2024年人工智能就绪度状况报告

By Fern Halper, Ph.D. TDWI's research examinesthe current state of AI, howready organizations are toimplement it, and key areascritical for success 2024State ofAI Readiness Table of Contents Research Methodology..................2The Scope and Importance of AI.............3The Overall State of AI Readiness. . . . . . . . . . . .4The State of Organizational Readiness for AI. . . . . .6The State of Data Readiness for AI............8The State of Skills and Operational Readiness for AI.10Building Data and AI Literacy. . . . . . . . . . . . . .13The State of Governance Readiness for AI.......14AI Model Governance..................15Considerations and Best Practices for AI Readiness..16About Our Sponsor...................18About the Author....................20About TDWI Research. . . . . . . . . . . . . . . . . .20 By Fern Halper, Ph.D. © 2024 by TDWI, a division of 1105 Media, Inc. All rightsreserved. Reproductions in whole or part are prohibitedexcept by written permission. Email requests or feedbackto info@tdwi.org. Product and company names mentionedherein may be trademarks and/or registered trademarksof their respective companies. Inclusion of a vendor,product, or service in TDWI research does not constitute anendorsement by TDWI or its management. Sponsorship ofa publication should not be construed as an endorsementof the sponsor organization or validation of its claims. Thisreport is based on independent research and representsTDWI’s findings; reader experience may differ. Theinformation contained in this report was obtained fromsources believed to be reliable at the time of publication.Features and specifications can and do change frequently;readers are encouraged to visit vendor websites for updatedinformation. TDWI shall not be liable for any omissions orerrors in the information in this report. Research Methodology This State of AI Readiness Report examines thecurrent state of AI and how ready organizations are toimplement it. It highlights key areas critical for AI success:organizational readiness, data readiness, skills and toolsreadiness, operational readiness, and governance readiness.It examines challenges organizations are facing in gettingready for AI. Additionally, it provides considerations andbest practices for moving forward with AI. For this study, TDWI examined several surveys andassessments that we run throughout the year. Datafrom this report comes primarily from the 2024 TDWIAI Readiness Assessment. Over 100 respondents fromvarious industries and company sizes have participated inthe assessment to date, and 113 completed responses arereflected in the figures in this report. Additionally, datafrom over 250 respondents to the 2024 TDWI Data andAnalytics Survey is used in this report. This report was sponsored by MongoDB, Pecan, and SAP. The Scope and Importance of AI From a technological standpoint, AI is an umbrella termencompassing a myriad of methodologies and techniques.It leverages advances in mathematics, computer science,computational linguistics, cognitive sciences, androbotics, among others. Popular AI technologies includemachine learning, natural language processing, andneural networks, which collectively drive the intelligentcapabilities of modern AI systems. TDWI sees organizationsbuilding AI models to predict churn and other customerbehavior, identify fraud, determine when maintenance willbe needed, recommend products, and predict disease,among other use cases. From a societal perspective, AI—particularly generative AI—has the potential to transformindustries, enhance decision-making, and solve complexproblems, making it an important force in the ongoingdigital revolution. MathematicsComputational LinguisticsRoboticsCognitive SciencesComputer Science MachineLearning NeuralNetworks Practically speaking, AI can provide tangible benefitssuch as deeper insights, increased productivity, improvedcustomer service, and greater operational efficiencies thatdrive cost savings and stronger top-line growth that deliverslarger profits. In TDWI research, for instance, we see thatorganizations implementing more sophisticated analyticssuch as AI are more likely to derive top- or bottom-linebenefits from their analytics efforts than others.1In otherwords, there is real, tangible value from AI. NaturalLanguageProcessing To achieve the benefits from AI, organizations need tounderstand the problems they want to solve and have a soliddata foundation that supports high volumes of diverse data.They will need to have organizational support and a culturethat champions AI. This includes the skills and training tomake AI a reality. Enterprises will need operational modelsand teams in place to deploy AI into production and ensurethat those models stay current. They will need to govern AIto ensure it meets compliance, quality, and ethical concerns. The Overall State of AI Readiness There are numerous interrelated factors that form thecurrent state of AI readiness. Readiness is not si