America’s AI Action Plan outlines a comprehensive strategy for the country’s leadership in AI. The plan seeks, in part, to accelerate AI adoption in the federal government. However, there is a gap in that vision: agencies have been slow to adopt AI tools to better serve the public. The biggest barrier to adopting and scaling trustworthy AI isn’t policy or compute power — it’s the foundation beneath the surface. How agencies store, access and govern their records will determine whether AI succeeds or stalls. Those records aren’t just for retention purposes; they are the fuel AI models need to power operational efficiencies through streamlined workflows and uncover mission insights that enable timely, accurate decisions. Without robust digitalization and data governance, federal records cannot serve as the reliable fuel AI models need to drive innovation.

Before AI adoption can take hold, agencies must do something far less glamorous but absolutely essential: modernize their records. Many still need to automate records management, beginning with opening archival boxes, assessing what is inside, and deciding what is worth keeping. This essential process transforms inaccessible, unstructured records into structured, connected datasets that AI models can actually use. Without it, agencies are not just delaying AI adoption, they’re building on a poor foundation that will collapse under the weight of daily mission demands.

If you do not know the contents of the box, how confident can you be that the records aren’t crucial to automating a process with AI? In AI terms, if you enlist the help of a model like OpenAI, the results will only be as good as the digitized data behind it. The greater the knowledge base, the faster AI can be adopted and scaled to positively impact public service. Here is where agencies can start preparing their records — their knowledge base — to lay a defensible foundation for AI adoption.

Step 1: Inventory and prioritize what you already have

Many agencies are sitting on decades’ worth of records, housed in a mix of storage boxes, shared drives, aging databases, and under-governed digital repositories. These records often lack consistent metadata, classification tags or digital traceability, making them difficult to find, harder to govern, and nearly impossible to automate.

This fragmentation is not new. According to NARA’s 2023 FEREM report, only 61% of agencies were rated as low-risk in their management of electronic records — indicating that many still face gaps in easily accessible records, digitalization and data governance. This leaves thousands of unstructured repositories vulnerable to security risks and unable to be fed into an AI model. A comprehensive inventory allows agencies to see what they have, determine what is mission-critical, and prioritize records cleanup. Not everything needs to be digitalized. But everything needs to be accounted for. This early triage is what ensures digitalization, automation and analytics are focused on the right things, maximizing return while minimizing risk.

Without this step, agencies risk building powerful AI models on unreliable data, a setup that undermines outcomes and invites compliance pitfalls.

Step 2: Make digitalization the bedrock of modernization

One of the biggest misconceptions around modernization is that digitalization is a tactical compliance task with limited strategic value. In reality, digitalization is what turns idle content into usable data. It’s the on-ramp to AI driven automation across the agency, including one-click records management and data-driven policymaking.

By focusing on high-impact records — those that intersect with mission-critical workflows, the Freedom of Information Act, cybersecurity enforcement or policy enforcement — agencies can start to build a foundation that’s not just compliant, but future-ready. These records form the connective tissue between systems, workforce, data and decisions.

The Government Accountability Office estimates that up to 80% of federal IT budgets are still spent maintaining legacy systems. Resources that, if reallocated, could help fund strategic digitalization and unlock real efficiency gains. The opportunity cost of delay is increasing exponentially everyday.

Step 3: Align records governance with AI strategy

Modern AI adoption isn’t just about models and computation; it’s about trust, traceability, and compliance. That’s why strong information governance is essential.

Agencies moving fastest on AI are pairing records management modernization with evolving governance frameworks, synchronizing classification structures, retention schedules and access controls with broader digital strategies. The Office of Management and Budget’s 2025 AI Risk Management guidance is clear: explainability, reliability and auditability must be built in from the start.

When AI deployment evolves in step with a diligent records management program centered on data governance, agencies are better positioned to accelerate innovation, build public trust, and avoid costly rework. For example, labeling records with standardized metadata from the outset enables rapid, digital retrieval during audits or investigations, a need that’s only increasing as AI use expands. This alignment is critical as agencies adopt FedRAMP Moderate-certified platforms to run sensitive workloads and meet compliance requirements. These platforms raise the baseline for performance and security, but they only matter if the data moving through them is usable, well-governed and reliable.

Infrastructure integrity: The hidden foundation of AI

Strengthening the digital backbone is only half of the modernization equation. Agencies must also ensure the physical infrastructure supporting their systems can withstand growing operational, environmental, and cybersecurity demands.

Colocation data centers play a critical role in this continuity — offering secure, federally compliant environments that safeguard sensitive data and maintain uptime for mission-critical systems. These facilities provide the stability, scalability and redundancy needed to sustain AI-driven workloads, bridging the gap between digital transformation and operational resilience.

By pairing strong information governance with resilient colocation infrastructure, agencies can create a true foundation for AI, one that ensures innovation isn’t just possible, but sustainable in even the most complex mission environments.

Melissa Carson is general manager for Iron Mountain Government Solutions.

The post Three steps to build a data foundation for federal AI innovation first appeared on Federal News Network.

Dec. 1–5 recap: This week showed the global scramble to out‑build, out‑train, and out‑ship AI, from datacenter deals to AI-powered smart glasses.

The post Daily Tech Insider Highlights the Escalating AI Arms Race Across Cloud, Code, and Consumer Tech appeared first on TechRepublic.

Microsoft will raise Microsoft 365 and Office 365 prices in July 2026, citing new AI and security upgrades for business and government users.

The post Microsoft to Hike Office Prices in 2026, Citing Major AI and Security Overhauls appeared first on TechRepublic.

Chinese-sponsored groups are using the popular Brickstorm backdoor to access and gain persistence in government and tech firm networks, part of the ongoing effort by the PRC to establish long-term footholds in agency and critical infrastructure IT environments, according to a report by U.S. and Canadian security offices.

The post China Hackers Using Brickstorm Backdoor to Target Government, IT Entities appeared first on Security Boulevard.

Dec. 1–5 recap: This week showed the global scramble to out‑build, out‑train, and out‑ship AI, from datacenter deals to AI-powered smart glasses.

The post Daily Tech Insider Highlights the Escalating AI Arms Race Across Cloud, Code, and Consumer Tech appeared first on TechRepublic.

Microsoft will raise Microsoft 365 and Office 365 prices in July 2026, citing new AI and security upgrades for business and government users.

The post Microsoft to Hike Office Prices in 2026, Citing Major AI and Security Overhauls appeared first on TechRepublic.

Microsoft’s annual shareholder meeting Friday played out as if on a split screen: executives describing a future where AI cures diseases and secures networks, and shareholder proposals warning of algorithmic bias, political censorship, and complicity in geopolitical conflict.

One shareholder, William Flaig, founder and CEO of Ridgeline Research, quoted two authorities on the topic — George Orwell’s 1984 and Microsoft’s Copilot AI chatbot — in requesting a report on the risks of AI censorship of religious and political speech.

Flaig invoked Orwell’s dystopian vision of surveillance and thought control, citing the Ministry of Truth that “rewrites history and floods society with propaganda.” He then turned to Copilot, which responded to his query about an AI-driven future by noting that “the risk lies not in AI itself, but in how it’s deployed.”

In a Q&A session during the virtual meeting, Microsoft CEO Satya Nadella said the company is “putting the person and the human at the center” of its AI development, with technology that users “can delegate to, they can steer, they can control.”

Nadella said Microsoft has moved beyond abstract principles to “everyday engineering practice,” with safeguards for fairness, transparency, security, and privacy.

Brad Smith, Microsoft’s vice chair and president, said broader societal decisions, like what age kids should use AI in schools, won’t be made by tech companies. He cited ongoing debates about smartphones in schools nearly 20 years after the iPhone.

“I think quite rightly, people have learned from that experience,” Smith said, drawing a parallel to the rise of AI. “Let’s have these conversations now.”

Microsoft’s board recommended that shareholders vote against all six outside proposals, which covered issues including AI censorship, data privacy, human rights, and climate. Final vote tallies have yet to be released as of publication time, but Microsoft said shareholders turned down all six, based on early voting. 

While the shareholder proposals focused on AI risks, much of the executive commentary focused on the long-term business opportunity. 

Nadella described building a “planet-scale cloud and AI factory” and said Microsoft is taking a “full stack approach,” from infrastructure to AI agents to applications, to capitalize on what he called “a generational moment in technology.”

Microsoft CFO Amy Hood highlighted record results for fiscal year 2025 — more than $281 billion in revenue and $128 billion in operating income — and pointed to roughly $400 billion in committed contracts as validation of the company’s AI investments.

Hood also addressed pre-submitted shareholder questions about the company’s AI spending, pushing back on concerns about a potential bubble. 

“This is demand-driven spending,” she said, noting that margins are stronger at this stage of the AI transition than at a comparable point in Microsoft’s cloud buildout. “Every time we think we’re getting close to meeting demand, demand increases again.”

Alphabet’s TPUs are gaining investor hype as analysts say the company could capture a share of the $900 billion AI-chip market.

The post Alphabet Emerges as Nvidia’s Biggest Threat in a $900B AI-Chip Race appeared first on TechRepublic.

Dive into Google’s 2025 Year in Search, from Gemini and AI-fueled trends to the movies, TV shows, and actors that kept the world searching across borders.

The post From AI Barbie to Squid Game 3: The Top Google Searches of 2025 appeared first on TechRepublic.

In an exclusive interview with TechRepublic, Lancium’s CEO explains how the $500 billion Stargate project is racing to build AI megacenters.

The post Stargate Attracts More Funding for Texas-Sized AI Factory appeared first on TechRepublic.

Other noteworthy stories that might have slipped under the radar: Akamai patches HTTP smuggling vulnerability, Claude Skills used to execute ransomware, PickleScan flaws.

The post In Other News: X Fined €120 Million, Array Flaw Exploited, New Iranian Backdoor appeared first on SecurityWeek.

The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains.

“Most organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,” says Shirley Hung, partner at Everest Group. “They have rigid, fragmented workflows that don’t adapt well to change, technology systems that don’t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.”

The central challenge, then, lies in rethinking how people, processes, and technology work together.

Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures—centralized decision-making, fragmented workflows, data spread across incompatible systems—are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.

“It is very important that humans continue to verify the content. And that is where you’re going to see more energy being put into,” Ryan Peterson, EVP and chief product officer at Concentrix.

Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a “virtual worker,” this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy.

“My advice would be to expect some delays because you need to make sure you secure the data,” says Heidi Hough, VP for North America aftermarket at Valmont. “As you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.”

Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate.

“Optimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,” says Hung.

Watch the webcast.

This webcast is produced in partnership with Concentrix.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Alphabet’s TPUs are gaining investor hype as analysts say the company could capture a share of the $900 billion AI-chip market.

The post Alphabet Emerges as Nvidia’s Biggest Threat in a $900B AI-Chip Race appeared first on TechRepublic.

Dive into Google’s 2025 Year in Search, from Gemini and AI-fueled trends to the movies, TV shows, and actors that kept the world searching across borders.

The post From AI Barbie to Squid Game 3: The Top Google Searches of 2025 appeared first on TechRepublic.

In an exclusive interview with TechRepublic, Lancium’s CEO explains how the $500 billion Stargate project is racing to build AI megacenters.

The post Stargate Attracts More Funding for Texas-Sized AI Factory appeared first on TechRepublic.