The Case Prep Problem: When Your Teams Spend More Time Finding Information Than Using It

There is a productivity paradox in knowledge work. Organizations invest millions in data systems, analytics platforms, and information infrastructure. Yet their most valuable people spend the majority of their time not analyzing or deciding, but searching and assembling.

A relationship manager preparing for a client meeting. A care coordinator reviewing patient records before an outreach call. A sales executive researching an enterprise account. A consultant getting ready for a strategy workshop. A product manager synthesizing customer feedback before a roadmap review.

All of these professionals are highly skilled. All of them are well-compensated. All of them have access to sophisticated technology. And all of them spend the majority of their preparation time on a task that creates zero value: finding and organizing information.

This is the case prep problem. And it is costing organizations far more than they realize.

The Hidden Tax on Expertise

I first saw the full scope of this problem at a major private sector bank in Asia. Relationship managers were the face of the bank to high-value corporate clients. These were experienced professionals with deep industry knowledge, strong client relationships, and sound business judgment.

But when I observed how they prepared for client meetings, I discovered something troubling. Out of a 60-minute preparation window, they spent 45 minutes hunting for information. Six different systems. Seven different report formats. Countless PDFs buried in shared drives nobody had organized in years.

They needed to understand past interactions. What was discussed in the last meeting? What commitments were made? What follow-ups were pending? That information lived in meeting notes scattered across three different people who had interacted with the client over the past year.

They needed to know the current relationship status. What products did the client have? What was the utilization? Were there service issues? That required pulling reports from the CRM, the product systems, and the service ticketing platform.

They needed context on the client business. Had there been any major news? Regulatory changes affecting their sector? Competitive moves they should be aware of? That meant searching news databases, regulatory update emails, and industry reports.

They needed to understand what might be relevant to recommend. Which products fit the client profile? What were other similar clients using? What recent innovations might address pain points this client had mentioned months ago? That meant looking through product catalogs, case studies, and innovation briefs.

By the time they assembled all this information, they had 15 minutes left to think about conversation strategy. What story to tell. How to position recommendations. What questions to ask. How to handle potential objections.

They were spending 75% of their time on information retrieval and 25% on strategic thinking. For a role that was supposed to be about relationship management and advisory expertise, that ratio was inverted.

The Pattern Across Industries

The bank was not unique. I have seen the same pattern everywhere.

At Optum, care coordinators were spending 60 to 90 minutes per patient case just reading through medical records. Electronic health records. Claims history. Care guidelines. Past call transcripts. Lab results. None of these systems talked to each other. Each required separate login, navigation, and manual extraction.

At an enterprise SaaS company, sales executives were spending hours researching accounts before important calls. Company websites. LinkedIn profiles. News articles. Earnings calls. Competitive intelligence. Analyst reports. Previous interaction history from the CRM. They were piecing together a picture of the buyer organization from dozens of fragments.

At an industrial automation company, product managers were trying to synthesize customer feedback to inform roadmap decisions. That feedback lived in support tickets, sales call notes, user interviews, survey responses, feature requests, and informal Slack conversations. Pulling it all together took days.

The tasks were different. The problem was identical. Valuable expertise was being wasted on information assembly because knowledge was fragmented across disconnected systems and unstructured sources.

Why This Happens

Knowledge fragmentation is not caused by incompetence or laziness. It is a natural consequence of how organizations evolve.

Systems are built at different times to solve different problems. The CRM came first. Then the ERP. Then the marketing automation platform. Then the customer support tool. Then the analytics warehouse. Each one made sense independently. Nobody designed the connections.

Data accumulates in different formats. Structured database records. Unstructured documents. Email threads. Meeting notes. Presentations. Spreadsheets. Each format requires different tools to access and different skills to interpret.

Knowledge becomes tribal. The person who has been in the role for five years knows where everything is. They have built mental maps of which system contains what information. They have shortcuts and workarounds. When they leave, that knowledge leaves with them. The new person starts from zero, struggling to find what their predecessor accessed effortlessly.

Organizational boundaries create information silos. Marketing has one set of systems. Sales has another. Product has a third. Customer success has a fourth. Each function optimizes for its own needs without thinking about how information flows to others who might need it.

The result is fragmentation. Knowledge exists, but accessing it requires manual effort, system-hopping, and cognitive load that exhausts people before they even get to the value-creating work.

The Real Cost

The visible cost is time. If your relationship managers spend 45 minutes per meeting on prep, and they have five meetings per day, that is nearly four hours per day spent on information assembly. Multiply that across a team of 100 RMs and you have 400 hours per day of highly skilled labor going to a task that creates no customer value.

But the invisible costs are worse. There is the opportunity cost. When people spend 75% of their time on information retrieval, they do not have time for strategic thinking, relationship building, or innovation. The work that actually drives business outcomes gets squeezed into whatever time is left after the mandatory information hunt.

There is the quality cost. When preparation is rushed, mistakes happen. Important details get missed. Conversations lack depth. Recommendations feel generic because there was not enough time to tailor them. Customer trust erodes when interactions feel unprepared or impersonal.

There is the stress cost. Constantly hunting for information is cognitively exhausting. The anxiety of wondering if you missed something important. The frustration of knowing the information exists somewhere but not being able to find it. The mental fatigue of context-switching between systems. This stress accumulates and leads to burnout.

There is the scaling cost. You cannot scale expertise if expert time is consumed by information assembly. Hiring more people does not solve the problem because each new person faces the same fragmentation. Growth requires linear increase in headcount instead of leveraging institutional knowledge.

And there is the attrition cost. Talented people leave organizations where they feel like their expertise is wasted on administrative tasks. Exit interviews reveal a common theme: “I did not go to business school to spend my day searching for files.”

The GenAI Solution

Generative AI changes the economics of case preparation fundamentally. Not because it is a better search engine, but because it can synthesize information across fragmented sources and present it in context.

At the bank, we built a knowledge assistant that ingested unstructured data from meeting notes, term sheets, product documentation, and policy manuals. It generated automated client briefings before each meeting. Past interactions summarized. Open service issues highlighted. Relevant product suggestions based on client profile. Regulatory changes affecting their specific sector.

The RM no longer spent 45 minutes hunting. They spent 10 minutes reviewing an AI-generated brief. Preparation time dropped by 75%. But more importantly, the quality improved. The AI could spot patterns and connections that a human might miss when looking at fragmented sources. It could reference details from two years ago that a human would have forgotten.

At Optum, we built a care summary engine that could parse multiple data sources and create unified clinical digests. What took 60 minutes of manual reading now took 15 minutes of reviewing a synthesis. Care coordinators had cognitive capacity back. They could focus on patient conversation strategy instead of information gathering.

At the SaaS company, we built a deal orchestration layer that would ingest account intelligence and create buyer-specific narratives. Sales executives could review an AI-generated account brief in minutes instead of spending hours doing research. They walked into meetings informed and confident.

The Pattern in the Solution

The pattern across these solutions is consistent. GenAI does not replace the expert. It prepares them. It handles the mechanical cognitive work of finding, reading, and organizing information. The human expert then applies judgment, builds relationships, and creates value.

This is augmentation, not automation. The relationship manager still leads the client meeting. The care coordinator still makes the patient call. The sales executive still conducts the negotiation. But they do it with better information, less stress, and more mental energy for the work that actually matters.

The business impact is measurable. At the bank, deal cycle times dropped by 28%. At Optum, patient engagement scores improved by 18%. At the SaaS company, win rates increased and sales ramp time decreased.

But the human impact may be more important. When people spend less time searching and more time thinking, the work becomes meaningful again. Attrition decreases. Satisfaction increases. Innovation emerges.

What Leaders Should Do

If you lead teams that do case preparation work, whether that is client meetings, patient outreach, sales calls, or strategic reviews, you likely have a knowledge fragmentation problem. The question is how severe it is and what you are willing to do about it.

Start by measuring. How much time do your people actually spend on information assembly versus application? Shadow them. Track it. You might be shocked by the ratio.

Identify the sources. Where does the information they need actually live? How many systems do they have to access? How much of it is unstructured? How often do they hit dead ends or discover information is outdated?

Ask about the pain. Where do they get stuck? What takes the longest? What causes the most frustration? What would they do differently if they had all the information instantly available and pre-synthesized?

Then explore solutions. Not necessarily technology first. Sometimes the answer is better process. Sometimes it is consolidating systems. Sometimes it is changing what information is actually necessary.

But increasingly, the answer is GenAI. Because GenAI can work with the messy, unstructured, fragmented reality of how information actually exists in organizations. It does not require you to fix your information architecture first. It meets you where you are.

The Opportunity

The case prep problem is one of the highest-ROI opportunities for GenAI in enterprises today. Because it affects highly skilled, highly compensated people. Because the time savings are immediate and measurable. Because the quality improvements compound over time. Because the human impact is meaningful.

Organizations that solve this will have teams that spend 75% of their time on strategic thinking and 25% on information review. That is the right ratio. That is how expertise should be utilized.

The technology exists. The question is whether organizations recognize the problem as strategic rather than just an operational annoyance. Whether they are willing to invest in amplifying expertise instead of accepting that information hunting is “just how things are.”

The companies that move first on this will have an enormous advantage. Not because they have better technology. Because they have better-prepared people who can focus on what actually creates value. That is a competitive advantage technology alone cannot replicate.