领导力、文化与2025年人工智能路线图

Headline partner Leadership, culture,and the future AI roadmap for 2025 04 03 05 KEY INSIGHTS PART 1:SCALING AI 10 13 PART 3:AI LEADERSHIPFOR THE FUTURE PART 2:DATA AND AI Find out moreabout DataIQPage 19 At DataIQ, we help data and AI leaders succeed by providing the insights,connections, and expertise they need to drive smarter decisions, reduce risk, andcreate measurable business impact. To find out more about becoming a member, our recognition program or joiningour wider community visitwww.dataiq.global/membership. Terms and conditions To see our terms and conditions please use this link:www.dataiq.global/terms-and-conditions IntroductionIntroduction Rachael Pimblett Content and Community Manager,DataIQ. Early in 2024, Forbes made the claim that “2023 was the Year of AI Hype – 2024 Is theYear of AI Practicality”. This research, in partnership with Blend, examines the extentto which that claim is true. As a global data leader empowerment company sat advantageously in the middleof all the high-level conversations worth having, we bring you evidence of what ourAI future is beginning to look like, practical or otherwise. This research was sharedwith hundreds of top US data and AI leaders with 87% of respondents in the DataIQUS 100, exemplifying the high caliber of experience. To journey through this shapeshifting and vastly expanding terrain, the report willbe split into three sections. The first, Scaling AI, will explore change in trends from2023 to 2024, including usage of AI in deployment, and ROI measurements acrossthe businesses surveyed. The second, Data and AI, will investigate the AI maturityof data-driven versus gut-based organizations. The third and final section, AILeadership, will explore the state of AI Leadership & Culture today using Blend’sCritical 7 Challenges to Navigate for Scaling AI. Key Insights •Growing AI Interest:25% increase in appetite for enterprise-wide AI adoption. •Implementation Progress:50+% have more than 12 models deployed, despiteobstacles.•Leadership Gap:A decade of data transformation has not diluted gut-feeldecision-making – a likely barrier to AI adoption.•Trust Issues:Data trust remains a key concern, lagging behind governanceimprovements.•Strategic Challenges:AI differs from traditional data, requiring new strategies forintegration and storytelling by data leaders. Part 1:Scaling AI Scaling AI must begin with understanding AI. Most people operating in a businesscontext will know what AI stands for. They may even know the specific types ofAI (classic, generative, and agentic). As AI lexicon has seeped into our publicdiscourse, so too has a basic knowledge of its functioning. Understanding matters most at the top of the power chain: this research has foundthat over 50% more businesses report that their CEO and CFO understand AI risksand pay-off in 2024 in comparison to 2023. Businesses are 25% more eager toimplement AI in 2024 compared to 2023 numbers. Consequently, businesses withgreater top-down buy in are more likely to prosper. “Where the CFOand CEO see therisks, rather thanthe benefits, ofAI, gut-baseddecision-makingis likely to reignsupreme.” What did AI roll-out look like in 2024 for most organizations? Over 50% oforganizations surveyed had 12+ AI applications in use, representing how mostbusinesses implemented AI for quick-fire solutions in small POCs. These POCsusually exist in pockets across organizations. DataIQ report However, 28% of organizations only have 3-5 applications in usage, whichdemonstrates a hurdle on the roll-out path. This hesitation often stems from initialsetbacks such as accuracy issues, cybersecurity risks, and challenges in explainingAI outcomes. Fig 1: Number ofAI applications inusage (2024) point-of-view What can organizations do to bridge the gap between initial AI enthusiasm tosustained deployment? “Make it work” AI doesn’t fail for lack of technology—it stalls when people don’t trust it, don’t know how to use it,or don’t believe it will help them. That’s why the transition from early AI experimentation to sustainable adoptionhinges on many areas but three stand out:Talent, Trust, and Change Management. Grow AI Talent –Technical training alone isn’t enough. Employees need to see how AI applies to their role inreal time. That’s why we recommend embedding AI training into daily workflows—like using copilots to generateproposals or summarizing meetings. When learning is contextual, adoption accelerates. Create Trust –Confidence grows through clarity. Equip teams with tools to validate AI outputs and understandtheir limitations. Leaders should normalize the probabilistic nature of AI—explaining what it can do, where it addsvalue, and when to rely on human oversight. Transparency builds trust faster than perfection. Enable Change Management –Successful adoption starts with people. Identify early champions and superusers who can model new ways of working. Use AI to personalize communication, highlight