人工智能时代的信息管理：33位首席信息官与技术官分享用例、挑战及见解

Information 33 CIOs and CTOsshare usecases, challenges, and insights Getting the data right, forthe right use cases TABLE OF CONTENTS 2Introduction 3Keeping up with AI: Thegovernance rules are changing The importance and value ofdata has never been clearer.But given the rapid evolutionof AI and related technologiesand their infusion into Laying the information 4Data quality anddata silos6Identifying roles, skills,and talent Strategies for persistent In three CIO Think Tank roundtables,held from June through August of 2025,IT leaders spanning diverse industries, from financial services to higher ed, healthcare, manufacturing, and retail, shared successes and speedbumps in managing 8Prioritizing use cases9Mitigating security 10Handling data The roundtables were facilitated by John Gallant, enterprise consulting director atFoundry, along with Amy Machado from industry research firm IDC, CIO editorial 10Managing business Challenged by data quality and sovereignty issues? Wrestling with staffing, skills,and education concerns? Searching for ways to reduce AI risks? Read on for ideas More AI use cases andlessons learned 11Productivity,automation, and 13Enhanced customerservice and CX WHAT IS CIO THINK TANK? 13Specialized industryapplications CIO Think Tank is a unique collaboration showcasing the ideas and expertise of top ITexecutives, IDC analysts, Foundry editors, and our exclusive vendor partner. Our goal isto explore and shape the future of the IT function and emerging technologies. OpenText 14AI agents: the future(that's happening now) 15Panelists and thanks Visit CIO.com for the full lineup of CIO Think Tank Roadmap Reports. Keeping up with AI: The Businessinitiatives AI use is advancing at warp speed,even as enterprise technology leaders wrestlewith foundational questions, starting with whether existing data governance policies Monetizing company data Some CIO Think Tank participants emphasized that they do. "It's just anotheropportunity to say all the existing policies and procedures still apply," said one CIO.Others argued that, while the goals of governance still apply, the manner in which AIuses data necessitates fresh thinking and updated policies. "The business outcomes Meeting compliance requirements Improving the customerexperience Among the challenges facing AI builders is how to set the right business expectations."The AI model life cycle is not like the application life cycle," said Jamil Badrudeen, VPAI and Financial Engineering at financial giant State Street. Applications are put intoproduction once the code works correctly; AI models "have to be tested and trained in Technologyinitiatives Further, the workforce needs new skills; models need new infrastructure. Line-ofbusiness leaders are excited about AI but remain blasé about investing in data cleanup. AI and tech leaders at the forefront of these efforts have tested numerous strategiesfor addressing the questions, challenges, and opportunities. But it's still early days forAI, and what works at one company may not play at another. Context matters, and thethink tank panelists — whose AI projects range from summarizing medical documents Machine learning/AI Security/risk management Even the easy cliché "It all starts with getting your data right" isn't universally applicable.Some companies have undertaken a major data infrastructure and governance overhaulbefore wading into AI; others report cherry-picking their best data to use as their AI Data/business analytics This Roadmap Report rounds up many of the panelists' key ideas, offering CIOs newpossibilities for moving their own work forward at the pace of...well, at the pace of AI. SOURCE: Foundry's State of theCIO Survey 2025 In part, these data gaps and inconsistencies stem from many organizations' historical "We started to require that everybody clean up their data sets [for AI], and there's zeroappetite for that — no one's interested in sorting through archival data and figuring outwhat your schema was, you know, 15 years ago," said David Chun, CIO of Montclair State For example, some companies have pockets of data that work well for specific usecases. That's an excellent starting place while data hygiene efforts are underwayelsewhere. "We started off with 'Where do we have the data? Where can we get the bestresults without having to waste a lot of time on all the data engineering tasks required tomake it acceptable for use in an AI project?'" said Andrew Scott, formerly chief digital Thedata hygiene thing is really bigfor me, because I’mdiscovering how much Another strategy is to lean on current vendors, who may be able to help identify andremediate "raw, redundant, or conflicting" data in specific systems or repositories, Several CIOs expressed confidence that AI itself can spot problems with data and insome cases, even help address underlying process issues as an AI project progresses.This provides another incentive to move ahead with se