Anjaiah Surgi explains how manufacturing enterprises are scaling AI across plants, dealer ecosystems, forecasting, and operations while balancing cloud infrastructure, data integration, governance, AI literacy, and measurable business outcomes.
As manufacturing enterprises accelerate their AI transformation journeys, the focus is rapidly shifting beyond isolated automation projects toward connected intelligence across plants, dealer ecosystems, supply chains, and customer operations. For traditional industries, the challenge is no longer just technology adoption, but building scalable AI-ready infrastructure while balancing operational continuity, data integration, governance, and measurable business outcomes.
In this conversation with CIO&Leaders, Anjaiah Surgi, CIO at Hindware, discusses how the role of the CIO is evolving from infrastructure management to strategic business leadership. He shares how the organisation is leveraging AI, cloud, analytics, computer vision, and predictive intelligence across manufacturing operations, dealer engagement, forecasting, and enterprise decision-making while addressing challenges around AI literacy, security, governance, and sustainable adoption.
CIO&Leader: AI adoption in manufacturing and dealer ecosystems comes with very different operational requirements compared to traditional SaaS environments. How are you balancing low-latency on-ground processing with growing compute requirements?
Anjaiah Surgi: You cannot continue relying entirely on traditional on-premise infrastructure and static server environments anymore.
That is where cloud and hybrid ecosystems help significantly, whether it is Azure, AWS, or GCP. In our case, we primarily operate on Microsoft Azure. The workloads are managed dynamically there, which means we do not have to constantly worry about latency or compute scaling.
Cloud and hybrid environments are what allow us to scale operations while maintaining performance and flexibility.
For example, if I look at our loyalty application ecosystem, we already have more than 60,000 influencers connected through the platform, and the network continues to grow.
If we had depended only on traditional on-premise infrastructure, scaling this environment would have been extremely difficult. Cloud and hybrid environments are what enable us to support this level of scalability and operational flexibility.
CIO&Leader: Legacy manufacturing environments often struggle with fragmented and siloed data. While evaluating technology vendors, how important is integration capability compared to raw AI power?
Anjaiah Surgi: Data remains the core fulcrum for every decision-making system, including AI.
Whether it is analytics, AI-enabled analytics, or transaction processing, if the underlying data quality is poor, the outcomes will also be poor. It is the classic “garbage in, garbage out” problem.
Data remains the core foundation for every decision-making system, including AI.
For example, while creating a sales order, if customer credit limits are not properly updated or integrated across connected systems, it creates operational and financial risks.
When we evaluate vendors or onboard software solutions, scalability, flexibility, and agility become extremely important because technology itself is evolving continuously.
A solution may work extremely well today, but if it cannot evolve alongside security requirements, scalability needs, or architectural changes after one or two years, then it becomes a limitation.
At the same time, integration remains absolutely critical. Our ERP backbone continues to be SAP. Any connected system must integrate properly with the ERP environment. That remains one of the core evaluation parameters for us.
CIO&Leader: Since manufacturing is traditionally not an AI-first industry, how are you addressing the AI literacy gap within the workforce?
Anjaiah Surgi: Any brick-and-mortar industry will face these challenges initially.
What we are doing is conducting continuous education and leadership training sessions across the organisation. We started with leadership teams and gradually expanded across the broader workforce.
Within IT itself, we have created a separate Emerging Technologies department dedicated to AI, ML, IoT, low-code/no-code platforms, and RPA initiatives.
This dedicated function conducts regular enablement sessions across locations throughout India. Over the last year alone, we have conducted more than 20 training and education programs, both virtual and in-person.
The momentum has been very encouraging. Employees are increasingly accepting and actively using AI tools.
Within the Microsoft ecosystem, Copilot access has been provided to employees for tasks such as drafting emails, summarising communication, reviewing quotations, and processing RFPs.
There is still a long journey ahead, but adoption levels are steadily increasing.
We also already have data scientists onboard within the organisation helping drive analytics initiatives across functions.
Some of our business reviews today happen completely paperless — without PPTs or Excel sheets — directly through BI dashboards.
The next stage we are moving toward is enabling teams to query systems conversationally instead of relying only on preformatted dashboards. As long as the data exists, users should be able to ask questions directly and receive insights dynamically.
CIO&Leader: How are you reshaping dealer and distributor experiences using AI-driven systems?
Anjaiah Surgi: We already operate distributor management systems integrated with our SAP ERP environment.
The next phase is enabling these systems with AI-driven intelligence.
This includes inventory visibility, market intelligence, and predictive recommendations around what dealers should order and when they should place those orders.
Dealers can already access visibility into nearby warehouses and depots from where products can be sourced.
Going forward, we are also building agentic AI-driven workflows where dealers can directly query systems conversationally to check order status, inventory availability, truck movement, and dispatch tracking in real time.
Today many of these processes require navigating traditional transactional systems. We are trying to simplify that experience significantly using conversational AI agents.
Dealers can already place orders through mobile applications. The objective now is to enhance that ecosystem further using AI-driven intelligence.
CIO&Leader: Are these engagement ecosystems aligned with India’s DPDP compliance requirements? And are you moving toward hyper-personalisation for dealers and customers?
Anjaiah Surgi: Yes. For all newly developed systems and applications, DPDP compliance is being built in from the beginning.
For legacy systems, we are currently conducting assessments and progressively making them compliant as well.
This includes consent management, data privacy controls, access governance, and related compliance requirements.
On personalisation, yes, we are already moving toward highly personalised engagement models.
Field officers using our applications receive contextual nudges about nearby retailers or business opportunities instead of relying only on static visit plans.
Similarly, dealers and distributors see highly personalised experiences where they only access their own operational data.
We are also increasingly building consent-driven engagement models around anniversaries, milestones, and relationship-oriented communication to strengthen long-term engagement beyond purely transactional interactions.
CIO&Leader: How has the role of the CIO evolved from managing IT assets to becoming a strategic business leader?
Anjaiah Surgi: Traditionally, IT was always viewed as a cost center. That perception is changing rapidly.
Today, boardroom conversations increasingly revolve around how AI improves productivity, drives growth, supports revenue generation, and optimises business performance.
The CIO role is gradually moving closer toward strategic business leadership.
For example, discussions today involve areas such as supply chain intelligence, demand forecasting, production optimisation, and plant efficiency.
In one of our manufacturing facilities, defect detection used to happen manually. About two years ago, we implemented computer vision-based defect detection systems on the production line.
Even with occasional false positives, the accuracy levels are now above 98%, which is significantly better than manual inspection.
Similarly, in demand forecasting, traditional human forecasting accuracy in sanitaryware could sometimes fall to nearly 50–60%.
With AI-enabled forecasting systems, we are now seeing accuracy levels move toward 85–90%.
That directly impacts inventory turnover, inventory carrying costs, manufacturing efficiency, and overall business responsiveness.
Earlier conversations around IT focused on infrastructure upgrades or ERP version upgrades. Today, conversations are centered around plant optimisation, forecast accuracy, regional growth opportunities, and operational intelligence.
These are fundamentally business discussions, and they are already happening.
CIO&Leader: Looking toward 2027, what AI-native business trends do you believe will reshape manufacturing and building material industries?
Anjaiah Surgi: We are already seeing connected plants becoming a major trend.
Many legacy machines that were previously disconnected are now becoming connectable because of evolving technologies.
That enables real-time insights and predictive intelligence for maintenance engineers and production supervisors.
Another major area is defect prediction and quality optimisation.
For example, in ceramic manufacturing, cracks may only become visible after multiple production stages. Now, using operational data and AI models, we are exploring how to predict and reduce such defects much earlier in the process.
AI models can significantly improve efficiency, reduce defects, and optimise manufacturing quality across production environments.
CIO&Leader: What do you see as the biggest challenges organisations still face while scaling enterprise AI?
Anjaiah Surgi: One of the biggest challenges is balancing innovation with organisational readiness.
Technology is evolving extremely fast — whether it is Claude models, Gemini Enterprise, or other emerging ecosystems. But organisations also need the ability to absorb and operationalise these technologies effectively.
Change management itself becomes a major challenge.
At the same time, security guardrails remain critical. Enterprises must be extremely careful about what technologies they adopt, how enterprise data is protected, and whether governance mechanisms are sufficiently strong.
Another important area is defining proper success metrics and measurable KPIs for AI initiatives.
It is not enough to simply implement AI systems. Organisations must continuously measure whether those systems are delivering sustainable value.
Implementing AI alone is not enough. Organisations must continuously measure whether those systems are delivering sustained business value.
For example, business reviews happening through BI dashboards instead of PPTs only succeed if the organisation sustains that behavioral shift over time.
If momentum drops after a few months, enterprises can easily fall back into siloed operations again.
Without measurable KPIs, sustained adoption becomes extremely difficult — regardless of whether the organisation is a traditional manufacturing enterprise or a high-tech company.