July 22, 2026

408 | 95% of AI Projects Fail: Here’s the Fix | Ben Tasker

Ben Tasker joins Jeff Mains to break down the real reason most AI rollouts fail: it's almost never the technology. From his time as Dean of AI at Southern New Hampshire University to building life-saving diagnostic tools at MaineHealth, Ben has seen firsthand how change management, human-centric strategy, and data quality determine whether AI creates value or amplifies chaos.

In this episode, Ben introduces the concept of the “AI Between Times” — the transition period where organizations are leaving old processes behind but haven't yet arrived at a fully AI-integrated future. He explains why 95% of AI implementations fail, what separates the 5% that succeed, and how founders and leaders can build a culture of continuous learning that makes their organizations genuinely AI-resilient. Ben also shares a practical four-step learning cycle (Learn → Practice → Apply → Reflect) and makes the case that adaptability and flexibility — not technical expertise — are the most critical skills to develop right now.

Key Takeaways

[0:00] — Hook: 95% of AI implementations fail — not because of bad tech, but because of what surrounds it: poor rollouts, unengaged leaders, and broken processes that get automated at scale.

[3:11] — Ben introduces the “AI Between Times” — the transition moment where organizations are leaving old processes but haven't reached AI maturity. Change management is the most overlooked factor.

[5:19] — What the 5% do differently: transparent campaigns that communicate why AI is being implemented, where, how, and what not to do — plus training pathways and upskilling resources.

[7:38] — Why people resist change: 66% of individuals currently hold negative AI sentiment, driven by news around layoffs, data centers, and high-profile failures. Trust and transparency are the antidote.

[7:52] — The danger of a “point solution” mindset: using AI to solve one small problem without mapping the full system. Ben's pizza-ordering example illustrates how a narrow implementation can actually increase costs, erode trust, and kill adoption.

[13:44] — AI doesn't fix a messy organization — it amplifies it. Garbage in, garbage out. Data quality and process integrity must come before the model.

[13:45] — Ben's healthcare origin story: his first job exposed the cost of poor data infrastructure and uncommunicated change. Physicians worked around the system because no one explained the why.

[15:27] — From community college to ICU: Ben's student success algorithm took 12 months just to get faculty buy-in — a timeline the data science team never anticipated. At MaineHealth, a similar algorithm flagged ICU patients for early infection, potentially saving thousands of lives.

[21:11] — Why people treat AI as just a search engine: ChatGPT reached 1 million users in 5 hours — faster than the iPhone, the computer, or the internet. Accessibility doesn't equal strategy. Most people use 2–3 use cases and miss the full picture.

[22:58] — Prompting is a skill. A prompt for a video, an image, and a report require different approaches. Without a learning plan, you're not using AI — you're guessing.

[26:20] — AI's primary objective is to interact with you more. The more you use it, the more personalized it becomes. But that also means being thoughtful about what data you share.

[31:25] — Ben's personal AI origin story: photographed workout equipment, had ChatGPT build CrossFit plans, then photographed his fridge for meal planning. That non-work use case built the prompting instincts he later applied professionally.

[33:14] — AI augmentation is the sweet spot — not replacement. Most roles will incorporate AI; the job titles that don't exist yet (Dean of AI, prompt engineer) are proof the economy is already shifting.

[35:48] — “Prompt engineer” has at least two completely different career paths — one for marketing professionals, one for PhD-level AI researchers. Organizations need to recruit by skills, not job titles.

[42:47] — What successful large-scale AI transformations have in common: leadership engagement, middle-layer exploration, and org-wide AI literacy — treated as a cultural shift, not a one-time training.

[44:00] — Context is the new data. Websites, job descriptions, PowerPoint decks — these become the data sources that AI ingests. If the context isn't clean and structured, the output won't be either.

[40:13] — The Learn → Practice → Apply → Reflect cycle: where most people break down is the reflection step. Without asking “What did I learn? Did I like it? What would I do differently?” the cycle can't repeat effectively.

[43:15] — Adaptability vs. flexibility: adaptability is accepting that change is coming; flexibility is navigating it once it arrives. These are the top two skills organizations are hiring for — and neither is a technical skill.

[44:10] — Individuals who know AI earn a 52% pay premium over those who don't.

[45:07] — The one investment SaaS founders can't skip: start using AI themselves and talk about it positively. Use your domain expertise to create context and systems that make your product AI-resilient before disruption forces the conversation.

Tweetable Quotes

“AI doesn't fix a messy organization. It amplifies it.” — Ben Tasker

“95% of AI implementations fail — and the 5% that succeed take care of the change management first.” — Ben Tasker

“You can use it doesn't mean you know how to use it. Just because you have access doesn't mean you have a strategy.” — Ben Tasker

“Data is the oil to the AI engine. Garbage in, garbage out — at scale.” — Ben Tasker

“If leaders aren't fully engaged with AI and using it themselves, the rollout is dead on arrival.” — Ben Tasker

“AI can amplify your organization, but it can also amplify your natural abilities. The two are not the same thing.” — Ben Tasker

“Context is the new data. If you're not setting up the system to succeed, the end result won't succeed either.” — Ben Tasker

“Individuals who know AI right now have a 52% premium on pay. If that's not an incentive, I'm not sure what is.” — Ben Tasker

“We're in the AI Between Times — not fully in the future, but moving out of the past. Change management is what bridges the gap.” — Ben Tasker

“It's not complete replacement. But it's also not forgetting to upskill your organization. Both have to happen.” — Ben Tasker

SaaS Leadership Lessons

1. Ship culture before you ship the feature. Most AI rollouts fail because leadership announces the tool but skips the story. Before your team touches a new AI system, build a transparent change management campaign: communicate the why, the where, the how, and critically — what not to do. Your people will fill silence with fear. Fill it with clarity first.

2. Don't automate a broken process — you'll just scale the chaos. AI amplifies what's already there. If your data is dirty, your workflows are fragmented, or your customer experience is inconsistent, AI won't fix it — it will magnify it. Map the full system front-to-back before you plug in any model. Finance, product, data, ops — all stakeholders belong at the table before implementation, not after.

3. Point solutions are the fastest path to failed adoption. The pizza-ordering example is a masterclass in what not to do. When you solve one narrow problem without thinking about the full customer or employee experience, you create friction that erodes trust — and trust, once lost, is expensive to rebuild. Think in systems, not spotfixes.

4. Leaders who don't use the tools can't lead the transformation. Mandatory AI training from a leader who has never opened ChatGPT is theater. Your team will follow what you do, not what you announce. Model the behavior. Use AI in your own workflows. Talk about it openly — including the failures. That psychological safety is what makes adoption stick.

5. Skills are more durable than job titles. Roles like “Dean of AI” and “prompt engineer” didn't exist three years ago. The titles will keep changing. What won't change is the value of skills: adaptability, systems thinking, communication, analytical reasoning. Build a skills inventory for your organization and create learning pathways before you need to fill a role. Your next AI hire might already be on your payroll.

6. The first 12–16 months are the egg — don't rush the hatch. Organizations that win with AI invest heavily in the foundation: data infrastructure, leadership engagement, org-wide literacy, and structured reflection. The ones that rush straight to implementation — running experiments with no strategy, no vision, no metrics — burn budget, burn trust, and burn out their teams. Slow down to go fast. The payoff compounds.