Welcome to Enterprise AI Today, your curated digest of cutting-edge AI case studies, implementation frameworks, and industry insights.
- AI Transformation Framework: New research reveals how the proven ADKAR change management model enables organizations to create structured, goal-oriented AI strategies.
- Global Trust in AI: A comprehensive study across 47 countries shows high AI adoption despite substantial ambivalence.
- Digital Risk Synchronization: To combat rising cyber threats, organizations need to synchronize business, IT, and security teams.
- Scaling AI in Government: State and local governments can scale generative AI by enhancing technological infrastructure, engaging the workforce, and building effective governance structures.
- AI in Cybersecurity: AI is rapidly changing the threat landscape with breakout times now often under an hour.
Want more AI case studies, best practices, and innovation insights? Check out AI Enterprise Today.
Paul Estes Editor-in-Chief
A Proven Framework for Enterprise AI
Brief: Enterprise AI falls flat without a framework. Most AI projects fail due to poor data, ROI misalignment, and lackluster change management. The ADKAR change management model offers organizations a structured pathway to successful AI transformation. Companies like Microsoft, AT&T, Shopify, and Walmart have already leveraged these principles to drive meaningful AI adoption and transformation.
- Awareness is critical—organizations must clearly explain why they're embracing AI and how it aligns with strategic goals.
- Creating desire among employees requires proactively addressing job loss fears by framing AI as a job enhancer and identifying internal champions to promote adoption.
- Building knowledge through comprehensive L&D programs is essential—after conducting skills gap analyses, organizations should develop personalized curricula for each department using diverse learning approaches.
Why it matters: With proven frameworks like these, organizations can become truly AI-first companies, develop competitive advantages, increase efficiency and innovation, and remain on the cutting edge of enterprise technology.
41 Case Studies Across 14 Industries
WORKFORCE RESEARCH
KPMG Global Study: Trust, Attitudes, and Use of AI
Brief: A comprehensive study examining the perspectives of over 48,000 people across 47 countries reveals how people trust, use, and understand AI systems, their views on impacts and governance, and expectations for regulation.
- Public adoption of AI is high, with 66% intentionally using AI regularly, yet AI literacy remains limited.
- Trust in AI presents a significant challenge driven by greater skepticism about safety and security than technical ability, and notable differences between economies.
- There's a strong public mandate for AI regulation—70% believe it's necessary, but only 43% view current laws as adequate, and 83% aren't unaware of them.
Why it matters: There’s considerable ambivalence toward AI use in society, stemming from the tension between safety/security trust and technical capabilities. AI adoption for performance gains has often outpaced governance and regulation. Effectively managing this tension is crucial for organizations looking to implement AI while building stakeholder trust.
Insights, Research, and News
- Boston Consulting Group reports that organizations must synchronize business, IT, and security teams to combat digital risks, as disconnected approaches leave vulnerabilities unchecked. This is a people and organization issue as much as a technical one, requiring alignment of incentives and fostering collaboration.
- Deloitte finds that US state and local governments can scale generative AI by creating AI marketplaces with necessary guardrails to reduce risks. Despite ongoing pilots, a large majority of organizations have deployed less than one-third of these pilots into production.
- McKinsey reports that AI has caused a 1,200% surge in phishing attacks since late 2022, while also becoming a game-changer for defense. Organizations are leveraging AI to reduce the mean time it takes to detect, respond to, and recover from threats and stay ahead of advanced attackers.
- IBM identifies that humility, recognition of varying worldviews, and multidisciplinary teams are essential for implementing responsible AI. Organizations must recognize that everyone has biases and be transparent about why they chose particular approaches to data or methodologies.
- World Economic Forum highlights that AI agents are beginning to take over administrative tasks, with 63% of use cases focused on repetitive categories like data collection and compliance support. This shift frees up skilled workers to focus on decision-making, creativity, and strategy while reducing burnout caused by high-volume, low-impact work.
- CIO.com identifies four goals forward-thinking CIOs target when building AI skills: increasing office productivity, improving core functions, developing organization-wide AI capabilities, and creating an AI culture. Construction company Arco saw 80 employees complete their enthusiast-level program, helping identify and implement high-impact AI use cases across the business.
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