Finding the right person to ask in a large company is one of the most underestimated productivity problems in knowledge-work organizations. In large organizations, expertise is invisible: it lives in people’s heads and in Slack threads that disappear within days. The result is that employees who need answers spend hours on a social investigation that should take minutes, and the experts who hold those answers get interrupted repeatedly by people who had no better option.
Expert discovery in large organizations breaks down not because people are bad at asking questions, but because the systems organizations rely on to surface expertise were never designed to do that job. According to APQC research, employees spend an average of 2.8 hours every week just looking for information or tracking down the right colleague to ask. That is nearly a full working day every two weeks spent compensating for a system that does not work.
You have been stuck on a problem for two hours. You know the answer exists somewhere inside your company: someone built this system, someone made this decision, someone dealt with this exact situation before. You just have no idea who that someone is.
So you guess. You ping the person whose name came up in a meeting last week. They do not know, but they point you to someone else. That person is helpful but not quite right. By the end of the day you have interrupted three people, scheduled a call you did not need, and burned most of the afternoon on a question that should have taken ten minutes.
This post is about why expert discovery fails in large organizations, and what actually makes it work.
Why Expert Discovery Is Broken in Large Organizations
In a ten-person startup, you know what everyone knows. You sit near them, you overhear their conversations, you see what problems they solve. Expertise is visible by default.
Scale that to two hundred people and the picture changes completely. Teams splinter into specializations. People develop deep knowledge that never gets written down anywhere. A senior engineer might be the only person who truly understands how a particular system was architected, but nothing in the company directory tells you that. A customer success manager might have spent months developing nuanced knowledge about a tricky client segment, but that knowledge lives only in her head and in Slack threads that nobody can find.
The result is what researchers call tribal knowledge: expertise that is only discoverable through word-of-mouth and direct relationship. As explored in what is tribal knowledge and how to stop losing it, this kind of knowledge is invisible until it disappears. In large or distributed organizations, there is rarely a central directory that maps which employees hold expertise in which areas. Finding the right person becomes a guessing game, and the people who win it are usually those with the largest internal networks, not those with the sharpest instincts about where knowledge lives.
New employees face this problem most acutely. Research on new hire onboarding challenges shows that the people who need expert access most have the weakest internal networks to find it. Senior employees, who are most likely to hold rare expertise, get interrupted constantly by people who had no better option than to guess. It is a compounding failure on both sides.
The Real Cost of Not Finding the Right Person to Ask
It is tempting to treat the expert-finding problem as a minor annoyance: a few wasted messages, a slightly longer time to answer. The numbers tell a different story.
Research by Gloria Mark at UC Irvine finds that it takes an average of 23 minutes and 15 seconds to fully regain focus after a single interruption. Every cold ping that lands on the wrong expert’s desk costs both people: the time spent answering a misrouted question, and the deep work that does not happen on either side of that exchange.
For the person asking, the cost is slower progress, more frustration, and a growing learned helplessness around finding internal expertise. Many employees eventually stop trying to find the right person and just schedule a meeting instead, pulling in everyone who might possibly know rather than the one person who definitely does. McKinsey research on knowledge work finds that employees already spend approximately 20% of their working week searching for information or tracking down colleagues to ask. The expert-finding problem is a significant share of that cost.
For the expert who gets interrupted, the cost is even higher. The most knowledgeable people in any organization tend to become de facto human search engines, fielding the same questions repeatedly because there is no other way for colleagues to surface their expertise. Their time gets carved up into fragments, their deep work suffers, and eventually many of them stop responding to cold pings entirely, which makes the expert-discovery problem even worse for everyone else.
Why Org Charts and Directories Fail at Expert Discovery
The obvious answer to “how do I find the right person to ask” is “look at the org chart.” But anyone who has worked in a large company knows how quickly that breaks down.
Job titles describe roles, not expertise. The person whose title says “Senior Engineer, Platform” may or may not be the right person to ask about the billing module. The “Customer Success Manager” title tells you nothing about whether that person has specific knowledge about a particular integration. Org charts are snapshots of reporting structure, not maps of knowledge.
Directories and internal wikis fare only slightly better. They capture what someone’s job is supposed to be, not what they actually know. And they go stale almost immediately. The person listed as the owner of a system may have moved teams six months ago. The skill listed in someone’s profile may have been added during onboarding and never updated. This is the same failure explored in why nobody uses your documentation: documentation systems fail at retrieval because they are organized around the writer’s mental model rather than the questions someone new would ask, and they go stale faster than anyone can maintain them.
The deeper problem is that formal systems capture self-reported expertise, which is unreliable in both directions. Some people overstate their knowledge. Many more undersell it, listing only the skills they were hired for rather than the broader expertise they have built through doing the actual work. The result is a directory that is simultaneously inflated and incomplete, and a knowledge infrastructure that experienced employees have no incentive to maintain.
Five Ways to Find the Right Person to Ask at Work
Given that formal systems fail, what do experienced employees actually do when they need to find the right person? These are the five approaches that work best for finding internal experts in organizations that have not yet built a systematic solution.
1. Start With the Work, Not the Person
The most reliable approach for figuring out who to ask at work is to start with the work itself, not the person. Rather than asking “who knows about X?”, ask “where has X been discussed or decided?” If your company has searchable channels in Slack or another tool, searching for the topic often surfaces not just the answer but the people who contributed to the conversation. Those people are your starting point.
2. Ask in Public Rather Than in Private
A question posted in a relevant team channel or public Slack channel reaches more potential experts than a direct message to one person. It also creates a searchable record that helps the next person who faces the same question. The downside is that it requires knowing which channel is relevant, which itself assumes some knowledge of how the organization is structured.
3. Use Warm Introductions
Rather than cold-pinging a name found in a directory, ask someone you already know to point you toward the right person. Warm introductions work partly because internal networks contain implicit knowledge about expertise that no directory captures, and partly because a warm introduction dramatically increases the chance of getting a timely, helpful response.
4. Look for Who Gets Cited
The people whose names appear repeatedly around a topic in documentation, meeting notes, or past decisions are often the de facto experts, regardless of their formal title. Looking at who has been cited or thanked in the relevant area is often more reliable than looking at who holds the relevant job title.
5. Invest Once in the Relationship
For repeated patterns of expert-finding in a specific domain, invest once in building the relationship rather than repeatedly making cold contact. A fifteen-minute conversation with the right expert, used to understand both the answer to your current question and how to find related answers in the future, is worth far more than five separate cold pings over the following months.
These five approaches work. But they share a limitation: they require the person seeking expertise to do significant detective work every single time. They treat expert discovery as an individual skill problem rather than an organizational infrastructure problem. Every new employee has to solve it from scratch. Every departure takes the solution with them.
The Systemic Fix: Internal Expertise Visibility at Scale
The more durable solution is to make internal expertise visibility a property of the organization rather than a skill of the individual. A knowledge expert finder built on demonstrated contributions rather than self-reported profiles changes the problem structurally: finding the right person to ask requires a search rather than a social investigation.
This means capturing expertise not through self-reporting, which is unreliable, but through demonstrated contribution. When someone answers a question, explains a decision, or walks through a process in a Slack conversation, that exchange contains real evidence of expertise. If that exchange is captured, attributed, and made searchable, it becomes a permanent signal that this person knows this domain. Over time, a picture emerges of who actually knows what across the organization, built from evidence of real work rather than from job titles or self-submitted profiles.
Peer validation matters here in a way that self-reported skill tags cannot replicate. When a colleague bookmarks someone’s explanation or explicitly recognizes a contribution as valuable, that signal carries weight that a job title cannot. The people who consistently receive peer recognition for contributions in a domain are, almost by definition, the ones worth asking about that domain. This is also why knowledge hoarding is rational in most organizations: expertise is a form of leverage, and the incentive to make it widely accessible is weak until the infrastructure changes what sharing actually produces for the expert.
The organizational cost of not solving this compounds over time. Research on institutional knowledge loss finds that 42% of role-specific expertise is known only by the person currently doing that job. When that person leaves, a new hire typically spends close to 200 hours working inefficiently, re-asking questions that were already answered and rediscovering things the team already knew. Every captured contribution reduces that number. And when expertise is siloed inside individual teams, the cost multiplies across every team boundary it crosses.
What Finding the Right Internal Expert Looks Like in Practice
Imagine you are a product manager who has just joined a 300-person company. You need to understand how a legacy pricing system works before a customer call tomorrow morning. You do not know who built it, you do not know who has touched it recently, and the internal wiki has a page that was last updated two years ago.
In most companies, your only options are to post in a general channel and hope the right person sees it, ask your manager who to talk to and wait for a response, or look through the org chart and make an educated guess. All three are slow. None is reliable. And the cost of guessing wrong is a customer call where you do not have the context you need.
In a company with working internal expertise visibility, you search for “pricing system” and find a Slack thread from four months ago where a senior engineer explained exactly how edge cases are handled. That engineer’s name is attached to the explanation. Her expertise in that area has been recognized by three colleagues who bookmarked the thread. Her current Slack status shows she is online. You send one targeted message with enough context to make it easy to respond. You have your answer before the day is out.
The difference is not luck or a better internal network. It is that the organization captured the knowledge when it was first created, attributed it to the person who contributed it, and made it searchable for anyone who came after. The expert was not interrupted by a cold ping from someone who did not know who to ask. The new employee did not spend hours on detective work. Both people got a better outcome from the same underlying knowledge.
The Right Person to Ask Is Already in Your Organization
Almost every large company has the expertise it needs to answer most of the questions its employees are asking. The knowledge exists. It was created when someone solved this problem the first time, explained this system when it was built, or worked through this exact situation with a client last quarter.
The challenge is not a shortage of expertise. It is that the expertise is invisible. It lives in people’s heads, in Slack threads that nobody can retrieve, and in the institutional memory of employees who may have already moved on. What companies lose when experienced employees leave compounds every week in pings that interrupt experts, decisions made without context, and new hires who spend months reconstructing what the team already knew.
The organizations that solve the expert-discovery problem will not do it by asking employees to fill in better skills profiles or by building more elaborate org charts. They will do it by capturing expertise at the moment it is created, validating it through the people who use it, and making it searchable for everyone who comes after.
When that infrastructure exists, finding the right person to ask stops being a social puzzle and starts being a search.
Pravodha is built to create exactly this infrastructure: surfacing expertise through the Slack conversations where knowledge is already being created and shared, attributing contributions to the people who made them, and making them peer-validated and permanently searchable. Rather than asking employees to maintain skills profiles or managers to maintain directories, Pravodha builds a living map of organizational expertise that updates itself as work happens. If your team is losing hours every week to the expert-finding problem, we would like to show you what a working knowledge infrastructure actually looks like.