AI Accelerator: Chris Lombardi, Principal Software Engineer

In this installment of AI Accelerators, we sit down with Chris Lombardi, Principal Software Engineer, to explore how his relationship with AI evolved from cautious early experimentation to a core part of how he designs systems and makes engineering decisions. Chris shares how AI now shapes the products he builds for clients, the first tool that got him genuinely excited, and where AI is delivering the most value in his day-to-day work. He also gets candid about where his skepticism lies, what AI still can’t do without human judgment, and what advice he’d give engineers building their careers in an AI-driven profession.

The AI Accelerator series is an ongoing collection of conversations with people across Confluence who are at the forefront of how we build and use AI.

From the intelligent features embedded in our solutions to the tools colleagues increasingly leverage to get their work done, to personal projects outside of office hours, each installment hears directly from people across teams and disciplines. Find out what’s working, what isn’t, and what they’re still figuring out. Ground-level perspectives from the people living it every day.

Q: How did your relationship with AI develop? Was there a clear moment when it shifted for you?

A: For me, it started gradually. Early large language models showed genuine promise, but they required significant setup and constant adjustment. They were interesting, but didn’t justify deep integration into my workflow. 

The turning point came when the models reached a level of capability where the friction dropped away. Suddenly, I could focus on intent rather than mechanics. I could describe an objective and receive output that was accurate, structured, and aligned with what I had in mind.

That shift changed what I considered possible. It became clear that AI could absorb the categories of work that consumed my time without advancing engineering insight. Offloading that work gives me more space to focus on the decisions that actually shape a system, while engineering judgment remains central. Since then, AI has become part of how I design systems, evaluate trade-offs, and maintain clarity in complex problem spaces.

Q: How does AI feature in the products you build, and what has it changed for clients?

A: The products I build benefit from clearer interpretation of data, better alignment with client needs, and stronger consistency across the experience. I use AI to examine system behavior and requirements at a broader scale, which gives me earlier sight of patterns, risks, and opportunities to refine things before they become problems.

I also apply AI-driven engineering rules and validations during development to keep the product consistent, stable, and true to its architecture, reducing defects and ensuring clients receive a more reliable foundation.

Beyond that, I use AI to enforce structure and coherence across workflows and interactions, reducing friction for users and helping them reach outcomes more directly. Combined with AI‑assisted solution design, I can validate ideas faster and deliver features that more closely match client intent. The result is a product that evolves quickly, maintains high quality, and aligns tightly with client objectives.

Q: What was the first thing you built with AI that genuinely excited you?

A: The first tool I built was a static validation utility that measured progress on a new system-wide feature implementation. This resulted in the ability to automatically check whether each part of the product was adopting the new standards we had introduced. Without AI, the optimal path would have been to track this manually and enforce it through reviews and testing, but AI allowed me to quickly build a targeted tool that benefited the entire team.

I look for solutions with AI rather than seeking validation for AI.

Chris Lombardi, Principal Software Engineer

Q: How are you using AI in your work right now? Where is it having greatest impact?

A: I use AI where it can reduce effort or improve clarity. That means analysing unfamiliar systems and turning around implementation quickly, translating vague or incomplete requirements into structured artefacts such as flows, contracts, acceptance criteria, validating assumptions, and generating and comparing solution options before committing engineering time. AI also acts as an on-demand knowledge bank, resurfacing relevant patterns, prior decisions, and domain context in real time.

A key change is how I decide what is worth solving. I now treat many previously high-difficulty, low-value problems as viable, because AI reduces the cost of exploration, design, and validation to something that fits within normal delivery constraints, sometimes almost immediately. I spend less time on mechanical work and more time on framing problems, defining constraints, and setting technical direction.

Q: Has AI made the work more enjoyable?

A: AI changed the amount of time that I spend on writing and maintaining software following a design decision to produce a working solution. Those steps used to consume a large portion of the day. Now they take far less time.

A typical day used to include long stretches of manual construction. I would translate intent into code, tests, documentation, and scaffolding. I would handle the repetitive parts of a design that were necessary. Today those steps are delegated. I still define the structure, the invariants, and the boundaries, but AI handles the first draft of the implementation. This lets me stay in the problem space longer and move through the solution space faster.

Using AI has expanded my strategic view of what engineering is. I no longer feel constrained by time or the limits of individual knowledge. The constraints now are the processes and practices required to use AI effectively and securely, which is a much more interesting set of constraints to work within. The work feels more empowering and far more high leverage.

I no longer feel constrained by time or the limits of individual knowledge.

Chris Lombardi, Principal Software Engineer

Q: Where does your skepticism sit — on the technology, or on how people use it?

A: I look for solutions with AI rather than seeking validation for AI. With any groundbreaking technology, a degree of skepticism is natural. Understanding how large language models are built, how they reason, and how to manage them turns skepticism into optimism.

By knowing its limitations and how to guide it, I can use AI to enable success rather than treat it as something to be proven useful.

It is important to bear in mind that AI augments rather than replaces human judgement. Because AI can project confidence and perform a wide range of tasks, it is important to design systems that reinforce user understanding and decision-making rather than obscure it. Meaningful results come from staying grounded with regards to how AI operates, what is true in the real world, and how we architect and engineer the systems around it.

AI augments rather than replaces human judgement.

Chris Lombardi, Principal Software Engineer

Q: What does AI still struggle with, in your experience?

A: AI still struggles to translate ambiguous or incomplete problem statements into complete, production-ready solutions. It can generate strong starting points, but it cannot infer the unstated constraints, architectural trade-offs, operational realities, or long-term implications that engineers account for as part of normal decision-making.

Even with well-structured inputs, AI often produces solutions that need correction, refinement, or realignment with the actual intent. Techniques exist that can reduce this gap, and they do improve consistency and reduce rework, but they do not eliminate the need for human judgement. AI cannot independently determine what is acceptable, what is risky, or what is strategically aligned. It cannot validate that a solution is correct, complete, maintainable, or safe. It cannot reason about the system as a whole or anticipate downstream effects. That remains the software engineer’s job.

Q: How should new engineers be thinking about their careers in an AI-driven profession?

A: Software engineering still has the same fundamental purpose: solving real-world problems with technology. Historically, the largest liability has been the volume of code engineers must maintain. The more there is, the more effort it takes to manage, evaluate, and translate decisions into working capabilities. With the appropriate application of AI, I already see that overhead decrease and expect a significant further decrease. Engineers will spend more time on what matters most: understanding problems, defining outcomes, designing systems, and navigating trade‑offs.

For new graduates or apprentices, my recommendation is to focus on AI in conjunction with patterns, practices, architectures, and technologies, understanding what exists, and when, why, and how to apply them together to be the most effective. That judgement is what will define successful careers. I would challenge new entrants to go further and create new patterns centered around AI, not just adopt the existing ones.

Q: What are you watching in AI right now?

A: I’m watching advancements in both model capabilities and hardware resources. Increased reasoning capacity, larger context windows, and controlled recursive processing will push the boundaries of what’s possible. That combination is what I believe will amplify AI.