Inside Invisible Technologies: CEO Matthew Fitzpatrick on AI, Human Collaboration, and Scaling Automation

Matthew Fitzpatrick's Expertise in Operations and Growth

Matthew Fitzpatrick brings extensive experience in scaling complex workflows and teams, combining consulting, strategy, and operational leadership. As the CEO of Invisible Technologies, he is dedicated to designing and optimizing comprehensive business solutions that blend human intelligence with automation to drive efficiency and transformative growth.

The Unique Approach of Invisible Technologies

Invisible Technologies specializes in business process automation by integrating advanced technology with human expertise. Their model doesn't seek to replace humans but to create custom workflows where digital workers (software) collaborate seamlessly with human operators. Their services cover data enrichment, lead generation, customer support, and back-office operations, allowing clients to delegate complex repetitive tasks and focus on strategic objectives. The company’s "work-as-a-service" model offers scalable, transparent, and cost-effective operational support.

Transitioning from McKinsey to Invisible Technologies

Having led QuantumBlack Labs at McKinsey, Matthew was drawn to Invisible Technologies because of the opportunity to operationalize AI at scale. The combination of a flexible AI software platform and an expert marketplace for human-in-the-loop feedback aligns with his belief that Reinforcement Learning from Human Feedback (RLHF) is critical for reliable generative AI implementations. Invisible supports AI across the entire value chain, including data cleaning, automation, chain-of-thought reasoning, and custom evaluations.

Key Lessons for Scaling AI Products

Matthew highlights two important lessons from his McKinsey experience: successful AI adoption requires organizational transformation alongside technology, and mastering the "last mile"—moving from experimentation to production—is crucial. Invisible applies these principles to help customers move beyond pilots and deliver real business value.

Trends in AI ROI for Enterprises

Enterprises achieving real ROI focus on aligning AI use cases with core business KPIs, investing in high-quality data and human feedback loops, and shifting towards tailored, domain-specific AI systems. These companies are scaling AI with clear purpose rather than merely experimenting.

Growing Demand for Domain-Specific Data Labeling

Foundation model providers like AWS, Microsoft, and Cohere are driving demand for specialized data labeling. Invisible maintains a highly selective expert pool with many trainers holding advanced degrees. This expertise is vital for accurate annotation and providing context-aware feedback to enhance model reasoning, accuracy, and alignment.

The Rise of Agentic AI

Agentic AI systems plan, decide, and act within set boundaries, functioning as teammates rather than simple tools. They are especially promising in handling complex, high-volume workflows such as customer support and insurance claims, reducing manual effort and increasing consistency while augmenting human teams.

Chain-of-Thought Reasoning in AI Training

Invisible trains models for chain-of-thought (CoT) reasoning to enable step-by-step logic critical in high-stakes scenarios like healthcare diagnostics, contract analysis, and financial validation. CoT improves transparency, debugging, and performance without requiring massive new datasets. Innovations like Tree of Thought and Self-Consistency build upon this groundwork.

Importance of Cultural and Linguistic Precision

Supporting over 40 coding languages and 30 human languages, Invisible emphasizes the importance of linguistic and cultural nuance in AI. Their multilingual trainers are embedded in the cultures they represent, ensuring models interpret context correctly and avoid compliance risks.

Overcoming Challenges in AI Production Deployment

Many AI models fail to reach production due to underestimated operational challenges, including the need for clean data and robust evaluation. Invisible combines technical expertise with production-grade infrastructure to bridge this gap, enabling successful model deployment.

Invisible's Approach to Reinforcement Learning from Human Feedback (RLHF)

Invisible treats RLHF as a sophisticated process focused on structured, high-quality human feedback capturing reasoning and context rather than simple binary signals. This depth helps models generalize better and align closely with human intent, supporting the development of robust AI systems.

The Future of AI-Human Collaboration

Matthew envisions AI not as a replacement but as infrastructure supporting human expertise. Future collaborations may see AI agents acting as copilots in healthcare, government, and finance, enhancing human capabilities and allowing experts to focus on higher-level tasks.

For more insights, readers are encouraged to visit Invisible Technologies.