GDS Group

“Dashboards are a thing of the past.” “We’re building architecture for the wrong user.” “Trust is the true deliverable.” At our latest Data & Analytics Summit, enterprise data leaders weren’t talking about incremental optimisation. They were questioning the foundations of modern data strategy altogether. Across sessions, one theme became impossible to ignore: AI is changing not just how organisations use data – but who data systems are being built for. The conversations focused on: → Why trust gaps are limiting AI adoption → Why context matters more than model sophistication → Why traditional dashboards and change management approaches are failing → How leading organisations are shifting toward decision-centric delivery → What AI-ready data actually requires in practice Featuring insights from leaders at IBM, Airgas, OhioHealth, Hackensack Meridian Health, and Pan-American Life Insurance Group. Download the full Insight Report: https://lnkd.in/eYaDR8UN

Workforce transformation, AI disruption, and evolving employee expectations are redefining what leadership looks like at the highest level of HR. At the CHRO Insight Summit | June 1 - 3 | Atalanta, the conversation centers on how today’s people leaders are building resilient cultures, developing future-ready talent, and leading through uncertainty with impact. Here’s a look at some of the leaders shaping that discussion: → Stacy Winsett with 𝐅𝐫𝐨𝐦 𝐀𝐬𝐡𝐞𝐬 𝐭𝐨 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 → Becky Sawyer with 𝐘𝐨𝐮𝐫 𝐎𝐫𝐠 𝐂𝐡𝐚𝐫𝐭 𝐖𝐨𝐧’𝐭 𝐒𝐚𝐯𝐞 𝐘𝐨𝐮: 𝐖𝐡𝐲 𝐂𝐇𝐑𝐎𝐬 𝐌𝐮𝐬𝐭 𝐑𝐞𝐛𝐮𝐢𝐥𝐝 𝐖𝐡𝐚𝐭 𝐏𝐫𝐞𝐬𝐞𝐧𝐜𝐞 𝐔𝐬𝐞𝐝 𝐭𝐨 𝐏𝐫𝐨𝐯𝐢𝐝𝐞 → Kristen Cooper with 𝐀𝐈-𝐑𝐞𝐚𝐝𝐲 𝐋𝐞𝐚𝐝𝐞𝐫𝐬: 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐢𝐧 𝐚𝐧 𝐀𝐈-𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐖𝐨𝐫𝐥𝐝 This is a peer-level environment for HR executives focused on creating organizations that are adaptive, human-centered, and built for the future of work. Register your interest 👉 https://lnkd.in/eknq2DBz

There’s a point where scale starts working against organisations instead of for them. More systems are added. More intelligence flows through the business. More teams move independently, making faster decisions across increasingly fragmented environments. From the outside, it can still look coordinated. Inside, it often feels very different. The latest edition of GDS Explore looks at what happens when organisations scale activity faster than they scale coherence and why many of the structures that once created control are beginning to struggle under AI acceleration. Read the latest edition 👇

One of the biggest challenge facing enterprise software delivery today is the growing cost of fragmentation. When critical data, workflows and decisions sit across disconnected platforms, leadership loses the visibility needed to predict outcomes with confidence. The result is slower execution, weaker governance, and increasing pressure on teams to bridge the gaps manually. In partnership with Broadcom, we brought together senior leaders to discuss what it takes to restore predictability at enterprise scale – creating alignment between strategy and execution without adding operational complexity. A huge thank you to the executives from BT Group, J.P. Morgan, Salesforce and more for contributing such honest, experience-led perspectives throughout the discussion. The quality of the conversation reflected exactly why peer-level engagement matters. 👏 Also a big thank you to the GDS team for bringing the evening together so seamlessly: Ben Thompson, Solveig Hilchenbach and Shaun Morgans.

“More risk. Less time. No margin for error.” That’s how Tim Cassidy framed the challenge facing security leaders right now – a theme that Bob Fabien "BZ" Zinga 🇺🇸🇮🇷🇺🇦 and Randy Raw explored further in this conversation around AI, trust and security leadership. What’s changing with AI isn’t just the threat landscape, it’s the expectation on security leadership itself. The old model of slowing things down, adding controls and sitting adjacent to the business is becoming increasingly difficult to sustain when organisations are under pressure to move faster with AI adoption. One point that really stood out was the idea that cybersecurity has to evolve from being seen as a blocker to becoming a function that truly understands the business, the risk appetite and where it can enable innovation safely. Because in an AI-first environment, the differentiator won’t just be technology. It’ll be leadership judgment. Hear from the voices in the room in the full executive perspective video 👉 https://lnkd.in/eujGDRgi

“Most digital transformations don’t fail loudly – they fail quietly.” That was one of the defining themes from our latest CIO Insight Summit. Across the event, senior technology leaders moved beyond AI hype and focused on a harder question: How do you turn AI into measurable enterprise value without increasing risk, complexity, or organizational friction? The conversations reflected a major shift in enterprise IT leadership: * AI is becoming core infrastructure, not experimentation * Governance is now a scaling mechanism, not a blocker * CIOs are being measured on outcomes, not innovation volume * Faster delivery means nothing without adoption and control * Many transformation programs fail because organizations digitize broken processes instead of redesigning them Featuring insights from leaders at, Dell Technologies, SpartanNash, Export-Import Bank of the United States and Laserfiche Download the full report to see how enterprise technology leaders are operationalizing AI, scaling governance, and turning transformation efforts into measurable business outcomes: https://lnkd.in/eJhZdK67

AI transformation is no longer a technology discussion. It’s a leadership challenge centred on trust, governance, operational readiness, and business value. That was the focus of yesterday’s executive gathering hosted in partnership with Amazon Web Services (AWS) – bringing senior leaders together for structured, peer-level conversations on how organisations move AI from experimentation to enterprise-wide impact. Conversations throughout the evening explored: ➡ How organisations move beyond AI experimentation into operational adoption ➡ Balancing innovation with governance, security, and compliance requirements ➡ The growing pressure to demonstrate ROI from AI investment ➡ Preparing data, infrastructure, and teams for enterprise-wide transformation ➡ Ensuring leadership alignment as AI strategies move from siloed initiatives to business-wide priorities A huge thank you to the executives who joined us from organisations including Wells Fargo, Visa, JPMorganChase. and many more for contributing openly to conversations grounded in real business pressures, priorities, and decisions. And a special shout out to the GDS team who brought the experience together: Amanda Brilhante, Jessica Herrod (née Paisley), Maya Reimers and Shaun Morgans The quality of the room determines the quality of the outcome and yesterday’s discussions reflected exactly that.

For the last 15 years, enterprise data strategies have been designed around human consumption. That assumption no longer holds. One of the more interesting perspectives raised by Carmen Malangone was that AI isn’t simply another layer on top of the data stack – it changes the nature of the consumer itself. Historically, data environments were optimised for people: Dashboards. Queries. BI layers. Decision support. Now organisations are designing for machine consumers as well – models, agents and AI systems that interact with data fundamentally differently than humans do. And that creates a far bigger leadership challenge than most organisations are acknowledging. Do you use AI to accelerate existing data foundations? Or do you redesign those foundations for machine-ready intelligence from the ground up? That distinction will likely define the maturity gap between organisations that operationalise AI successfully and those that remain stuck in experimentation. Hear from other voices in the room in the full executive perspective video 👉 https://lnkd.in/eXSCnANA

Across executive infrastructure discussions, one theme keeps surfacing: Enterprise infrastructure was designed for a more stable world. → Stable demand. → Stable costs. → Stable operating environments. That stability is disappearing. AI, geopolitical pressure, regulatory change, and operational complexity are converging at the same time — forcing organizations to rethink the assumptions their infrastructure strategies were built on. What’s emerging in response is a clear shift in mindset, from standardization to adaptability. Because increasingly, the real risk isn’t infrastructure cost alone. It’s whether infrastructure strategies can absorb constant change without creating operational fragility. That may become one of the defining infrastructure challenges of the next decade. See the key insights below 👇