Ask most technology vendors where industrial AI is headed and you’ll hear about self-optimizing plants, prescriptive intelligence, and lights-out manufacturing. Ask Bühler India, and the answer is more measured and arguably more instructive for CIOs operating in emerging markets.
Bühler, the Swiss group that supplies grain processing, food manufacturing, and advanced materials technology worldwide, is working on two fronts at once. Internally, it is rebuilding its enterprise IT architecture around a global Information Technology and Processes (ITP) framework. Externally, it is selling AI-enabled products such as sorters, energy control systems for malting, and moisture-dosing tools for flour milling. In India, which has more than 1,000 Bühler employees, both efforts are shaped by one principle: the technology has to fit the market it lands in.
In an interview with Express Computer, Dipu Pillai, Head of Group Services at Bühler India, and Shashidhar Subramanya, Head of Corporate Technology and Research & Training Centers (Middle East, Africa and India), described how that philosophy plays out, from the ERP core to the factory floor.
Building the backbone: standardise the core, flex at the edge
In ERP, that means harmonising the core and standardising end-to-end processes, among them order to payment, procure to pay, manufacturing, inventory, and finance, using Bühler’s global templates. The payoff, Pillai explained, is that “this improves visibility across plants and business units while accommodating genuine local requirements.”
On cloud, the company has avoided dogma. “We follow a cloud-first but deliberately hybrid approach, selecting SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service) or edge solutions based on resilience, security, performance and cost,” Pillai said. The approach also lets Bühler “benefit from innovations by partners such as SAP and Microsoft.”
The people layer matters as much as the platform. Bühler has created an internal AI community focused on the responsible use of approved in-house tools. “Across our 1,000-plus employees in India, we run training to help teams use our in-house solutions, experiment on the same and to define clear problem statements for improved adoption,” Pillai said.
AI in engineering: sharpen the question before spending on the answer
Inside Bühler’s research and engineering teams, AI is not replacing the expensive tools. It is deciding
AI helps teams “go through large data sets to spot anomalies and correlations that would otherwise stay hidden,” he said, which “sharpens how we frame the problem before any actual development work begins.”
The second use case is simulation. Full-scale models are costly to set up and run, so Bühler uses AI as a quick filter. “It lets our teams do a quick initial assessment and rule things in or out before committing to the costlier simulation tools,” Subramanya said. His summary of the current state of play: “AI’s real value for us right now is in sharpening the problem and pre validating ideas before we invest in the more expensive stages of testing.”
The KPIs: real gains, honestly framed
Many technology leaders overstate AI’s impact. Subramanya is notably careful, treating AI alongside robotics and digitalisation, “since in practice they work together on the floor.” He pointed to three KPIs.
Delivery and utilisation. On-time delivery performance and capacity utilisation are showing “steady, consistent improvement.” He was candid that this “isn’t purely an AI story, it sits somewhere between RPA (Robotic Process Automation), AI and broader digitalisation, but the gains are real and sustained.”
Inventory. Bühler’s factories rotate roughly 30,000 to 40,000 active SKUs, a scale at which manual analysis breaks down. “We now use AI and statistical data models to set optimal inventory levels, which keeps parts reliably available for machine assembly,” he said.
Cost efficiency. Reducing in-house value-addition cost year on year “used to be driven almost entirely by lean and process improvement initiatives,” Subramanya said. “Increasingly, robotics and digital solutions are forming a meaningful part of that three percent as well.”
Then came the caveat that gives the rest of his answer credibility: “I would be cautious about calling this maturity just yet. We are still in the early stages and there is a lot more ahead of us.”
Traceability: the technology exists, the upstream data doesn’t
Food processors face growing pressure to prove provenance from farm gate to finished pack. Bühler says its tools are ready. “Between Bühler Insights and other platforms we’ve built, we have what’s needed to pull value chain information together into a traceability solution,” Subramanya said.
The obstacle is upstream. “Raw materials and ingredients change hands across a lot of interfaces before they ever reach a Bühler facility, and a lot of that handoff still isn’t digitized, so the data is hard to collate in one place.” The opportunity, he said, is to work with customers to digitise and connect steps “from farm reception through trading and logistics, so the entire value chain becomes visible, not just what happens inside the plant.”
Within Bühler’s own factories, and for parts of the chain outside them, the data can already be pulled into “a single traceability picture for a customer who needs it.” The limiting factor is data quality, which “varies by market.” How hard Bühler pushes the solution, he said, “depends on where the demand and the data readiness are.”
Predictive maintenance: start with detection, not prescription
Perhaps the most counterintuitive message concerns predictive maintenance. The industry narrative treats prescriptive AI, which tells operators exactly what to fix, as the destination. Subramanya argued that for much of Bühler’s customer base, simple anomaly detection delivers most of the value.
“Simple anomaly detection on its own already adds a huge amount of value, especially in markets where processes are still being standardized and operational expertise can vary across sites,” he said. In many plants, operators “may not always have access to the training, tools, or experience needed to consistently identify anomalies when they occur and respond to them effectively.” Given the markets Bühler serves, “India, Africa, and the wider Global South, that gap in operator training is real and significant.”
Bühler offers both ends of the spectrum. “For markets that want full automation and full prescriptive recommendation, we have solutions built for that,” Subramanya said. “But we’re not taking our eyes off simpler systems either.” For India and Africa, his “focus stays on simple systems, trend monitors, and anomaly detection, tools that give the plant owner or operator a reasonably clear picture of where things are heading.”
That, he said, reflects a broader stance: “Our digital strategy isn’t a one-size-fits-all approach applied globally. It’s shaped by what a specific market needs.”
Toward zero waste: staged, not all at once
Bühler has set a global mandate to cut energy, waste, and water by 50 percent in its customers’ value chains. In South Asia, Subramanya sees the first gains coming from a mundane source. “A large share of the waste we see comes from exactly the kind of anomalies we’ve been discussing, plants running outside their set parameters without anyone catching it in time.”
He declined to put a number on the savings, noting that “the exact number varies a lot by plant and context,” but said the impact is “repeatable, meaningful,” not “marginal.” It appears as “real savings in reprocessed material, along with better line availability, because the plant isn’t losing time reprocessing.”
Commercial proof points already exist: AI-powered sorters, energy control for malting, moisture recommendation in flour milling, and tools that let customers review operations and “build a learning cycle so they perform better the next time round.” Combined with timely alerts when parameters drift, Subramanya called it “a genuinely powerful combination.”
Crucially, the path is incremental. “The path toward Bühler’s waste reduction targets doesn’t depend on all regions adopting the complete package at once,” he said. “It can happen in stages, matched to what each market can realistically take on.”
Bühler India’s story cuts against the grain of AI hype. Its leaders are not promising autonomous plants. They are standardising the ERP core, running a hybrid cloud, training a thousand-plus people to define good problems, and deploying the simplest AI that moves a number: a flagged anomaly, a better stock level, a cheaper first-pass simulation.
For CIOs in emerging markets, the lesson is that AI maturity is not a single destination but a ladder, and the right rung depends on data readiness, operator skills, and local demand. The organisations that win will not be those that deploy the most sophisticated models, but those that match capability to context, and are honest enough to say, as Subramanya did, that they are “still in the early stages and there is a lot more ahead of us.”