As AI lowers the barriers to software development, how should finance leaders think about technology investment? Does AI fundamentally change the build versus buy decision, or make specialist providers even more valuable?
To build on the conversation started by CTO Amjad Zoghbi in our May edition, we sat down with Tyler Kellner, CFO at TRG Screen, to talk about how AI is changing the economics of software, why opportunity cost is often overlooked, and how organizations can make smarter technology investment decisions.
AI has undoubtedly changed what's possible when it comes to software development. If you're looking for a relatively straightforward solution, every team and company can build something significantly faster today than they could have a year ago.
But lowering the speed barrier doesn't automatically mean building in-house becomes the right decision.
Take ERP systems as an example. Most organizations could decide to build their own accounting platform, but very few would. They'd recognize there's enormous value in using software that's been developed and refined by teams who specialize in solving those problems for hundreds or thousands of other companies. They know the best practices.
I think market data management deserves to be viewed in much the same way.
The question isn't simply whether you can build it. It's whether recreating years of specialist knowledge and operational experience is really the best use of your time and resources, or whether you're better off standing on the shoulders of people who've already solved those problems.
That's a very different decision.
For me, this is where the conversation becomes much more interesting.
The biggest cost isn't necessarily what you spend building a solution. It's what your best people aren't doing while they're building it.
And based on our client conversations, it’s hard to find a market data team that has surplus capacity. Every team is asked to do more with less. They are under pressure to deliver more insights and faster decisions with shrinking budgets and finite resources. Their expertise is incredibly valuable. Should firms divert those same people into designing, building and maintaining internal software?
That's the opportunity cost organizations often overlook.
These highly skilled professionals could instead be helping the business optimize market data costs, strengthen governance, improve compliance or support better decision-making. Those are the activities that directly create value for the organization.
AI has changed what's technically possible. It hasn't changed the fact that maximizing your people's expertise is one of your most impactful decisions.
The first question I'd ask is whether this is an area where your business genuinely brings unique expertise.
If you're solving a problem that's specific to the way your organization operates, then AI makes it much more realistic to build lightweight tools, workflows and automations yourself.
But if you're dealing with a more complex operational discipline, like market data management, I'd start with a proven foundation rather than building everything from scratch.
You're not simply buying software. You're buying a solution with workflows that have been refined over decades, across hundreds of customer environments. The solution brings deep domain expertise and teams that continue to evolve those solutions as markets, vendors and regulations change.
From there, AI gives organizations the flexibility to build and tailor in a way that makes sense for their own business. That's why I prefer thinking about solutions rather than software. It's the combination of technology, expertise and specialist services that ultimately delivers value.
The biggest one is everything that comes after the first version.
AI can help you build something remarkably quickly. We've all seen examples that are genuinely impressive. But building something once is very different from owning it for years.
Markets evolve. Business processes change. Teams grow. Compliance requirements shift. The software has to evolve alongside all of that. Someone has to own that responsibility, and that's where organizations often underestimate the long-term commitment they're taking on.
There's also a financial dimension that's easy to overlook. Even within TRG Screen, we've seen AI consumption costs grow more quickly than we initially anticipated. The return has absolutely justified that investment, but it's a good reminder that AI doesn't eliminate cost. It changes where those costs appear.
The same applies when building tools internally. Development will be faster, but maintaining a reliable, compliant, enterprise-ready solution remains an ongoing investment.
I think this is one of the biggest misconceptions in the AI conversation. The assumption is often that AI narrows the gap between internal teams and specialist providers. I think, in many cases, it can widen it.
Software providers are using exactly the same AI technologies to accelerate product development, respond more quickly to customer feedback and deliver new capabilities faster than ever before.
The difference is that they're doing it on top of years of product development, deep market expertise and real-world experience gained from working across hundreds of customers.
You're not comparing an internal AI project with yesterday's software platform. You're comparing it with a platform that's evolving just as quickly, backed by people whose full-time job is solving these problems.
That's an important distinction.
I don't think the future is a binary build versus buy decision.
AI is making it easier for teams to create lightweight tools, automate workflows and tailor solutions around the way they work. At the same time, organizations still need robust systems of record for the operational capabilities that are critical to the business.
The future is about being much more deliberate about where you build and where you rely on specialist partners.
Ultimately, every investment decision comes back to the same question. Where do your people create the greatest value?
If AI allows your teams to focus on the work that's genuinely strategic while trusted partners continue to evolve the operational foundations underneath, that's where I think firms will see the greatest return.