This is the second of five articles in a series titled How to Actually Apply AI in Your Company (A Guide for the Executive Tasked with Making AI Work).

Here’s a truth that Silicon Valley doesn't want to admit: The difference between AI success and failure usually has nothing to do with AI.

The Flow Efficiency Imperative

To understand flow efficiency, we first need to define key terms:

• Flow Efficiency: The ability of a process to consistently move work from start to finish with minimal delays, interruptions, or unnecessary complexity. It’s about optimizing the flow of tasks and decisions to ensure smooth operations and predictable outcomes.
• Process State: The intermediate stages and conditions through which a task moves before completion. Excessive state introduces unnecessary delays and increases the likelihood of errors.
• Coupling: The degree to which different tasks, teams, or systems depend on each other. High coupling creates bottlenecks and slows overall progress.
• Specialization: The focused assignment of tasks to individuals or systems with specific expertise. While specialization can enhance efficiency, over-reliance on single-person ownership risks creating bottlenecks.

Flow efficiency thrives when single-person ownership is limited outside of specific tasks, hand-offs between people or teams are streamlined, and excessive “state” is minimized. By focusing on these factors, businesses can create processes that are more agile, scalable, and ready for optimization.

The data is unequivocal: Manufacturing plants with high flow efficiency see up to 3x better returns from AI adoption compared to their more "flexible" counterparts. This isn’t just about production lines; it’s about creating predictable, measurable processes that can be meaningfully optimized. Whether you're processing insurance claims or onboarding customers, the principle holds.

When production processes are designed for flow efficiency rather than flexibility, you get:

• Richer, more consistent data for AI training.
• Clearer patterns for prediction.
• Higher stakes for proactive intervention.
• More standardized outcomes to measure.

The Standardization Paradox

"But wait," you’re thinking, "isn't AI supposed to help us handle variation and complexity?"

This is where most companies get it fundamentally wrong. AI isn’t magic—it’s math. Every variation in your process isn’t just another case to handle; it’s exponentially more complexity for your AI to learn. Before you write a single line of code, you need to:

1. Map process variants
2. Quantify the business value of each variation.
3. Eliminate variations that don’t deliver clear value.
4. Standardize the handling of necessary variations.

One financial services provider learned this the hard way. They spent months training AI to handle their "highly customized" client onboarding process. Then someone asked a simple question: "Why do we have 14 different ways to collect the same five pieces of information?" By standardizing their input processes first, they cut onboarding time by 65% before implementing any AI.

The Exception Trap

Here’s where most process optimization efforts go off the rails: exceptions. Every organization has them, and most build their processes around them. This is backwards.

Instead, create a hierarchy of process handling:

1. Core Process: Design for the 80% case.
2. Standard Variations: Document and standardize common deviations.
3. True Exceptions: Create clear escalation paths.
4. Augment rather than Automate: Automation is a buzzword and it's great when it's possible but plan to augment and consider Automation an eventual goal.

The key insight: Exceptions shouldn’t drive process design—they should inform your AI implementation strategy. When you know which exceptions truly require human judgment, you can design AI systems that augment rather than replace human decision-making.

The Managerial Complement

Managerial capacity is a critical complement to both process optimization and AI adoption. It refers to the ability of an organization's leaders and managers to effectively oversee, coordinate, and refine processes and systems. Organizations with higher managerial capacity consistently achieve significantly better returns from AI investments. This is because effective managers:

• Discern between necessary and unnecessary complexity.
• Clearly articulate and uphold success metrics.
• Skillfully integrate AI insights into existing human workflows.
• Foster discipline to maintain standardized processes and refine them over time.
• Empower teams with the right tools and budgets to enable continuous improvement. These managers understand that process enhancement is a perpetual journey, not a one-time task.

Moreover, they prioritize ongoing investment in tools and systems rather than treating it as a one-off expense. Startup founders frequently fall into the trap of thinking they can "build a tool" to quickly resolve an operational problem. While this might seem like a quick win, developing workflow-based systems that lock down a nascent process can stifle growth and limit adaptability. Instead, founders should focus on creating task-based tools that enable flexibility and allow workflows to evolve alongside their business needs. For organizations unable to budget for continuous internal tool development, leveraging third-party solutions can provide scalable, adaptable options to maintain operational efficiency while preserving the ability to iterate as needed.

The Path Forward

Before you invest another dollar in AI, ask yourself:

• Have you mapped your actual processes (not just the official ones)?
• Can you quantify the cost of process variations?
• Do you have clear protocols for handling exceptions?
• Have you invested in the managerial capacity needed to maintain optimized processes?

The harsh reality is that AI won’t fix broken processes—it will only make them fail faster. But for organizations that do the hard work of process optimization first, AI can deliver extraordinary returns.

Remember: The goal isn’t to eliminate all complexity. It’s to ensure that the complexity in your processes is delivering real value. Everything else is just technical debt waiting to happen.

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Sit-Back and listen to a Podcast Version of This Series

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Enjoy!

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