New Study Reveals Crucial Role of Information Management in AI Success

It cannot be argued that good decisions come from good information. So, in a world of rapid generative AI adoption, what does this mean for organizations who want to take advantage of this exciting new technology, while also maintaining the integrity of their information and, of course, their business decisions?

In something that will be shocking to no one, I, of course, believe that effective information management is the foundation of AI success, and those who fail to prioritize their IM strategies will experience challenges during adoption.

It’s also not just me saying this; this finding comes directly from a new study by AvePoint, the AI and Information Management Report, in collaboration with the Association for Intelligent Information Management and the Centre for Intelligent Policy Leadership. The report provides comprehensive insights into the role of information management in AI success, based on the perspectives of over 750 business leaders across 16 countries and 10 industries.

As information managers, we inherently understand the significance of our data and content, but it's crucial to recognize that others are only just beginning to understand its importance and place appropriate significance on its management (look! We were right all along!). As organizations become aware of the value of information, some for the first time while adopting AI, they will look to us for guidance on overcoming the data challenges holding back their AI success. It's essential to be prepared.

In this post, I’ll examine the report's findings and explore how you can use these insights to enhance your organization's AI strategy.

The Current State of Data in the Age of AI

The AIIM 2023 State of the Intelligent Information Management Industry found 78% of organizations feel overwhelmed by the vast volume, velocity, and variety of information from technology usage. The AI and Information Management Report highlights exactly why this may be the case.

Survey respondents reported that 50% of their organizational data is over 5 years old and likely contains redundant, obsolete, or trivial (ROT) data. Yet only half of organizations actively enforce a data retention policy and just 29% of organizations leverage automation in most aspects of their information management strategy.

Additionally, the architecture of organizational storage may not be ideal for AI use. Many organizations still store data in self-hosted storage and physical documents, leading to separate and disparate organizational storage that can be challenging for both people and AI to navigate. As much as we’d love to use AI for content generation, it’s impossible to do that when the content is stored in a cardboard box or filing cabinet.

It's also no surprise that the recent Gartner Case-Based Research AI Services, 2023 found that the largest challenges facing generative AI adoption were a lack of knowledge and skills on the adopter side and "data-readiness" on the IT side.

AvePoint, with over two decades of experience in data management, has seen firsthand how tricky AI adoption can be without proper data management. One customer even reported they had to postpone their AI pilot to get their data in order first.

When you invite AI into your digital workplace, it will expose the vulnerabilities that exist there (and quickly!) – whether related to data security, poor quality data, inefficiencies in data management, or because data hoarding has become the norm. That’s because, to build AI that truly understands your business, you need a data foundation that maps every workspace, piece of content, and user to the right business context. We need to transform this chaos into order and enrich data with business ownership, risk postures, and relevance to power new AI experiences.

The AI and Information Management Report confirms that proper information management is the key to achieving this. This is not something we didn’t know, but it’s nice to have it confirmed.

The Root Cause: Information Management Deficiencies

Study findings reveal a major disconnect between how prepared organizations think they are for AI, and the reality of their data readiness. While 80% of respondents believed their data was ready for AI prior to implementation, over half ended up grappling with poor internal data quality and organization issues when deploying AI models and solutions.

There are a lot of possible reasons for this, from out-of-date content being used in AI generation and lack of critical analysis of AI-generated content to insufficient security and access policies in place to secure sensitive information; the list of issues an organization could face from poor information management is endless.

This perception gap highlights a foundational and alarming disconnect; organizations are not acknowledging the strategies that must be in place to manage data in the age of AI. Despite claims of having robust information management strategies, a closer look reveals glaring gaps. Nearly half of organizations lack fundamental measures such as archiving strategies, retention policies, and lifecycle management solutions.

It's evident that amidst the AI hype, there's a certain "data delusion" prevailing. This sentiment is echoed in a recent AIIM report, which found that two-thirds of organizations rate themselves as below average in managing information lifecycle, governance, and compliance. So, why is there disparity between perceived readiness and actual capabilities?

The answer lies in a fundamental blind spot: organizations are rushing into AI adoption without adequately preparing their data infrastructure. For many companies, information has been left unmanaged, sometimes for years, creating a breeding ground for inefficiencies and errors. It is only now, with the deployment of more advanced tools, that these longstanding issues are being brought to light.

The rush to embrace AI may have stemmed from an eagerness to reap its promised benefits. However, the reality is that years of neglecting information management practices are now coming back to haunt these organizations, leaving them ill-prepared to harness the full potential of these technologies. Yet, the study reveals those who take a proactive stance toward rectifying these issues and investing in data readiness report higher confidence levels and are better positioned to leverage AI's potential advantages. What does this mean for businesses? It's a call to action to prioritize information management strategies. Now more than ever, companies must scrutinize their data management approaches and address pressing questions like, "Why is this pulling information from 5 years ago?" or "Why does the intern have access to this file through the AI?" The answer is clear: it's time to elevate our information management strategies.

The most compelling evidence from the report supports this: organizations with mature data strategies are 1.5 times more likely to achieve advantages such as improved efficiency, enhanced decision-making, and optimized resource allocation from their AI investments.

The Path to AI Success Runs Through Information Management

As the AI revolution charges full steam ahead, with the report finding 80% of companies plan to increase spending in this space in 2024, the challenges many organizations are facing will be exacerbated as their AI usage increases.

The report's finding is clear: Effective information management is the secret weapon for AI success. The insights provided by the AI and Information Management Report offer a roadmap for organizations looking to enhance their AI strategy. By investing in information management and adopting AI-powered data management solutions, organizations can overcome the challenges of implementing AI and reap the benefits of accurate and effective AI systems.

Already, 89% of executives acknowledge a high level of data quality is critical for success. As AI comes to full swing, with Gartner predicting 10% of all data generated will come from AI by 2025, organizations will need to focus even more on information management to ensure their data is of high quality and trustworthy.

The good news is that, as information managers, we are uniquely primed to help organizations meet this challenge. We are the ones who will ensure that data quality is no longer a barrier to AI success and our organizations will reap the benefits.

To learn more about the role of information management in AI success, download the AI and Information Management Report today. Take the first step towards AI success and start prioritizing your data management strategy.