In this interview from Phi Moments @ Google Cloud Next 2026, Mehul Trivedi, group vice president of technology at John Wiley & Sons, joins Debopriyo Nag, practice lead for data and analytics at Quantiphi, to talk with theCUBE's Rebecca Knight about how a 219-year-old publishing company is dismantling a decade of fragmented data infrastructure to build a unified, AI-ready lakehouse on Google Cloud. Trivedi details how Wiley's legacy ecosystem — built across disparate business units over 10 years — created a data debt that blocked meaningful AI and analytics work. He outlines the three-part case for choosing Google Cloud and BigQuery: cost consolidation, a fully integrated AI and ML stack and open-source flexibility through formats such as Parquet and Iceberg. Nag explains how Quantiphi deployed its in-house AI agent platform, Codeaira, to automate query translation, validation and migration across 300 terabytes of data and 30,000 tables — compressing what typically takes two years into six to nine months.

The conversation also explores how the migration is reshaping Wiley's workforce. Rather than displacing engineers, Trivedi notes, Gemini-assisted tooling frees teams from routine pipeline and reporting work so they can focus on building semantic models, uncovering new data relationships and developing future-ready products. Nag reinforces a lesson drawn from years of enterprise migrations: lifting and shifting data to the cloud without first contextualizing it will not unlock AI value. From piloting a scalable lakehouse architecture to positioning Wiley for the next decade of AI competition, both guests make clear that data readiness — not model selection — is the decisive factor in enterprise AI success.

In this interview from Phi Moments @ Google Cloud Next 2026, Erik Ashby, senior director and head of product research at Helpshift, joins Ram Kasi, head of the EMEA GCP business unit at Quantiphi, to talk with theCUBE's Rebecca Knight about how goal-driven AI agents are transforming player support from a source of friction into a genuine competitive advantage. Ashby traces Helpshift's evolution from mobile-first in-app customer service — now embedded in over two billion devices — to a family of AI agents built around a single reframing: shifting the objective from solving problems to actively engaging players. That pivot, developed in partnership with Quantiphi on Google Cloud, produced measurable results including higher player CSAT scores and a nearly 2x reduction in reopen rates. He explains how the Care AI agent works dynamically to get players back in the game — resolving issues, offering contextual rewards and handing off to human agents with full context — rather than routing them through rigid chatbot workflows.

The conversation also explores how Quantiphi served as the engineering backbone behind that transformation, bringing what Kasi describes as experience, expertise and scale to a product roadmap demanding rapid adaptation as AI models evolved every few months. Kasi underscores Quantiphi's platform-first philosophy — building shared infrastructure for security, grounding, scalability and integration so each new agent can be deployed faster and more securely than a siloed approach would allow. Ashby notes that gaming has historically been the proving ground for emerging technology, and that the engagement principles Helpshift developed for players apply equally to any brand seeking genuine consumer connection. From building goal-aligned architecture over off-the-shelf AI layers to choosing partners who can flex at the pace of change, both guests provide a practical roadmap for senior leaders ready to move beyond experimentation toward measurable business impact.

In this interview from Phi Moments @ Google Cloud Next 2026, Ankur Manake, Honeywell Forge data and AI leader at Honeywell, joins Dinesh Kabaleeswaran, regional sales leader for North America at Quantiphi, to talk with theCUBE's Rebecca Knight about the journey from AI experimentation to enterprise-scale production in complex industrial environments. Manake describes the challenge of "legacy spaghetti" — a sprawling mix of sensor data, asset metadata and unstructured content across heterogeneous industrial systems — and explains why layering AI on top of existing processes inevitably produces stalled pilots. True transformation, he argues, requires rethinking workflows alongside domain experts, treating AI not as an added tool but as the new standard way of working. The north star, he notes, is reliable AI that is consistent, trustworthy and transparent enough for industrial teams to act on with confidence.

The conversation also explores how Kabaleeswaran and Quantiphi approached the Honeywell engagement as a unified team, drawing on Safi Bahcall's "Loonshots" framework to maintain a dynamic equilibrium between aggressive innovation and operational stability. Kabaleeswaran breaks down how running tightly scoped sprints — measured in weeks, not months — surfaced early wins that turned skeptics into believers and built the trust needed to scale. Both guests reflect on their Phi moments: for Kabaleeswaran, it was seeing real outcomes emerge mid-sprint; for Manake, it was watching customers experience a genuine breakthrough with technology that changed how they operated. Looking ahead, Manake outlines a vision of an "intelligence neural network" — individual nodes of AI capability that must be intelligently connected to eventually drive autonomous industrial outcomes. From starting with the right-sized problem rather than the technology to leveraging crisis moments as catalysts for change, the discussion offers a practical roadmap for executives navigating their own AI transformation journeys.

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.