Flywheel Tooling: Leveraging AI-Driven Development to Fix the "Dusty Corners" of your Organization
How to align the velocity of AI-driven development to your organization's core objectives.
Flywheel tooling is the concept of leveraging rapid, AI-driven software development to find and fix the interconnected, “dusty corners” of an organization. It rests on three fundamentals at the intersection of operations, software engineering, and AI. Together, these components create a powerful cycle of efficiency; hence, the term flywheel.
Inefficient processes have a compounding, negative impact on downstream workflows.
Building small, internal tools leveraging the speed of AI-driven development is now extraordinarily efficient.
AI systems always magnify output – good or bad.
AI Magnifies Indiscriminately
Intuitively, leaders often consider broken processes as low-hanging fruit for AI adoption. In reality, this approach can compound the problem. When workflows are poorly understood, data sources are disconnected, or employees are not well trained, introducing AI merely exacerbates existing chaos. Instead, constructing simple, methodical software substrates around these processes ensure they have a strong foundation before AI automation is considered.
If a process is well structured, AI can significantly reduce workload while maintaining quality. The opposite is also true. AI systems magnify indiscriminately.
Example: Disparate Data Modeling
Let’s imagine an organization with inconsistent modeling and forecasting practices – a common, big-picture problem. Generally, departments have agreed on a few key drivers, but ultimately each maintains their own series of manual spreadsheets populated by disparate sources of data. Finance forecasts revenue, Operations maps workflow capacities, and People performs headcount planning. Each of these modeling processes spawn dozens of inefficient secondary processes, catalyzed by inefficiencies of the first. This is our flywheel – but it’s moving in the wrong direction.
Stale Data: Each model’s key drivers (closed deals, average revenue per account, etc) are stale as soon as they’re imported into the model.
Misaligned Definitions: Each department defines model variables slightly differently, conflicting with other departments.
No Single Source of Truth: Models are isolated from each other and thus, unable to share a single source of truth.
Connecting these spreadsheets to an AI tool will only serve to magnify existing problems and further confuse teams. Instead, the business can first invest in standardizing these models into a structured software system – either building a solution in-house or procuring a solution like Looker or Power BI. This transition has an immediate and lasting impact — moving a neglected process to one that can stand on its own. Only then, can AI automations be considered a viable option.
Basic Framework
Conceptually, flywheel tooling looks something like this:
Discover: Identify broken or neglected processes throughout the organization.
Isolate the Root Cause: Drill down past the symptoms to isolate the real reason the process is broken or neglected.
Define the Objective: What are we trying to accomplish? Does it address the root cause? How will we measure success?
Build or Integrate: Where appropriate, build or integrate lightweight software solutions around the process and its data.
Refine & Evolve: Using this new foundation, evolve and refine workflows to meet quality expectations.
Automate: Only once these workflows have matured, consider it a candidate for AI automation.
Hidden Benefits
Implementing flywheel tooling has several key benefits outside of general process improvements:
It forces leaders to face neglected processes head-on. Understanding processes is important; understanding what processes are broken is imperative.
It channels the power and velocity of AI-driven software development towards meaningful, impactful solutions that align with the organization’s larger objectives. Without this, engineers can fall victim to the “building because we can” mentality.
It provides subject-matter experts, who may otherwise not have the opportunity, to contribute to new, innovative solutions. This can shift the morale from change fatigue to ownership and excitement.
These types of small, internal software structures can usually be built by pairing an engineer and a SME without distracting from bigger-picture product roadmaps.
Avoiding the “Build Because We Can” Mentality
While flywheel tooling can be a powerful concept, it also requires operational discipline. As the speed of development has increased, so has the “build because we can” mentality – from both technical and non-technical teams. Many organizations have handed out tools like Claude Code with little discretion, creating a massive backlog of software that is fragile, not well-understood, and riddled with security concerns. Without careful planning, this software is primed to be the “dusty corners” of tomorrow.
Prior to AI-driven development, the “build because we can” mentality had a natural limiter: time. Today, teams must practice a new level of rigor, ensuring that this new “superpower” is continuously aligned with the organization’s objectives.
In Summary
The “dusty corners” of your organization are not ready for AI automation. This approach will only magnify their weaknesses.
Instead, work with your technology team to leverage the velocity of AI-driven development to help address these neglected processes, first getting them in working order.
Only when the process is stable should it be considered a candidate for AI automation.
Use this cycle of flywheel tooling to help align the velocity of AI-driven development to your organization’s core objectives. Foster innovation in the right direction.
We’ll discuss Flywheel Tooling at length in an upcoming title, APPROACHABLE AI FOR BUSINESS LEADERS.
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The distinction between using AI to accelerate software development and using AI to automate the work itself is especially useful.
I’m seeing a related dynamic in an enterprise voice-agent project. The technology works, but the process depends on business rules that were never explicit enough for a machine to follow. Human agents have been quietly interpreting that ambiguity, making the workflow appear more stable than it actually was.
The complexity had not disappeared. It had been transferred into the judgment of the people doing the work.
That hidden transfer does more than keep the process moving. It trains people to absorb ambiguity rather than require the organization to resolve it. Repeated long enough, the workaround becomes the operating model.
That makes the discovery and root-cause work within flywheel tooling especially important. The tacit judgment inside the existing process has to become visible before it gets encoded. Otherwise, faster development may formalize the workaround instead of repairing the underlying process.
AI doesn’t only magnify the dusty corners. It reveals the invisible human work that allowed them to function.