Planning is the New Search: Transforming the Knowledge Economy

Planning is the New Search: Why Tomorrow’s Knowledge Economy Runs on Intent

In the vast and ever-expanding sea of digital information, search has long been our anchor. It’s how we navigate the web, sift through mountains of data, and make sense of the world at the click of a button. But as the complexity of knowledge work deepens, search is reaching its limits. Enter planning: a transformative approach poised to reshape how we organize, retrieve, and act on information. Planning isn’t just a tool for tomorrow’s workflows—it’s the foundation of the next wave of productivity.

Search: A Reactive Model

Traditional search thrives on queries. You type in a phrase, and an algorithm churns out the best guesses for what you need. This reactive model has served us well, but it’s inherently limited. Search assumes:

• You know what to ask: Often, knowledge workers aren’t even sure how to phrase their questions.
• It’s an isolated task: Search isn’t connected to a larger workflow, requiring workers to piece together fragmented answers manually.
• Information exists in silos: While search engines excel at finding external information, internal systems—emails, chats, and knowledge bases—often remain disconnected.

Over the last years, we have learned the art of "search". While in our heads we do formulate an objective ("I have to finish the report by tomorrow"), what we do is keyword search. The "planning" therefore happens implicitly, often within our heads and experience.

These limitations create massiv inefficiencies, leaving workers siloed, and always starting from scratch, drowning in data but starved for actionable insights. The result? Wasted time, missed opportunities, and a growing gap between information and execution.

The Mission: From Search to Planning

Search helps us retrieve information, but retrieving knowledge is not the same as getting work done. In modern organizations, work is rarely a single-step process—it unfolds as a series of interdependent tasks. Yet, most knowledge work today lacks a structured way to move from information to execution.

When faced with complex tasks, teams often rely on fragmented search, scattered conversations, and ad-hoc decision-making to piece together a plan. The real challenge isn’t just finding the right information—it’s structuring work in a way that ensures efficiency and consistency. This requires:

• Breaking work into the right set of subtasks so that each step logically builds toward the goal.
• Assigning the right tasks to the right people or AI agents based on expertise, precedent, and automation potential.
• Orchestrating execution so that tasks progress smoothly, dependencies are managed, and new information is dynamically incorporated.

Without an approach that ensures output accuracy, organizations fall into reactive workflows, where every case is handled from scratch, knowledge is siloed, and inefficiencies compound. What’s needed is a system that not only retrieves knowledge but actively organizes it into action. This is where planning comes in.

Our Planning based Approach: Intent-Driven Knowledge Work

Planning flips the script. Instead of reacting to queries, planning anticipates needs, organizes context, and bridges the gap between knowledge and action. Here’s how it works:

• Intent before execution: Planning systems begin with a clear understanding of what you’re trying to achieve. Whether it’s solving a customer complaint or drafting a strategy, planning aligns information retrieval with end goals.
• Integrated knowledge: Instead of treating each search or query as isolated, planning builds a dynamic map of related tasks, threads, and insights. The go-to technology for this is knowledge graphs, which map relationships between data points and workflows.
• Memory and adaptability: Planning systems learn from past interactions. Organizations work in patterns, whether explicitly documented or implicitly lived. By clustering precedents and ranking relevant knowledge, planning systems surface the most useful information proactively.
• Human-AI collaboration: Planning isn’t about replacing human judgment but enhancing it. Advanced systems bring humans into the loop for critical decisions while automating repetitive workflows. Think of it like Google Maps: it doesn’t replace your freedom to drive but collaborates with you to recommend the best path.

The Shift: From Search Boxes to Action Frameworks

Imagine a world where, instead of Googling or searching your internal Confluence/SharePoint for “How do I resolve the IAM issue we have with the new application,” you’re guided through a workflow that:

• Processes an unstructured email reporting an IAM issue, extracting and organizing relevant details automatically.
• Refers to precedents by analyzing how similar issues were resolved previously, such as “calling the department at number XYZ and requesting required credentials or permissions.”
• Automatically assigns the right individuals or teams to handle each subtask, like verifying credentials or contacting external vendors.
• Predicts resolution time based on historical data and surfaces the key information and tools needed to address the issue efficiently.
• Escalates uncertainties to people or agents who successfully resolved similar problems in the past, ensuring consistency and leveraging prior expertise.

This is the future planning unlocks: turning information retrieval into actionable frameworks. Instead of living in search boxes, knowledge becomes a living, breathing network of actions and outcomes.

Why Planning Matters in the Quest for 10x Productivity

So far, most companies are adopting LLM models by introducing company chatbots or providing knowledge workers with a version of RAG agents (e.g., Microsoft Co-Pilot, Gemini, etc.). It makes sense why this is the first use case—most knowledge work revolves around search. However, many use cases were solvable years ago with solid ElasticSearch solutions. Excluding documents, better knowledge bases already offered somewhat decent search solutions. This may be why many still question the true value of tools like Co-Pilot.

But what if, instead of providing every knowledge worker with a Co-Pilot, we devised a plan with subtasks that we know from precedent works? By agreeing on the “right way” to complete a task, we could identify who successfully handled similar cases in the past. This approach allows us to:

• Break large cases into smaller “micro” tasks and delegate them to the appropriate team members and AI agents.
• Identify which micro tasks—like search or basic transfer tasks (e.g., customizing emails)—can be automated entirely by AI.
• Massively reduce case throughput time by leveraging infinitely scalable AI agents.

By grounding planning in company precedent, we unlock the true value of AI agents in business workflows. Organizations naturally work in patterns, even if they aren’t explicit. The next generation of workflow platforms must infer these patterns to orchestrate reliable workflows, enabling the 10x productivity we all hope for.

Capturing Corporate Memory

Capturing work patterns and corporate memory is not just best practice—it’s the foundation of reliable end-to-end workflows for AI agents. The benefits include:

• Retention of knowledge: Thousands of precedents remain accessible even when experienced employees leave.
• Shared work patterns: Implicit workflows become visible and available to the entire organization.
• Dynamic documentation: Reflective agents document edge cases while minimizing unnecessary details.

In summary, capturing work patterns and corporate memory is priority number one on the strategic roadmap to AI-driven productivity.

The Tech Behind the Shift

Planning as the new search is already taking shape through technologies like:

• Knowledge graphs: Mapping relationships between data points, entities, and workflows to make knowledge retrieval smarter.
• Agentic workflows: AI agents that not only search but also plan, cluster, and optimize tasks within broader processes.
• Language-based AI: Natural language models simplifying complex planning into intuitive, conversational steps.
• Human-in-the-loop systems: Ensuring oversight and adaptability in workflows powered by machine learning.

Looking Ahead

As we shift from reactive search to intent-driven planning, we’re not just improving productivity—we’re redefining it. Planning transforms knowledge work from a fragmented, siloed process into a cohesive, goal-oriented experience. For organizations and individuals alike, it’s a leap from asking, “Where do I find an answer?” to answering, “What do I need to get done?”

It’s in our nature as humans to think with intent and then work backward to achieve it. For the first time, we have the technology to build software that does the same.

This is why our core product mission at Interloom is to build the first “Navigation System for Work”—enabling knowledge workers to act with intent and guiding tasks toward their desired outcomes.

By adopting planning-centric systems, organizations can unlock unprecedented levels of efficiency, innovation, and collaboration. The question isn’t whether planning will replace search—it’s how quickly we can adapt to this new paradigm. The knowledge economy of tomorrow runs on intent, and the time to start planning is now.