Building smarter data frameworks in the age of AI

When properly collected, stored, analyzed, and managed, data becomes a powerful asset for outpacing the competition. Data is a critical asset, and managing its growth, security, and compliance isn’t an easy feat, especially because of artificial intelligence’s (AI’s) increasing use and prevalence.

During its Exploring the Information Frontier roadshow stop in Atlanta on April 15, Iron Mountain hosted an expert-led session that inspired fresh thinking and delivered practical guidance for navigating the complexities of today’s fast-changing data environment. The discussion focused on strengthening collaboration between IT and Records and Information Management (RIM) teams, leveraging AI to enhance data management, and exploring best practices for building resilient, robust data frameworks.

Participants walked away with these five essential takeaways.

1. Make data ethics a strategic priority
   

   Companies can build trust by embedding data responsibility into culture, governance, and AI strategy. Responsible AI requires more than just technical excellence - it demands intentional safeguards to ensure fairness, accountability, and transparency. This includes using AI to drive innovation and protect sensitive data, uphold privacy standards, and ensure data is managed with integrity across its life cycle.

   Proactively incorporating data ethics into business practices helps organizations stand out in the marketplace, while reducing risk and building lasting trust with all stakeholders, whether customers, partners, or regulators.



2. Design AI governance with human oversight
   

   Responsible AI starts with thoughtful design that includes building in human oversight at every stage. To mitigate bias, reduce risk, and ensure the accuracy of AI-driven decisions, organizations should implement structured human checkpoints. These may take the form of peer reviews or approval workflows that ask colleagues to confirm the accuracy of AI outputs before they’re acted upon.

   While biased - or hallucinogenic - AI often reflects human shortcomings, it must ultimately rise above them. That’s why governance structures should focus not only on technical performance but also on ethical soundness and contextual awareness. A key part of this process is asking better, more intentional questions because the quality of AI outcomes is shaped by the clarity and depth of human input.



3. Shift from “no” to “know” in cross-functional collaboration

   Help shift the mindset from automatically resisting AI to getting curious and engaged. Encourage open conversations, share information freely, and create space for teams to ask questions and explore how AI could impact their work. It’s about building a culture where people want to understand the possibilities, and not receive pushback by default.

   By building knowledge-sharing channels and cultivating a spirit of collaboration, organizations can align technical, ethical, legal, and business perspectives - laying the foundation for more responsible and effective AI development.



4. Use education and continuous learning to drive responsible innovation

   Embrace failure and learning as essential parts of innovation and resilience. Where possible, take a FAIL methodological approach – First Attempt In Learning – to create a culture of understanding and evolving alongside AI and other technology. Each misstep provides valuable insights that help teams build more effective, responsible AI solutions.

   Failure deepens understanding because it confronts gaps in knowledge and/or skills. It helps employees develop the mindset to keep on trying, adapt strategies, and push through challenges. Failure also encourages experimentation and develops critical thinking as people try to understand why something didn’t work and how to improve it.



5. Tie data management directly to cost and sustainability

   Align retention and disposition policies with both budget and environmental, social, and governance (ESG) goals. Unchecked data growth – and often data sprawl – drives up storage costs and contributes to energy waste and operational inefficiencies. When organizations treat data as limitless, they often overlook the financial and environmental impact of storing redundant, outdated, or unnecessary information.

   The most effective way to insert sustainability into data management is to frame it in financial terms. By connecting data practices to cost savings and ESG performance, organizations can make smarter decisions that benefit the bottom line and the planet.

   By linking data management to measurable financial and sustainability goals, companies can reduce their digital footprint while improving efficiency. Implementing clear retention and disposition policies helps control costs, supports ESG commitments, and shifts sustainability from a compliance concern to a bottom-line business strategy.

Next stop as the data journey continues

The eight-city roadshow series kicked off in Atlanta and will take place in seven other North American cities in 2025. And Iron Mountain can’t wait to get on the road again.

The Exploring the Information Frontier roadshow continues in Houston on May 20, with subsequent stops in Toronto, Minneapolis, Dallas, Boston, New York City, and Los Angeles. Find out more here.