The Real Reason Tech Products Fail

Nov 18, 2025

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Our latest episode features Jessica Randazza Pade, Head of Brand Activation & Commercialization at Neurable. Named to Campaign US’s 40 Over 40 and ELLE Magazine’s 40 Under 40, Jessica is an award-winning global digital marketer, business leader, and storyteller. She explains why AI is not a value proposition, how to turn vague use cases into measurable outcomes, and why making technology invisible is often the strongest competitive advantage.

“If the user can’t articulate what’s different in their life because of your product, you’re selling a vitamin—not a painkiller.”

Listen on Apple Podcasts | Spotify

Shape Our 2026 Research

We’re mapping where teams are struggling with AI adoption and what tools, frameworks, and support they need in 2026. Your input directly shapes our annual research and the topics we cover.Take the survey → https://tally.so/r/Y5D2Q5

AI has lowered the cost of prototyping but raised the bar for adoption. Most AI products fail because they launch demos instead of durable workflows, rely on large models where small ones would work better, ignore trust, or sell “time savings” instead of business outcomes. Organizations resist tools that feel risky, inaccurate, unproven, or misaligned with real workflows. Complicated architecture, poor UX, weak personalization, and unclear ROI all compound the problem.

Here’s a sample of it:

#3: Your product doesn’t actually learn. Fake personalization destroys trust.#4: One hallucination can end adoption permanently.#8: “Saving time” is not a business case—outcomes are.#11: Organizational silos suffocate AI products.#17: Without a workflow and measurable ROI, you don’t have a product.

AI will not save your product. Only reliability, trust, workflow clarity, governance readiness, and measurable value delivery will.

Read the full article → https://ph1.ca/blog/why-your-AI-product-will-fails

The Year of AI Value

This video covers why 2026 marks a turning point where AI is judged not by novelty or intelligence but by measurable ROI, workflow impact, and operational reliability. It explains why businesses are shifting from “AI features” to fully redesigned AI-enabled systems.

We are past the point of buying AI based on promises

AI buyers no longer invest because the tech is impressive. They invest when it:

* delivers measurable ROI

* reduces operational and compliance risk

* integrates into existing workflows

* produces consistent results

* overcomes organizational resistance and silos

If you’d like us to create a full episode on why AI products fail, add a comment to this post.

The AI Adoption Curve Is About to Flip

This video explains how organizations are moving from experimentation to structural integration, redesigning roles, responsibilities, and workflows around AI. It also highlights early signals that distinguish “tool usage” from true operational adoption.Watch →

Featured Thinker: Stuart Winter-Tear

This week we’re spotlighting the insightful work of Stuart Winter-Tear, founder of Unhyped. His writing reframes LLM inconsistency as a reflection of the chaotic and contradictory data ecosystems they’re trained on—challenging assumptions about rationality, coherence, and system behavior.

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