Questions Information Leaders Need to Ask About AI Governance, Ethics, Data, and Implementation

I recently had the opportunity to sit down with The Association Adviser podcast to discuss one of the most critical questions facing organizations today: how do we responsibly integrate AI into our operations? The episode, "AI is Here - Is Your Association Ready?", is now live, and I wanted to share some key insights from our conversation.

As information management practitioners, we're not just dealing with technology adoption—we're navigating fundamental questions about how AI will reshape our organizations, our workforce, and our relationships with stakeholders. Here are some of the key questions (and my answers) that I believe information leaders should be asking right now.

Key Insights from the Conversation

What makes for strong AI governance or ethics policy?

Best policy is one that's understood and followed—clear, concise, and created through collaboration. AI policies should optimize beneficial impact while reducing risks and adverse outcomes, which means success is measured by both ROI and avoidance of risks and mistakes.

The essential components? Regulations, IP and copyright protections, data privacy, staff accountability and human oversight, transparency about usage, training, and commitment to fundamental best practices.

Are there helpful uses for AI that my organization is currently not taking advantage of?

Beyond content generation and personalization, I see three areas with huge potential:

• Facilitating human-to-human connections - Using AI to enable deeper person-to-person interactions, not replace them
• Operational efficiencies - Focusing on strategy and relationships while AI handles the operational heavy lifting
• Improving the experience - Making interactions faster, more accurate, and ultimately more valuable through agent-to-agent transactions, smarter search and discussion thread generation, and automated development of meeting minutes and summaries

Content generation is useful, but I'm less enthusiastic about it becoming our primary focus. We need to use AI for creating better experiences and automating the manual, tedious work—not just churning out more content.

How should my organizations think about our data ecosystem?

Here's the reality: your data ecosystem goes beyond your primary customer relationship management system. When I look at AIIM, we use event registration, LMS, AMS, CRM, marketing automation, email—that's 7-9 systems in our core tech stack. Those systems contain a goldmine of data, particularly when combined.

Here's what I tell people about data valuation: Don't undervalue your data. Think beyond storage costs. Data value is a fuzzy number, hard to calculate, but focus on uniqueness and volume:

• Does anyone else have this data?
• How much historical data do you have to show trends?
• How clean and accurate is the data?
• How much data do you have? Sample sizes matter.

Then imagine the applications. If this data was high quality and highly accessible, what could you do with it to better serve your stakeholders? What would the perfect dashboard look like for making decisions?

The Bottom Line

AI presents real opportunities and real risks for organizations. Our challenge isn't to rush toward mass adoption, but to be intentional about where we invest, how we protect data and user experience, and how we personalize journeys to focus on individual needs.

As I mentioned in the interview, we need to reach a new point of maturity as organizations—one that leverages AI to augment our foundational human strengths, not replace them.

Listen to the full episode.