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“If your data isn't in good order with the proper security, your ability to leverage AI is greatly diminished and you have to go back and pay that tech debt."
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Enterprises eager to embrace the AI future are running into a formidable roadblock: their own legacy technology.
In every major IT transition, the past can become an albatross that strains modernization efforts and chokes innovation. Much of the technical debt is a natural extension of how modern businesses operate. Any company that has ever done an acquisition, for example, likely had (or still has) IT complexity to deal with.
But from supply chain to financial services, older technology is turning AI adoption into a slog. Nearly 70% of companies report that legacy systems are slowing down AI scale, according to a survey from Harvard Business Review. But addressing the problem is also well worth it: 76% saw “strong returns” from upgrading their dated infrastructure, according to the report..
TCC CIO Lynn Murphy-Dubbink is well-versed in technical debt. She previously worked at an insurance carrier that went bankrupt in part because the company struggled under the weight of IT complexity. Lynn also worked at Salesforce, one of Silicon Valley’s most prolific acquirers. Now at TCC, she’s helping the authorized Verizon reseller begin to introduce AI into operations.
“The technology debt that has come about over the years becomes a ball and chain,” Lynn told CIO News. “If you're not keeping pace with all the disparate systems, that tends to slow down corporate strategies.”
We talked to Lynn about why legacy IT debt isn’t a blame game, how to get started on the modernization journey, and the power of grassroots AI adoption.
Data comes home to roost
When legacy debt is left unaddressed, AI agents struggle to navigate the older, often disconnected environments, leading to inaccurate or misleading intelligence and disjointed workflows.
The systems need continual context to function. But many enterprises weren’t built to ingest, transform, and deliver data to thousands of endpoints at real-time speeds, with the security that modern businesses demand. A resounding 96% of companies said data pipeline performance impacted accuracy and performance, according to a Broadcom survey.
“We’ve been talking about data forever, and now that is really coming home to roost,” Lynn said. “If your data isn't in good order with the proper security, your ability to leverage AI is greatly diminished and you have to go back and pay that tech debt.”
But as companies start to address the problem, Lynn cautioned that it’s not about embarrassment, anger, or blame. And companies shouldn’t expect to rid themselves of legacy IT debt completely. Instead, it’s about accepting the current state. Then, by linking modernization efforts to outcomes, companies can start to build a realistic blueprint for moving forward.
“Almost every organization has to cross that chasm at some point,” she said. “There’s not an apology that's needed over the past, but an acknowledgement of how we get from where we are to where we need to go.”
Inspire, but let them lead
For CIOs, it’s a good reminder that no matter the pressure to invest in AI, they have to excel at basic IT hygiene. But in the AI era, the IT department should also be seen as a source of inspiration for others in the business. Often, they’re uniquely equipped to connect the dots between the desired outcomes and the technology to make them possible, said Lynn.
For CIOs, that means staying technically adept with the latest AI tools and techniques. But while the CIO can energize, it’s ultimately up to other leaders to carve their own unique path to AI adoption.
“We’re not going to be a very effective IT organization if we're not at pace or ahead of the trends,” Lynn said. But “it’s not IT that’s driving it. As leaders become more empowered and understand the tools, they'll be better able to guide their teams and the right use case.”
This grassroots approach to AI adoption is the catalyst for more responsible, secure, and impactful scale. For example, TCC is starting to explore how agentic AI can automate and accelerate processes and decision making, with a goal of refocusing time and attention towards customer interactions.
“There’s this ‘Aha!’ moment where you start to really realize the power and what it can do,” said Lynn. “It’s rewarding to see how each person applies it to their role. As they improve their own personal productivity, we’re anticipating more departmental AI use cases, then enterprise-wide AI use cases,” she added.
Ultimately for CIOs it’s about harnessing the deep technical knowledge most have, while also being open-minded and eager to explore what’s next.
“There is no substitute for actually learning the tools,” said Lynn. “If we don't prioritize that time, we won't unlock the next horizon. That's our next biggest challenge as business goes on: we’ve got to carve out that time.”
