Adam Cheyer is a pioneering AI technologist whose innovations have fundamentally shaped today's intelligent interfaces. As co-founder of Siri Inc. (acquired by Apple), he served as a Director of Engineering at Apple's iOS group, and later co-founded Viv Labs (acquired by Samsung), Sentient Technologies, and played a founding role in Change.org.
Adam Cheyer was Chief Architect of CALO, one of DARPA's largest AI projects, authored over 60 publications and holds more than 25 patents
In recognition of his achievement, he received his alma mater Brandeis University's 2024 Alumni Achievement Award - for transforming a long?standing AI vision into everyday tools used by hundreds of millions.
Now represented by Champions Speakers Agency, he continues to speak globally on how organisations can harness AI with responsibility, scale, and impact.
Q1. How do you see the role of data management in enabling AI capabilities and bringing data to life for organisations?
Adam Cheyer: "AI systems are built on two foundations: algorithms and data. The algorithms themselves are well established, but without high-quality, well-organised data, they can't deliver real value. Data is the fuel that powers every AI application, and managing it effectively is now a mission-critical skill for any organisation developing AI.
"With the rapid acceleration of AI in recent years - especially in the past six months - the ability to handle, refine, and govern data has shifted from being a technical advantage to an essential requirement across industries."
Q2. What challenges have you faced when managing large data sets?
Adam Cheyer: "I've been building AI systems over 30 years, so it's changed a little bit over time. Clearly, the first issue is just storage and management and processing of the data. The data now is so large. Back in the 80s and 90s that wasn't quite as essential, it was smaller data sets, but today the data sets are huge.
"So, you need a system that can store it efficiently in a distributed way, and we've used various systems over the years to do that. You need a system that can process this huge amount of data in parallel at scale.
"One of the key areas in data management for me is data quality. Even if you work with data companies - and when we were a start-up, and then even at Apple for instance - many of the data sources come from other places, other vendors, and surprisingly the data is not always in perfect clean form.
"So, you need to have a process and tools and a pipeline that goes through and takes that data, cleanses it, adapts it, and often if you have multiple sources you need to integrate data together, and that can be a real challenge.
"There are standard systems, ETL systems etc., but sometimes you need proprietary algorithms. As an example, with Siri, when we were a start-up, you would get millions and millions of restaurant name data and business name data.
"If you had something like Joe's Restaurant and Joe's Bar and Grill - are they the same or not? That's a real problem. Joe's - probably you'd say yes, but Joe's Pizzeria and Joe's Grill maybe not, right? And so, how do you know?
"There's a lot of work that goes into cleansing, integrating data.
"And then the final thing I'll mention, which is a big topic in data management, is privacy and security. Once you have data coming in from users, there are standards, issues, and regulations that mean you need to be able to ensure that the data you have is accessible only by the right people, that it is secured and protected, and that it keeps privacy as much as possible - standardised.
"At Apple, we had a number of techniques and teams, and there's a lot that goes into that. So, you need good systems, good processes, and to set up your organisation to be able to handle all of these challenges."
Q3. How do you manage data privacy when building large AI systems?
Adam Cheyer: "Absolutely, so it is a challenge. Your first tendency is, well, we just record everything, but I think that'...
