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From the security standpoint, OAuth is an essential starting point, says Fox. She adds that more advanced security frameworks, particularly around zero-trust architectures and granular identity management, will become indispensable. APIs turn observers into doers APIs are widely seen as a linchpin for evolving agentic AI, but the current landscape is far from perfect. Enterprises still face challenges like inconsistent data practices, fragmented standards, competing integration protocols, and rising security concerns. As a result, some leaders approach the space with cautious optimism rather than full adoption. “Because the technology is moving at lightning speed, the focus should be on creating a connected, secure, and scalable environment where AI can thrive responsibly,” says Chaplin. For him, unlocking real value from agentic AI will require stronger governance at the data layer, including clearer policies for categorizing, defining, securing, and monitoring enterprise data. Still, these roadblocks shouldn’t stall momentum. APIs will be essential to move autonomous agents beyond basic conversation and into meaningful, real-world execution. And, over time, organizations that master AI-to-API orchestration will likely outperform peers. “APIs are the lifeblood of agentic AI,” says Blundell.” Without API access, agents remain observers rather than doers.” source

The technical breakthroughs powering next-gen data masking  The latest advancements in data masking focus on preserving computational efficiency while maintaining high-security standards across enterprise environments. Real-time, in-memory data masking dynamically applies obfuscation at the query layer, eliminating reliance on pre-masked datasets. This ensures transaction-heavy applications, AI models and real-time analytics remain performant and compliant. Another breakthrough is format-preserving encryption (FPE), which retains the structure of masked data, ensuring seamless processing in legacy systems and structured datasets. Additionally, differential privacy techniques introduce controlled noise, allowing AI models to train securely without exposing sensitive data. Modern context-aware masking dynamically adjusts obfuscation levels based on user roles, location and risk assessment, crucial for multi-cloud architectures. K2view’s micro-database approach ensures real-time, granular masking at the point of access, eliminating the need for pre-processing. The data masking tools enable fine-grained, role-based masking by encapsulating data entities in dedicated micro-databases, ensuring low-latency compliance enforcement for AI-driven workflows.  With federated learning and decentralized AI models, homomorphic encryption, secure multiparty computation (SMPC) and masked data lakes are shaping the future of privacy-preserving AI. The ability to mask data in motion, at rest and during computation is critical for next-gen enterprise security.  How data masking fuels hyper-personalized customer experiences  The demand for hyper-personalization in digital services is rapidly growing, powered by AI-driven recommendations, dynamic user interfaces and contextual customer engagement. However, achieving personalization at scale requires enterprises to process vast amounts of sensitive user data in real-time while adhering to strict compliance regulations such as GDPR, CCPA and PCI-DSS. This is where data masking becomes an enabler rather than a constraint, allowing businesses to extract valuable insights while safeguarding user privacy. Dynamic data masking (DDM) allows real-time customization of content and services without exposing personally identifiable information (PII). AI-driven personalization engines can process masked data to analyze behavioural patterns, predict customer needs and deliver contextually relevant recommendations without breaching compliance. Techniques such as tokenization and synthetic data generation further allow enterprises to simulate real customer interactions while eliminating privacy risks. By integrating role-based and context-aware masking policies, organizations ensure that only authorized AI models, analytics tools and business teams access the appropriate levels of detail. This enhances personalisation accuracy and fortifies trust and regulatory compliance, allowing enterprises to deliver seamless, hyper-personalized customer experiences without exposing sensitive data. Future-proofing your enterprise: Why adaptive data masking is non-negotiable  Traditional static masking approaches become impractical as enterprises scale AI-driven operations and real-time analytics. Adaptive data masking is essential for maintaining both security and usability across complex, distributed ecosystems. Unlike conventional methods, adaptive masking leverages context-aware policies, real-time risk assessment and automation to dynamically adjust data obfuscation levels. In multi-cloud environments, adaptive masking frameworks integrate with identity and access management (IAM) systems to enforce security policies based on user roles, geolocation and access context. AI-powered risk-based masking further enhances security by applying different masking levels depending on threat-intelligence insights and behavioral analytics. Industries handling sensitive data, such as finance, healthcare and telecommunications, must ensure compliance with evolving regulations such as GDPR, CCPA and HIPAA. By implementing automated, real-time masking policies, enterprises can facilitate secure AI model training, fraud detection and real-time decision-making while ensuring privacy and performance scalability. Future-proofing data security with adaptive masking is no longer optional, but rather a strategic imperative for resilient, AI-driven enterprises. source

Keith: And what problems are you solving? Obviously, you wouldn’t have started the company if there wasn’t a problem to fix. Matt: A few things. First, it’s about getting to the data where it is. We’re a powerful engine for querying data from data lakes. But often the challenge is: how do you get the data into one place? We can augment that process by connecting to various sources. Especially with AI, the effectiveness of your model depends on the quality of the data it gets. We think of ourselves as the fuel that powers AI. Also, we allow experimentation and production-level usage without forcing you to centralize everything. You can experiment quickly and move to production faster. Uniquely, we support both cloud and on-prem environments. So we can reach across clouds, across regions—even back to on-prem. Keith: Was the growth of cloud computing one of the main reasons data started getting so fragmented? Matt: That’s part of it. Companies moved to the cloud but still have legacy systems on-prem. Some keep data on-prem due to privacy or security needs. In M&A scenarios, a company might acquire another with data in Google Cloud while they use AWS. There are lots of reasons for the fragmentation. Keith: So, what kinds of problems would a company face if they weren’t using Starburst? Matt: They’d likely need more tools and would rely heavily on ETL processes to move everything to one place. ETL has its role, but we don’t make it a requirement. You can connect directly to the data source, experiment, and decide whether you want to move it later. This gives you faster time-to-value with less complexity. Keith: All right, let’s jump into the demo and see what you’ve got. Matt: Yeah, absolutely. Let me jump over here. In this demo, we’re pretending to be an airline or travel company. We have data across different sources. source

Salesforce. Integration in retail Goñi sees demand for AI agents rising across all sectors, but the retail industry is where the action on agentic AI has been the most dynamic. Here, Goñi explains, use of agentic AI has been focused on “optimizing the customer experience and logistics operations.” Fernando Herranz, director of digital experience and ecommerce at Leroy Merlin Spain, agrees, seeing the promise of “a progressive but constant integration” of agentic AI with the company’s comprehensive omnichannel model, with beneficial impacts for both Leroy Merlin’s clientele and its teams. “AI is allowing us to move toward a more fluid and personalized relationship with the customer, and we do this by combining strategic vision with controlled and measurable applications,” he says. The company is exploring use of agentic AI, for example, in store automation processes, digital content generation, and personalized support, both online and in physical stores.   source

Retailers across categories are navigating shifting consumer expectations, digital fragmentation and broader market volatility — all while under pressure to deliver measurable business results. While many transformation efforts stall under the weight of over-planning or fail to scale beyond pilots, I share a practical blueprint rooted in real-world execution. At Kendra Scott, our longstanding focus on purpose, community and customer-first values has given us a strong foundation. Yet, like many lifestyle brands, we faced the challenge of translating that ethos into a modern, high-performing digital experience. In response, we launched a strategic 3-year transformation initiative to reverse prior digital underperformance. Within two years, we achieved 50% growth in digital revenue, despite slower retail demand. Before this, I led the North America ecommerce strategy for a major jewelry brand, launching the digital business and scaling it into a multihundred-million-dollar operation with sustained double-digit CAGR. That experience deeply shaped my approach to scalable transformation, which came to life fully in this recent work. Our modular structure makes the framework applicable to businesses of any size — small, mid-sized or enterprise-level — adapting easily to varying digital maturity.  source

Matt Kulangara joins State Compensation Insurance Fund as CIO New CIO appointments, May 2025 Humana appoints Japan Mehta as CIO Shekar Pannala named CIO for The Hartford Genworth announced Morris Taylor as CIO Prakash Kota joins UKG as CIO Ahold Delhaize USA welcomes Ann Dozier as CIO Gerald Charles, Jr., joins Coke Florida as CIO Arhaus appoints Allison Sutley as CIO Neil Cowles named CITO for Kaiser Permanente Ha Hoang joins Commvault as CIO Precision Medicine Group welcomes Eric Hodgins as CITO Sprinklr appoints Sanjay Macwan as CIO Joe Locandro named CIO at Rimini Street New CIO appointments, April 2025 Providence welcomes Cherodeep Goswami as CIDO Jennifer Flynn named CIO at Raytheon Academy Sports + Outdoors appoints Sumit Anand CIO Michael Bongiovanni joins RateFast as CIO Textron Aviation appoints Luke Pankey as CIO Mike Blandina joins Snowflake as CIO Books-A-Million promotes Brandon Waters to CIO Revlon appoints Ralph Marshall as CIDO Anisha Vaswani named CICO for Extreme Networks The Mutual Group appoints Vineet Bansal as CITO Silicon Labs welcomes Radhika Chennakeshavula as CIO Leigh Williams appointed CIO at Augusta Health Morgan State University names Timothy Summers CIO Sandesh Parulekar joins Workers Credit Union as CIO New CIO appointments, March 2025 Citizens Inc. promotes Paula Guerrero to CIO Breakthru appoints Glenn Remoreras as CIO Santhosh Kumar announced as CIO at Panera Bread Ametek appoints Isabel Wells as CIO Leo Bodden named CIO for WMCHealth Freeman welcomes Sanjay Shringarpure as CIO Antonio Carriero joins Fossil Group as CDIO University of Vermont welcomes Kellie Campbell as CIO AMS appoints Alan Segal as CDTO New CIO appointments, February 2025 Boeing appoints Dana Deasy as CIO Kristie Grinnell named CIO at TD Synnex Diane Schwarz joins Smurfit Westrock as CIO AssuredPartners appoints Jason Sickle as CIO Brad Novak joins DXC Technology as CIO Samir Shah joins Brunswick Corporation as CIO Hanger announces Lety Nettles as CIO Leslie’s names Maryann Byrdak CIO AIT Worldwide Logistics appoints Ann Nemphos as CIO Sunil Gupta appointed chief information officer with TNT Crane & Rigging Mistras Group welcomes Rick Fabrizio as CIO Sandy Venugopal joins CoreWeave as CIO Marcus & Millichap names Evan Wayne CIO Carl Lucas appointed CIO at Woolpert Scale Venture Partners promotes Bob Genchi to CIO source

The case against the ticket  Let’s call the ticket what it is: a workaround. It’s a relic from a time when humans were the only means of diagnosing and resolving technical problems. It made sense in a human-centered workflow; document the issue, assign it to a team and wait for resolution. But at scale, tickets reveal themselves as friction points.  They cost time. They slow MTTR. They create queues. They burn out staff. They require interpretation, enrichment, triage and escalation, often before resolution even begins. Worse, they reinforce a culture of reactivity: “Did you create a ticket?” becomes the reflex instead of “Did we fix the root cause?”  From what I’ve seen working with global enterprises across telecom, financial services, manufacturing and the public sector, tickets aren’t just operational overhead — they’re opportunity cost. Every minute spent opening, routing or waiting on a ticket is time that could have been spent solving problems or advancing strategic goals. Employees wait for someone to open a ticket, wait for someone else to pick it up, and wait again for someone to act. It’s not just inefficient; it’s momentum lost across the business.  source

The process automation vendor deploys more than 40 agents across the company to save time and money in its finance, tech support, and other functions. source

Hertz got its start renting out a dozen Model T Fords more than 100 years ago. Today, Hertz operates in 160 countries and has more than 20,000 employees and 500,000 vehicles in its fleet. To streamline an operation with so many moving parts, the company has deployed Hertz Connected Fleet OS, an AI-enabled operating system for fleet management. “This is all around our purpose for our customers, which is making sure we have the right car, at the right place, at the right time,” said EVP and CIO Tim Langley-Hawthorne at Palantir’s AIPC last month, before stepping down from the role soon after. “It orchestrates customers, vehicles, and workforce. Those are the three critical components for businesses like ours on the ground.” Hertz has leveraged Palantir Foundry and Palantir Artificial Intelligence Platform (AIP) to create a set of AI-powered applications to help it increase efficiencies in vehicle turnaround, reduce maintenance expenses, predictively allocate its workforce across field locations, and match the best car to customers. source