edge over those who don't.

For this technological transformation to take place, industries must navigate four critical challenges

First

AI is only as effective as the data it learns from. Aging infrastructure produces unreliable data that compromises how well these models perform. Hence investing in modern IoT sensor networks and robust data infrastructure are a non-negotiable for industries looking to technologically upgrade themselves
Second

There is an expertise gap because running these systems requires a mix of skills like machine learning (ML) knowledge, operational technology experience and deep understanding of industrial processes. Most organisations don't have these capabilities internally. And, because AI systems require ongoing refinement, the capability gap widens quickly
Third

The challenge of integration and interoperability. Existing SCADA and plant-control systems were not designed keeping AI connectivity in mind. Thus, connecting these systems and at the same time being mindful of not disturbing the day-to-day operations takes careful planning and strong change management. Organisations that underestimate this complexity often stall midway through AI transformations
Finally

There is a security challenge. When you connect sensors across a facility and move operations to cloud, you are opening yourself up to new cyber-attack vulnerabilities. Hence cybersecurity should be a design level priority rather than an afterthought

To address these challenges, the industrial leaders need to immediately act

First

Start by honestly assessing where you are. Do you have the sensors needed to collect quality data? Where would this technology help the most? Asking these questions and an honest technological self-assessment could help in knowing what your priorities are
Second

Make a real business case with precise numbers. Show what you would save- less wasted water, lower energy bills, better compliance. Tie these potential benefits to your company’s bigger goals like managing risk and hitting ESG targets. AI investments compete for capital against other priorities, hence precision in this regard matters
Third

Start with strategic AI pilots in a manageable environment and learn what works, fix what does not and refine your tech models accordingly
Fourth

Build cross-functional teams that combine domain expertise with AI capabilities and treat water intelligence as a core organisational competency that requires the right-team and sustained investment

The industrial water crisis has already arrived, and artificial intelligence can manage water at the scale and complexity that modern industry requires. Regulations are not going to ease up, water isn't going to become more abundant, investors won't stop judging you on resource efficiency and your competitors will not slow down their AI adoption.

So, organisations that lead the next decade will be those that treat AI-powered water management at the center of their strategy, treating it as important as capital planning and supply-chain resilience.

The technology exists. The business case is proven. The window for decisive action is open, but it might not remain open forever.

The shift to AI-powered, autonomous water management is not optional. It is inevitable. The only question is whether you will lead it or be left behind by it.