Indian enterprises have spent the past two years running AI pilots. Scaling them is proving far harder. The obstacle is rarely the model—it is the data underneath it. Customer records sit in one system, transaction logs in another, operational signals in a third, and much of it is batch-processed, stale by the time an AI agent tries to act on it. The result is a familiar pattern: demonstrations that impress in a boardroom and stall in production.
Confluent’s argument is that this is a streaming problem before it is an AI problem. Built on Apache Kafka and Apache Flink, its platform continuously moves and processes data across legacy systems, clouds, and silos, delivering what the company calls real-time context to AI agents and applications. Its newer Confluent Intelligence layer pushes that further—streaming agents that observe, decide, and act inside live data pipelines rather than querying a warehouse after the fact.
Rohit Vyas, Director, Solutions Engineering, Confluent India speaks with Zia Askari from TelecomDrive.com about the company’s strategy for India and its future plans.Â
How does IBM’s $11 billion acquisition of Confluent specifically address the data fragmentation challenges that Indian enterprises face when moving from AI pilots to production-scale deployment?
Enterprises, SMBs and start-ups all faced varied degrees of challenges to source right tech and right price and right roadmap, as much as possible, covering the varied challenges of the dynamic business environment. This often led to multi-sourced solutions whose integration points, open source mix, varied licensing and support regimes and roadmap ambiguities added to the TCO conundrum.
With IBM Confluent, the buyers have the ease to source full stack data infrastructure, all the way from hardware to software and everything in between (services, support, engineering roadmap and consulting) at an attractive TCO and open integration patterns available therein. This is a win-win to aid sourcing velocity to address evolving business asks.
How will the integration of Confluent’s real-time streaming capabilities with IBM’s WatsonX platform change the operational efficiency and AI readiness for existing Indian customers like Swiggy, Meesho, and Jio Platforms?
The integration brings together Confluent’s real-time data streaming and pipeline capabilities with IBM watsonx’s data lake and orchestration capabilities. Confluent enables enterprises to bring data together from multiple sources, apply governance, transform it using technologies such as Flink, and make it available in the data lake.
The orchestration layer becomes particularly important as enterprises move towards agentic AI. You can have the best individual agents, but if there is no coordination between them, the overall system will not be effective. Enterprises need the right agents to interact at the right time, with the right context and clear handovers between them, without agents working at cross-purposes.
By bringing these capabilities together, enterprises can connect real-time data, governance and orchestration. This gives AI systems access to the right context in real time, enabling them to make more informed decisions and operate more effectively at scale.
How does Confluent’s streaming governance and IBM’s sovereign cloud strategy help Indian businesses comply with the Digital Personal Data Protection (DPDP) Act while maintaining real-time data flows across hybrid environments?
India is, by far, one of the largest data creators in the world, and the real opportunity is to ensure that this data can be made meaningful and put to work for the country. For enterprises, that also means having the flexibility to build and manage their data infrastructure in a way that works for their specific requirements.
Through our portfolio, we give customers that flexibility. They can choose to fully self-host their infrastructure on-premises, opt for a fully cloud-hosted SaaS model, adopt a hybrid approach, or bring their own cloud. We meet customers where they are, while also helping them meet the requirements of their regulators.
Governance is equally important, particularly as AI adoption scales. Whether it is training data, RAG data, reference data or inference data, Confluent helps ensure that data coming into AI models from systems of record, across OLTP and OLAP environments, is properly governed. This includes applying schemas and data contracts, establishing appropriate access mechanisms, and maintaining clear controls around who can access the data, what they can access, for how long, and who is responsible for auditing that access.
Ultimately, the objective is to give enterprises the flexibility to use their data wherever it resides, while ensuring that it remains governed, secure and fit for AI.
Confluent/IBMs portfolio data contract capabilities aid with secure definition of roles, actions, audit trails, service accounts and reporting to satisfy the ask of the regulator about the who-what-why-when and where of the data ownership, management and lifecycle stages until decomm per the choice of the original data owner. Confluent portfolio is GDPR compliant. DPDR draws a lot of inspiration from GDPR with some extended controls. These are ably addressed by the Confluent/IBM portfolio capabilities.
What is the future roadmap for GenAI and Agentic AI in India? What specific GenAI and agentic AI use cases are being prioritized for the Indian market, especially in high-volume sectors like real-time commerce and telecommunications?
Agentic AI is growing because enterprises are looking to make decisions on the fly, with fewer manual interventions and more intelligent systems, so that processes can become faster, more responsive and more effective.
We have seen the concept of design thinking being applied to problem-solving. I believe we now need to move towards what I would call design consciousness. This means that we are not looking at technology in isolation. We are bringing together the data required to understand and solve a problem in its business context, while keeping the desired business outcome at the centre. When you put these elements together, the agentic ecosystem becomes much more relevant and useful to the end user.
This is where the combination of Confluent and IBM becomes particularly interesting. With Confluent, you have a real-time data pipeline that can bring data together from multiple sources, apply governance, transform it using technologies such as Flink, and make it available across the enterprise, including in data lake environments. With IBM watsonx, you have capabilities around data, AI and orchestration that help bring these components together.
Orchestration is particularly important as enterprises start working with multiple AI agents. You can have the world’s best agents, but if there is a lack of coordination between them, the overall system will not be effective. You need the right number of agents working in the right context, with the right handoffs between them, so that they complement each other rather than compete or duplicate work.
Telecom is a very interesting example of where this can come together. One of the key metrics telecom companies track is ARPU, but improving ARPU requires much more than simply delivering telecom services. It involves ensuring resilience and performance, managing costs effectively, understanding customer behaviour, and identifying opportunities for cross-sell and upsell.
The two large telecom companies in India also have businesses beyond traditional telecom, including super-app ecosystems that bring multiple services to customers. To make those ecosystems work effectively, data needs to flow seamlessly across the entire customer journey. From customer acquisition and service provisioning to resilience and telemetry, cost management, revenue optimisation, cross-sell and upsell, there are multiple points where intelligent, real-time decision-making can create value.
That is where Confluent’s strength comes in, and where we see the combination with IBM becoming even more powerful. By bringing real-time data, governance, transformation and orchestration together, enterprises can build agentic systems that can actually operate in the context of their business.
The opportunity ahead is to take this approach across more use cases and help enterprises move from individual AI agents to connected, intelligent ecosystems that can make better decisions in real time.
Please share your go to market strategy for India? How many such partners are working with Confluent today and what kind of partner growth are you looking for in the near future?
IBM, as a mothership, has always been partner-first, and that is a philosophy we at Confluent share as well. Together, we are even more focused on building a partner-first go-to-market approach.
Whether it is global system integrators, regional system integrators, Big Four consulting firms, or more specialised boutique partners, we work across the entire ecosystem. We believe partners are one of the most effective ways to reach customers, particularly when you consider the scale of the market and the limited number of people any one organisation can have on the ground.
Our partners bring a level of scale and customer proximity that is difficult to replicate. They understand their customers closely and have the expertise and relationships to take solutions to market effectively. That makes them a critical vehicle for us to expand our reach and drive adoption.
We continue to invest significantly in this ecosystem, with dedicated teams focused on working with partners and enabling them to take our solutions to customers. We also have incentives in place to encourage joint go-to-market efforts, whether that involves selling and implementing a licence, providing operational services, or taking end-to-end responsibility for a customer engagement.
Our role is to enable and support our partners and be a force behind them in making those customer engagements successful. Ultimately, the scale and reach that our partner ecosystem brings is immense, and we are proud to work with some of the leading names across the industry.
A lot of System Integrators and Solution Providers in India are targeting the AI space today. How can they benefit by working with Confluent today?
We bring two things to our partners: openness and trust.
For partners looking to expand their portfolio, Confluent gives them the opportunity to move beyond their existing areas of expertise and participate across a larger part of the data value chain. For instance, a partner that already works with data lakes can bring Confluent into that environment and offer customers a more end-to-end proposition.
It is also not just about reselling subscriptions or services. Partners can actually build with Confluent. Our Build with Confluent initiative has been running for more than two years, encouraging technology partners to develop connectors and make them available through the Confluent Marketplace. This gives them an opportunity to monetise their own capabilities while creating greater stickiness within their customer environments.
Ultimately, this gives partners another way to differentiate themselves. They are not limited to being resellers; they can also become IP providers, building their own capabilities around the Confluent platform and creating a stronger, more differentiated proposition for their customers.
