In this keynote preview from Google Cloud Next 2026, theCUBE's John Furrier and co-host Alison Kosik set the stage for three days of wall-to-wall coverage by dissecting Google's strategy to capture the AI control plane — the horizontal connectivity layer that routes data across systems and enables agents to operate at enterprise scale. Furrier frames Gemini not as a standalone model but as an orchestrator, evolving toward something closer to an AI operating system. He cites a Databricks milestone as a marker of how far the transformation has advanced: machines are now writing more code than humans.

The conversation also explores the competitive dynamics shaping the AI landscape, where model-level leapfrogging between Anthropic and OpenAI matters less than who owns the systems those models integrate with. Furrier argues that enterprise adoption — catalyzed first by AI-assisted coding and now being supercharged by agentic workflows — is the defining battleground at this event. The organizational stakes are equally significant: CFOs are shifting into operator roles, chief people officers are being drawn into AI workforce decisions, and tokens are emerging as a new enterprise currency. From Google's TPU investments to the agent ecosystem forming around Gemini, Furrier and Kosik outline what Google must prove this week and why winning the enterprise remains the decisive test of any AI platform's staying power.

In this interview from Google Cloud Next 2026, Giovanni Carraro, senior vice president of alliances at Kyndryl, joins Hector Genaro Moran, chief information officer of PiSA Farmacéutica, to talk with theCUBE's John Furrier and co-host Alison Kosik about the partnership driving enterprise cloud modernization and the shift from AI experimentation to agentic production. Carraro describes how AI has decisively cleared the experimentation phase — the question is no longer if, but how to deploy it at scale. He outlines how Kyndryl's expertise in mission-critical systems enables clients like PiSA to migrate legacy SAP environments to Google Cloud, unlocking decades of manufacturing data trapped in systems of record. Moran notes that for a regulated pharmaceutical manufacturer still running on-premises infrastructure, the move to cloud is not a technical checkbox but a strategic foundation — the prerequisite for everything that follows.

The conversation also explores the governance challenges that shadow AI introduces and why frameworks built for traditional IT cannot be repurposed wholesale for autonomous agents. Carraro argues that as enterprises evolve from managing people and processes to orchestrating people, processes and agents together, governance must advance at the same pace. He also highlights Kyndryl's expanded partnership with Google, including new Distributed Cloud services designed to match the right workloads to the right environments. Both guests converge on leadership transformation as a defining theme: Moran envisions a new generation of board members who treat AI literacy as a core competency rather than an IT concern, while Carraro frames the modern executive as a champion of change rather than a guardian of stability. From migrating a Latin American pharmaceutical leader off legacy infrastructure to envisioning hybrid organizations where humans and agents operate side by side, the discussion maps a clear path from cloud foundation to AI-native enterprise.

In this interview from Google Cloud Next 2026, Mike Thompson, director of cloud product at AMD, joins Tim McArdle, senior principal of FinOps at Sabre, to talk with theCUBE's John Furrier and co-host Alison Kosik about how compute efficiency and strategic cloud migration are freeing up budget to fund the next wave of AI innovation. Thompson points to the industry-wide shift from model training to inference as the defining compute trend of 2026, noting that surging demand is exposing a long-overlooked problem: many enterprises run servers at only 10% utilization. McArdle illustrates the opportunity firsthand — Sabre migrated a massive CPU-intensive workload to AMD instances on Google Cloud with zero code changes, achieving a smaller footprint, faster performance and immediate cost savings, all while scaling to over 50,000 vCPUs.

The conversation also explores how those infrastructure savings are being reinvested directly into agentic AI development at Sabre, which has moved 99% of its compute capacity to Google Cloud. Thompson details how migrating general-purpose workloads to AMD typically drives 30 to 50% OpEx savings — headroom that enterprises can redirect toward new AI applications without waiting for budget cycles to reset. Both guests make the case for x86-based containerization as the foundation of a resilient hybrid cloud, highlighting its portability advantage over multi-architecture environments. McArdle underscores the cultural dimension of modernization, noting that the real barrier is not learning new technology fast enough — it's letting go of established ways of working. From decades of on-premise legacy infrastructure at Sabre to the emerging discipline of AI FinOps, the conversation maps a practical path for enterprises looking to optimize existing budgets while competing in an increasingly AI-native world.

In this interview from Google Cloud Next 2026, Gaurav Syal, vice president and global head of AI, cloud and infrastructure services, EMEA, at Tata Consultancy Services, joins Amit Kapur, chief AI and services transformation officer at Tata Consultancy Services, to talk with theCUBE's John Furrier and co-host Alison Kosik about how enterprises are crossing the divide from AI experimentation to production-ready agentic deployment. As Google's Diamond partner and its top-ranked talent development competency holder, TCS earned five awards at Google Cloud Next 2026, spanning agent development, security and talent. Syal explains how TCS operates as "customer zero" for Gemini Enterprise — deploying the platform internally across 30,000 employees before recommending it to clients. Kapur frames the broader market shift: enterprises are moving away from isolated use cases toward fundamentally reimagining entire workflows on AI, not sprinkling it onto existing processes.

The conversation also explores TCS's approach to cultural transformation, including "AI Fridays" — recurring sessions where employees collectively log 12,000 hours in a single day tackling real-world problems, with 240,000 hours of AI learning accumulated to date. Syal details two standout deployments: an APAC retailer where agentic AI autonomously manages product replenishment by negotiating with suppliers, handling compliance and fulfilling orders end-to-end; and a UK bank that realized $50 million in business value across more than 50 use cases including fraud detection and mortgage lending. Kapur outlines a repeatable engagement model — immersing C-suite leaders in 60-minute hands-on sessions, then delivering a production-ready proof of value in 12 to 16 weeks. From seven Gemini experience centers spanning the globe to the emerging convergence of digital and physical AI in capital-intensive manufacturing environments, the discussion provides a practical roadmap for enterprises ready to move from curiosity to conviction.

In this interview from Google Cloud Next 2026, Philip Larson, managing director of the Google Cloud Partner Network at Google, joins theCUBE's John Furrier and co-host Alison Kosik to discuss how Google is rebuilding its partner ecosystem for the agentic AI era. Larson explains that the redesigned Google Cloud Partner Network (GCPN) functions as an umbrella spanning ISVs, services partners, data providers and marketplace partners — all aligned to guide enterprises through what he describes as an unavoidable transformation to the agentic enterprise. He details a $750 million investment covering AI training, sandbox credits and deployment vouchers, and underscores that the GCPN itself is built on Google's own unified AI stack, with agents embedded across every stage from partner onboarding through to delivery and support.

The conversation also explores how Larson's team eliminated the friction that typically undermines partner programs — stripping out unnecessary external audits in favor of metrics that actually drive outcomes: certifications, co-sell activity and closed statements of work. A standout example is the agentic SOW analyzer, which now influences 90% of funded engagements just six months after launch. Larson notes that partners are demanding new features at a pace that has compressed release cycles from annual to monthly, reflecting the energy around what he calls a once-in-a-generation opportunity. With 500 agentic customer use cases announced at the event, he outlines a near-future vision where partner agents communicate directly with GCPN agents to surface real-time go-to-market recommendations — building a competitive moat he believes no other hyperscaler can replicate at scale.

In this interview from Google Cloud Next 2026, Paul Lewis, chief technical officer of Pythian, joins theCUBE's John Furrier and co-host Alison Kosik to discuss the critical shift from AI experimentation to production-ready, AI-native operations across the enterprise. Lewis, a four-time theCUBE guest at Google Cloud Next, argues that last year's AI momentum stalled because enterprises confused building with operating — most pilots never reached production. He outlines the two core failure modes: selecting the wrong use case and lacking the operational discipline to sustain an AI system once deployed. To frame ROI more precisely, Lewis introduces the "five minutes versus two week rule": saving one person two weeks per quarter far outweighs distributing five-minute savings across a thousand employees.

The conversation also explores the structural gaps standing between enterprises and production AI. Lewis details Pythian's service model — a field CTO advisory practice, an end-user center of excellence built around Workspace and Gemini, an Agentic COE and a managed XOps layer — designed to close the distance between keynote demos and real deployment. He highlights a persistent and underappreciated barrier: data readiness. A significant share of enterprises, Lewis notes, need to fix siloed databases and broken data foundations before any AI investment makes sense. The discussion surfaces Google's cross-cloud lakehouse as a breakthrough for federated data access, while Lewis cautions that migrating away from legacy data pipelines requires recreating years of embedded business logic — a task agents can assist with but cannot fully automate. From C-suite education workshops to the principle that replaceability should be every AI project's primary non-functional requirement, Lewis provides a clear-eyed roadmap for enterprises navigating the gap between AI ambition and operational reality.

In this interview from Google Cloud Next 2026, Asutosh Padhi, senior partner and global leader of firm strategy at McKinsey and Company, joins theCUBE's John Furrier and co-host Alison Kosik to discuss why 80 to 90% of enterprise AI initiatives fail to deliver business value — and what C-suite leaders must do differently to close the gap. Drawing on McKinsey research and direct CEO conversations, Padhi identifies three root causes: insufficient C-suite ownership, fragmented data foundations and an organization's inability to absorb new technology into core workflows. He challenges the conventional wisdom of starting with simple use cases, arguing that tackling the toughest business problems is what drives the executive attention and organizational alignment needed to produce results that scale.

The conversation also explores McKinsey's concept of "distinctiveness" — the modern successor to core competency — and how companies are using digital twins of their business ontologies to unlock AI value without waiting years to remediate legacy data infrastructure. Padhi outlines how this approach underpins an "AI management operating system" that enables faster insights and superior decision-making from the C-suite to the frontline. He segments today's executives into three cohorts: proactive innovators, cautious wait-and-watchers and those in outright denial — warning that the latter two risk ceding compounding advantages to faster movers. On workforce impact, Padhi draws on McKinsey Global Institute research projecting that one-third to half of all roles will be fundamentally transformed, citing the Mayo Clinic's 50% increase in radiologist hiring as evidence that AI augments rather than eliminates human capability. From the three traits defining tomorrow's leaders — technology fluency, speed and human judgment — to the broader arc of AI as the most profound general-purpose technology revolution in living memory, Padhi provides a strategic framework for organizations navigating the shift from AI experimentation to AI-native operations.

In this interview from Google Cloud Next 2026, Ram Poornachandran, vice president of AI and architecture at F5, joins Gary Newe, vice president of solutions engineering at F5, to talk with theCUBE's John Furrier and co-host Alison Kosik about moving agentic AI from proof of concept into production-ready enterprise deployment. Poornachandran draws a sharp distinction between personal and process productivity, explaining that while individuals are adopting AI tools daily, real enterprise gains require redesigning business processes from the ground up — not bolting AI onto legacy workflows. He and Newe agree that organizations reaching for production are finding their underlying infrastructure, security and compliance posture simply isn't ready, and that closing that gap demands a solid foundation before scaling agent fleets.

The conversation explores the specific security and observability challenges of deploying AI agents at scale. Newe argues that traditional packet-level monitoring cannot serve non-deterministic systems — enterprises must instead track intent, context and decision trails in real time. Poornachandran underscores the need for continuous agent evaluation to prevent drift as new data and models are introduced. The discussion also covers F5's announcement of its AI Guardrails and AI Red Team solutions, now integrated into Google's Agent Cloud framework and available for one-click deployment on Google Cloud Marketplace. From building governance frameworks that fast-track approved agent workloads to applying the hard-won lessons of DevSecOps to the emerging world of AIOps, the pair outline why security and governance are not optional additions but the foundational layer on which any AI-native enterprise must be built.

In this interview from Google Cloud Next 2026, Rebecca Potts, director of North American strategic industries, partner sales at Google, joins Mike Thiessen, U.S. chief clients and markets officer of PwC, to talk with theCUBE's John Furrier and co-host Alison Kosik about why trust — not technology — is the critical variable in scaling enterprise AI. Potts explains how generic AI pitches have given way to industry-specific agentic solutions, citing personal shopping agents for retailers and democratized financial planning as concrete examples of AI expanding market reach. She details how Google's forward-deployed engineers build working agents tailored to a customer's own data within one to two weeks, turning "show me, don't tell me" into a practical trust-building onramp. Thiessen frames the speed challenge through a responsible AI lens — defining who can use it, what for and under what governance controls — as the prerequisite for sustainable, wide-scale adoption.

The conversation also explores what separates organizations scaling AI from those stuck in place. Thiessen describes three archetypes — realists, zealots and skeptics — and warns that a single wrong answer from an agentic solution can send an entire team back to manual processes when governance guardrails aren't in place. He and Potts address the board-level make-versus-buy debate, the rise of shadow AI and the shift of AI conversations from IT departments to CFOs and COOs. Both guests identify intellectual curiosity and empathy as the defining skills for leaders in this era — not technical expertise alone — echoing PwC's "human-led, tech-enabled" philosophy. From converting skeptics with provable use cases to driving measurable revenue growth through hyper-personalized B2B engagement, Potts and Thiessen provide a practical roadmap for enterprises ready to move beyond pilots and into full agentic transformation.

In this interview from Google Cloud Next 2026, Maura McEnroe, chief marketing officer of UnitedHealthcare, joins Ritesh Mangal, senior vice president at Wipro, to talk with theCUBE's John Furrier and co-host Alison Kosik about how a cloud-native data foundation is enabling agentic AI to reshape healthcare marketing and enterprise operations. McEnroe, who brings a rare CIO-to-CMO background, explains how those converging roles reflect a broader shift — marketing is now inseparable from technology, driven by real-time personalization, self-service and continuous member engagement. She and Mangal detail a three-way collaboration between UnitedHealthcare, Wipro and Google Cloud that migrated the company's claims application to the cloud, transforming static data pipelines into a dynamic, AI-accessible foundation for hyper-personalization and predictive member behavior.

The conversation also explores how AI is reshaping organizational structures, flattening hierarchies and collapsing the traditional distance between technology and business teams. McEnroe describes a shift toward joint accountability through shared KPIs, where use cases are built collaboratively and brought to life in a single day rather than over months. Mangal underscores that the transformation extends beyond any single function: every role in the enterprise must now be AI-native, with technology embedded across marketing, finance, sales and operations. The discussion also surfaces the challenge of managing a fragmented MarTech stack, where disparate tools slow execution and complicate personalization at scale. From navigating the data-readiness imperative — ensuring clean data serves not just internal systems but outward-facing AI agents querying public information — to the emerging reality where agents interact primarily with other agents rather than humans, McEnroe and Mangal map a clear path from AI-assisted workflows to fully AI-native business models.

In this interview from Google Cloud Next 2026, Fawad Shaikh, global vice president of business development at TELUS Health, joins Saurabh Mishra, global head of Google Cloud business at Quantiphi, to talk with theCUBE's John Furrier and co-host Alison Kosik about dismantling healthcare's legacy data silos to build a foundation for AI-native operations. Shaikh describes how the industry's tolerance for fragmented data has hit a breaking point — driven by COVID-era lessons and new regulatory mandates including FHIR data-sharing standards from CMS and provincial legislation in Canada. He details how TELUS Health's acquisition-led growth of roughly a dozen companies in five years compounded the challenge, demanding a disciplined unification strategy built on master data management, metadata governance and purpose-built ingestion pipelines. Mishra explains how Quantiphi's Codeaira AI platform automated legacy code migration, enabling the team to operate at a pace traditional engineering alone could not sustain.

The conversation also explores the tangible ROI that emerges once data foundations are secure. Shaikh points to a flagship result: consolidating 11 data sources into a single automated report cut generation time by 80%, saving roughly 15 hours per agent per year. Two AI use cases illustrate the broader potential — automated patient-document matching for e-fax referrals that cut processing burden in half, and a reusable microservice that eliminated a third-party payment processor entirely. Mishra introduces the "launchpad mindset," arguing that enterprises racing to deploy AI agents without first solving their data challenge are skipping the hard part. He advocates for building reusable data products — purpose-built segments aligned to specific business problems like provider performance or employee productivity — as the scalable sandbox on which agents can reason reliably rather than retrieve blindly. From combining top-down leadership alignment with grassroots innovation to rallying an entire organization around a single data mission, both guests provide a practical roadmap for healthcare and enterprise leaders navigating the tension between core modernization and scaling new intelligence.