By Gavin Barfield
TENNIS is gaining traction in the Philippines, following Alex Eala’s rise to global fame this year. The first Filipina to break into the world’s top 100 female tennis players, the 20-year old now stands alongside Filipino athletes who have put the nation on the sporting map.
In many ways, tennis — a sport requiring skill, precision, and strategy, is a fitting metaphor for how organizations approach agentic AI. Giving an amateur player the best racket, shoes, or access to the best tennis courts is not enough. To become truly competitive, that player needs consistent training and high-quality, trusted input from coaches. Likewise, even the most advanced AI agents are ineffective without the input of trusted, unified data.
Without trusted data, AI outputs are often inaccurate, overly generic, or simply lacking the specific context needed to drive meaningful action. It’s no surprise that, according to a Salesforce study, the majority (85%) of Filipino service professionals using AI believe having better access to data will improve the support they provide. The reality is that many organizations, 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.
DOMINATE THE AI COURT WITH CONTEXTBusinesses 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 organization’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 also encompasses a vast, often untapped trove of unstructured information that includes everything from documents, e-mails, 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 meaningful, trusted, and actionable outputs.
To return to our tennis analogy, giving an AI agent data without context is like sending a player to Wimbledon without any trusted, quality training or coaching. Enterprise knowledge is the coach’s playbook — clear drills, step-by-step guidance, and a game plan based on an analysis of the competitors’ strengths and weaknesses. Having that crucial context gives the athlete a full picture of the game, enabling smarter decisions and a better chance of winning.
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 enterprise context, such as the customer’s interaction history, seasonal purchasing patterns, and even sentiment from previous e-mail conversations, that agent won’t be able to grasp the specific context to provide a helpful solution.
WHEN ORGANIZATIONS HIT A WALLThis context gap is where many organizations, including businesses in the Philippines, stumble in their AI journey. Enterprise knowledge is often notoriously difficult to achieve, 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 wonder agents struggle to get a full picture of the business, let alone offer useful or trusted outputs.
Data silos mean there’s no single source of truth. Without it, agents can’t reason effectively, understand nuance, or make confident, informed decisions. Instead, they risk making superficial or even incorrect choices, which erodes trust and limits their ability to drive real value.
THE POWER OF UNIFIED DATAThe only way for agentic AI to truly succeed is to connect all of this disparate data and infuse it with real-world business context. When AI agents have access to the full picture, they’re able to act more intelligently, adapt to dynamic scenarios, and deliver meaningful outputs.
Platforms like Salesforce Data Cloud connect both structured and unstructured data sources into one unified, integrated platform. Its zero copy technology allows organizations to access and query their data in real-time, without the need to extract, transform, and load their data across multiple sources — a process which can be costly and time-consuming. All of this data grounds Agentforce, Salesforce’s suite of AI agent tools. It ensures that AI agents are grounded in all your business data, while providing robust security, governance, and compliance.
When done right, agentic AI can be a game-changer. 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 feel intuitive and relevant.
GETTING TO MATCH POINTA skilled tennis player can’t win with just the best equipment; they need rigorous training and trusted, quality coaching. The same is true for AI agents: they require trusted, unified data and crucial business context to deliver their full potential. As the AI rally heats up, organizations that connect their data and ground it in real context will gain a decisive edge — and have the best shot at winning the Grand Slam.
Gavin Barfield is vice-president and chief technology officer for solutions at Salesforce ASEAN, where he leads a regional team of engineers in developing integrated technology solutions to support customers’ digital transformation.
