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Chief digital and information officer Pragati Mathur details how the oil and gas production company moves from AI potential to performance. source

Inconsistent results, hallucinations, and a lack of use cases tolerant to inaccuracies have sent generative AI into a downward hype cycle. source

Academia follows the industry’s lead with CAIOs and AI strategies embedded at every level. source

To win in the Agentic Age, don’t chase return on investment. Instead, build a data foundation first. Only once that foundation is built can a company begin to unlock real value.  It’s an approach that goes against the instincts of most companies and executives. The common instinct is to identify a high-profile project with the potential for a fast return on investment. Too often, that is a recipe for disaster, said Mihir Shah, a former Fortune 500 CIO, tech executive, and current member of the Reltio Advisory Board, who has helped companies navigate these challenges for more than 30 years.  “Most companies have an IT graveyard of half-completed data projects,” he said. “In public companies, it is very difficult to invest a lot of money over many years and keep that going. This is where the CEO and the Board come in.”  Begin with a foundational data model The first use case every company should tackle is building a foundational data model with unique IDs for its customers, products, clients, and other key pieces of data, he said. Only once that taxonomy has been established, and all data has been unified in one location, can leaders across the business identify use cases that grow revenue or margin.  There are natural allies within most companies who will support this kind of foundation work, Shah said. CFOs are often the first to turn to, as they and their teams understand best how hard it is to aggregate financial information across business silos. Compliance teams also need visibility into the entire business across business units. Both are often early adopters of unified data strategies.  When leading a foundational effort, though, a chief data officer must show progress along the way, Shah said, who led a similar effort at a large financial institution earlier in his career.  “In my case, I had a four-year program, but within two quarters, you have to show use cases that offer not necessarily the highest ROI but something to show that we are making progress and creating value for the business,” he said.  Real-time personalization drives success  In today’s market, competitive advantage no longer hinges on brand loyalty, product features, or price. It depends on delivering hyper-personalized, real-time experiences that anticipate and adapt to customer needs. Take these two examples: A global hotel chain uses AI agents to unify data from booking systems, loyalty programs, and guest preferences. When a frequent traveler books a room, the system instantly customizes the experience—assigning a preferred room, offering tailored upsells, and notifying staff of personal preferences. The result: higher satisfaction, increased revenue per booking, and deeper loyalty. A medical device company deploys agents to monitor real-time sensor data from field equipment. When anomalies are detected, the agents proactively initiate a service order, alert the customer, and dispatch a technician—often before the user even knows there’s a problem. Downtime is avoided, and trust is reinforced. These are not future visions—they’re today’s reality, powered by agentic AI and data in motion. Unlike batch-processed, siloed data, real-time, unified data allows agents to personalize interactions, automate decisions, and act with foresight, driving both profitability and customer loyalty at scale. Businesses that unify and activate data in real time can meet customer expectations in the moments that matter most. It’s no surprise that companies leading in personalization generate 40% more revenue than their peers. The common thread: data mastery drives market leadership. C-suite leadership is required at this moment To seize the advantage, C-level leaders must act now and reimagine their data strategy. This is not a departmental issue. It is also not just a CIO or CDO agenda—it’s a CEO and Board-level imperative. Enterprise data is becoming the most valuable asset that a company has. Leaders must ask: Are our decisions—human or AI—based on the freshest, most complete data? If the answer isn’t a confident “yes,” it’s time for change.  That means breaking down silos, unifying core data domains (customer, product, operations), and replacing batch-based processes with real-time data pipelines. It also means implementing robust governance and observability so AI systems can make trusted, automated decisions. Enterprises need to build robust data pipelines, AI-driven insights, automation frameworks, and real-time decision-making engines as the foundation for agentic AI. But technology alone isn’t enough. A cultural shift is essential. Data must be recognized as a strategic asset at the highest levels. Every executive must become a data leader: CEOs should view real-time data as a strategic growth lever, essential for spotting market shifts, delivering a premium customer experience, and enabling agility. CMOs and CX leaders should push for unified customer data platforms that enable personalization at scale, because irrelevant engagement kills loyalty. (Companies using real-time data have seen up to a 40% increase in customer retention.) CFOs and risk leaders should demand live data for financial reporting and risk mitigation, catching anomalies before crises occur. CIOs and CDOs must champion flexible, cloud-based architectures that support agentic AI enterprise-wide. Winning in this new era requires data-driven leadership that cuts across roles, silos, and disciplines. Your data strategy is your business strategy For years, companies have chased digital transformation. Now, the shift is toward autonomous transformation, where intelligent agents and automation drive decisions and actions in real time. In this new era, success hinges on delivering the right data to the right place at the right moment. Organizations that can do this will operate like real-time nervous systems—sensing change, making informed decisions, and taking instant action. They’ll delight customers, optimize operations, and outpace competitors. Those clinging to static data and delayed systems will find themselves running the race while staring into the rearview mirror—outmaneuvered and out of time. The message to CEOs, COOs, CIOs, CDOs—and especially CFOs—is clear: Make your data move, or risk being moved aside. The future belongs to companies that treat unified, trusted, real-time data as the fuel for continuous intelligence and innovation. Moving from data at rest to data in motion is not a technical upgrade. It’s a strategic necessity. Because in the agentic

Artificial intelligence (AI) adoption is accelerating, and quantum computing is steadily moving from theory to reality. In fact, the United Nations declared 2025 the International Year of Quantum Science and Technology, signaling quantum’s shift from experimental research to real-world applications. For IT leaders, the convergence of AI and quantum represents both unprecedented opportunity and an emerging security crisis. From deepfake-enabled phishing campaigns that are eroding trust to the looming quantum decryption threat that could expose decades of secured data, IT leaders face a race to rethink their defenses. Foundry reached out to the CIO Experts Network, a community of IT professionals and technology industry influencers, to explore the most vexing data security challenge for IT leaders over the next few years — and solutions for countering these threats. Their responses point to a dual challenge: countering today’s AI-enabled threats while preparing for the disruptive impact of quantum computing. The quantum clock is ticking Vivek Singh, senior vice president of IT and Strategic Planning at PALNAR, warns that quantum computing could render encryption useless across email, WhatsApp, virtual private networks (VPNs), authentication protocols, digital signatures, and more. “A critical challenge lies in the potential for retroactive decryption. If someone’s sensitive data is stolen today, it could be decrypted in the future with quantum capabilities,” he says. “From my perspective, the most vexing challenge is managing the dual pressure of preparing for postquantum cryptographic resilience while simultaneously defending against increasingly sophisticated AI-enabled cyberthreats — we can say AI-driven threats like deepfakes, automated phishing, voice calibration, and intelligent malware. “ Gene De Libero, principal consultant at Digital Mindshare LLC, agrees that the quantum deadline is near. “AI-powered attacks are learning and adapting faster than security teams can respond,” he says. “We’re seeing malware that breaks out in 51 seconds and 560,000 new variants created daily. Meanwhile, bad actors are stealing encrypted data right now, banking on quantum computers’ cracking it later. The real problem: You need to start quantum-safe crypto migration by 2026 while fighting AI threats that evolve faster than you can patch.” AI: Faster, smarter, harder to detect Peter Nichol, data and analytics leader for North America at Nestlé Health Science, notes that AI-powered adversaries are already changing the game and that the rise of AI agents is expanding the attack landscape. “Today’s organizations face an increasingly complex and exposed digital surface area. Adversaries are also harnessing AI, automation, and crime-as-a-service ecosystems. Threats are no longer one-dimensional — they’re multivector, AI-driven, and quantum-enabled. Dynamic data security risks are accelerating as we move toward quantum-enhanced AI [QAI],” he says. “The speed and sophistication of these threats now exceed conventional defense thresholds. Quantum annealers, using variational algorithms, can rapidly train reinforcement learning agents, allowing autonomous malware to morph in hours rather than weeks and learn how to bypass detection on first execution.” Nichol also points to the rise of AI-powered algorithmic collusion — malicious behaviors that emerge without explicit coordination and are nearly impossible to detect. These self-taught, cooperative threat patterns evolve independently, adapting in ways traditional defenses can’t anticipate. “A clear example is AI-driven botnets using reinforcement learning to evade detection. Multiple agents are deployed into a cloud-based microservice environment, where they independently learn which behaviors help them persist, spread, and avoid detection, such as altering email spelling, adjusting image-to-text ratios, or adapting to traffic throttling,” says Nichol. The lack of coordination, combined with subtle behavioral drift, renders traditional detection methods ineffective. Nichol warns that AI agent collusion is creating decentralized, self-evolving threats that outpace conventional security models and demand entirely new defensive strategies. Defending today while preparing for tomorrow Ed Fox, CTO of MetTel, frames the issue as “a two-front war: securing an ever-expanding, real-time digital frontier while racing to future-proof its very foundations. “The most vexing data security challenge for IT leaders is securing the explosion of decentralized, multimodal data generated by autonomous AI agents, especially as our industrial systems increasingly connect to the internet, creating new physical vulnerabilities. IT leaders must simultaneously defend against dynamic AI-driven threats that can impact physical operations now, while strategically preparing for a massive cryptographic overhaul to protect from future quantum attacks.” Some see the path forward as requiring business alignment and cultural change alongside technical defenses. Arsalan Khan (@ArsalanAKhan), a speaker, adviser, and blogger, argues that the biggest risk isn’t just technical — it’s strategic. “While IT leaders can address the technical aspects — from data governance frameworks and postquantum cryptography readiness to AI threat detection — it’s business leadership that must drive a culture of shared responsibility. In my view, the most vexing data security challenge for IT leaders today is not the technology itself but convincing business leadership that data security is no longer just an IT issue; it’s an enterprise imperative.”  AI and quantum are converging to create a perfect storm for IT security. The challenge is not just to defend against sophisticated AI-enabled attacks now but also to act decisively before quantum computing renders today’s safeguards obsolete. Learn how OpenText can help secure your organization against the dual threats of AI and quantum cryptography. source

This momentum is exciting, but also overwhelming. With limited capacity, technical debt, and governance still evolving, teams often face the same question: Where do we begin? According to MIT Sloan Management Review, legacy infrastructure and mounting tech debt remain core barriers to scaling AI efforts effectively. Enthusiasm without focus leads to scattered pilots, shallow proof-of-concepts, and siloed tools that never scale. To move beyond experimentation, we need a smarter way to decide: which use cases should we prioritize — and why? From possibilities to priorities To make meaningful progress, organizations need a clear and shared lens for evaluating which AI use cases deserve attention now — and which can wait. source

Dan Roberts: Given the diversity and complexity of the ever-changing role of CIO, there is no one single way of getting there. Can you talk about your path and how it’s shaped your leadership philosophies and style? Katrina Redmond: I have undergraduate degrees in biochemistry and psychology, and a master’s degree in industrial engineering with a focus on quality assurance and control. I was actually planning on going to med school, but I had a bad auto accident that set me back from that journey. I ended up staying in the process space at GE, and the direction I went there has really informed my entire career. I started off in process work and getting Six Sigma certified, becoming a Green Belt, then a Black Belt and Master Black Belt. From there, they launched me into sourcing and inventory leadership, operations management, facilities management, and various other operational roles. At one point, I was at a GE business that was struggling on the IT front, and they asked me if I would take on both quality and IT. I said, ‘Absolutely, but I’m not technical.’ And they said, ‘We need your process skills more than your technical skills.’ So I transitioned into a CIO role, and I’ve stayed in the role basically ever since. source

But implementation taught me that ‘seamless’ and ‘simple’ aren’t the same thing. Each cloud provider had different APIs, security models and operational procedures. What we called ‘unified management’ still required teams to understand the nuances of multiple platforms. We solved the technical integration challenge, but we didn’t eliminate the operational complexity — we just centralized it. Now, in my role as technical leader for F5’s BIG-IP management plane, I see the other side of that equation. I work with enterprises that have implemented these hybrid solutions and are struggling with exactly the operational burden we thought we had solved. They’re managing applications across multiple clouds, dealing with different load balancing requirements for each environment, and trying to maintain consistent security policies across platforms that were never designed to work the same way. The F5 report’s finding that 94% of organizations deploy apps across multiple environments, with a median of four different public cloud vendors, isn’t just a statistic to me — it’s the daily reality of the enterprises I support. When I see that 79% have moved applications back from public clouds to on-premises, I recognize the disillusionment. The hybrid cloud flexibility we promised became hybrid cloud complexity that they couldn’t manage. source

Years ago, as a VP of IT for a Fortune 500 company, I found myself in a paradox of success. I had a corner office, a talented team and a reputation as someone who could handle any crisis, but I was trapped in a relentless cycle of meetings, emails and urgent demands. My days stretched from 6 AM to midnight, constantly putting out fires and making snap decisions without time for genuine strategic thought. The breaking point came during a particularly brutal week when I realized I had attended 37 meetings in five days. I was exhausted, my team was burned out and despite all our activity, we weren’t making meaningful progress on our strategic initiatives. That experience led me to discover the transformative power of what productivity experts call a strategic pause. The hidden cost of hyperconnectivity My experience wasn’t unique. Research from Harvard Business Review shows that executives now spend nearly 23 hours a week in meetings, up from less than 10 hours in the 1960s. This dramatic increase has coincided with declining productivity and rising burnout rates across organizations. In my own company, I noticed team anxiety increasing, decision-making quality deteriorating and innovation suffering as everyone struggled to keep up. source