Formula 1's reliance on engineering precision mirrors how organizations must build unified data foundations to enable effective agentic AI systems that deliver consistent, contextually relevant business outcomes at scale.
20 Dec 2025 | 4103 Views | By Deepu Chacko, Salesforce India
One of the most thrilling events on the Formula 1 calendar,the VegasGrand Prix just concluded, offering a glimpse into the pinnacle of motorsport. The F1 car is an adrenaline-filled showcase of how cutting-edge engineering and precise design come together in the relentless pursuit of speed and performance.
In ...
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One of the most thrilling events on the Formula 1 calendar,the VegasGrand Prix just concluded, offering a glimpse into the pinnacle of motorsport. The F1 car is an adrenaline-filled showcase of how cutting-edge engineering and precise design come together in the relentless pursuit of speed and performance.
In many ways, F1 is also a fitting metaphor for how organisations approach Agentic AI. Just as a huge part of an F1 driver’s success is determined by their car, an AI agent’s performance is also informed by data. A driver’s skill is crucial, but it can’t overcome the limitations of a slow car. Similarly, the best AI agents are ineffective without trusted and unified data.
Without trusted data, AI outputs are often limited. They can even become generic in some cases and miss key nuances or fail to translate into real business impact. In fact, 49% of IT security leaders in India say their organisation’s data foundation is not yet ready to support agentic AI at scale. The reality is that many organisations, in their rush to embrace the latest AI advancements, are overlooking a critical element for success: the ability to scale their solutions and consistently deliver reliable outputs.
Context Turbocharges the AI Engine
Businesses have been told time and again: “Your AI is only as good as your data”. They invest significant effort into cleaning and structuring their data. Yet, many still struggle to achieve meaningful and useful outputs from their AI agents.
The problem? AI agents don’t just need data; they need context — the deep, nuanced understanding of the business, embedded in an organisation’s enterprise knowledge.
This “enterprise knowledge” isn’t just the sum of a company’s data stored in intentionally created, curated, and maintained databases. While structured information – such as customers’ names, contact details, financial transactions, and product information – is essential, it represents only part of the picture.
True enterprise knowledge goes far beyond this and extends into a largely untapped layer of unstructured information that includes everything from documents, emails, customer interactions, internal guides, and even the nuances of teams’ knowledge. Without harnessing both structured and unstructured knowledge, even the most advanced AI agents will fall short of delivering outputs that businesses can truly and act on.
To return to our F1 analogy, giving an AI agent data without context is like sending a driver onto the track alone with the car. With only the car’s dashboard for guidance, the driver is effectively racing in the dark. Enterprise knowledge, on the other hand, acts as the race engineer's voice in the driver’s ear, offering real-time insights into car performance, track conditions, weather shifts, and race strategy. That context is what allows the driver to adapt in the moment and make winning calls under pressure..
For instance, a marketing team could leverage Agentforce to execute a hyper-personalized account-based marketing (ABM) campaign at an unprecedented scale. AI agents research target accounts, analyze data, and draft personalized outreach transforming the process into an intelligent system.
Consider a customer service agent tasked with resolving a billing dispute. Raw transactional data might tell the agent what a customer purchased and when. But without wider enterprise context, such as the customer's past interaction, recurring issues, seasonal purchasing patterns or even the sentiment in their emails, the agent cannot fully understand the situation or provide a helpful solution.
Where Organisations Stall on Track
This context gap is where the opportunity lies. Enterprise knowledge is often notoriously difficult to activate, locked away in hundreds of disconnected systems and buried in unstructured formats like Slack messages, PDFs, meeting recordings, and support tickets. With the average enterprise juggling over 897 applications, 71% of which are disconnected, it’s no surprise that AIagents struggle to build any kind of coherent view of the business.
Data silos mean there's no single source of truth. Without one, AI agents struggle to interpret information properly, miss important context and hesitate on critical decisions. Instead, they risk making superficial or inaccurate responses, eroding trust and undermining the value they are meant to create.
The Power of Unified Data
The only way for agentic AI to truly succeed is to connect thus fragmented data and anchor it with real-world business context. When AI agents have access to the full picture, they can respond with greater intelligence, adapt in real time and produce outcomes that feel relevant and grounded in reality.
When done right, agentic AI shifts from being an experiment to becoming a core business advantage. Sales teams can send personalized messages based on real-time customer insights. Service agents can resolve issues faster and with more empathy, and customers can get product recommendations that are intuitive and relevant.
Getting to the Chequered Flag
A skilled F1 driver can’t win with just a car; they also need real-time insights and strategy from their race engineer. The same is true for AI agents: they require trusted, unified data and business context to deliver consistent, real-world impact. As the AI race heats up, organisations that successfully connect their data and ground it in a real business context will gain a decisive edge, powering their way to the chequered flag in this AI Grand Prix.
With India strengthening its AI ecosystem through national missions and dedicated Centres of Excellence, businesses that unify data will be in pole position to shape this transformation. This is where India's opportunity truly crystalises. Unburdened by the decades of legacy systems that encumber many mature economies, India has the unique advantage of being able to leapfrog directly to a unified, AI-ready data infrastructure.
By applying this modern approach to its vast and diverse digital-native population, India is creating a living sandbox for solving complex, real-world problems at a scale. The solutions and best practices that emerge whether in optimizing agriculture, delivering personalised healthcare to remote populations, or building more resilient supply chains will extend far beyond India and help shape how the world applies AI in practice.
India's leadership in AI will ultimately be defined not just by the technology itself but by its ability to apply it practically and responsibly at scale, to solve humanity's most pressing challenges. This will rest on one critical foundation - a clear, unified and forward-looking approach to data.
Deepu Chacko is VP- Solution Engineering at Salesforce India. Views expressed are the author's personal.