AI Knowledge Curation: A New Role or the Next Evolution of Product Management?

As AI continues to reshape the workplace, companies face a growing challenge: How do we make internal knowledge accessible, structured, and useful for AI-driven decision-making?

Today, vast amounts of critical information—product roadmaps, strategic priorities, customer insights, and team progress—are scattered across wikis, Slack channels, Notion docs, and spreadsheets. Without proper organization, AI can’t effectively synthesize and provide insights.

This has led to discussions around a new potential role: The AI Information Curator, responsible for structuring internal knowledge so that AI can surface meaningful insights for stakeholders. But is this really a new role—or just the next natural evolution of product management, program management, and knowledge work?

AI Needs Structured Knowledge—But Who Provides It?

AI models like large language models (LLMs) excel at analyzing and reasoning across large datasets, but they aren’t magic. They rely on access to well-structured, up-to-date, and relevant knowledge to deliver useful insights.

In most companies, this information already exists—but it’s unorganized, buried in fragmented documentation, or outdated. Without a systematic approach to managing and curating this knowledge, AI outputs can become inaccurate, irrelevant, or misaligned with corporate goals.

To address this, someone (or something) needs to:

Document what teams are working on and why it matters.

Ensure knowledge is structured so AI can retrieve and reason against it.

Keep information up to date to prevent misinformation.

Align AI insights with business objectives so decision-makers can trust AI-generated recommendations.

But does this require a new role, or can AI itself take on much of the burden?

The AI Information Curator: A New Role or an Expansion of Existing Ones?

While some companies might create a dedicated AI Information Curator, a more likely outcome is that this responsibility is absorbed into existing roles—especially those already responsible for cross-functional alignment, documentation, and strategy.

1. Product Managers (PMs)

PMs are already at the intersection of strategy, execution, and communication. Instead of just writing product specs and roadmaps, they’ll increasingly curate knowledge for AI to provide insights on:

• How product decisions align with business objectives.

• What teams are working on and why.

• Key learnings from customer feedback, experiments, and past launches.

  
  

2. Program & Technical Program Managers (TPMs)

TPMs already structure work across teams, ensuring execution stays aligned with strategy. Their role could evolve into maintaining AI-ready project documentation, ensuring AI can generate useful progress updates, highlight dependencies, and even suggest improvements.

3. Knowledge Managers & Product Operations

These roles already focus on organizing and maintaining internal knowledge, making them natural candidates to oversee AI-driven knowledge repositories. Rather than manually updating wikis, they might focus on optimizing AI models to retrieve and synthesize knowledge accurately.

4. AI & Data Teams

Enterprise AI architects and data scientists will play a key role in ensuring that internal AI systems can ingest, process, and retrieve company knowledge effectively. They’ll work with PMs and knowledge managers to build AI-driven documentation systems rather than relying purely on human curation.

AI as a Collaborator, Not Just a Consumer

While the immediate challenge is feeding AI the right information, the real opportunity lies in leveraging AI to assist in knowledge management itself.

How AI Can Help Product & Knowledge Workers:

AI-powered synthesis: AI can summarize meetings, extract key themes from Slack discussions, and auto-generate documentation.

Automated reporting: PMs and TPMs can ask AI to generate status updates tailored to different stakeholders.

Idea generation & strategy validation: AI can highlight knowledge gaps, surface competitive insights, and suggest product improvements.

Dynamic knowledge retrieval: Instead of manually structuring information, AI can learn to organize and retrieve insights contextually.

The shift is clear: Workers will provide knowledge to AI, but AI will also help workers structure and maintain that knowledge.

The Future of AI-First Knowledge Management

Rather than companies creating a brand-new AI Information Curator role, we’re more likely to see existing roles evolve to work with AI as a knowledge partner.

Short term (1-2 years): PMs, TPMs, and knowledge managers start structuring knowledge in AI-friendly ways.

Medium term (3-5 years): AI-driven knowledge management tools emerge, reducing manual documentation burdens.

Long term (5+ years): AI takes on much of the knowledge curation process, while humans focus on refining, verifying, and making strategic decisions.

In this new paradigm, AI won’t just be a passive consumer of knowledge—it will become an active participant in shaping, structuring, and retrieving it.

Conclusion: The AI-Powered Evolution of Work

The movement toward curating knowledge for AI isn’t a passing trend—it’s the next frontier in AI-powered business operations. However, this doesn’t require a new, standalone role. Instead, product managers, program managers, and knowledge workers will need to adapt their roles to collaborate with AI, ensuring that company knowledge is structured, accessible, and continuously updated.

The real winners won’t be those who simply document knowledge for AI—but those who learn to work with AI to manage and leverage knowledge dynamically.

So, will companies create an AI Information Curator? Maybe in niche cases. But in most organizations, AI will become the co-pilot rather than the passenger, reshaping knowledge management as we know it.