Results, Risks, and What Comes Next

By Greg Ingino, Chief Technology Officer, Litera

This is Part 3 of a three-part series on how Litera transformed its R&D organization over the last 18 months. Part 1 covered the adoption arc and the headline business outcomes. Part 2 walked through the Mates program, the system of purpose-built agents that automates the full lifecycle from idea to market. Here, I'll share the output and quality data, what we're watching closely, and the principles I'd carry into any organization attempting the same shift.

04 Output per Engineer Climbed Across the Board

Rising AI usage matters only if it reaches output, and it did. Indexed to the start of the period, code changed per developer grew about two and a half times, reaching 246 on a base of 100. Pull requests merged per developer more than doubled, to 263 on the same base. These are per-person measures, so they isolate genuine productivity at the individual level.

Quarterly Detail

There is a useful nuance in the planning units. Story points completed and completed epics both rose over the period, though far less steeply than code volume and per-developer output. The gains concentrated in throughput and code. The cleanest reading of the table is the gap between two rows: total code changed rose by well over 100% while the developer count fell by roughly a tenth. The organization is producing substantially more with fewer people, and the per-developer figures show this as real leverage at the individual level.

05 Quality and Security Held as Volume Rose

A productivity surge usually raises the risk that quality quietly erodes. Here the data runs the other way. As the volume of code changed rose every quarter, the rate of customer-reported defects per million lines fell by about two thirds. Output and quality moved in opposite directions, which is the result you want and the one that is hardest to achieve. Even in absolute terms, total customer defects have fallen over the period even as deployment volume climbed, the clearest sign that the added velocity has not come at the cost of quality.

Two mechanisms explain this. QualityMate expanded test coverage faster than humans could have managed alone, so more of the new code arrived with tests attached. And because engineers spend less time producing first drafts, more of their attention goes to reviewing and hardening what the system generates. As QualityMate absorbed the bulk of test creation, we redeployed many of our quality engineers, who were among our highest AI adopters, into other parts of the organization to lead the same transformation there.

The discipline is in keeping the suite healthy as it grows. QualityMate retires stale tests about as fast as it authors new ones, so the total stays close to flat, regression time stays down, and coverage keeps climbing.

A healthy test suite matters more than a large one. AI now automates about 85% of tests while the suite size holds roughly flat.

Security followed the same path. Vulnerability density fell by more than three quarters over the period. SecureMate carries much of this load, surfacing and helping remediate issues earlier in the lifecycle where they are cheaper to fix.

06 Faster Response, More Carried per Person

The productivity and quality gains compound into operating leverage. Average customer bug resolution time roughly halved, and the ratio of developers to quality engineers widened as QualityMate absorbed a growing share of test work. Each of these is a measure of how much the team can carry per person. One operational note worth recording: as the pace rises, geographic proximity has become an advantage with teams that build and run a feature sitting close together. This means real time work, adjustments, and issue resolution become more collaborative and faster. This is one interesting finding that higher automation has brought our people closer together.

Set against the Q1 FY2025 baseline, the leverage shows up clearly across the board. Code per developer is the standout, but the operational measures moved just as decisively in the directions that matter, including a 65% drop in customer defect density. Doing more, with better quality, with fewer people is the whole story in a single chart.

07 What We Are Watching

Change at this pace creates real tensions, and it would be dishonest to present only the gains. We are tracking a few items closely, and the most pressing one now sits at the end of the pipeline.

Go-to-Market and the Pace of Change

The speed of release has become its own challenge. We can now ship faster than the organization can absorb, which has pushed the constraint downstream to go-to-market. Every release carries change that has to be learned and enabled across the company, and customers have to be prepared for the volume that is coming. Bringing go-to-market into the automated pipeline as a first-class phase, so enablement and customer readiness move at the speed of delivery, is a current focus.

Human Touchpoints as the Next Bottleneck

With most of the lifecycle automated, the human touchpoints that remain, the reviews and the sign-offs, are becoming the limiting factor on speed. We are developing confidence scoring to clear those touchpoints without sacrificing safety. The aim is to keep firm security and quality gates in place so that anything shipping or being approved still carries the right oversight, while routine work passes through automatically and only the cases that genuinely need a person are routed to one. Finding ways to do this without giving up that oversight is the next unlock for velocity.

The Rising Cost of Consumption

As our use of AI scales, the cost of running it rises, and so far, that cost is well justified by the value we are seeing. The discipline we are building is to weigh each body of work on its own terms, the cost to produce it against the value it returns, and to ask whether a given piece of work should be done at all. That cost-to-value judgment is the next thing to watch.

DevOps Controls and Automation

Moving at this pace only works if the delivery pipeline can keep up. We are watching the maturity of our DevOps automation and the quality and security gates inside it, to be sure the controls that protect the firm scale alongside the speed. The goal is a pipeline where those controls are automated into every release, so velocity and oversight rise together.

Team Sentiment

The early going was hard. The pace of change and the shift in how the organization works asked a great deal of people. As people learned the tools and folded them into their everyday work, however, sentiment rose above where it was before we started this transformation. We keep a close watch on it, because the people doing the work are what give the gains lasting impact.

Customer Defects as Velocity Rises

As we keep increasing velocity, the priority we watch most closely is making sure the number of customer bugs in our deployments does not climb with it. Total customer defects have fallen over the period even as throughput grew and holding that line as we go faster is the goal.

Lead Times at the Planning Edge

R&D and release lead times have held roughly steady. POMate is closing this gap at the front of the lifecycle and extending the same discipline through Define and Build is the next priority.

Releasing faster than the organization can absorb has made go-to-market the next constraint. Enablement and customer readiness now have to move at the speed of delivery.

08 How to Pursue the Same Shift

The approach is portable. If I were starting again in another organization, these are the principles I would hold to.

Set a Bold, Measured Goal and Blow Up the Old Way of Working

Commit to a specific, public, almost impossible target, and align the organization on the few metrics that prove it, why each one matters, and an honest baseline. Reaching it takes more than incremental gains; it means dismantling the old ways of working while the technology is still maturing.

Put Dedicated Owners on It

This does not happen in the gaps of a day job. Stand up a dedicated team with its own learning and development environment and a clear owner for adoption and governance, and push ownership of the Mates to engineering managers, so accountability sits where the work happens.

Build Governance In From the Start

Put governance up front, before the rollout scales. Being late is costly: it slows everything down and puts the firm at risk. Make security, quality, and oversight part of the foundation from day one.

Automate the Entire Lifecycle

Start in a single area, master it, and build out from there. The largest gains came from removing handoffs, so put a named owner on every step from idea to go-to-market and orchestrate them through one layer, until a team can run a feature end to end on its own.

Automate Your DevOps Pipeline, With Gates

Velocity only holds if the pipeline can carry it. Automate the DevOps pipeline so releases move at the pace of the work and build quality and security gates into it so every release earns the right oversight automatically.

Plan Go-to-Market from the Start

Treat go-to-market as a first-class part of the lifecycle from day one. Plan for the internal enablement each release demands and for preparing customers for the change coming, so delivery speed does not outrun the organization's ability to absorb it.

Pick Your Tools and Get Good at Them

The market ships a better model or tool nearly every week, and the pull to chase each one is real. Teams that flip-flop spend their time evaluating and never build momentum. Pick a small set, commit, and get good at them. We standardized on Cursor and Devin and went deep, and that focus is what let us keep executing while others were still comparing.

Ground Every Agent in Shared Knowledge

A living, centralized knowledge repository is what lets agents hand work to one another without losing context. Build it alongside the product team.

Find Your Champions and Give Room to Explore

Run pilots and ask for volunteers; the champions become obvious fast and show everyone the art of the possible. Give teams room to experiment and permission to fail, so those champions can lead.

Automate the Sign-Offs, Keep Humans on Judgment

Confidence-scored review agents handle routine approvals and route the rest to the right reviewer, so people spend their time where judgment matters.

The trajectory is favorable, and the evidence here points to gains that are durable and compounding. The next phase is consolidation: extending the same discipline to the workflows that are not yet automated, holding quality firm, and bringing go-to-market up to the speed of delivery. A closing thought for anyone taking this on. Design your organization for the velocity it will create from the very start, because the bottleneck moves downstream to learning, enablement, and customer readiness. Build the capacity to absorb change with the same intent you bring to creating it.

Greg Ingino is Chief Technology Officer at Litera.

← Read Part 1: The AI Productivity Inflection

← Read Part 2: The Mates Program: One Orchestrated Pipeline

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