Many AI efforts stall in the same place. Teams cannot find the truth about how their systems work fast enough to change them safely. Hours disappear into rebuilding context that should be easy to find, so a simple system question turns into Slack archaeology and a repo hunt.
Atlassian’s 2025 State of DevEx report found that 50% of developers lose 10+ hours each week, and 90% lose 6+ hours or more, to organizational inefficiencies. In practice, a big share of that loss shows up as time spent finding the truth about how systems work. Using AI effectively in an organization involves resolving that bottleneck.
AI efficiency comes from making systems easier to explain and safer to change, with less rework downstream. If “efficiency” in your organization is already being translated into cost pressure, reset the frame. Cost matters, but it follows uncertainty and rework.
Let’s walk through how to use AI as a capability lift, through three pragmatic patterns.
Pattern 1: Use Private AI to Make Your Codebase “Answerable”
When teams cannot quickly explain how their systems work, delivery slows before anyone touches code. Documentation drifts as well, and critical system knowledge lives with a few people, or disappears when they’re unavailable.
Plus, estimates get padded because basic questions take too long to answer, like who is talking to this service, what consumes this queue, or what does this table represent in the real world. Todd Parker, Solutions Architect at Axian, often hears from client teams: “I don’t know, go read the code.”
Private AI is a practical response to that gap. As described by Todd, think of this as an internal AI bot that can be given access to source code to ask questions right away. “Private” here means access-controlled workflows that keep code and data inside approved boundaries, with answers traceable to internal sources.
If you are deciding how to use AI in a complex codebase, start by making the codebase answerable. Begin with a quick structural scan, note the patterns you see, and provide that context before you ask anything deep. Done well, this is how to use AI better without turning it into a side project.
Questions worth asking early:
- What is this component doing, and where is it used?
- How does this database layout translate to the real world?
- Who is talking to this service, including through queues and other non-obvious paths?
Outputs to demand:
- A living system map of services and dependencies
- A short domain glossary that matches the code
- An onboarding primer that explains how the system works
The payoff is reduced uncertainty and less guesswork baked into decisions and estimates. Efficiency.
Pattern 2: Use Functional Principles to Reduce Maintenance Risk
AI will not reliably compensate for a codebase your team cannot confidently understand. Hidden state and side effects make it hard to predict what a change will break, so teams ship regressions and spend longer diagnosing incidents.
You can reduce that maintenance risk without a functional rewrite. Apply functional principles where complexity hurts most:
- Reduce mutable state and keep side effects at the edges so intent stays visible.
- Keep core logic as small “data in, data out” transformations that are easy to test and reuse.
These choices improve outcomes leaders care about. Onboarding gets easier because engineers can follow the flow without tribal knowledge. Releases get steadier as behavior is easier to trace and the change risk stays contained.
Treat this as an engineering policy. Pick one boundary, like business rules or integration adapters, and set senior review expectations that reward clarity. This is AI efficiency in practice, because you spend less time debugging surprises and more time shipping.
Pattern 3: Use AI Tooling to Increase Throughput and Communication Without Lowering the Bar
AI earns its place when it turns vague work into a usable first pass and turns technical notes into language that leaders can act on. Ownership stays with the person who ships the change. As Todd Parker puts it, “If you’re checking it in, that’s your code.”
If you are working out how to use AI day to day, aim it at work that burns senior time but does not require senior judgment. Use it to break a problem into steps and generate a first pass, then apply senior review. Expect a draft you can shape and test, not a finished answer.
It also helps when you need alignment. Todd advises feeding bullet notes into AI and asking it to tailor the message for a specific audience, so technical observations become clear inputs for leadership decisions and risk calls.
To scale this without lowering the bar, you need a way to tell whether AI is reducing work or adding noise. A simple way to check for AI efficiency is to evaluate its outcomes. On a regular cadence (often quarterly), compare lead time and change failure rate against your current baseline.
Dream Bigger: Build Clarity, Then Speed
Use AI better by treating it as a capability lift that improves predictability. When teams spend less time rebuilding context, they spend more time making safe changes.
The playbook is straight forward:
- Make the codebase answerable with private AI so teams can get answers quickly.
- Apply functional principles so behavior stays legible and changes become safer.
- Use AI tooling to speed up first passes and improve decision communication, with senior ownership.
AI efficiency shows up here as less rework and steadier delivery.
Looking for assistance with applying these patterns safely? Contact Axian to de-risk AI adoption and build a measurable operating model.