IT leaders seeking promising AI use cases may be overlooking a potential goldmine: deploying the technology to assist with major IT modernization and digital transformation initiatives.
IT leaders seeking promising AI use cases may be overlooking a potential goldmine: deploying the technology to assist with major IT modernization and digital transformation initiatives.
Leading CIOs at a handful of large enterprises have leveraged generative AI and related tools to rewrite code, redesign workflows, and migrate databases during major digital transformation efforts, a practice that isn’t as widespread as its potential suggests.
“The potential is actually, I don’t want to say ‘limitless,’ but it is,” says Vijay Thangella, head of AI and machine learning at contact center technology provider Avaya. “There’s a very high potential.”
Avaya, using AI tools built from frontier models, claims it can help customers cut contact-center modernization timelines drastically. Revamping legacy contact centers typically requires IT teams to spend months deciphering and manually rebuilding years of workflows, routing logic, integrations, and business rules. AI-assisted modernization can cut the time to a few days, Thangella says.
Managed services provider Ensono also uses AI tools to help customers with tasks such as code discovery and cleaning up legacy code. While most organizations still haven’t used AI to assist with digital transformation, the technology can significantly cut the costs and time associated with large IT modernization projects, says Brian Klingbeil, chief strategy officer at the company.
Gartner AI analyst Arun Chandrasekaran sees significant potential to accelerate digital transformation initiatives through use of generative AI and related tools, in particular for gathering requirements, mapping legacy code, and performing code quality assurance.
In context
- Topic: Estrategia y Gobierno de TI — Decisiones de portafolio, costo total, gobierno y marcos de referencia.
- Source: CIO
- Published: 20/10/2026
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Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.
Why it matters
There is a wide gap between a company that has technology projects and one that has a technology strategy. The first accumulates initiatives; the second decides what it will not do. Moves like this are usually the moment somebody at the top finally wrote down the list of what gets left out.
My filter is always the same: does this change a decision, or only a tool? If at the end of the quarter the same people are approving the same things by the same criteria, there was no transformation, there was a purchase. The difference shows up in the committee minutes, not in the press release.
What usually goes wrong
The classic mistake is confusing the plan with the execution. A three-year roadmap gets approved in a two-hour committee and nobody revisits it until there is a problem. Plans that work get reviewed every quarter and corrected without drama; the ones that do not get defended instead.
What to watch
- What gets switched off to fund the new thing; if nothing does, the budget will hurt in six months.
- How the relationship with current vendors is left, which is usually where the dependency nobody measured is sitting.
- Who ends up owning the decision, and with what budget of their own rather than borrowed.
How I read this entry
I would treat it less as a technology story and more as a market signal. If one player moves this way, in twelve to eighteen months the rest will have to answer something. Better to decide now, calmly, what that answer will be, rather than improvise it when a client asks.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
