An average engineer takes 64 seconds to respond to a message but loses 23 minutes regaining focus due to context switching, which means nearly 25-30% of their time is wasted on interruptions, not actual work.
This is not just an employee gripe—it’s a crisis for businesses everywhere. Enterprises rely on an average of 625 applications, and employees spend nearly 25% of their workweek hunting for data buried in endless SaaS apps, Slack threads, PDFs, email chains, and legacy systems.
Every wasted minute navigating a fragmented search means slower decisions, delayed projects, and a workforce drowning in inefficiency.
The cost? Billions lost in productivity. Missed deadlines. Slow decisions. Frustrated employees.
Legacy enterprise search makes people dig. DevRev’s conversational search helps them decide.
Most AI-enhanced tools still rely on bolt-on fixes—clunky interfaces, keyword dependence, and disconnected data. The result? Delayed answers, scattered insights, and frustrated teams.
This blog breaks down how DevRev’s AI-native search flips the model—using real-time context and natural language to cut search time by 30% and turn every query into action. No more searching. Just solving
How conversational search is redefining AI and cutting search time by 30%
Employees aren’t just looking for files; they’re looking for insights.
28% of company time is lost due to reading and responding to internal and external communication, according to an HBR report. That’s nearly one-third of the workweek wasted—lost in emails, Slack threads, meeting notes, and endless searches, trying to piece together scattered information from disconnected apps.
However, the problem here is not about too much communication–it’s about the struggle to find concise information from the details.
Support leaders can’t track what was resolved. Product managers can’t spot which features are frustrating users. Engineers waste hours digging through internal docs. Legacy search leaves teams in the dark—fragmented, slow, and siloed.
DevRev’s AI-native search rewrites how teams find answers—real-time, contextual, and cross-functional.
Conversational AI makes it possible—ditching keyword-matching systems that spit out noise and replacing them with context-aware intelligence that delivers a single, relevant answer the first time.
It’s not just a search. It’s understanding.
Conversational search—a truly modern enterprise search experience, a new way to discover even on the go.
For eg: A product manager is preparing for a leadership review. Instead of sifting through endless Slack threads, Jira tickets, and scattered documentation, they simply ask,
“What were the top customer complaints about our latest release?”
Conversational search pulls insights from support tickets, engineering updates, and past discussions—delivering a precise, cited answer.
This is what a proper conversational search looks like: active, intelligent, conversational language and informed action that delivers a seamless search and insightful solution.
Why legacy and AI-bolted enterprise search is a productivity killer?
AI-powered search is supposed to increase productivity. But instead of answers, employees get endless lists of files, documents, and chat threads, hoping to piece together what they need.
The irony of AI enterprise search is that it was meant to be a solution, designed to increase efficiency, reduce redundancy, and empower employees with instant access to knowledge.
Yet, somewhere along the way, it became just another workplace bottleneck—one that keeps growing, silently eating away at productivity. For over two decades, enterprises have been using multiple SaaS tools–CRM, email automation, live chat, ticketing software, and knowledge base management—to ease their work and increase productivity.
Companies have spent billions on AI-driven search, but the problem persists. Why?
1. Siloed knowledge everywhere
Siloed knowledge doesn’t mean the knowledge is lost—it is locked away. When information is scattered across Jira, Confluence, Salesforce, Slack, email threads, and PDFs and operating in its own silo, your employees indulge in searching for a long time with repetitive questions.
They navigate a maze of disconnected systems, outdated documents, and fragmented conversations. Where does your data lie? Where do your people lie? Where are you getting your work done today?
And that’s the core issue: knowledge isn’t accessible when it matters most. Employees spend more time searching than solving, hopping between tools that don’t talk to each other, trying to piece together fragmented insights.
This is a crisis of accessibility. AI-bolted and legacy enterprise search needs to improve this because they retrieve files, not intelligence. They surface 10 versions of the same document but won’t tell you which one is relevant. They find customer tickets but won’t connect them to product issues. They return links, not answers.
Search shouldn’t stop at retrieval. It should drive decisions. Until AI can connect the dots between people, knowledge, and action, it’s not solving the problem—it’s just reformatting it.
2. Keyword-based search lacks intelligence
Your employees don’t need 10,000 search results—they need the one right answer.
Yet, legacy and AI-bolted enterprise search still function like a glorified keyword matcher. It understands and treats search like a game of “exact word bingo,” retrieving documents based on isolated keywords rather than understanding context, search intent, or relationships.
We have all this information from customers, sales, and technology failures. But what does it mean? How does it help us make better decisions? Even if knowledge is there, there is no way to reference it, leverage it over time, and get answers.
Consider this: A product manager searching for “customer complaints about the latest release” doesn’t need a list of Jira tickets, Slack threads, and Confluence docs stuffed with the words “customer” and “complaint.” They need a single, concise, and structured insight connecting and consolidating all those complaints to product issues, support trends, and customer sentiment shifts.
This is why employees often get either too many results or none at all. So, essentialism in bolted enterprise search needs to be ameliorated with conversational search; it isn’t about doing more—it’s about doing it the right way.
3. Searching for the information still feels like work
Despite AI’s transformation of entire industries and the way we work, employees still struggle with one fundamental challenge: finding information. A McKinsey report highlights that employees spend 1.8 hours every day—9.3 hours per week—just searching for information.
So, the problem isn’t a lack of knowledge—it’s the fragmentation of knowledge buried in different tools that is hard to retrieve.
Search isn’t just an inconvenience. It’s an enterprise-wide bottleneck that affects productivity, decision-making, and even customer experience.
Siddhartha Garikapati, Associate Director & Technical Program Management, Razorpay, summed up this challenge with a real scenario:
We have been trying to track down every product spec written over the last decade—thousands of documents from hundreds of people. A new hire needed context on past decisions, but all the critical information was buried in Google Drive, scattered across emails, and lost in Slack threads. There was no easy way to find it. This isn’t just a search problem—it’s a productivity crisis.
Ironically, enterprise search was designed to make work more manageable, but is it really?
When every search leads to more searching, is enterprise search truly solving the problem, or is it just adding to the workload?
4. No real-time adaptability to the changing data
We know business data isn’t static—it constantly evolves with product updates, customer interactions, and organizational shifts. Yet, legacy enterprise search engines fail to keep up. They retrieve documents as they existed yesterday, last week, or last year, not as they are relevant today.
This creates a fundamental gap between what employees need and what legacy search delivers.
Eg: A support engineer troubleshooting an API outage needs the latest report—fast.
But legacy search delivers noise: outdated tickets, irrelevant threads, and zero context.
Instead of solving the issue, they’re forced to repeat searches, switch tools, and dig through disconnected systems—chasing answers that should’ve been instant.
We deliver one search experience across all your data sources, ensuring sourced and cited information you can trust. Our enterprise search goes beyond links—it provides answers, connecting data to solve your toughest questions.
How conversational AI unlocks enterprise intelligence for a better search experience
We’ve been told enterprise search is “intelligent”—that one query gives you everything you need. But in reality, it floods you with noise, misses the context, and leaves you searching more than solving.
Every day, employees have to fight against their limitations in hunting, digging, and piecing together the siloed information from the fragmented tools, hoping to find the answer to their search.
But after the revolution of AI, enterprise search was given a blown-up hype—that it was easier than before. Yet again, it failed. Why?
Legacy enterprise search saw AI only as a trending feature, not as a product. They adopted AI to sustain themselves in the market, which resulted in bolting AI on top of keyword-based systems instead of reinventing how search should work. It returns links, not answers. It offers documents, not decisions, back to square one.
AI isn’t just another feature. It’s your newest team member—one that listens, learns, adapts, and drives action.
To escape the trap of half-baked automation, businesses must stop treating AI as a bolt-on tool and start building around it as a product that works with humans, not around them.
Instead of isolated AI enterprise search, it should be embedded into workflows, support tickets, product enhancements, and add-ons across the team. So, information can be retrieved without hands-off or delays.
Legacy vs. AI-native enterprise search: A quick, clear comparison
Feature | Legacy Enterprise Search | AI-Native Enterprise Search |
|---|---|---|
Speed & relevance | Slow, keyword-based, and often returns outdated or irrelevant results. | Instant, AI-driven answers with real-time, context-aware results. |
User effort | Need multiple searches, manual filtering, & system-hopping. | Delivers precise answers without redundant searches. |
Data unification | Siloed data across different tools, making search fragmented. | Unified search across all integrated data sources. |
Context understanding | Matches keywords but lacks deeper understanding. | AI-driven search grasps intent, relationships, and context. |
Trust & accuracy | Results may be unreliable or outdated. | Sourced, cited, and verified information you can trust. |
Scalability & adaptability | Hard to scale and adapt as new data sources grow. | Continuously learns and evolves with organizational data. |
Yet legacy and AI-bolted enterprise searches often fail to deliver the answers and insights for the search queries.
DevRev offers a powerful alternative to siloed data, disconnected systems, outdated documents, and endless search results. It provides you with direct, concise, and consolidated information for your conversational search queries.
Here’s how DevRev redefined enterprise search with an AI-native foundation:
1. Knowledge graph: Offer a unified search experience
Businesses today operate across multiple tools—CRM, Slack, emails, PDFs, and ticketing systems—resulting in scattered and snowed-under data. Employees struggle to find answers because data is buried in silos, making searches inefficient and time-consuming.
This inefficiency occurs because the enterprise operates without a unified and strong AI-native platform. Yes, without an all-in-one platform, even the smartest AI fails to deliver the contextual and consolidated answer to search queries that should be retrieved from multiple sources.
Knowledge Graph is DevRev’s answer to the problem. It is the cerebrum of the brain that holds the entire data by connecting all the touchpoints—customer, product, engineering, and support—under one record.
If a support leader is trying to understand why the CSAT score dropped last quarter. Legacy enterprise search gives: historical support tickets, product release notes, internal engineering discussions, and feedback logs.
But DevRev AI-native enterprise search offers: why the spike in support tickets, how many tickets count on the whole, how many have been resolved, Internal engineering logs showing recurring bugs, and customer complaints from Slack and CRM flagged with urgency.
This is how DevRev’s Knowledge Graph redefines how businesses search, connect, and act on information by breaking barriers between teams and the tools.
2. Airdrop: To unify the data
Data silos has always hindered productivity in enterprises. Support teams operate within ticketing systems. Sales teams rely on CRMs. Engineering teams track issues in Jira and leadership teams make decisions based on fragmented dashboards, which results in disconnected data and inefficiencies.
DevRev Airdrop eliminates this fragmentation by creating a unified, real-time data ecosystem that seamlessly synchronizes information across tools. With bidirectional syncing, every update made in one system is instantly reflected across all connected platforms, ensuring teams always have the most accurate, up-to-date data at their fingertips—eliminating gaps, reducing manual effort, and driving efficiency.
Beyond syncing, Airdrop intelligently maps and structures data using AI-powered analytics. It links the related documents so employees don’t have to manually piece together the information and consolidate all business data into a single searchable platform.
While legacy enterprise search may help employees find data, DevRev Airdrop ensures they find insights.
3. Agentic AI: Be conversational, not a bot
AI-bolted enterprise search promised efficiency, but most implementations have fallen short.
Instead of delivering meaningful insights, it relies on rigid keyword matching and pre-programmed responses, often reducing AI to a basic FAQ responder.
However, DevRev’s AI Agent is different; it doesn’t act just as a first-line response but engages with and understands the search intent, context, and reasons like a real human team.
As Neeraj Matiyani put it: We need to make AI small, pervasive, multilingual, multimodal, and conversational.
Unlike legacy enterprises, where AI implementation can take months, DevRev No-Code Agent AI is different. It is simple, easy to integrate, and seamlessly fits into your system. It quickly learns from real-time conversations, customer complaints, and tickets while continuously updating the system for search queries.
AI agents get smarter day by day, as employees don’t just search; they ask, interact, and receive contextual answers instantly.
The future of AI-native enterprise intelligence in India
India isn’t just riding the AI wave—it’s helping shape it. As organizations rethink how they operate, AI is moving from experimental edge to operational core, becoming essential to staying relevant in a fast-evolving global market.
India’s ability to leapfrog outdated models into an AI-first world is its greatest advantage.
However, AI-bolted enterprise search still falls short and spits out links, not insights. It relies on keyword matching, not understanding.
“We thought AI would solve our problems, but most of it still functions like a keyword matcher. Employees don’t need 10,000 search results—they need one right answer.” - As Gautam Anand, Head Mobile & Net Banking, HDFC Bank.
Businesses no longer want AI that makes them search better—they want AI that helps them find answers faster.
Ready to see the AI-native enterprise search in action that transforms your employee search experience?
Book a demo to see how DevRev helps your employee to move from search to answer.
