Host Mohsin Ali speaks with Josh Shaner, CEO of Braive, about why AI projects fail. Learn the real reasons: unclear ownership, rushed rollouts, and misaligned incentives. Discover why culture and governance beat fancy models, how to build accountability, and why phased approaches work better than big bets.

PureLogics Pulse Podcast Chapters

00:00 – 01:06 | Opening Hook: Why AI Projects Really Fail

The episode opens by challenging the assumption that AI failures are driven by bad models, reframing the problem around ownership, accountability, and execution gaps.

01:06 – 02:05 | Podcast Welcome & Episode Context

Mohsin Ali introduces PureLogics Pulse and sets the stage for a practical discussion on AI failures, lessons from 2025, and what organizations must rethink in 2026.

02:05 – 03:55 | Guest Introduction & AI Employees Explained

Josh Shaner introduces his background and explains the concept of “AI employees” as narrowly focused, always-on digital workers designed to support—not replace—humans.

03:55 – 05:15 | AI Replacing Humans: Myth vs Reality

The discussion addresses common fears around AI replacing jobs and emphasizes the continued need for human oversight, judgment, and accountability.

05:15 – 07:28 | Rushing into AI Without Planning

Josh explains why AI adoption pressure is increasing and how leadership-driven launches often fail when frontline teams are not involved early.

07:28 – 10:10 | Ownership Gaps & Real-World Failure Example

A real deployment story highlights how lack of buy-in and missing SOPs can derail even well-functioning AI systems.

10:10 – 12:08 | Bad Models vs Lack of Ownership

The conversation breaks down why AI failure is rarely technical alone and how responsibility is shared between agencies and clients.

12:08 – 14:22 | MVP vs Production-Grade AI Systems

Mohsin and Josh discuss why AI systems require optimization time and why expecting perfection at launch creates long-term technical debt.

14:22 – 16:55 | Frontline Resistance & Incentive Misalignment

Josh explains why resistance usually appears at the frontline and how forcing new workflows without proof leads to rejection.

16:55 – 19:55 | Replacing Mundane Work, Not People

The episode highlights why AI adoption succeeds when it removes low-value tasks instead of threatening core roles.

19:55 – 21:43 | Silent Rejection & “Soft” Sabotage

Josh explains how AI initiatives fail when tools increase workload or introduce errors, causing teams to quietly abandon them.

21:43 – 23:45 | Discovering Real vs Assumed Workflows

The importance of stakeholder interviews, process mapping, and focusing on measurable ROI before building AI solutions.

23:45 – 25:18 | Early Red Flags in AI Projects

Key warning signs include unrealistic timelines, end-to-end expectations, scope creep, and disengaged stakeholders.

25:18 – 27:22 | When Leaders Should Pause or Kill AI Initiatives

Josh explains why agencies and leaders must be willing to say no, reset expectations, or stop initiatives to avoid sunk-cost failures.

27:22 – 29:51 | Scaling AI Across Regions & Teams

The discussion covers phased rollouts, test markets, and why large-scale AI deployment must account for regional and operational nuance.

29:51 – Conclusion | Final Advice for Agencies in 2026

The episode closes with a clear takeaway: AI success depends on honest expectation-setting, human ownership, phased execution, and treating AI as an evolving system—not a magic bullet.