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Businesses have grappled with — or in some cases, ignored — information governance for years. The introduction of generative AI has added further complexity and urgency to maintain robust information governance policies to ensure data remains accurate, accessible and secure.
The urgency is real. A recent report from Acrolinx found 57% of the 162 Forbes Global 2000 customers surveyed view using AI tools as a risk, and three quarters say their companies regulate AI internally. Still, six out of 10 are unsure whether the introduction of AI in the workplace has increased or decreased transparency and decision making.
The situation has a bit of a “wild, wild west” feel right now, as companies navigate the opportunities and risks of GenAI adoption and maintain compliance with emerging laws and regulations. But as we explore the potential and consequences of these tools, we can’t ignore the data that is being pulled, used, stored and meshed inside organizational databases, alongside other sources, further murking up the waters for the future.
Information governance is the key to unlocking GenAI's potential as these tools spread through our organizations.
The Foundation: Information Governance for AI Success
A solid foundation of information governance is at the heart of effective AI-driven knowledge sharing, said Megan O’Hern-Crook, director of archives and information management at History Associates Incorporated (HAI).
Many organizations are eager to implement AI solutions, but O’Hern-Crook warns against moving without a solid strategy in place.
“Many public and private sector clients of ours are trying to figure out how to use AI for efficiency, but without critical data structures in place this will never be possible,” she said. “AI is only [as] good as its underpinning and without the right governance structures in place, an AI tool will never be optimized.”
What needs to happen, O’Hern-Crook said, is following the “right order of operations.” That is:
- Clean up relevant records, raw data, servers and other databases with macro-structures.
- Then work to see what AI tools can support once the data is clean.
The “right order of operations” ensures that AI tools have high-quality, well-organized data to work with, leading to more accurate and useful knowledge sharing outcomes.
Related Article: 2023: The Year AI Governance Came Into Its Own
Emerging Challenges and New Risks
Generative AI tools have introduced new risks for organizations to address, especially in an environment where the use of these tools is still widely unregulated — and experience with them is limited.
Companies face three main challenges related to AI and information governance, said Dave Trier, VP of product at ModelOp, an AI governance firm.
- Lack of visibility. “Enterprises are seeing a 30% annual growth rate in AI models, but less than 2% of enterprise execs know what they are and how they're being used,” Trier said.
- Rapidly evolving technology, including generative AI. The proliferation of tools is causing confusion and dissonance inside organizations. Trier said many companies now have more than 20 AI technologies across teams and business units.
- New rules and regulations. “In 2024, at least 45 states introduced AI bills, and the EU AI Act went into force on August 1,” Trier said. “All of this significantly increases risk across the business — including financial, brand, IP, security, privacy, operational and regulatory risk.”
These challenges are occurring in an environment where the pace of change is rapid, said Catherine Brooks, PhD, a professor and interim dean at the College of Information Science at the University of Arizona.
“In the face of emerging AI tools, the key is going to be to think ahead of ourselves as best we can in order to mitigate unintended negative impacts that are already emerging.”
5 Best Practices to Set an Information Governance Foundation
Technology adoption should always be approached from a strategy standpoint, Rita McGrath, a Columbia Business School professor and the founder of capability-building advisory firm Valize.
She and the other experts we spoke with laid out some best practices to help organizations harness the power of AI while maintaining strong information governance:
1. Establish a Governance Framework
McGrath recommends creating a governing body with real power and budget control to oversee AI initiatives. The group should meet regularly to ensure alignment with organizational strategy and to make informed decisions about resource allocation.
2. Implement Robust Data Management
O’Hern Crook said before AI tools are deployed, it’s also important to clean up and organize existing data. This should include:
- Consolidating and standardizing data across the enterprise
- Implementing consistent naming conventions and metadata practices
- Regularly auditing and updating data to ensure ongoing accuracy and relevance
It’s not a one-time process, but an iterative one that must underpin a reliable repository of enterprise-wide information, she said.
3. Ensure Traceability and Oversight
Trier said it’s important for organizations to establish risk-based governance policies and implement key AI governance capabilities including:
- A “single source of truth” inventory for all AI/ML use cases and models.
- Automated controls to enforce compliance.
- Streamlined reporting for enterprise-level AI transparency.
4. Prioritize Training and Education
Gaining clarity around how generative AI will and won’t be used is important. That clarity then needs to be translated into education and information that can guide employees’ actions and decisions.
The goal, Brooks said, is “to train a workforce with analytic skills and with creative tools for managing, preserving and making information useful and reliable for other people.” In addition, she said, “we need to avoid the use of AI tools 'too much' while being open and purposefully training people to use AI tools in ways that are beneficial and useful to them.”
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Brooks recommended:
- Clearly communicating expectations for AI use in knowledge creation and sharing.
- Training employees on ethical AI use and its role in knowledge management.
- Developing analytic skills to effectively leverage AI-enhanced knowledge systems.
5. Foster Transparency
As AI becomes more integrated into knowledge-sharing processes, transparency becomes crucial. Brooks suggests organizations consider labeling AI-assisted materials and being open about how AI is used in knowledge management processes.
That transparency may ultimately be required by federal and state governing authorities. For now, though, it should still be a priority for businesses as part of their overall information governance practices.
Related Article: AI Governance Is a Challenge That Can't Be Ignored
The Future of AI Goes Nowhere Without Strong Governance
AI and GenAI tools offer opportunities for organizations and employees to access and share information, create new efficiencies and drive innovation and creativity. As AI technology continues to evolve, the organizations that succeed in leveraging it will be those that maintain a strong foundation of information governance.
The use of these tools, though, needs to be considered from a thoughtful perspective tied to ongoing education and training for employees and a commitment to transparency and accountability. AI holds great promise, but it is up to us to use it wisely.
