The enterprise technology conversation has spent the last two years asking how quickly organisations can adopt AI. The more important question now is how quickly employees can use the information already available to them to make decisions and get work done. This is where a new business metric deserves attention from CIOs: Time-to-Knowledge.
Time-to-Knowledge is the time it takes an employee to go from needing information to having enough relevant, reliable context to act on it. It is different from search speed because finding a document is not the same as finding an answer. In a large enterprise, the real challenge is bringing together information from multiple systems and understanding what it means in the context of a specific task.
Founder
OneTab.ai
The timing for this conversation is important because enterprise AI adoption is no longer theoretical. Microsoft and LinkedIn’s 2024 Work Trend Index found that 75% of knowledge workers globally were already using AI at work, with 92% of knowledge workers in India using AI. The same study found that 79% of business leaders globally considered AI adoption critical to remain competitive, while 59% were concerned about quantifying productivity gains. (The Official Microsoft Blog)
Yet adoption does not automatically translate into business value. McKinsey’s 2025 State of AI report found that 88% of organisations were regularly using AI in at least one business function, up from 78% the previous year. However, nearly two-thirds had not yet begun scaling AI across the enterprise, and only 39% reported an impact on enterprise-level EBIT. (McKinsey & Company)
This gap between adoption and impact points to a larger problem. Enterprises have invested heavily in AI tools, but many are still measuring success through usage, pilots, licences or employee adoption rather than through how work itself has changed.
Time-to-Knowledge offers a different way of looking at the problem.
Consider a salesperson preparing for a customer meeting. The information required may include the customer’s purchase history, open support issues, previous conversations, contract details and the status of an ongoing opportunity. All of this information may already exist within the organisation, but it could be spread across the CRM, email, support platforms, shared documents and collaboration tools.
The challenge is not data availability. The challenge is the time required to turn scattered data into usable context.
The same issue appears across customer support, finance, procurement, HR and operations. A support employee may need to search previous tickets before responding to an unusual issue. A finance professional may need to trace emails and documents to understand an approval. An operations manager may need to speak to several teams before understanding why a process is delayed.
Each delay may appear insignificant in isolation. Across thousands of employees and millions of decisions, however, these delays become a meaningful productivity cost.
This is where AI can change the equation. Employees are increasingly accustomed to asking AI systems questions in natural language and receiving answers within seconds. The expectation for enterprise technology is therefore changing. Employees no longer necessarily want to know which system contains the answer; they want the right answer, with enough context to trust it and act on it.
For enterprises, solving this requires more than putting a chatbot on top of existing systems. AI needs access to relevant enterprise knowledge, an understanding of organisational processes and the right permissions to determine what information can be accessed and what actions can be taken.
IBM’s 2025 CEO study reinforces the importance of this foundation. Only 25% of surveyed CEOs said their AI initiatives had delivered the expected ROI over the previous few years, while just 16% had scaled AI enterprise-wide. At the same time, 68% identified integrated enterprise-wide data architecture as critical for cross-functional collaboration, and 72% said proprietary data was key to unlocking the value of generative AI. (IBM Newsroom)
This makes Time-to-Knowledge a useful business KPI for CIOs. When AI is introduced into customer support, organisations should measure how long it takes an employee to find the information required to resolve a case. In sales, it could measure the time required to build a complete account picture. In finance or operations, it could measure how quickly the relevant information can be assembled for a decision or approval.
The baseline matters. Before deploying AI, organisations can measure how long a workflow takes today. After deployment, the same workflow can be measured again. This provides a much clearer picture of whether technology is reducing friction and creating measurable business value.
Time-to-Knowledge can also expose problems that traditional IT metrics often miss. If employees consistently struggle to find information, the issue may not be a lack of technology. It could indicate fragmented applications, poor documentation, inconsistent processes, data silos or unclear ownership.
This becomes even more important as organisations move towards AI agents. Deloitte’s 2025 India research found that more than 80% of Indian organisations were exploring autonomous agents, while 70% wanted to use GenAI for automation. Yet only 29% reported being able to fully scale up to 30% of their AI proofs of concept. (Deloitte)
An AI agent cannot execute a workflow effectively if the information required to make the next decision is fragmented, outdated or inaccessible. The intelligence of the model is only one part of the equation. The other is whether the organisation can provide the right context at the right time.
This is why the next step is to move beyond Time-to-Knowledge towards Time-to-Action. Finding an answer is useful, but the real value comes when that answer allows an employee or AI agent to make a decision and complete a task.
For CIOs, this means the technology scorecard needs to evolve. Uptime, cybersecurity, infrastructure costs and system performance will continue to matter, but they are no longer enough to explain the business value of enterprise technology.
The organisations that benefit most from AI may not necessarily be those deploying the largest number of models or tools. They may be those that systematically remove the small delays that sit between a question and an answer, an answer and a decision, and a decision and an action.
Time-to-Knowledge provides CIOs with a way to measure that shift — and in an enterprise where every minute between knowing and doing carries a cost, it could become one of the most important measures of technology’s business impact.
Authored by Saket Dandotia, Founder, OneTab.ai