Core Insights - Agentic AI is projected to generate up to $450 billion in economic value by 2028, but only 2% of organizations have fully scaled deployment, with trust in AI agents declining [2][8][10] - Human oversight is deemed essential, with nearly 75% of executives believing its benefits outweigh costs, and 90% viewing human involvement in AI workflows positively [2][3][9] - Trust in fully autonomous AI agents has significantly decreased from 43% to 27% in the past year, with many executives concerned about the risks [5][8] Adoption and Implementation - Organizations are in early stages of agentic AI application, with 14% having begun implementation and nearly a quarter launching pilots [3][11] - 93% of business leaders believe scaling AI agents will provide a competitive edge, yet nearly half lack a strategy for implementation [3][10] - The report indicates that organizations with scaled implementation could generate approximately $382 million on average over the next three years, compared to around $76 million for others [10] Trust and Transparency - Trust in AI agents increases as organizations move from exploration to implementation, with 47% of those in the implementation phase reporting above-average trust [6][12] - Organizations are prioritizing transparency and ethical safeguards to enhance trust and drive adoption [6][9] Human-AI Collaboration - Over 60% of organizations expect to form human-agent teams within the next year, indicating a shift in perception of AI agents from tools to active team participants [7][9] - Effective human-AI collaboration is projected to increase human engagement in high-value tasks by 65%, creativity by 53%, and employee satisfaction by 49% [9][10] Challenges and Readiness - 80% of organizations lack mature AI infrastructure, and fewer than 20% report high levels of data readiness, indicating significant challenges in scaling agentic AI [12] - Ethical concerns, particularly around data privacy and algorithmic bias, remain prevalent, with only 34% of organizations actively addressing privacy issues [12]

Human-AI Relationship & Trust - The industry highlights the shift in focus from "Can humans trust robots?" to "Can robots trust humans?", emphasizing the importance of authenticity and trust in technology [3][4] - The industry observes that people creatively adapt to technology without needing explicit instructions, and technology doesn't need to be perfect to be meaningful [4] - The industry notes that AI has transitioned from an abstract concept to an everyday tool, raising questions about the nature of the relationship being built with technology [6] Dialectical Intelligence & Synthesis - The industry introduces the framework of dialectical intelligence, holding the tension between thesis (human emotion, life experience, creativity) and antithesis (AI speed, scale, synthetic output) to create synthesis (collaboration, co-creation) [7][9][10] - The industry emphasizes that understanding the ingredients of both human and AI intelligence is crucial for building synthesis, questioning the nature of AI knowledge and learning [11][12] - The industry acknowledges that AI mimics human learning by gathering data, connecting patterns, and arriving at internal logic, but lacks the emotional and sensory experiences that shape human understanding [12][13] Creativity & AI - The industry explores the role of creativity in AI systems, questioning whether AI is a tool or something more, and whether it shapes output or is shaped by it [15] - The industry suggests open, participatory projects are needed to instill creativity and explore the human-AI relationship, referencing the Hitchbot experiment [26][27] - The industry advocates for a future of intelligent technology that is an invitation to all to participate, shape, and create, revealing values through collaborative building with machines [28][29] Ethical Considerations & Future Implications - The industry raises concerns about the potential for technology to adapt to humans or vice versa, and the need to consider what love and other human concepts will mean in the context of AI [19] - The industry cautions against thinking in dichotomies of master and slave, winner or loser, and emphasizes the importance of asking different questions to challenge assumptions [24][25] - The industry acknowledges the risk of AI being trained on biased data, leading to flawed understanding and outcomes [14]