If most companies are using the same AI systems, how can they stand out and get ahead?
And as agentic AI becomes table stakes, what do enterprises need to keep in mind to make AI work?
And how can we even trust an AI-powered workplace when most people can't even explain the basics of AI?
We're learning from the experts.
Accenture's Mary Hamilton joins the Everyday AI show to talk about building trust in an autonomous workplace, how we can prepare for the future of work, and four emerging AI trends you can't miss.
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Topics Covered in This Episode:
- AI-Powered Autonomy Shaping Future Work
- Generative AI’s Impact on Business Transformation
- Accenture Technology Vision 2025 Overview
- Key Trends: Autonomy and Enterprise AI Adoption
- Human Capability Expansion via AI Tools
- Trust, Explainability, and Responsible AI Practices
- Agentic AI Models and Productivity Shifts
- Continuous Learning Loops in Workplace AI
- AI-Powered Robotics and Multimodal Integration
- Personalization and Brand Voice with AI Agents
Timestamps:
00:00 "AI's Impact on Business Autonomy"
03:33 Accenture's Global Consultancy Overview
09:48 Technology as a Game-Changing Partner
12:16 Reinventing Responsible Tech Use
14:31 Building Trust Through AI Interactions
18:17 Building Trust in Enterprise Data
23:20 Embracing AI: Active Learning Loop
26:24 "Embracing Efficiency with AI Agents"
Keywords:
AI powered autonomy, generative AI, large language models, future of work, automation, business transformation, Accenture, innovation centers, strategic visioning, co-creation, ecosystem partners, digital core, technology consultancy, technology reinvention, enterprise AI adoption, operational efficiency, Technology Vision 2025, AI trends, human-like capabilities, language barrier, technology acceleration, digital agents, digital transformation, customer interaction, trust in AI, responsible AI, data platform, knowledge graphs, AI-driven robotics, warehouse automation, personalization at scale, brand voice in AI, digital twin, agentic models, observability, traceability, explainab
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