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Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail

AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments. Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.

Vibe Coding Economically: Which Framework Is the Cheapest? (Rails vs Django vs Laravel)

Token costs used to be something most developers ignored. They simply dismissed them as theoretical. Now, they’re showing up in your Cursor/Claude Code bill, in every pasted error, in that package the AI pulled in without asking, or in that clarification round you didn’t plan for. Most developers choose a framework based on what they've used before, what the job description asks for, or simply whatever was used on their last project.

Automotive Industry Trends 2026: AI in Automotive Software Development

Since the first vehicles were rolled out to customers, automakers have competed to deliver the newest features and the greatest benefits to the driving experience. Today, that competition is less about shaping a car’s physical characteristics and more about making cars smarter and more connected to the world around them. With thousands of car models and trim levels available worldwide, there is a fierce need to find new ways to stand out from the competition.

Five things your logs will never tell you

A customer escalation hit my queue when I was on the customer smoke jumpers team at an observability vendor. My team was the group that parachutes into Fortune 500 accounts one bad week from churning and usually after a big customer outage. The customer had filed a billing dispute three weeks earlier and their on-call engineers were stuck. They had our full stack: logs, metrics, traces, end-to-end instrumentation, every product we sold and some we didn’t. They could see the request came in.

Stop Rebuilding Data Models From Scratch: Meet SpotterModel

Your data engineering team shouldn't be the bottleneck between a business question and a governed answer. SpotterModel turns a natural language prompt into a deployable data model. This release does the heavy lifting on complex calculations, and lets you roll back to any previous model state, anytime, so a bad change never costs you hours of rebuilding. It maps your relationships, dimensions, and measures instantly, and you stay in control of table selection and the build process the whole way.

Introducing AI Transport v0.2.0

Version v0.2.0 of @ably/ai-transport reorganises the SDK to better support a wide range of interaction patterns. Everything in an AI session – input, output, agent lifecycle, control signals – is captured durably, allowing you to easily build the sophisticated interaction patterns that support modern AI user experiences. When we first built @ably/ai-transport, we modelled an AI conversation the way most people first picture it: as a request and a response.

Building a Digital Banking Platform From Scratch: Architecture Decisions That Scale

Building a digital banking platform from scratch in 2026 is becoming less about launching a banking app and more about designing the right architecture from day one. The industry is moving through a major infrastructure shift. According to McKinsey Financial Services Insights, global fintech revenues crossed nearly 650 billion dollars in 2025, growing at roughly 21 percent year over year.