Feature
Zoho Corporation has positioned its AI-native platform, Zia Hubs, as a solution to one of enterprise computing's most persistent challenges: making sense of the vast amounts of unstructured business data scattered across email chains, chat threads, PDFs and disparate cloud systems.
The platform, launched last month, represents Zoho's broader strategic shift from passive AI assistants to proactive digital agents designed to execute end-to-end business tasks with minimal human oversight. Just last week, the company introduced a series of new pre-built AI agents and a proprietary LLM to further this vision.
“We’re no longer asking users to tag, classify or file their data manually,” said Ramprakash Ramamoorthy, director of AI at Zoho Corporation. “Zia Hubs does that autonomously. What we’ve built is not just smarter search — it’s contextual reasoning across your entire business knowledge base.”
The Enterprise Knowledge Crisis
Most enterprise data today is unstructured, scattered across outdated file formats, embedded in conversation threads and siloed across disparate systems. Business information is often locked away in email chains, Slack chats, PDFs, wikis and cloud documents stored across isolated platforms. Traditional search and document management tools frequently fall short, unable to retrieve meaningful insights from this fragmented, context-poor content. This means knowledge workers search for information instead of applying it, slowing decision-making.
This obstacle to enterprise productivity is an opportunity for innovation, Ramamoorthy told Reworked.
The Role of Zia Hubs
At the heart of Zoho’s AI-driven knowledge strategy is Zia Hubs, its AI-native platform designed to make enterprise knowledge searchable, dynamic and intelligently organized. Powered by large language models (LLMs), semantic search and advanced document parsing technologies, Zia Hubs autonomously ingests and interprets content from across business systems, including email, chat, documents and third-party SaaS applications, and restructures it into knowledge assets. These assets are grouped into context-aware “knowledge hubs,” continuously updated and easier to explore.
“Zia Hubs applies AI not just to find documents, but to understand them,” Ramamoorthy told Reworked. The platform identifies entities, relationships and user intent, then makes that information accessible through intuitive, natural-language queries. With dynamic updating and context awareness, it reduces the need for workers to dig through multiple disconnected systems, making relevant information available when needed.
One of Zia Hubs’ differentiators is its native integration across Zoho’s ecosystem. Unlike standalone AI tools requiring customization or third-party connectors, Zia Hubs is embedded into Zoho’s data fabric.
This means it can extract richer signals — from customer relationship management metadata and project timelines to permissions and usage patterns — and apply them to fine-tune its understanding of context. “AI works best when it knows the structure of your business,” Ramamoorthy said. “Because we own the platform, Zia doesn’t just guess — it learns from your workflows, your permissions, your context.”
This AI-driven knowledge platform is part of a broader Zoho vision to shift from passive AI assistants to proactive digital agents that execute tasks end-to-end with minimal human intervention. This happens with the Zia Agent Studio, a no-code/low-code environment. These agents resolve IT tickets, qualify leads, manage approvals and optimize deal closures — all while learning from organizational data and workflows.
Ramamoorthy sees this shift as redefining how employees work. “AI doesn’t replace human judgment; it amplifies it,” he said. “With Zia, employees are freed from repetitive tasks and can focus on creativity, insight and strategic decision-making.” This collaboration between human and machine is intended to make workforces more agile and employees happier.
Knowledge Hubs Are Only as Good As Their Content and Data
Outside Zoho, there are those who see the promise while emphasizing necessary caution. “Knowledge hubs succeed only when the data foundation is clean,” said Nic Adams, CEO of Orcus. “AI excellence cannot compensate for unmanaged access rights, poor tagging or broken data retention policies.” While he applauds Zia’s use of a unified vector index and granular permission controls, even the best AI will stumble with disorganized or outdated content, he warned.
Adams also outlined risks with AI interpreting unstructured data: hallucinated relationships, outdated or stale information contaminating insights, hidden biases baked into historical emails or chat logs and “context bleed” where confidential data may leak into broader suggestions. “Every answer must link back to the original source, with diffs and timestamps to enable human review,” he said.
This need for transparency aligns with Zoho’s approach. “Transparency is not optional,” Ramamoorthy said. “Businesses need to see how an insight was derived, where it came from and what it means. That’s built into how Zia works.” Zia Hubs shows its work, so users can validate, audit and refine the AI-generated interpretations.
Zia Hubs’ Adaptability
From a practical standpoint, John Yensen, president of Revotech Networks, confirms the platform’s adaptability. “Zia Hubs stands out by automatically ingesting emails, PDFs, chats and documents into a unified, searchable repository,” he said. “Many tools still rely on manual tagging or rigid folder structures, but Zia’s autonomous ingestion is a game-changer.” The system’s flexibility across industries includes sales, support and R&D, providing the training data represents each business’s terminology.
Yensen cautioned, however, about the persistent risk of AI hallucinations, where the system confidently returns incorrect or outdated information due to misreading context or stale content. “I always recommend a quick human check on critical decisions,” he advised. “Broadly, AI can get you 80%-85% of the way there, but errors will slip through if left unchecked.”
Security and privacy are paramount concerns in any AI deployment. Ramamoorthy pointed to Zoho’s data governance: The company owns and operates its own data centers, unlike many AI vendors relying on third-party clouds. “Zia’s AI agents adhere to strict access controls, and businesses can configure them within compliance frameworks,” he explained. “We don’t have an ad-based revenue model, so AI adoption never comes at the cost of data security or regulatory compliance.”
Looking forward, Ramamoorthy envisioned a future where Zia’s AI agents become more collaborative and cross-functional. “We’re building toward intelligent collaboration, where multiple agents can work together dynamically to manage end-to-end business operations autonomously, but still transparently,” he said. He also highlighted the importance of explainable AI, so decisions remain interpretable, auditable and aligned with real business needs.
Adams anticipated rapid advances as well: real-time embedding refreshes to eliminate stale data lag, multimodal parsing of images and voice notes, on-device differential privacy, incremental departmental learning and policy-aware retrieval that flags sensitive or regulated data automatically.
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Both experts agreed that AI-powered knowledge hubs represent a fundamental leap in enterprise productivity, if implemented thoughtfully. “Zia is designed to enhance human decision-makers, not bypass them,” Ramamoorthy said. “That’s how we make AI truly responsible.”
Editor's Note: Read more questions around unstructured data and AI:
- 3 Steps to Using Unstructured Data With AI — Without clean, well-governed inputs, even the most advanced AI tools will fail to deliver reliable results.
- Knowledge Graphs: The Secret Sauce Behind AI Development — Pairing knowledge graphs with LLMs boosts AI’s reasoning, accuracy and explainability.
- AI's Double Edged Sword — Generative AI, like all tools, has its upsides and its downsides. To get the most out of the tool you need to understand both.