To quantify the real-world impact of its AI tools, Block tracks a simple but powerful metric: "manual hours saved." This KPI combines qualitative and quantitative signals to provide a clear measure of ROI, with a target to save 25% of manual hours across the company.

Assembled initially replaced a manual spreadsheet process. Their success came from understanding the spreadsheet was a symptom of deeper pains like headcount planning, real-time dashboards, and agent utilization. The real value was in solving these complex operational problems, not just digitizing a spreadsheet.

Digital transformation is not a one-time project but a perpetual flywheel of improvement. True change comes from re-engineering processes and empowering people first. Technology and platforms are the final step, not the starting point, enabling a company's ongoing evolution.

The core bottleneck in agile manufacturing isn't the machinery, but the manual creation of work instructions, often done in PowerPoint. This slow, error-prone process prevents rapid iteration and keeps factory workers operating on outdated information. Automating this "atomic unit of information" is critical to creating a robust industrial base.

Proving the ROI for developer productivity tools is challenging, as studies on their impact are often inconclusive. A more defensible business model focuses on outright automation of specific tasks (e.g., auto-updating documentation in CI). This provides a clear, outcome-oriented value proposition that is easier to sell.

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.

Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

The biggest mistake in AI adoption is simply automating an existing manual workflow, which creates an efficient but still flawed process. True transformation occurs when AI enables a completely new, non-human way of achieving an outcome, changing the process itself rather than just the actor performing it.

A traditional IT investment ROI model misses the true value of AI in pharma. A proper methodology must account for operational efficiencies (e.g., time saved in clinical trials, where each day costs millions) and intangible benefits like improved data quality, competitive advantage, and institutional learning.