Information governance has long resided in the background of many consulting firms: retention schedules, file plans, and access rules managed by risk, IT, and records teams while partners focus on winning work and serving clients. It shows up as something you are expected to “have in place” rather than something that actively shapes deal strategy, AI roadmaps, or integration plans. The working assumption is familiar: smart consultants, reputable platforms, and generic firmwid

ISO 27001 is widely recognized as the gold standard for information security management, giving organizations a structured way to protect the confidentiality, integrity, and availability of their data. ISO 42001 does something similar for artificial intelligence: it provides a formal management system for how AI is designed, built, deployed, monitored, and improved. Layered on top of these, the EU AI Act and emerging U.S. AI rules push organizations to treat AI not as a serie

The federal government’s gradual shift—accelerated in 2022—toward using commercial records storage centers has opened up a rare moment of opportunity for the industry. Agencies that once relied on government-operated facilities are now looking outward, and they’re asking far more sophisticated questions about compliance than ever before. For commercial records centers, this shift is significant, because it brings NARA standards and federal expectations directly into everyday

The National Archives and Records Administration (NARA) is the federal agency responsible for preserving and managing government records, establishing recordkeeping standards, and ensuring public access to America's documentary heritage. All too often, private sector organizations think that federal recordkeeping standards don't apply to them—that NARA's guidance is purely a government affair with little relevance to their business realities. Leadership sees federal requireme

As artificial intelligence (AI) continues to rapidly accelerate and evolve within healthcare systems, the development and deployment of clinical AI models has become a cornerstone for organizations seeking to improve patient outcomes and operational efficiency. However, this approach comes with substantial risks related to bias, equity, and discriminatory healthcare delivery. In particular, the rise of AI-driven clinical decision support systems has transformed how healthcare

All too often, companies think that they have proudly “solved retention” by investing in an extensive SaaS platform that now delivers...

This past week, the General Court of the European Union dismissed challenges against the European Commission’s adequacy decision for the...

In early 2025, the Commonwealth Bank of Australia (CBA) attempted to make headlines for innovation. The bank announced it would replace...

If an AI model helps triage a chest pain patient at 2:07 a.m., can you prove—instantly—which data, code, and policy version made that...

In March 2025, Iranian state-owned Bank Sepah suffered a devastating cyberattack orchestrated by the hacker group Codebreakers. The...

In July 2025, the Massachusetts Attorney General reached a $2.5 million settlement with a student loan company that had deployed an AI...

In today’s high-pressure business climate, executives are expected to close complex transactions with speed, accuracy, and clear returns....

Boxes of old files aren’t just dusty—they’re dangerous. For Group Purchasing Organizations (GPOs), physical record storage has become an...

Many organizations have poured resources into DEI over the past decade—launching initiatives, hiring Chief Diversity Officers, and citing...