If you want real improvement—not just more dashboards—workflow efficiency metrics have to start with something most teams avoid: visibility. In Part 2 of our interview with Michael Toguchi, we move from "big ideas" into the operational reality leaders face every day: shadow tools, duplicate systems, fuzzy ROI, and the pricing pressure that shows up when AI makes work faster.

This conversation is a reality check for ops leaders, engineering leaders, and consultants trying to scale without drowning in tool sprawl—or measuring productivity in ways that break trust.

Workflow efficiency metrics only work when the workflow is visible. If work lives in shadows, your data will lie.

About Michael Toguchi

Michael Toguchi is the Chief Strategy Officer at eResources, where he leads strategy for technology that supports complex, high-stakes workflows across higher education and mission-driven organizations. With 25+ years in digital transformation, Michael helps teams reduce tool sprawl, eliminate manual bottlenecks, strengthen compliance, and measure improvements in ways that translate into real operational capacity and impact.

Tool Sprawl Starts as "Helpful" (Until It Becomes Expensive)

Every organization eventually meets the "skunk works" problem: someone builds a spreadsheet, a quick app, a mini database, or a side process that solves a real pain—fast. It's well-intentioned. It's also how silos form.

Over time, those small fixes become a parallel organization:

• Data gets duplicated in multiple places
• Teams report numbers that don't match
• Leaders lose confidence in what's "true"
• Tech debt grows quietly because no one owns it end-to-end

Michael's warning is simple: when every department solves problems in isolation, the organization pays for it later—usually in rework, compliance risk, and decision-making paralysis.

Shadow tools don't just create tech debt—they create decision debt.

Workflow Efficiency Metrics Start With Transparency, Not Control

The fix isn't to ban spreadsheets or crush experimentation. Michael's approach is more practical: shine the light on the workflow, then standardize intentionally.

That means asking better questions:

• Who is doing this work today—and why?
• Where does the data enter, and where does it leave?
• Which steps exist because the system is unclear… versus because the work is truly necessary?
• What systems must integrate so people aren't forced into duplicate entry?

Transparency isn't micromanagement. It's a shared map. And once everyone sees the same map, you can make changes that stick.

"Shine the transparency light on the workflow." Then decide what to standardize and integrate.

Workflow Efficiency Metrics That Matter: Time Saved → Capacity Gained

A big takeaway from Part 2 is how Michael thinks about measurement. Leaders often struggle here because "value" feels subjective—until you translate it into something tangible.

Instead of measuring activity ("tickets closed" or "hours logged"), focus on outcomes:

• time reclaimed
• errors reduced
• handoffs eliminated
• cycle time improved
• compliance risk reduced

Michael shares a practical framing: if you reclaim even a slice of time—say 15% of a team's capacity—that's not just a feel-good metric. It's a lever you can pull:

• that capacity becomes more customers served
• more projects shipped
• more support coverage
• fewer burnout-driven departures

In other words, the metric isn't "time saved." The metric is what the organization can now do because time was saved.

Time saved is only "real" when it turns into capacity, quality, or revenue.

When AI Shrinks Time, Time-and-Materials Pricing Breaks

Then Michael hits the business-model shift that a lot of teams are quietly wrestling with: AI compresses time. Work that took weeks can take days. The value may be the same—or higher—but the hours shrink.

If you sell hours, you're forced into a bad choice:

• charge less (even if the impact is huge), or
• justify hours that no longer make sense

Michael's answer is to move up the stack: value-based pricing, retainers, and partnership models—ways of charging for outcomes, access, and expertise instead of minutes on a clock.

That shift requires maturity: you must be able to explain your value clearly and measure the results you're creating. Which brings us right back to the point of the episode…

Workflow efficiency metrics aren't just internal tools. They're how you prove impact when "time spent" stops being the story.

Value-priced work + retainers make sense when time shrinks—but outcomes still matter.

Closing Thoughts on Workflow Efficiency Metrics

Part 2 is a playbook for modern leaders: reduce tool sprawl with transparency, measure efficiency without eroding trust, and adapt your pricing model as AI changes the relationship between time and value.

In a world where speed is easier to buy, the winners will be the teams who can see the workflow, measure what matters, and price the impact.

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