Summary4 min read

Podcast Summary

Podcast: No Bullsh!t Leadership
Host: Martin G Moore
Episode: Moment 165 – How To Avoid Expensive Tech Project Failures
Date: March 22, 2026

Overview

This episode delivers Martin G Moore's direct, pragmatic insights into why so many technology (and particularly AI) projects go off the rails, ending up late, over budget, and under-delivered. Drawing from decades of experience, Marty breaks down the core reasons for repeated failure and urges leaders—especially non-tech leaders—to develop their “bullshit detectors” and take a more active, accountable role in technology initiatives.

Key Discussion Points & Insights

The Disappointing Track Record of Tech Projects

Tech project delivery is notoriously unreliable:
Less than 10% of technology projects meet original costs and timelines, only a fraction achieve the business value they promised.
- [01:00] “Less than 10% of technology projects meet their original estimates for cost and time, and an even smaller percentage achieve the functional richness that the original business case promised.” — Martin G Moore
AI Projects Are No Panacea:
A surge in AI investments hasn’t improved outcomes. In fact, most organizations see disappointing returns.
- [01:35] “According to McKinsey, 80% of AI projects are failing to scale, and Accenture reports that only 4% of AI projects have delivered noticeable productivity gains.” — Martin G Moore

Six Underlying Problems Behind Tech Project Failures

Lack of Industry Standards
- [02:10] “The IT industry has low barriers to entry, and professional standards are patchy. Surprisingly, this has never been effectively addressed in the 50 years that we've been running major commercial technology projects.”
Inexperience
- Many companies lack internal expertise because major tech projects are infrequent. Over-reliance on outside firms—often staffed by junior consultants—exacerbates risk.
- [02:33] “Many of these firms tend to put young, inexperienced people onto the project to build their capability. What could possibly go wrong, right?”
Intangibility of Software
- Software’s unseen nature makes it hard to specify requirements up-front.
- [02:54] “Software is intangible... it's incredibly hard to visualize and describe what the finished product should look like. This often relies on taking the 'I'll know it when I see it' approach.”
The “We’re Different” Fallacy & Customization
- Insisting on custom solutions increases complexity and makes lessons from previous projects difficult to apply.
- [03:18] “Most companies fall into the fallacy of saying ‘we’re different,’ which then leads to excessive customization... customization increases both complexity and cost.”
Poor Scope Control
- Requirements emerge during the project, leading to continual scope expansion and spiraling costs.
- [03:44] “Requirements tend to be discovered as the project team progresses the design process, and scope... tends to grow out of control.”
Lack of Accountability
- Business leaders offload responsibility to IT, failing to oversee progress or keep strong people engaged, while red flags are concealed.
- [04:10] “Tech projects are largely left to tech people. Business leaders... take too little interest... they don't know how to read the play when a project begins to go south... their tech friends will obfuscate to avoid discovery until there's no way to hide it any longer.”

Memorable Quotes & Moments

[04:37] “When it comes to tech, non-tech leaders have very underdeveloped bullshit detectors.”
A call to arms for business leaders to upgrade their project skepticism and oversight.
Cross-promotion of Deep Dive:
[05:00] “In episode 363 of the No Bullshit Leadership podcast, I give you 10 surefire ways to exponentially increase the likelihood of success for your next technology investment.”

Episode Guidance and Takeaways

For leaders:
- Don’t default to a hands-off approach—take full ownership of tech initiatives.
- Recognize and mitigate the six core dangers: low standards, inexperience, software intangibility, excess customization, scope creep, and lack of accountability.
- Invest in developing your ability to detect nonsense and push for transparency.
Next Step:
- For listeners wanting actionable strategies, Marty suggests listening to episode 363 for a full roadmap to tech project success.

Timestamps for Important Segments

| Timestamp | Segment | |-----------|----------------------------------| | 00:25 | Introduction and statistics | | 01:35 | AI project failure rates | | 02:10 | Problem 1: Lack of standards | | 02:33 | Problem 2: Inexperience | | 02:54 | Problem 3: Intangibility | | 03:18 | Problem 4: Excess customization | | 03:44 | Problem 5: Scope control | | 04:10 | Problem 6: Lack of accountability| | 04:37 | Business leader call to action | | 05:00 | Recommendation: episode 363 |

By reframing tech project risk as a leadership challenge—rather than merely an IT problem—Martin G Moore empowers leaders to be proactive, informed, and vigilant, dramatically improving chances for a positive tech ROI.

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Transcript2 lines

[00:02]
A
Welcome to the no Bullshit Leadership Podcast. In a world where knowledge has become a commodity, this podcast is designed to give you something more access to the experience of a successful CEO who has already walked the path. So join your host, Martin Moore, who will unlock and bring to life your own leadership experiences and accelerate your journey to leadership excellence.
[00:26]
B
Welcome to Moments with Marty, your short, sharp shot of leadership insight to help you to stay on track between our weekly episodes of the no Bullshit Leadership Podcast. Virtually every organization is racing to adopt AI, and although AI offers the promise of groundbreaking productivity, the projects required to deliver it will suffer from exactly the same problems as every other IT project that I've seen in the last 40 years. Less than 10% of technology projects meet their original estimates for cost and time, and an even smaller percentage achieve the functional richness that the original business case promised. Gartner, a consulting Firm, predicted that $5.6 trillion would be spent on it in 2025, an increasing percentage of which is being spent on AI projects. But the outcomes rarely live up to the hype. According to McKinsey, 80% of AI projects are failing to scale, and Accenture reports that only 4% of AI projects have delivered noticeable productivity gains. Big projects are hard, whether they're in construction, infrastructure or procurement, and tech is no different. But there seems to be a certain mystique around technology projects. Why is your tech project so unlikely to deliver the promised return on investment? The first problem is a lack of standards. The IT industry has low barriers to entry, and professional standards are patchy. Surprisingly, this has never been effectively addressed in the 50 years that we've been running major commercial technology projects. The second problem is inexperience. Very few companies build good IT project capability because they rarely take on large scale tech projects, and when they do, they tend to hire external firms to help. As you might imagine, many of these firms tend to put young, inexperienced people onto the project to build their capability. What could possibly go wrong, right? The third problem is one of the biggest and also the hardest to overcome. Software is intangible, unlike infrastructure projects. It's incredibly hard to visualize and describe what the finished product should look like. This often relies on taking the I'll know it when I see it approach. The fourth problem is that most companies fall into the fallacy of saying we're different, which then leads to excessive customization. But customization increases both complexity and cost, and IT means that the lessons from previous projects are even more difficult to capture. The fifth problem, and one of the most common, is poor scope control requirements tend to be discovered. As the project team progresses the design process and scope well, it tends to grow out of control, unlike the more tangible project types that have finite delivery parameters. And last but not least, if those five problems haven't already killed your project's roi, lack of accountability will. Tech projects are largely left to tech people. Business leaders who are the ultimate beneficiaries of the system, tend to take too little interest. They allocate weak people to project liaison roles because their best people are needed in the business, they don't pay sufficient attention to project milestones, and they don't know how to read the play when a project begins to go south, which their tech friends will obfuscate to avoid discovery until there's no way to hide it any longer. When it comes to tech, non tech leaders have very underdeveloped bullshit detectors. In episode 363 of the no Bullshit Leadership podcast, I give you 10 surefire ways to exponentially increase the likelihood of success for your next technology investment. If you want to take a deeper dive into how you can avert a tech project disaster, have a listen to episode 363 of the no Bullshit Leadership podcast, 10 steps to a positive ROI in your AI and tech investments. We'll leave a link in the show Notes. I really hope you enjoyed this moment and that it gives you that extra little spark to be a no Bullshit.