OJ Laos is Director of AI at Armanino, where he leads enterprise AI and innovation strategy across the firm’s service lines and internal operations. With over a decade of experience in software management and product delivery, he develops scalable, AI-powered solutions that address complex challenges from data integration and workflow automation to custom applications that enhance decision-making. As product manager for Armanino’s proprietary IP, including pre-built analytics platforms and Power Platform tools, he helps clients and teams unlock value through responsible, high-impact technology.

OJ began his career in Silicon Valley, where he built software to improve pricing transparency and operational efficiency for automotive dealerships. At Armanino, he brings that same product-focused mindset to enterprise transformation. He collaborates with business leaders across industries, facilitates hands-on innovation workshops, and drives cross-functional initiatives that embed AI into everyday processes. He’s committed to bridging vision and execution, helping leaders move from AI experimentation to sustained impact.

Can you expand on Armanino’s launch of the Firmwide Digital Group and the vision behind this initiative?

At Armanino, we treat innovation as a core component that touches every department. The launch of the Firmwide Digital Group, or FDG, is our way of making that commitment tangible. What started as a focused, grassroots “AI Inner Circle” of early adopters grew into a firmwide initiative driven by a single idea. AI should not be on the sidelines. It belongs in our processes, playbooks, and people.

The FDG helps shift our mindset from being curious about technology to creating measurable, scalable impact. We are working with our 2,800-plus team members to help them understand how to use AI in meaningful ways. We are also building systems that empower people to rethink the work itself. At the same time, we are listening to our teams. We ask what issues we can address with AI that may otherwise go unnoticed. We explore how one department’s idea can spark change across the firm. Whether in audit, tax, consulting, business management, or operations, our goal is to make AI useful, usable, and integrated throughout. This is how we create a future-ready firm that leads with AI as a mindset rather than chasing it as a trend.

How is Armanino preparing its workforce at scale to adopt and apply AI in day-to-day operations?

Scalability without accessibility creates resistance. That is why the FDG is not just rolling out tools. We are changing how we work, learn, and share success. We use micro-learning, gamified onboarding, and podcast-driven storytelling. These formats meet our people where they are.

We made the complex easier to approach by creating a feedback loop that teaches and listens equally. True transformation does not happen through keynotes or dashboards. It happens when someone on the front lines solves a real challenge and shares it. That shared experience can spark the next breakthrough.

Our goal is to make AI feel like a partner, not a platform. When we succeed, people stop asking whether they should use AI. Instead, they ask how they can use AI to make something better. That shift is what makes scale sustainable.

What are some of the most impactful AI use cases emerging for enterprises? Where does the technology still face limitations?

The biggest wins are not always visible but they save real time. One of our most impactful use cases involved reviewing tens of thousands of engagement letters. What used to take thousands of hours now takes a fraction of the time, and accuracy has improved.

Another strong use case is synthesis. Our teams use generative tools to summarize meetings, pull relevant research or playbooks, and draft strategic reports. AI has proven to be a time multiplier, helping our people stay focused on their most important work.

We also recognize that AI is not perfect. It is not always right, but it is highly useful. When paired with expert judgment, it can speed up and improve everything we do. That is why training and compliance are essential. Even experimentation must be responsible.

Our firmwide goal is to unlock the full potential of our people by pairing their skills with the most intelligent tools available today.

What should guide executive decision-making on AI at the board level?

It starts with alignment. If AI is only a line item on your IT roadmap, you are already off track. The right question is not what tool to buy. It is what problem you are solving. Define your use case and start small. Build gradually and deliberately.

After that, consider the full ecosystem. Successful transformation happens when your people, platforms, and goals are in sync. At Armanino, we launched Copilot alongside a centralized model for governance, training, and feedback. That combination keeps everything secure and aligned.

Culturally, you need leadership that is just as curious and willing to learn as frontline teams. Great leaders promote experimentation and reward progress by showing results. The most successful firms will be those with the clearest focus and not necessarily the largest budgets.

What risks or considerations are essential for responsible and sustainable AI implementation?

We believe in structured experimentation. Responsible design gives innovation the direction it needs. That is why we built clear AI rules from the start. These include privacy, compliance, data integrity, and ethical safeguards. Guardrails do not limit us. They give our people confidence to move quickly.

You also need shared language and shared responsibility. From the beginning, we brought legal, IT, and risk teams into the process. Transparency is another essential element. When AI tools operate in a black box, it erodes trust. People need to understand how tools work and why safeguards like data privacy are critical.

To make AI sustainable, you need ongoing feedback. What worked six months ago might not be effective today. The technology evolves and your approach should evolve with it.

How important is collaboration across departments and industries in creating scalable AI strategies?

Cross-functional collaboration turns isolated wins into lasting transformation. Many of our most valuable AI use cases came from departments outside the usual suspects. Our audit, tax, and consulting teams identified points of friction and found solutions. The FDG helped validate and scale those ideas across the organization.

AI is the most collaborative technology we have seen. It improves through iteration, adapts as it is used, and grows when knowledge is shared.

What are some of the most common misconceptions executive teams have about enterprise AI?

A major misconception is that AI is a magic button. Some expect it to deliver instant transformation. When results fall short, enthusiasm fades. In practice, AI needs thoughtful integration, training, and oversight. It works best when guided by people who understand both the system and the business context.

Another false belief is that AI adoption is a binary choice. It is not. It is more like forming a habit. You test, adjust, and improve. Over time, AI becomes a natural part of how teams operate and make decisions.

Executives also sometimes chase what is new instead of what is necessary. Flashy tools may get attention but real gains come from solving practical issues. These include inefficiencies like bottlenecks, duplicated workflows, and manual steps.

To avoid these traps, focus on clarity. Know what you are trying to solve. Bring in the people who live that problem daily. And measure progress based on real impact, not surface-level metrics.

Looking ahead, what advancements in corporate AI are most promising?

We are entering a new phase where AI shifts from assistant to active participant. We are already seeing autonomous agents that manage entire workflows. These agents work across ERP systems, trigger actions, and respond to live data. They are starting to mirror human decision-making at machine speed. Instead of telling your AI what steps to take, you will ask it to let you know when the task is done.

We are also excited about modular, model-agnostic platforms. These tools adapt to your systems, your data, and your business flow. They do not force you into rigid processes. Instead, they integrate naturally with how your teams already work.

The real breakthrough, however, will depend on how people change their perception of AI. The future is not about deciding whether to use AI. It is about reaching a point where that question no longer needs to be asked.

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