TechVoices

Doug Marquis, Chief Technology Officer at Zywave, says AI is moving quickly from experimentation to production in insurance — but the real challenge is not simply connecting a large language model to a workflow. In a highly regulated industry like insurance, AI systems need domain-specific intelligence, observability, governance, and the ability to explain how they reached an answer.

Identity security is undergoing its biggest transformation in decades as enterprises shift from managing human users to governing vast numbers of AI agents. In this interview, Saviynt Chief Trust Officer Jim Routh explains why traditional identity governance architectures are becoming obsolete, how organizations should build AI governance around specific business use cases, and what enterprises must do to securely manage autonomous AI agents as they become a permanent part of the workforce.

At Pure Accelerate 2026 in Las Vegas, Everpure Vice President of Product Management Chad Kenney outlined the company’s evolution from a storage provider into a broader data management platform designed for the AI era.

By now, the “testing the waters” phase of adding GenAI to M&A workflows is coming to a close. A recent Deloitte survey on GenAI in M&A of 1,000 corporate and private equity business leaders found that 86% of organizations have already begun to integrate GenAI into dealmaking.

Technology news headlines are dominated by AI companies, hardware vendors, and cloud providers racing to build faster processors, larger LLMs, and new automated agentic AI services for every function and industry. Meanwhile, inside the enterprise, data storage managers and data engineers are working on a more fundamental problem: making unstructured data usable in lakehouses so AI pipelines have something worth running on. The scale of the problem is heady.

Artificial intelligence has become one of the easiest ways for organizations to demonstrate productivity gains. Every week brings another announcement about how much faster teams can write code, create reports, analyze data, draft documentation, or complete administrative tasks. The conversation is usually accompanied by impressive numbers that show hours saved, output increased, and workflows accelerated. Most of those claims are directionally correct.

For years, data sovereignty was treated primarily as a legal and compliance issue. Organizations focused on understanding regional requirements, updating policies, and ensuring regulated data remained where it was supposed to. But data sovereignty is no longer just a compliance challenge. It is becoming a scale challenge. As countries continue to introduce localized data requirements, organizations are being forced to regionalize infrastructure, controls, vendors, and operational processes.

As enterprises race to deploy AI, many are discovering that model performance is only part of the challenge. Data quality, governance, security, and regulatory compliance have become equally important concerns, particularly in industries handling sensitive information.

AI is dramatically increasing developer productivity, but many companies are struggling to translate those gains into measurable business outcomes. In a TechVoices interview, GitLab Chief Product and Marketing Officer Manav Khurana explained that AI-assisted coding is creating unprecedented speed, but also introducing new issues around reliability, security, governance, infrastructure scale, and cost control.

Magnific CEO Joaquin Cuenca believes artificial intelligence is ushering in a new era of creativity, one in which AI serves as a collaborator rather than a replacement for human creators. Speaking at the Upscale Conference in San Francisco, Cuenca described how Magnific has evolved from its origins as Freepik, a search engine for free images, into a comprehensive AI-powered creative platform designed to help users generate films, advertisements, marketing campaigns, and other visual content.