Podcast Summary: "The 92% AI Failure: Unmasking Enterprise's Trillion-Dollar Mistake" – E146

How I Invest with David Weisburd

Introduction

In episode 146 of "How I Invest with David Weisburd," host David Weisburd interviews Matt, the CEO of Invisible Technologies, to dissect the alarming statistic that 92% of AI initiatives in enterprises fail to meet expectations. Released on March 14, 2025, the episode delves into the complexities of AI model development, the hurdles enterprises encounter in deploying AI effectively, and the strategic solutions Invisible Technologies offers to bridge this significant gap.

AI Model Building Challenges

The conversation kicks off with a discussion on AI-native solutions and the fundamental flaws in how AI is typically integrated into business processes.

“Most of the material benefit you're going to see is when you clean sheet any process to be like, how would I design this process knowing all the AI tools I have from scratch?” [00:08]
— Matt, CEO of Invisible Technologies

Matt emphasizes that true value in AI implementation arises not from merely replacing human tasks with AI but from redesigning processes to leverage AI's strengths from the ground up. He argues that human-machine interfaces are pivotal, allowing for a symbiotic relationship where both technology and humans enhance each other’s capabilities.

Invisible’s Business and Growth

Matt provides an overview of Invisible Technologies, highlighting its impressive growth and market position.

“We ended 2024 at $134 million in revenue, profitable. We were the third fastest growing AI business in America over the last three years.” [00:42]
— Matt

Invisible Technologies has rapidly become a key player in the AI industry, focusing on AI training and model fine-tuning. Matt discusses how Invisible's approach involves using both technology and human expertise to ensure AI models are not only built efficiently but also tailored to meet specific enterprise needs with high accuracy.

Enterprise AI Adoption and Challenges

The dialogue shifts to the broader challenges faced by enterprises in adopting AI. Matt cites a startling statistic:

“The stat that I've seen most frequently cited is that about 8% of models today make it to production.” [03:21]
— Matt

Despite massive investments in AI, only a small fraction of models succeed in being deployed effectively within enterprises. Matt attributes this failure rate to several factors, including poor data quality, lack of clear definitions for success, and inadequate fine-tuning of AI models for specific business contexts.

Invisible’s Solutions for AI Integration

Matt elaborates on how Invisible Technologies addresses these challenges through its two main business components: reinforced learning and feedback systems, and enterprise AI fine-tuning.

“We have two big components of our business, what I call reinforced learning and feedback which is the process on any topic where model is being trained.” [02:22]
— Matt

Invisible leverages a pool of highly specialized experts to train AI models on niche topics, ensuring that models can handle complex and specialized tasks with high accuracy. This approach is complemented by the company’s focus on enterprise AI fine-tuning, which involves adjusting AI models to function seamlessly within specific business environments.

Process Redesign and AI Integration

A significant portion of the discussion centers on the necessity of re-engineering business processes to fully harness AI’s potential. Matt compares the current state of AI in enterprises to factory workers who incrementally improve productivity without altering the production line’s core structure.

“It's unclear the degree to which they actually save any work. They kind of tweak a lot of things on the margin.” [13:42]
— Matt

He argues that true efficiency gains from AI require comprehensive process redesigns rather than superficial adjustments. Invisible Technologies assists enterprises in rethinking their workflows, enabling them to integrate AI in ways that transform operations fundamentally rather than just enhancing existing practices.

Data Quality and Organization

Matt underscores the critical role of data quality in AI success. He points out that many enterprises struggle with legacy data systems that are poorly organized and hinder effective AI utilization.

“When good AI meets bad data, the data usually wins.” [05:07]
— Matt

Invisible Technologies addresses this issue by helping companies structure and master their data domains, ensuring that AI models are trained on clean, well-organized data. This foundational step is crucial for achieving the high accuracy and reliability required for enterprise-level AI applications.

Evaluating AI Model Performance at Scale

The conversation delves into the complexities of evaluating AI model performance, especially when models generate large volumes of outputs, such as investment memos.

“We spent the last eight years... building what's called semi private custom evals where we effectively set parameters and use human feedback to score those parameters.” [07:25]
— Matt

Invisible Technologies has developed sophisticated evaluation frameworks that use subject matter experts to consistently assess AI outputs against predefined criteria. This ensures that only high-quality outputs are deployed, maintaining the integrity and reliability of AI applications within enterprises.

Future of AI in Enterprises

Looking ahead, Matt is optimistic about the evolving landscape of AI in enterprises. He envisions a future where AI models are not only successfully deployed but also continuously optimized to meet dynamic business needs.

“I'm very optimistic that over the next five, six years we're going to see many, many more examples of great gen AI use cases in production.” [16:09]
— Matt

Matt outlines Invisible Technologies’ strategic direction, which includes expanding their process platform to incorporate modern data infrastructure, application development environments, and sector-specific tools. This evolution aims to facilitate the creation of highly customized AI applications tailored to various industry requirements.

Invisible’s Approach to Customization and Scalability

Invisible Technologies focuses on providing scalable solutions that can be tailored to specific enterprise needs. Matt discusses how their platform allows for the customization of AI workflows, enabling businesses to design and deploy AI solutions that align perfectly with their operational processes.

“The motion of how do I deploy a machine learning model with accuracy. You've seen a bunch of really good examples of that.” [04:52]
— Matt

This approach ensures that AI implementations are not only accurate but also seamlessly integrated into the existing business frameworks, enhancing overall efficiency and effectiveness.

Addressing Common Enterprise AI Use Cases

Matt shares insights into successful enterprise AI use cases, such as Moody's chain-of-thought reasoning model and Klarna’s AI-driven contact center, highlighting the importance of achieving near-perfect accuracy in these applications.

“If you think about what Invisible there we have this kind of AI process platform where we trot out any individual task into a set of stages and then insert kind of feedback analytics at all of those different steps.” [16:09]
— Matt

These examples illustrate how tailored AI solutions can transform specific business functions, setting the stage for broader adoption and more innovative applications across different industries.

Conclusion and Future Vision

In concluding the episode, Matt reiterates his belief in AI’s transformative potential for enterprises. He emphasizes that while the path has been challenging, strategies focused on process redesign, data quality, and model fine-tuning are key to unlocking AI’s full value.

“I don't think that every enterprise will have to build some capabilities around... What do I want to get out of these models? How do I train and validate these models?” [23:45]
— Matt

Matt envisions Invisible Technologies as a pivotal force in this transformation, providing the necessary infrastructure, expertise, and support to ensure that AI models not only reach production but also deliver substantial business value.

Notable Quotes

1. “Most of the material benefit you're going to see is when you clean sheet any process to be like, how would I design this process knowing all the AI tools I have from scratch?” — Matt [00:08]

2. “We ended 2024 at $134 million in revenue, profitable. We were the third fastest growing AI business in America over the last three years.” — Matt [00:42]

3. “The stat that I've seen most frequently cited is that about 8% of models today make it to production.” — Matt [03:21]

4. “When good AI meets bad data, the data usually wins.” — Matt [05:07]

5. “I'm very optimistic that over the next five, six years we're going to see many, many more examples of great gen AI use cases in production.” — Matt [16:09]

6. “We spent the last eight years... building what's called semi private custom evals where we effectively set parameters and use human feedback to score those parameters.” — Matt [07:25]

7. “I don't think that every enterprise will have to build some capabilities around... What do I want to get out of these models? How do I train and validate these models?” — Matt [23:45]

Final Thoughts

Episode E146 of "How I Invest with David Weisburd" offers a comprehensive examination of why a vast majority of AI initiatives in enterprises fall short and how Invisible Technologies is positioned to change that narrative. Through Matt’s insights, listeners gain a nuanced understanding of the strategic adjustments necessary for successful AI deployment, emphasizing the importance of process redesign, data integrity, and customized model training. This episode serves as an invaluable resource for investors and enterprise leaders aiming to navigate the complex AI landscape and harness its full potential for transformative business growth.