Why Is Search Available, Yet Not Usable?

In the past, when working on enterprise AI solutions,
issues around search would almost always come up.

In the field, we consistently heard things like:

• When searching past cases, too many similar results appear
• It takes too long to reach the needed information
• People don’t know how to search effectively
• Images, PDFs, and drawings are difficult to find

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At first glance, these look like separate problems.
But in reality, they all stem from the same root cause.

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👉 The information exists.
But it is not usable.

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It’s not that information cannot be found.
👉 It cannot be retrieved in a usable form.

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As a result, in practice:

• Time is spent searching
• People end up asking others
• Past knowledge is not reused

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👉 Search exists, but it is not being used.

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The Limits of Traditional Search

Traditional search is simple:

Query → Index → Ranking → Result

It returns items that match keywords or are similar.

This mechanism is rational,
but it does not align with how work is actually done.

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Why It Doesn’t Work

What people need in practice is not similar information.

👉 They need usable information.

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However, search systems:

• Do not understand context
• Do not understand intent
• Do not understand the situation

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As a result:

👉 You can find things, but you cannot use them.

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In reality, people end up:

• Repeating searches with different conditions
• Opening multiple documents
• Extracting and organizing relevant parts

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👉 The work after search is heavier than the search itself.

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The Fundamental Problem

The key issue is this:

👉 Search stops at “finding.”

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Results are returned.
But the real work does not end there.

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What Work Actually Requires

What people want to do is:

• Understand cases
• Identify differences
• Organize decision factors
• Decide the next action

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👉 Finding information is not the goal.

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The Real Problem Lies After Search

More importantly:

👉 The difficulty is not in searching, but in what comes after.

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Actual workflows look like this:

Search
→ Open multiple candidates
→ Read necessary parts
→ Gather related information
→ Compare
→ Organize key points
→ Decide what to do

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👉 Search is only the first step.

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Why the Post-Search Process Is Heavy

There are four main reasons:

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① Information Is Fragmented

Information is scattered:

• Estimates → file servers
• Specifications → PDFs
• Drawings → separate systems
• Communication → email/chat
• Data → Excel/DB

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👉 Search is not information collection.

You must gather from multiple sources.

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② Finding Information Is Not Enough

Even if you find information, it cannot be used as-is.

You need to check:

• Are the conditions the same?
• Are assumptions aligned?
• Is the customer similar?
• Is it outdated?
• What were the results?

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👉 What is needed is interpreted and comparable information.

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③ Organization Depends on Humans

People mentally judge:

• This is usable
• This is not
• This is outdated
• This is similar

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This is flexible, but:

• Time-consuming
• Inconsistent
• Dependent on experience

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④ The Process Is Not Accumulated

This is the biggest issue.

Even if you:

• Gather information
• Compare it
• Identify key factors
• Make a decision

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👉 The process itself is not recorded.

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So each time:

👉 Everything starts from scratch.

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The Core Insight

The real problem is not search itself.

👉 It is the fragmentation of information collection and organization.

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What Needs to Change

Improvement is not about search accuracy.

What is needed is a system that includes:

• Collecting information
• Connecting relationships
• Structuring for comparison
• Clarifying meaning and differences
• Linking to action

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Conclusion

What is needed is not the evolution of search.

👉 It is the evolution of information collection and organization.

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What Comes After Search

Future systems must do more than return candidates.

They must:

• Collect related information across sources
• Remove duplication
• Organize comparison points
• Show similarities and differences
• Explain why information matters
• Suggest next steps

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👉 Only then does search become usable.

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Reframing Search

Traditional search was:

👉 A tool to find information

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What is truly needed is:

👉 A system to collect, organize, and support decisions

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Search must be redesigned as:

👉 A process, not a function

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Direction of the Solution

Search must evolve:

👉 From retrieving information
👉 To supporting the next action

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Search as a Process

Search should not be a single operation.

👉 It should be designed as a flow.

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Decision Trace Model × Multi-Agent

Search Through Decision Trace Model

Applied to search:

Event → User request
Signal → Understand intent and context
Decision → Determine search strategy
Execution → Collect information
Aggregation → Organize and compare
Explanation → Provide reasoning
Log → Record and reuse

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What Changes

Traditional:

👉 Input → Result

New:

👉 Understanding → Strategy → Collection → Organization → Explanation

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👉 Search shifts from answering to supporting thinking.

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Multi-Agent Decomposition

This cannot be done by a single process.

It requires roles:

• Intent → Understand purpose
• Context → Understand situation
• Retrieval → Multi-method search
• Aggregation → Structure information
• Ranking → Prioritize usability
• Explanation → Provide reasoning
• Learning → Improve over time

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Cross-Media Integration

All data is treated uniformly as signals:

• Text → semantic vectors
• Images → features
• PDFs → structure + meaning

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👉 Different formats become searchable together.

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Final Transformation

Search evolves:

👉 From keyword matching
👉 To handling intent, context, and reasoning

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👉 From returning information
👉 To guiding action

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Use Cases

Engineering

Before:
Find documents → too many → unclear usability

After:
Structured comparison + reasoning

👉 From searching → decision support

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Sales

Before:
Manual reuse of past proposals

After:
Structured proposal generation

👉 From experience → reproducible process

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Knowledge Sharing

Before:
Information exists but unused

After:
Structured problem-solving flow

👉 From stored knowledge → usable knowledge

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Common Transformation

Search shifts:

👉 From “finding” → to “using”

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Before:

• Find information
• Humans organize
• Humans decide

After:

• Collect information
• Structure relationships
• Extract meaning
• Drive action

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👉 Search becomes a decision-support process.

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Final Conclusion

Decision Trace Model × Multi-Agent transforms search:

👉 From a tool to find information
👉 To a system that drives work forward

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Ultimately:

👉 Search becomes a foundation that supports decision-making

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The Real Change

Search evolution is not about:

• Improving accuracy
• Optimizing algorithms

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It is about redefining:

👉 What search is for

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Traditional search:

👉 A tool to find information

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Future search:

• Understand context
• Enable comparison
• Provide reasoning
• Lead to action

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👉 A system that supports decisions

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Decision Trace Model × Multi-Agent:

• Understand intent
• Capture context
• Organize information
• Extract meaning
• Provide explanations
• Enable action

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👉 All as a unified structure

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Final Shift

Search transforms:

👉 From something you “look for”
👉 To something that “you can use”

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Ultimately:

👉 Search becomes a foundation for action within work and experience

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This is not just an evolution of search.

👉 It is a transformation of the relationship between people and information.

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※ For technical details, see “Search Technologies”