Fixing the Release Cycle: How a Kanban Transformation Delivered Liquidpoint’s First On-Time Release in Two Years
When Liquidpoint acquired Associated Options, I transitioned from the trading floor to the corporate office. My new assignment? Figure out which developers were causing delivery delays.
They hadn’t shipped a quarterly software release on time in over two years. Sometimes, they skipped releases entirely.
Spoiler: it wasn’t the developers.
The Problem
Liquidpoint was stuck in a waterfall model, paired with long quarterly release cycles. Sales would lock in scope six months in advance, then change priorities mid-cycle to land deals. Developers received full scopes up front and naturally bounced between Item A, then B, then suddenly F when Sales asked.
Each cycle ended with frustration.
The data told the real story:
- 23 items were scoped.
- 23 items were delivered.
- 65 items were started.
This wasn't bad estimation. It was an invisible work-in-progress overload—untracked, unmanaged, and slowly eroding trust in the entire process.
The Fix: Kanban With Backbone
I didn’t just recommend Agile—I implemented Kanban Agile with strict operational rules.
- Sales could reprioritize the backlog at any time.
- But once an item entered In Progress, it couldn’t move back until it was finished—QA’d and merged into the main branch.
This created clarity for developers and structure for the business.
To make it stick, I gave both sides a win:
- Sales got visibility and real-time backlog control.
- Engineers got assurance that they’d never have to discard partially completed work again.
And because new work couldn’t begin until the last item cleared QA, devs had space between items to focus on innovation, tech debt, and backend performance.
The Results
The rollout was, frankly, anti-climactic—in the best way.
The chaos disappeared. Releases shipped. People stopped talking about “the process” and got back to doing real work.
And that original question—“Which developers are underperforming?”—finally had a real answer. Clean systems exposed clean data: accuracy, rework rates, QA bounces, throughput.
What I Learned
This experience reaffirmed a core belief that now anchors my entire operational playbook:
The problem you're hired to solve is almost never the real problem.
Symptoms lie. Systems reveal the truth.
Want help uncovering your team’s invisible inefficiencies or rebuilding trust in your delivery process? Let’s talk.