Enterprise AI, from GPUs to governance—LLMOps, retrieval, inference efficiency, and data pipelines powering production AI.
Identity sprawl, unpredictable workload patterns, and autonomous systems that can outrun their own guardrails. At Everpure's Accelerate, financial-services infrastructure leaders laid out five areas where agentic AI is forcing a rethink.
An Everpure-hosted panel featuring Options Technology VP Andrea Moccia and financial services infrastructure leaders laid out the recurring failure patterns that trap AI pilots in permanent purgatory, and the architectural decisions that let a handful of firms break through.
Avon's Head of Applied AI and Business Value has watched enterprises promise to replace people with tools once already. A decade on, the data underneath still isn't ready, and now the tools act on it without a human in the middle.
Nagarjun Rajendran, Cloud and Technology Solutions Manager at Bosch Digital, shares why GPU utilization alone cannot tell a team what is limiting its production inference.
Bill Wright, Head of Government Affairs at Everpure, says federal agencies keep calling data their most valuable asset while their systems still treat it as a byproduct of individual applications.
As more AI systems act without real-time oversight, leaders are weighing what a wrong decision would cost before handing over more work.
Fernando Faustino, Executive Advisor for Digital Transformation and AI at Grupo Souza Lima, on the half of the AI energy story that gets less attention, where the models help generate the power.
AT&T's Ashok Kumar argues that enterprise AI maturity depends on looking beyond token consumption to the value produced, the context required, and the infrastructure costs underneath it.
Sahar Mirmahmoodi, Digital Product Lead for Laboratory Automation at Eppendorf, argues that transformer shortages and power limits give enterprises a reason to decide which data earns its storage.
AI memory increases what a system can connect, not just what it knows. María Luisa Redondo Velázquez, Global CISO at TK Elevator, says that's where the new risk lives.
Peter Corless, Principal Product Marketing Manager at Redpanda, says agents need tightly scoped access and audit trails they can’t alter.
Robert Alvarez, Principal AI Solutions Architect at Everpure, argues that enterprises need documented context, a data intelligence layer, and sensitivity-based model routing before scaling AI.
Trideep Patra, Chief Technology and Transformation Officer at Apex Technology Systems, argues that agents need lineage, classification, and policy as code before they touch enterprise data.
Volkswagen Group's Dr. Praveen Mishra on shifting supply chains from reactive recovery to continuous prediction, and the foundation of baselines, oversight, and data residency it requires.
On the Data Wire podcast, Nomad Futurist Foundation CEO Nabeel Mahmood explains why AI’s labor pipeline matters as much as its compute.
Ashish Yadav, Senior Data Engineer at The Modern Data Company, argues that with models and platforms available to everyone, the advantage moves to semantic rules, governed model access, and how fast a company can turn its own data into decisions it trusts.
A Bloomberg Businessweek Radio broadcast at Accelerate 2026 exposed the financial and operational pressure now shaping infrastructure decisions, from a CFO navigating a component supercycle to a federal cloud CEO modernizing under compliance constraints.
An Phan, Senior Data Infrastructure Engineer at Hippo Harvest, explains why physical AI forces data teams to rebuild pipelines around reality, replay, and edge compute.
Shivali Naik, Senior Technical Architect at Snowflake, explains why traditional linear data pipelines break once AI moves toward production.
Sathish Kumar Subramani watches enterprise AI systems closely before governing them, since the costliest failures pass every check while quietly handing customers wrong answers.
Surajiwale, Staff Machine Learning Scientist at Etsy, explains why recommendation systems are shifting from pointwise prediction to LLM-based generative architectures.
Shammy Narayanan, SVP of Data, AI, and Platform Architecture at Welldoc, explains why clinical AI depends on breaking down the data silos that leave half of healthcare's information stale, fragmented, or inaccessible.
Abdulkader Khouja, Data Engineering Consultant at Artefact, on how enterprises are confronting the physical layer beneath AI.
Bijit Ghosh, board chair at Atlas Cloud, made the case for a control plane that coordinates compute, memory, and data over the systems enterprises already run.
Satish Anandhan, Senior Architect and Principal Engineer at Salesforce, says the safest AI strategy is architecture that can change its mind.
Northern Trust SVP and Global Head of ALM Armel Roméo Kouassi on data governance, legacy systems, and sovereignty as the defining variables for AI in banking.
Doug Aguiar, former CMO of Golden 1 Credit Union, argues that brands preparing for physical AI should start with purpose and experience architecture rather than technology.
Ezhil Arasan, Principal Cloud Architect at WEPA, argues that AI success starts with platform readiness rather than model selection.
Robotics and AI engineer, Rangel Alvarado, discusses why the next leap in humanoid robotics depends less on better robots than on the development environment beneath them.
David Corliss, Principal Data Scientist at Grafham Analytics, says enterprises can’t scale AI safely without data scientists who understand where models are reliable, where they’re estimating, and when they need human oversight.
Andrew Ballard, Enterprise Data and Strategy specialist at Thoughtworks, draws a line between useful AI training data and unnecessary noise to prioritize useful data pipelines.
Robert Alvarez, Senior AI Solutions Architect at Everpure, explains why DeepSeek v4 signals a shift from model performance races to infrastructure efficiency, and why 2026 is the year enterprises confront that AI's real bottleneck has moved from models to the systems underneath them.
Kenza Rchi, a Senior IT Analyst for AI Scaling at Philip Morris International, highlights the role of principles and policy in scaling AI infrastructure, and how enterprises must ground deployment in solid fundamentals.
Xin Tu, a Risk Executive in AI, Data, IT, and Cyber, Financial Services, explains why governance teams that position themselves as blockers get routed around, and how redesigning frameworks for speed and risk classification turns governance into the accelerant enterprises need.
Ted Murena, Director of Global Marketing Technology at CBRE, explains why the speed of AI-assisted development is outpacing the governance, documentation, and organizational alignment required to ship durable enterprise software.
Ivan Ruskevich, Principal Consultant at RaspberryTea.ai, explains why data governance only works when it is designed around ownership, accountability, and decision-making clarity rather than tooling, catalogs, or checkbox compliance.
Kevin Frank, VP and Data Warehouse Manager at Glacier Bancorp, says the institutions that separate AI governance from data governance and consolidate their platform architecture will capture AI's upside
Prashant Parida, Leader of AI Strategy, Transformation, and Portfolio Management at IBM, argues that AI adoption requires both top-down and bottom-up collaboration and suggests a four-quarter budget approach with quarterly governance.
Satyabrata Pradhan, Senior Program Manager at General Motors, on how continuous AI validation is catching defects earlier and raising the quality floor for software engineering.
As AI infrastructure investment surges, technology executive and futurist Nabeel Mahmood explains why energy constraints, labor shortages, and catastrophic downtime risk are the variables most leaders are ignoring.
Shammy Narayanan, VP of AI and Data at Welldoc, explains why enterprise AI productivity starts at the top and why internal teams are an organization's greatest AI asset.
SupportLogic Founder & CEO Krishna Raj Raja discusses why an "AI First" mandate and reinvention, not efficiency, is the key to business transformation in the modern enterprise.
AI and data science leader Deborah Wall shows how codified processes, technical rules, and governance make AI a force multiplier for businesses.
Will Rowlands-Rees, Chief AI Officer at Lionbridge, says enterprise AI performance now hinges on context-rich training data, validation rigor, and deliberate bias control.
Audit Executive Xin “Cindy” Tu shares her playbook for embedding governance in AI from day one and transforming audit into a strategic partner for responsible innovation.
Ingmar Mier, Managing Director of Metatron Marketing, says organizations are transforming AI adoption by addressing data quality, aligning workflows, and empowering teams to unlock real value across the enterprise.
Jai Sisodia, Managing Director of Cybersecurity, Privacy, and AI at Cycore, explains why enterprises need structured governance frameworks and functional ownership to safely scale agentic AI from experimentation into core infrastructure.
Enterprises struggle to govern probabilistic AI with deterministic mindsets. Google AI Infrastructure Architect Yujun Liang explains the leadership challenge and shares his three-layer framework for managing risk through secure infrastructure, data quality, and human validation.
Shreyas Kanoongo says global AI governance isn’t one-size-fits-all and companies need market-specific frameworks to manage cultural and regulatory challenges.
Nikki Mehrpoo, AI Governance Architect and Founder of iGovernAI, reframes AI adoption as a professional duty, calling for documented standards that withstand legal and regulatory scrutiny.
Wes Wright, Chief Healthcare Officer at Ordr, describes how AI-powered access to insight is changing executive participation while leadership fundamentals stay intact.
Nanoprecise's Faizan Wani explains the DPO's shift to Data Guardian, the risk of internal AI use, and why governance needs to be built into AI architecture from the start.
Nirav Sheth, VP of Worldwide Sales & Customer Success Engineering at Everpure, discusses how AI’s transition to production will push businesses to modernize their infrastructure in 2026.
Steve Tulk, Chief Technology Officer at Vaniam Group, shows how AI increases the value of human judgment in regulated work by turning experts into the trusted front end of interpretation.
Rosa Chang Claro, Director of Strategic Engagement at Dynatrace, positions observability as the control layer enterprises need to trust AI agents, prove value, and govern risk at scale.
Ronitt Mehra, AI Engineer at Amneal Pharmaceuticals, shows how a pipeline-first approach and autonomous remediation framework can turn fragile multiagent AI into resilient production systems.
Carrie Goetz, Principal and CTO at StrategITcom, outlines why data centers cannot meet AI demand without early exposure, stronger training, and a modernized talent pipeline.
Palanivel Rajan Mylsamy, Director of Engineering Program Management at Cisco, shows how compute scarcity and strong governance now define whether enterprise AI can scale.
Tim Sanders, Chief Innovation Officer at G2, explains why AI's value is unlocked not by new tech, but by trusting agents with the autonomy to do their work.
Shahab Siddiqui, a Digital Strategy Consultant, explains why owning the full AI stack is the only way to achieve true digital sovereignty.
Eros Marcello, a Senior AI Consultant with experience across Eli Lilly, Meta, and Apple, explains why AI is forcing the fusion of product design and engineering.
Nate Moore, Founder of Enlite IT Solutions Inc., explains how to combat Shadow AI in healthcare by enabling safe AI adoption with sanctioned sandboxes.
Maincode CEO Dave Lemphers shifts national AI from defensive "sovereign" concepts to collaborative "Australian-Made AI," driving tangible solutions & global partnership.
Ravi Kiran, VP of Engineering at KnackLabs, explains how layering 'invisible AI' onto legacy systems boosts productivity and increases enterprise adoption.
IBM's Prashant Parida on why banking AI fails. Banks layer apps on broken foundations, creating 'AI theater.' A ground-up approach builds real value & trust.
Matthew Crowson, MD, Director of AI/GenAI Product Management at Wolters Kluwer, on how healthcare's cultural "build reflex" is a key obstacle to ROI.
David Johnson, Global AI Practice Leader at North Highland, explains why bottom-up strategies lacking executive vision might cause high failure rates for enterprise AI.
Eric Johnson, Director at S&P Global and Senior Technology Editor at JOC.com, explains why AI's value in logistics is cost mitigation to offset new tariffs and fees, not transformative savings.
Sumeet Agrawal, VP of Product Management at Informatica, explains why enterprise AI projects fail when leaders focus on the agent instead of the infrastructure.
Amanda Estiverne-Colas, Director and Head of Payments Practice at Perficient, explains why banks, fintechs, and policymakers are coming together to tackle industry-wide challenges.
Brad Kaufman, Global Consulting Head for R&D in Life Sciences at Wipro, explains why so many enterprise AI projects fail to deliver ROI, and how leaders can solve the issue with sustainability.
Dheeraj Maken of Everest Group advocates for a tiered AI model in banking, where AI handles low-risk tasks and humans oversee critical decisions.
Pankaj Misra, Director & Region Head of North America at Maveric Systems, proposes a shared AI exchange model for smaller community banks to modernize.
Satyabrata Pradhan, Senior Program Manager at General Motors, explains how the next tech transformations will solve complex challenges in the auto industry.
Brajesh Jha, SVP and Global Leader of AI Value Transformation at Genpact, on pivotal agentic AI moments in the enterprise.
Dave Berg, CPO at Constellation Network, discusses the EU AI Act.
Microsoft introduced its first homegrown AI models, MAI-Voice-1 and MAI-1-preview, to reduce reliance on OpenAI.
South Korea aims to be a top-three AI power with a 100 trillion-won fund and tech giants like SK Telecom, LG, and Naver.
Eric Snyder of the Wilmot Cancer Institute discusses how a "hype-first" approach to AI undermines data integrity.
Stanford Health Care and Qualtrics expand their partnership to deploy AI agents for automating administrative tasks in healthcare.
Google partners with Indiana Michigan Power and Tennessee Valley Authority to pause non-essential AI workloads during grid stress.
