As uncertainty surrounding the conflict in West Asia continues with no clear resolution in sight, companies are increasingly evaluating digital solutions to navigate emerging operational and supply-chain challenges. To manage the challenges on multiple fronts. Birla Opus Paints is exploring AI and other digital technologies that could help improve raw material price predictability and secure critical resources to minimise supply-side disruptions during geopolitical conflicts and other stress events. Surbhi Gupta, Head - Digital, Birla Opus Paints speaks with FE FUTECH.
India relies heavily on Middle Eastern crude imports and is therefore vulnerable to prolonged disruptions in the region. The paints industry is significantly dependent on crude-linked inputs such as titanium dioxide, solvents and resins. The escalating conflict in West Asia could potentially trigger prolonged supply-chain disruptions across industries. How is Birla Opus leveraging its digital infrastructure to build resilience against such commodity and logistics volatility ?
Every disruption, similar to the current West Asia crisis, creates opportunities to improve existing process norms and establish a new normal. COVID-19 is a classic example. The pandemic brought to the fore the importance of remote working and demonstrated how a certain level of connectedness could be maintained through digital solutions. A similar situation is now emerging in the ongoing Gulf conflict, where supply-chain disruptions are once again highlighting the importance of digital resilience.
Increasingly, these developments are demonstrating the need for digital solutions that may not predict events with complete accuracy, but can at least provide directional insights into potential disruptions and help mitigate risks arising from macroeconomic factors.
As part of this ongoing effort, my responsibility as a digital officer is to ensure that we continue building more resilient and robust systems capable of withstanding future uncertainties. We are currently evaluating and discussing several such solutions with the relevant functional teams for future implementation.
The broader objective is to improve predictability around market trends as well as raw material pricing. The focus is on building a more connected ecosystem across the supply chain, covering not only inventories of raw materials and spare parts but also broader operational dependencies, so that the company can avoid supply crunches during periods of uncertainty.
While these solutions are still at a conceptual stage and have not yet been fully designed, discussions and initial frameworks are already progressing in this area. The company plans to further develop and scale these initiatives over the coming year.
One specific use case we are actively exploring is the prediction of raw material pricing. This solution would take into account multiple external factors, including developments reflected in news flows as well as signals emerging at the vendor level. By combining and analysing a wide range of parameters, we want to assess whether meaningful predictive insights can be generated.
On the supplier risk aspect, these insights could further support supplier-risk assessment by helping evaluate the long-term dependability of suppliers and identifying which suppliers are better equipped to withstand market disruptions compared with others.
Overall, these kinds of comparative capabilities are areas we plan to gradually build into the system. At present, we are still conceptualising the broader framework and determining the best approach for implementation. However, if successfully deployed, this could become a significant differentiator for the business.
Where are you in the journey from AI experimentation to deploying agentic AI in production across areas such as demand forecasting, dealer engagement and dynamic pricing? Additionally, what governance guardrails are you putting in place?
We have launched a couple of AI-led solutions, including a voice bot in our contact centre to handle incoming contractor calls, and an exterior visualiser that helps customers visualise different colour schemes for their properties.
Our AI strategy is vendor-agnostic. Apart from working with various LLM models readily available in the market, we are also building our own AI platform to reduce costs and ensure that indigenously developed capabilities can be reused across multiple use cases.
The contact centre voice bot for handling incoming contractor calls has stabilised since going live about two months ago. The bot was designed after analysing patterns across the end-to-end journey of typical contractor interactions. For example, it can manage the complete workflow associated with a complaint raised by a contractor, including the required follow-up actions.
The exterior visualiser enables potential customers to visualise how their properties would look with different colour schemes, either within their home settings or on the exterior facades of their homes. The solution is also being used by institutional teams working on projects, where they need to showcase multiple colour schemes to project owners and demonstrate which combinations would best suit a particular institutional project. The visualizer is powerful enough to provide a ‘clean’ image of the exterior façade without any obstruction to the view.
Earlier, this process typically required a turnaround time of two to three days. This has now been reduced to just a few minutes.
On the agentic side, we are building an agentic platform for end consumers that will enable them to access any service they may require from Birla Opus. The platform is being designed to support multiple consumer interactions across the painting services journey, including handling customer queries, collecting NPS survey responses, providing service updates, and enabling customers to raise complaints whenever required.
The entire communication platform will be powered in the background by an agentic layer, where multiple AI agents will work together to provide consumers with a unified interface and a seamless experience. Whether a consumer walks into a franchisee store or engages with the company online, the platform will be able to connect and integrate all customer journeys. This is how the company is approaching the broader agentic framework for its consumers.
You mentioned solutions such as the exterior visualiser and cleanup capabilities, which would require significant computational power. Is the infrastructure cloud-enabled, and what kind of AI and compute infrastructure are you planning to put in place to support these workloads?
We have adopted a cloud-first strategy. Following a hybrid model, our solutions are hosted across multiple hyperscalers.
AI solutions are hosted on different AI platforms depending on the specific use cases being addressed. For generative AI-based use cases, we are currently relying largely on paid LLMs. At the same time, we are also experimenting with the use of open-source LLMs for certain core use cases.
We use GPUs for high-computational AI workloads. Overall, the infrastructure follows a balanced mix depending on the nature of the AI solution, and the workloads are distributed accordingly.
We are largely leveraging GPUs for more analytical use cases, where multiple AI workflows will be running at the same time and also looking at some use cases requiring SLMs
AI governance guardrails are considered crucial in the BFSI sector. How relevant are they for the paint industry?
In the paint industry, similar to other sectors, AI guardrails are focused on ensuring that AI-generated code remains clean and free from issues such as data poisoning or hallucinations. It is also important to ensure that proprietary company data is not used by external AI models or LLM providers, and that control over the data remains within the company’s perimeter.
The company is in the process of implementing guardrails and AI hygiene practices across each of these areas as part of its coding guidelines. These standards are being followed uniformly by both the company’s internal teams and its vendors.
Upskilling employees in AI will be critical to future-proofing the company against emerging challenges. What kind of programmes do you plan to undertake in this regard?
An AI training agenda has been outlined across Aditya Birla Group (ABG). The group operates the Gyanodaya learning centre, where AI upskilling programmes are conducted for teams across its various companies. Initially, the focus has been on leveraging AI to improve personal productivity.
At the group level, there is a platform called ‘IntelAct’, an enterprise-grade ChatGPT-like application that is being used by our teams. We are also exploring additional solutions to provide training in prompt engineering and the effective use of AI for personal productivity.
For the upskilling of our technical teams, we are focusing on enabling AI-assisted coding practices, understanding the potential security implications associated with AI, and building adequate governance and control mechanisms.
What challenges have you faced working with vendors, startups, Big and small, and how do you find long and short-term workarounds?
The shift towards AI is not necessarily creating challenges in isolation, but it is fundamentally changing the way organisations and technology partners are expected to operate. With advancements in AI, there is now a broader expectation that product companies and implementation partners must demonstrate how they are leveraging newer technologies to improve productivity, accelerate turnaround time and deliver stronger cost efficiencies. The technology world has moved away from the way developers wrote code even 5 years back
As part of our engagements with vendors and partners, we are increasingly asking them to demonstrate how AI can help improve delivery productivity, reduce implementation timelines and generate measurable cost benefits. This expectation extends not only to implementation partners but also to product partners, where discussions are now centred around how they can leverage AI within their product roadmap and pass on those efficiencies and benefits to us as clients. This is expected to become an even bigger focus area over the coming year.
One of the key challenges, however, continues to be talent availability. Even beyond our own organisation, there remains a broader shortage of AI-related talent across vendor ecosystems. At the same time, while working with certain startups, we have seen very encouraging traction and strong results for some of the AI solutions being developed collaboratively.
However, there is still significant work that needs to be done across the industry. Vendors and technology partners need to reassess their security posture in light of emerging AI-related threats. More broadly, the technology services industry as a whole needs to rethink how it designs, delivers and adapts solutions in response to the rapidly evolving technology landscape, while also communicating clearly to customers how they plan to evolve alongside these changes.
How can vendors improve productivity and pass the cost benefit to you? Can you give an example of this?
From the product side, we are engaging with vendors to understand what kinds of AI-led components they are planning to include in their product roadmaps. The objective is to assess how these AI-enabled features can improve operational efficiency and potentially reduce the overall subscription or licensing costs associated with the solutions provided by product vendors.
On the implementation side, the focus is on achieving faster turnaround times for projects. The expectation is that vendors will leverage AI-assisted development and coding practices to deliver projects within shorter timeframes. This can help improve execution speed and lower the cost of development.
Birla Opus has entered the top three decorative paint brands by revenue in just over a year, backed by an investment of ₹10,000 crore and a network of more than 45,000 tinting machines and 50,000 dealers. Given this pace of growth, how do you measure digital’s direct contribution to revenue at this scale?
The following initiatives illustrate how digital solutions contribute to the company’s overall revenue. Various business operations have been digitally enabled, including programmes that incentivise contractors to purchase paint buckets. The process has been simplified to allow contractors to scan a paint bucket and earn reward points, which they can redeem for direct benefit transfers (DBTs). This translates into instant monetary benefits for the contractors and encourages them to make purchases immediately, resulting in faster cash inflows for the company.
Under the company's Paintcraft services model, customer leads are instantly converted into business opportunities through the customer relationship management (CRM) solution. Using this model, the company is able to respond to customer enquiries posted online within 30 minutes. Thereafter, a visit by a company representative is scheduled at the customer’s convenience. Once the products are finalised, a quotation can be generated for the customer within minutes. This is helping improve conversion rates.
The digital solutions designed for the on-ground sales team provide regular updates on key focus areas, including how sales plans can be improved and integrated based on the type of dealer, as well as the parameters that should be reviewed during dealer interactions. This is enhancing employee productivity. During the year, the company plans to leverage AI-led solutions to achieve business KPIs faster and more efficiently than before.
The ease of doing business enabled through digital solutions for channel partners is directly contributing to revenue growth by improving engagement levels and enabling faster turnaround times for consumers and employees.
Broadly, what feedback have you inferred from the familiarisation programmes undertaken for the board on the recent developments in IT and digital?
The leadership team and the board are already convinced about the transformative potential of AI and view it as one of the most critical technologies for the future. The broader focus is not merely on achieving incremental gains, but on leveraging AI to drive exponential growth that can meaningfully contribute to both the top line and bottom line.
To support this vision, Birla Opus has prepared an AI Vision document, which has been circulated to all functional heads. The document outlines the company’s AI implementation strategy, the formats in which AI will be adopted, the resources required, and the potential business value expected to be generated through these initiatives.
During the familiarisation programmes, discussions have largely centred around the company’s progress on its AI journey and the kind of support required to scale AI adoption across the organisation. The board has been particularly focused on understanding how AI can be operationalised effectively at scale.
At the same time, functional heads across departments are also keen to drive AI-led technology initiatives within their respective functions, reflecting a broader organisational push towards AI adoption.
