By Hank Marquis

Ace Your Digital Transformation

Image Credit: pexels.com/@volkerthimm

How to Ace Your Digital Transformation

Great IT outcomes aren't about charisma, charm, or clever messaging. They come from understanding how work actually gets done—and what quietly gets in the way. In my book Completely Satisfied, that meant examining what people perceive every day, what they expect from their tools, and where those two fall outside acceptable tolerance. Done manually, that work takes time, coordination, and serious analytical effort.

Ops tools fix what breaks. Experience tools report how it feels. But neither explains root causes, which is why friction compounds and revenue quietly bleeds away. Employee experience is where digital friction shows up first, shaping how effectively work gets done and how reliably outcomes follow.

Before tools like Hailee existed, organizations relied on interviews, surveys, spreadsheets, and long workshops to interpret experience data. Teams mapped perceptions and expectations, calculated tolerance ranges, aligned activity importance, and reconciled everything against benchmarks and real workgroup conditions. The method worked—but it was slow, expensive, and hard to repeat at the speed digital transformation demands.

Hailee automates that same method. The principles from Completely Satisfied don't change—only the time it takes to see what's holding execution back.

Reliability

Reliability reflects the gap between the dependable service people expect and what they actually receive. Outages, inconsistent behavior, and unstable performance push perceptions below reasonable tolerance. Traditionally, teams gathered uptime data, coded survey responses, and compared perceptions to benchmarks to locate the breakdown. Hailee performs the same analysis automatically, using workgroup expectation and perception patterns to explain where reliability erodes execution.

Assurance

Assurance concerns trust—whether people believe systems are accurate, secure, and competently supported. Expectations form around transparency and dependability, and every interaction either reinforces or undermines them. Manual analysis required interviews, risk reviews, and alignment sessions to uncover where confidence slipped. Hailee applies the same logic instantly, showing where assurance gaps emerge across a workgroup.

Tangibles

Tangibles cover interfaces, navigation, documentation, and how information is presented. People compare every system to the best tools they already use and notice immediately when something feels harder than it should. Historically, this meant usability studies and detailed feedback analysis. Hailee identifies where tangible design choices fall short of expectations and how that friction affects high-importance work.

Empathy

Empathy reflects whether people believe their input matters and whether changes are communicated clearly. When feedback disappears or decisions arrive without explanation, trust erodes. Manual assessment required reconstructing feedback patterns and communication timelines. Hailee uses the same framework to show where expectations of inclusion and responsiveness break down.

Responsiveness

Responsiveness captures how quickly people and systems react when work is blocked—support response times, ticket handling, and system speed. Manual analysis meant correlating performance data with support feedback and activity importance. Hailee automates this process, showing how delay and waiting compound friction across the workgroup.

This is not guesswork. It is the structured method from Completely Satisfied: expectation and perception analysis, tolerance ranges, gap identification, activity importance, and benchmark comparison. What once took months of manual effort can now be done in minutes.

Digital transformations don't fail because leaders lack ambition or employees resist change. They fail because leaders can't see what is actually holding work back. When friction becomes visible, transformation shifts from belief-driven programs to evidence-driven execution.

Please comment or reach out and let me know what you think, I'd love to talk with you!

Best,
Hank

— END —