Information Management Blog

The AIIM Blog

Keep your finger on the pulse of Intelligent Information Management with industry news, trends, and best practices.

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Closing the Ownership Gap in AI-Era Information Management

Machines Can Recommend. They Can’t Be Accountable.

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Artificial Intelligence (AI)  |  Certified Information Professional (CIP)  |  Intelligent Information Management (IIM)

Essential Skills for Information Professionals in the AI Era

Technology is automating much of what information management professionals have traditionally done: classifying, tagging, labeling, maintaining. That's not a threat if we're willing to evolve. The ability to absorb change, identify new opportunities, and play the human in the loop is going to define who thrives and who gets left behind.

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14 Steps to a Successful ECM Implementation

Making an ECM implementation successful requires planning and attention to detail. The best way to create the right solution is to identify organizational goals and priorities. Learn how to manage a successful implementation in our free guide.

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Artificial Intelligence (AI)  |  Information Governance  |  Intelligent Information Management (IIM)

A New AI Executive Order and What Information Professionals Need to Know

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Intelligent Information Management (IIM)

The People Behind the Practice: Why This Year's Research Matters

Research That Follows the Community AIIM defines intelligent information management as a practice that integrates people, processes, information, and technology to drive digital transformation and achieve better business outcomes. We invest in regular research to better understand this practice, and the focus of that research tends to reflect the times.

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Data Management  |  Intelligent Information Management (IIM)

Why We Should Rethink the Term “Unstructured Data”

I think we need to talk about what we call “unstructured data.”

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Blog Feature

Artificial Intelligence (AI)  |  Data Management

The Three C’s of Data Readiness for AI

Do We Even Need to Prepare Data for AI? I've heard different versions of this question across the world. Some wonder if we're overthinking it. Others suggest we should just focus on understanding AI and the data will somehow get ready on its own. Do we even need data to get ready for AI?

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