From Storage to Strategy: Five Steps to Achieve Trusted AI

Business leaders recognize that success with AI depends on more than just the technology itself. At Iron Mountain’s Dallas stop on the Exploring the Information Frontier series, executives and strategists examined how AI-savvy leaders can prepare their organization’s data ecosystems, governance protocols, and workforce attitudes for the age of intelligent automation.

The central message was that AI is only as strong as the foundation it’s built on. To thrive, organizations must commit to implementing resilient data practices, developing ethical stewardship, and cultivating a workforce empowered to partner with technology.

Attendees of the Aug. 13 event left the experience with five actions to guide their journey from AI ambition to readiness for the innovative technology.

1. Build AI on a strong data core
   

   AI innovation requires the right data. Models thrive on inputs that are “ACS” – accurate, compliant, and structured. When data is fragmented or inconsistent, the risks multiply to include unreliable outcomes, missed opportunities, and potential regulatory exposure. A disciplined data foundation creates the conditions for AI to generate reliable, actionable insights that leaders can confidently use in their decision making.

   Building this strong core demands ongoing commitment to everything from governance to integration to data cleansing. Those treating data quality as a living process, rather than a box to check, will better position themselves to adapt as new tools, regulations, and business priorities emerge. A resilient data ecosystem ensures that AI strategies sustain and scale, rather than simply launch and plateau.

2. Treat anonymization as non-negotiable
   

   The rate of cyberattacks and data breaches is escalating, making anonymization a business imperative. Protecting sensitive information avoids reputational damage and also creates an environment where data can be safely shared and leveraged without risk.

   Embedding anonymization into workflows lets organizations balance innovation with responsibility. It builds trust by assuring customers and stakeholders that data is used ethically. Companies that respect privacy stay compliant, strengthen credibility, and stand out. In the era of AI, trust is as critical as the technology.

3. Rethink data ownership in a shifting landscape
   

   The traditional boundaries of data ownership are blurring as AI agents and multimodal systems become more sophisticated. Who controls data, how it is used, and who ensures its stewardship are questions that require clear answers. The new data leader must balance responsibilities across compliance, cybersecurity, governance, and privacy, often serving as the connective tissue between technology and business strategy.

   This expanded role turns data ownership into a strategic advantage. Companies that proactively define ownership structures and embed accountability into their processes can move faster and with more confidence. In a marketplace where regulators and consumers increasingly demand transparency, ethical data leadership is a way for organizations to differentiate and build trust.

4. Recognize the end of proprietary model dominance
   

   The breakneck pace of AI advancement is making it unrealistic for most organizations to build and maintain proprietary models. Agility is now the new currency. Open-source and marketplace solutions offer a faster, more flexible path to adoption – allowing companies to tap into cutting-edge innovation without the burden of developing everything in-house.

   By embracing shared ecosystems, organizations can stay innovative while focusing resources on aligning AI with business goals. Proprietary models will remain useful in some cases, but scale and speed will favor those who leverage the broader AI marketplace.

5. Embrace AI as a workforce amplifier
   

   Despite fears of job displacement, AI’s true potential is amplifying the workforce. AI delivers a speed and precision that enhances, rather than replaces, human judgment. It enables people to shift their focus from routine tasks to high-value, strategic activities. Framing AI as a partner rather than a competitor also helps drive adoption. Employees who see AI as an enabler of their success are more likely to embrace it, experiment, and uncover new ways to create value. Organizations that prepare their people will unlock AI’s full potential as a capability enhancer.

The road ahead

The Dallas event made it clear organizations are treating AI as a partnership between data, governance, and people will be best positioned to thrive in this era of transformation.

As the pace of AI innovation continues to accelerate, powerful leaders won’t be those who simply adopt AI – but those who embed it thoughtfully and responsibly into the fabric of their business.