E146: The 92% AI Failure: Unmasking Enterprise's Trillion-Dollar Mistake

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Mar 14, 2025

In this discussion, Matt Fitzpatrick, CEO of Invisible Technologies and former head of McKinsey’s QuantumBlack Labs, reveals the staggering fact that only 8% of AI models succeed in production. He dives into the critical data quality issues hampering adoption and emphasizes the importance of fine-tuning AI to meet enterprise needs. With real-world success stories from Moody's and Klarna, Matt outlines how human feedback is essential for training models. He also shares insights on the future of AI in transforming business processes and the innovative vision of Invisible Technologies.

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INSIGHT

Low Enterprise AI Adoption Rate

Only 8% of AI models make it to production, highlighting the gap between development and deployment.
Moody's and Klarna are rare examples of successful enterprise AI integration.

INSIGHT

Friction in Enterprise AI Adoption

Substantial investment in general AI model development has not translated to widespread enterprise success.
The challenge lies in fine-tuning these models for specific enterprise contexts and achieving high accuracy.

ADVICE

Data Quality is Key

Organize and structure your internal data before implementing AI solutions.
Poor data quality hinders AI adoption, so ensure data is accessible and well-defined.

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In this episode of How I Invest, I sit down with Matt Fitzpatrick, CEO of Invisible Technologies and former head of McKinsey’s QuantumBlack Labs. Matt shares his deep insights on enterprise AI adoption, model fine-tuning, and the challenges businesses face when integrating AI into their workflows. We explore why only 8% of AI models make it to production, how enterprises can overcome friction points, and the future of AI-powered enterprise solutions. If you’re curious about the intersection of AI and business strategy, this episode is a must-listen.

Highlights: Enterprise AI Adoption: Why only 8% of AI models reach production and how companies can improve their success rates.

AI Fine-Tuning for Enterprises: The importance of domain-specific AI models and why general AI solutions don’t always fit enterprise needs.

The Data Challenge: Why poor data quality is the biggest barrier to AI adoption and how companies can fix it.

Human-AI Collaboration: The role of human feedback in training AI models for enterprise use cases.

Real-World AI Success Stories: How companies like Moody’s and Klarna successfully implemented AI solutions.

Invisible Technologies’ Vision: How Invisible is building an AI-powered enterprise services platform to modernize business operations.

Future of AI in Business: How AI-native companies will rethink workflows and software applications from the ground up. Matt’s Journey: From leading McKinsey’s AI lab to scaling one of the fastest-growing AI companies.

Guest Bio: Matt Fitzpatrick is the CEO of Invisible Technologies, a rapidly growing AI-powered services company. Before joining Invisible, Matt led McKinsey’s QuantumBlack Labs, where he worked on large-scale AI and data transformation projects. He has extensive experience in enterprise AI adoption and has helped some of the world’s largest organizations modernize their operations using artificial intelligence.

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Sponsor: At Carta, we’re on a mission to connect the participants in private capital — from investors and LPs to their portfolio companies — through a suite of financial intelligence and reporting software that’s purpose-built for venture capital and private equity. Since 2018, we’ve been empowering GPs, CFOs, and CEOs to escape obsolete systems of disconnected FP&A tools — all in favor of a single, unified software suite that helps them make impactful strategic decisions and deliver outstanding results for their investors. Learn more at: www.carta.com/howiinvest.

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LinkedIn: David Weisburd: https://www.linkedin.com/in/dweisburd/ Matt Fitzpatrick: https://www.linkedin.com/in/matthew-fitzpatrick-97b62722/

Links: Invisible Technologies: https://www.invisible.co/

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(0:00) Episode preview (1:46) Training vectors and core business components of Invisible Technologies (3:21) Successful enterprise models and future AI evolution (5:07) Challenges and evaluation of AI model deployment at scale (7:49) Human feedback and accuracy requirements in AI models (10:03) Fine-tuning AI models for enterprise applications (12:27) McKinsey's successful enterprise AI use cases (13:23) AI's impact on productivity and process redesign (15:39) Invisible Technologies' growth strategy and focus on enterprise fine-tuning (17:22) Building a modern ServiceNow with GenAI and avoiding obsolescence (18:26) Modernization of legacy systems and AI native application development (20:03) Shifting focus in the AI ecosystem and agentic layer (22:30) Comparing AI native CRM to traditional CRM systems (23:18) Invisible Technologies' future vision and optimism about enterprise AI (24:33) Closing remarks