How Tata Realty is building AI-led customer journeys
As India’s real estate sector undergoes rapid digital transformation, enterprises are increasingly looking beyond basic automation towards intelligent, predictive, and deeply personalised customer experiences. For large real estate developers, the focus is no longer limited to digitising workflows alone. The next phase is about simplifying highly emotional and complex journeys such as home buying through AI-led systems, unified data platforms, and contextual customer engagement.
In an exclusive interaction with Express Computer, Girish Hadkar, Chief Information and Digital Officer, Tata Realty & Infrastructure, and Arundhati Bhattacharya, President and CEO, South Asia – Salesforce, discuss how AI agents, predictive intelligence, data governance, and unified enterprise platforms are reshaping customer experiences, operational visibility, and decision-making across the real estate ecosystem.
Real estate is shifting from reactive operations to predictive intelligence
Speaking about Tata Realty & Infrastructure’s larger business vision, Hadkar says the company’s focus continues to remain centred around building sustainable, secure, and digitally enabled spaces across residential and commercial real estate. “In terms of our mission, we want to create sustainable spaces, secure spaces, smart spaces, and social spaces. I think that’s what the customer is looking for,” Hadkar adds.
According to him, the role of AI inside the organisation is fundamentally changing how decisions are being made. “If you see, the decision-making earlier was largely based on experience and intuition. It is still there, but now you have data-backed analysis,” he explains.
Hadkar points out that enterprises are now increasingly moving from reactive systems towards predictive operating models. “The focus of the shift is clearly from fixing problems to pre-empting problems and not getting them to happen,” he notes.
This predictive capability is now being applied across multiple operational areas, including forecasting, construction management, customer servicing, and maintenance planning. “Whether it is sales forecasting, predictive maintenance, or even construction risks, we can identify bottlenecks that can come in the future,” Hadkar adds.
AI is enabling hyper-personalised customer journeys in real estate
According to Hadkar, AI adoption is helping transform customer engagement from transactional interactions into highly contextual and personalised journeys.
The company is now leveraging listening platforms, communication analytics, and sentiment intelligence to better understand customer behaviour and intent.
He emphasises that contextual engagement is becoming one of the most important differentiators in customer experience strategies today.
AI adoption is being designed to augment employees, not replace them
Both Hadkar and Bhattacharya stress that despite growing AI automation, human judgement and empathy remain central to customer engagement.
Hadkar explains that Tata Realty initially approached AI deployment through an “assistant mode” strategy designed to improve employee productivity. “We had a strategy that by implementing AI, we first have to implement data assistant mode,” he says.
He illustrates this using the example of CRM managers handling large customer ticket volumes daily. “Earlier, a CRM manager had to manually go through 20 to 60 tickets and decide what should be prioritised first. Now AI helps identify the priority, communication context, and the next best action.”
This allows employees to focus less on repetitive operational work and more on meaningful customer interactions.
Bhattacharya adds that enterprises must carefully determine where autonomous AI can operate independently and where human intervention remains necessary.
“Depending upon the criticality, or maybe when emotions are running high, a human being in the loop is important,” Bhattacharya observes.
She also clarifies that AI agents should never impersonate humans during customer interactions. “At no point in time are we going to have agents masquerading as human beings,” she says.
Faster sales cycles and real-time decision-making are already showing results
According to Hadkar, unified operational visibility and AI-driven intelligence are now delivering measurable business outcomes.
“There is visibility available into what is happening at what time, and decisions are now more real-time,” he says.
The organisation is also using AI-led sentiment analysis and customer feedback intelligence to continuously refine engagement models and personalise journeys. “All the interactions and feedback are getting analysed in real time. We are learning how to respond to customers and curate personalised journeys,” Hadkar explains.
This has significantly improved lead conversion efficiency and customer responsiveness. The impact is also visible in sales velocity. “Earlier, selling used to happen in three days. Now it is happening in one day,” Hadkar reveals.
Tata Realty now envisions an AI-led home-buying assistant
Hadkar shares that their next major focus is simplifying the entire home-buying process through intelligent AI orchestration.
He points out that customers today often struggle with multiple operational complexities ranging from documentation and taxation to banking coordination and financial formalities.
“At each point in time, the customer keeps worrying whether they are doing the right thing,” Hadkar notes.
To address this, Tata Realty now plans to build what Hadkar describes as a “digital signature AI” capable of orchestrating documentation, banking coordination, and transactional workflows across the customer journey.
“Our vision is to now create a digital signature AI who will manage all these banking activities for the customer,” he explains.
The larger objective, according to him, is to help customers enjoy the emotional aspect of home ownership without operational anxiety.
“It will enable our customer to enjoy the process of buying their first home,” Hadkar says.
Unified data ecosystems are becoming foundational for enterprise AI
Bhattacharya emphasises that enterprise AI adoption cannot succeed without strong data integration and governance frameworks.
“In any organisation, fragmentation of data is the biggest challenge,” she says.
She explains that enterprises today operate across multiple structured and unstructured data systems, making unified intelligence difficult without a consolidated data layer.
“The idea is to create a single source of truth for the customer, and then that becomes the source of information to help make decisions,” Bhattacharya explains.
She also highlights the importance of allowing enterprises to leverage existing data investments instead of forcing complete infrastructure replacement. “We don’t want customers to throw away all the intelligence they have already built,” she adds.
Governance, consent management, and privacy controls are central to AI scaling
According to Hadkar, governance capabilities have been deeply integrated into the company’s AI architecture from the outset. “The very reason that we opted for this platform was to have the ability of setting the guardrails that we want,” he points out.
Capabilities such as consent management, notice management, access controls, and data masking are now tightly embedded into the organisation’s operational framework.
“The entire consent management, notice management, customer preferences – everything is available at one single place,” Hadkar says.
He stresses that enterprise AI systems must remain grounded entirely on trusted internal enterprise data. “All the communication that the agent does is only based on our clean data,” he adds.
The next phase of AI will be built around interconnected enterprise agents
Looking ahead, Bhattacharya believes enterprise AI systems will evolve rapidly from isolated chatbots into interconnected multi-agent ecosystems capable of orchestrating complex business decisions autonomously.
“Today robots are meant for one journey. In the next wave, they will get infused with agentic capabilities,” she says.
According to her, enterprises will increasingly rely on multiple specialised agents collaborating together to execute larger workflows.
However, she also acknowledges the pace of AI evolution is creating continuous pressure on enterprises and employees alike.
“The speed at which this is building is really very fast,” she observes.
For enterprises attempting to remain competitive in the AI era, continuous learning and adaptability will become essential.
“People like us have to keep enabling our people. They have to keep learning new things,” Bhattacharya concludes.
