How Qualcomm’s CIO is placing big bets on AI to support the chip company’s diversification push

Qualcomm CIO Atilla Tinic says more internal usage of AI can support the semiconductor company's diversification efforts.

Atilla Tinic spent three decades in the telecommunications sector, but it was his most-recent role in the field—serving as chief information officer at EchoStar—that inspired his next gig as CIO at semiconductor company Qualcomm.

During Tinic’s time at EchoStar, the satellite internet provider had grand ambitions to become the U.S.’s fourth major carrier under its Dish brand, a diversification plan that didn’t exactly pan out as envisioned. Last year, Dish ditched those aspirations, and in June 2026, the subsidiary filed for bankruptcy after deals to sell spectrum licenses to AT&T and SpaceX hadn’t closed in time.

But at Qualcomm, efforts to diversify the business away from relying too much on the volatile smartphone market have been far more fruitful. Last year, Qualcomm unveiled new AI accelerator chips to better compete with Nvidia and AMD. There have also been reports that Qualcomm is working with OpenAI on a new smartphone AI chip.

And last month, Qualcomm’s shares jumped when it unveiled new fiscal 2029 revenue targets during the company’s investor day, including projections that non-handset revenue would reach $40 billion and data center sales would total $15 billion. Qualcomm’s revenue and earnings results for the first two quarters of 2026 exceeded Wall Street’s expectations, even as the global smartphone market is expected to contract at the steepest rate on record.

“I was intrigued by the diversification strategy,” says Tinic of joining Qualcomm in February 2025. He found it alluring to join the company as it aimed to remake its business with bigger bets on data centers, automotive, the internet of things, and other adjacent markets.

One of Tinic’s earliest moves when stepping into the CIO role was the creation of centralized data and AI teams, rather than embedding those resources into various infrastructure and application teams.

He has also supported the generous deployment of AI tools across the company, including AI coding assistants, Microsoft Copilot, and generative AI features from vendors including cloud-based software provider ServiceNow, Salesforce’s messaging platform Slack, and Atlassian’s project management software product, Jira. 

“Things are moving so quickly that I think if anyone thinks that they can completely go it alone, they’re going to be left behind,” says Tinic.

Some internal AI use cases that are changing workflows include the use of AI agents to validate purchase orders autonomously, with the AI system even assigning an accuracy score to each document so that customer service team members can spend their time focused on hunting down the exact details that are wrong or unfilled.

Another example came from Tinic’s IT team, which created an autonomous AI agent to handle almost the entire process for refreshing a worker’s laptop. 

Within Qualcomm, Tinic says AI tools are widely adopted to support the software testing life cycle, as well as for research and personal productivity. What’s more challenging is how to rethink end-to-end workflows in a manner that’s different from past technology waves, where automation was on the margins. Rethinking work is an even greater unknown as autonomous AI agents become more popular.

“With AI agents and having a digital workforce, they can work differently; they can do a lot of reasoning,” says Tinic. “Your workflow itself really needs to be rethought.”

Tinic says he assesses progress through three impact metrics: volume, velocity, and quality. So, for example, if a software developer is using AI, Tinic and Qualcomm will look at the volume of work delivered, whether the product is going to market faster, and if it has fewer defects. Within the help desk, Qualcomm will track the volume of tickets resolved through AI, but also the rate of re-opened tickets.

Getting all three right is key to enabling Qualcomm to scale its internal AI capabilities, which ties back to the company’s goals to diversify its business. “I really want to repurpose people for those high-value tasks that are helping the business grow, as opposed to administration tasks,” says Tinic.

One major AI initiative that predated Tinic’s time at Qualcomm, but that he continues to support, is an AI council made up of legal, security, and IT experts to ensure that any new large language models, AI tools, or data sets associated with any of these projects are secure.

“In prior years of my career, I would have looked at governance as a slow-moving bureaucracy,” says Tinic. But he has evolved his thinking, believing that this structure gives employees confidence to use authorized AI tools. “It’s like the old adage, you can go faster when you know you have brakes.”

The high rates of internal adoption of AI have led Qualcomm to closely monitor its AI token spend, a question that’s become more central across the technology sector.

Tinic has established token limits, which he says are “generous,” but if an employee hits that limit, there will be a conversation about different decisions that can be made regarding their AI model selection. For some advanced cases, Anthropic’s Claude Opus model may be required, but for a lower-stakes use case like documentation creation, the lower-cost Claude Haiku is sufficient.

“Everybody who’s talking about moving from experimentation to production is probably dealing with costs that have hit them that they weren’t maybe projecting,” says Tinic.

John Kell

CIO Intelligence is taking a three-week summer break. I’ll be back in your inboxes Aug. 5. In the meantime, send thoughts or suggestions to CIO Intelligence here.

This story was originally featured on Fortune.com