Just as Kaizen and “China cost” revolutionized physical product businesses over 40 years, AI is initiating a similar, decades-long optimization cycle for intellectual property and human-centric processes. Companies that apply this “digital Kaizen” to lean out workflows will gain a compounding cost and efficiency advantage, similar to what Danaher achieved in manufacturing.

Most AI coding tools automate the creative part developers enjoy. Factory AI's CEO argues the real value is automating the “organizational molasses”—documentation, testing, and reviews—that consumes most of an enterprise developer’s time and energy.

Boom Supersonic accelerates development by manufacturing its own parts. This shrinks the iteration cycle for a component like a turbine blade from 6-9 months (via an external supplier) to just 24 hours. This rapid feedback loop liberates engineers from "analysis paralysis" and allows them to move faster.

A study of 100 R&D leaders found teams spend a staggering 70% of their time on communication-related tasks: 30% on information lookup and 40% creating documentation. This administrative burden is a primary bottleneck slowing speed-to-market for new products.

To drive adoption of automation tools, you must remove the user's trade-off calculation. The core insight is to make the process of automating a task forever fundamentally faster and easier than performing that same task manually just once. This eliminates friction and makes automation the default choice.

The most powerful automations are not complex agents but simple, predictable workflows that save time reliably. The goal is determinism; AI introduces a "black box" of uncertainty. Therefore, the highest ROI comes from extremely linear processes where "boring is beautiful" and predictability is guaranteed.

AI tools like LLMs thrive on large, structured datasets. In manufacturing, critical information is often unstructured 'tribal knowledge' in workers' heads. Dirac’s strategy is to first build a software layer that captures and organizes this human expertise, creating the necessary context for AI to then analyze and add value.

To identify prime automation opportunities, analyze your company's existing SOPs. These documents explicitly list the sequential steps, data sources, and transformations in a predictable process. If a process is documented for frequent human use, it's a strong candidate for a high-value automation workflow.

Just as electricity's impact was muted until factory floors were redesigned, AI's productivity gains will be modest if we only use it to replace old tools (e.g., as a better Google). Significant economic impact will only occur when companies fundamentally restructure their operations and workflows to leverage AI's unique capabilities.