How Going ‘All In’ on AI Yielded Business Benefits for Software Developer

Committing to AI led to staff turnover in the short run but also strengthened the organization by enabling both existing and new workers to focus on higher-value work that benefits customers, explains Eric Vaughan, CEO at two software companies.

While many businesses are wrestling with exactly how and where to adopt AI, Eric Vaughan, CEO of Ignite Tech and GFI Software has put AI at the heart of both companies. That has included replacing the 80 percent of his staff who were reluctant to make AI central to their jobs and, at one point, devoting 20 percent of every employee’s time to learning AI.

After a year-long transition, Vaughan says the benefits are clear: stronger results, not just in efficiencies, but more importantly, innovation. “The biggest benefit of AI isn’t cost cutting,” Vaughan says. “It is the ability to eliminate routine work for everyone from the CEO to the CIO to a software engineer to free their time up for more important, critical tasks. AI can enable every worker to become the best version of themselves – but only if they see the potential and are willing to work to achieve it.”

In this interview, Vaughan discusses his work at both software businesses (which are part of the same holding company). He explains their commitment to AI, the challenges his organizations have worked to overcome, and lessons he sees for CIOs and others.

Bob Scheier: When did you make this major shift to AI?

Eric Vaughan:  When my friends and family who are completely outside of the tech sector started asking questions about ChatGPT, it was an “Aha moment.”

Coupled with NVIDIA’s CEO, Jensen Huang’s comment that this was the “iPhone moment for AI,” I realized I didn’t want to be behind on this, and I didn’t want my companies to be behind. We are in the technology industry, and our customers expected us to lead. This was such a monumental shift, globally, in every walk of life, that we had to bet the farm on it and remake both companies.

What did that mean in practical terms?

Our software development strategy, our innovation strategy, our investment strategy, all are now AI-based. For example, we don’t do feature upgrades for software products that don’t include AI. The roadmap for both our small and medium-sized business and enterprise products revolves around taking what we already have and using AI to enhance them. This initially meant we introduced “co-pilots” to augment our existing product base. But with our new team of AI innovation specialists, we also designed and created two new AI products from the ground up in less than nine months.

How did you begin the shift?

In the first quarter of 2023, we had an all-hands meeting, and I put a big red X over the usual agenda and said, “That’s not what this meeting is about. We’re here to talk about how generative AI is changing our world and how to quickly adapt to it.”

We spent 2023 doing what we could to retrain our legacy team to inspire them about AI and their future. We allowed our entire staff to spend up to $1,200 each on AI tools, getting the “pro” versions so they could learn without justifying that spending. In Q1 of 2024, we dedicated every Monday, literally 20% of our company-wide payroll, on nothing but enabling our teams to learn AI tools and developing their AI expertise.

How did they respond?

It was mixed. Cultural changes are difficult and some just didn’t believe in the process. Others openly rebelled against the change, seeing this as an affront to their years of experience. At that point we said, “We understand, and we wish you well in your next endeavor.”

How many people left?

From Q2 of 2023 to Q2 of 2024, we replaced approximately 80% of our entire team. I’m neither proud nor happy about that, and it’s not what I expected. But my conscience is clear because we gave everyone every chance to succeed.

Who left and who stayed?

What surprised me was that the marketing, sales, and financial people loved it because they saw how AI could make them more efficient. The technical people were the most resistant. I had expected that they, being more technical, would be the earliest and most enthusiastic adopters. They didn’t believe that AI could write code as well as they could with their years of experience. But AI has been trained on all the languages, which made unique language knowledge obsolete. 

Who were the new hires?

They were all recruited as “AI Innovation Specialists” for various domains, such as finance, marketing, sales, support, and software engineering. But the first criterion was their aptitude in and knowledge in AI tools for their domain. We were also insistent on finding people who were plugged into the latest trends because they are changing literally every day. We came up with a very unique way of describing AI-First, that we used as a hiring rubric. In essence, being AI-First means identifying opportunities to leverage artificial intelligence for solving complex business challenges. We had everyone we needed to build an AI-first team by Q3 2024.

We also used industry conference appearances and interviews like this one to spread the word that “If you really want to get into AI, this is where you should be.” It worked.

What has the payback been on this shift?

Our two companies combined have revenue in the nine-digit range, with more than $2 million in revenue per employee. For fiscal year 2024, our EBITDA (earnings before interest, taxes, depreciation, and amortization) was 75 percent. At the end of 2024, we had developed two brand new, patent-pending AI-powered solutions we built from the ground up in less than eight months. That’s proof of the approach from my view. 

Did AI allow you to reduce your headcount?

We reduced our staff by about 15 percent but that was never our aim. Our aim was to eliminate mundane and routine work and enable people to do their best job by doing deep thoughtful research into how to better serve our customers.

For example, we eliminated a lot of our Level One customer support representatives (people who handle the simplest, most common problems) but upskilled many of those 
individuals as Level Two agents, resolving more complex issues and helping us create automated support systems.

What lessons have you learned about using AI in your business?

Rather than spending a lot of time and money trying to change the attitudes of people who cannot change, create your new AI-focused job descriptions and have your existing staff apply for them. That way, you’ll quickly find out who isn’t on board with AI. Replacing those who weren’t immediately all-in would have been quicker and less painful.

Which AI models are you using?

We’re not building our own models but constantly testing both open-source and proprietary models. My advice, and our approach, is to build systems that are model agnostic with a software layer using what we call an AI API, or A(I)PI, through which your applications can call multiple models, so you’re not locked into one vendor.

You recommend not using terms such as “agentic AI” and RAG (Retrieval-Augmented Generation). Why?

The people who make the buying decisions -- CEOs, CxOs and other leaders attuned to the bottom line – don’t buy features, they buy benefits. If you use the term “AI” in your customer conversations, explain how these tools can actually deliver tangible benefits. Speak in terms of reducing repetitive work and freeing up your team to do more creative work rather than quote industry buzzwords. That’s the lingua franca of people who actually buy.

What does your experience offer to CIOs, including those at public companies?

Start at the top. Your C-suite must understand AI and use it themselves to identify the best use cases and strike the right balance between governance and innovation. They cannot have a hands-off attitude and outsource these decisions, similar to the early days of the Internet when CEOs had their admins type their emails. Be sure your legal team is also educated first because they will need to address new liability issues and changing regulations in different geographies. Without that education, they will likely prescribe overly protective and cautious approaches, which will both mitigate advances using AI and drive AI innovators on their staff to other companies.