Systems | Development | Analytics | API | Testing

Ep 79 | Why Some AI Products Strike a Chord (and Others Don't)

You recognize the tune, but something feels off. That's how Marlon Davis describes many of today's AI initiatives: AI karaoke. Organizations are rushing to add AI to products, but too often they're layering technology onto solutions without fully understanding the customer problems they're trying to solve. In this episode of The AI Forecast, Paul Muller sits down with fractional Chief Product Officer at Devlnio, Marlon Davis, to explore how organizations can move beyond superficial AI efforts and build products that deliver meaningful customer value.

How Booking.com Scaled Agentic Analytics for Self-Service

At Snowflake Summit '26, Chris de Groot, Manager of Data Engineering Customer Service, and Jay Stricks, Group Product Manager, Insights Platform, took the stage to share Booking.com's massive data transformation. In their session, "Booking.com's Data Travels: Platform Foundations to Agentic Analytics," they laid out a masterclass on how to make a colossal, fragmented data landscape entirely AI-ready.

How to Measure Embedded Analytics ROI for Busy End Users

Most analytics programs fail the ROI test for one simple reason: they measure dashboard output, not workflow impact. A team can ship reports, charts, and alerts, yet still miss the real question: does the analytics change what busy people do next? That is the core issue for embedded analytics ROI. How do we measure whether embedded analytics actually delivers business value for busy end users, frontline teams, and executives?

Stop extracting data: Run serverless Python natively in BigQuery!

Python UDFs now Generally Available (GA), you can run custom Python code natively, securely, and serverlessly inside your SQL statements. Write standard SQL, import libraries like BeautifulSoup, or run machine learning tokenizers with zero infrastructure management. Speaker: Products Mentioned: BigQuery, Python User Defined Function.

Improve Compliance, Visibility, and Control in Your Financial Close

Four specialists. 75+ years of combined close experience. One roundtable. We asked them to dissect the month-end close: where governance quietly erodes, why a one-day delay can cascade into seven, what happens when your best reconciliation person is unavailable, and where automation is genuinely delivering results today. 45 minutes of practical close strategy from people who know exactly where the process breaks, and how finance teams can fix it.

Real-Time Hyper-Personalization in 2026: Architecture Guide

Hyper-personalization in 2026 is the ability to act on a user's current intent within the current session, using signals from across the journey. Batch customer data platforms (CDPs) can't do this. They can't capture intent as it forms, can't hold session state, and can't activate inside the intent window.

How to Eliminate Training-Serving Skew With a Unified Real-Time Streaming ML Pipeline (2026 Guide)

The problem. Predictive ML pipelines that maintain separate batch and streaming code paths for the same features carry training-serving skew, the gap between the features a model was trained on and the features it sees at inference time. Skew silently degrades model accuracy and doubles infrastructure cost. The recommendation. Adopt a unified streaming (kappa) architecture.