What does the future of responsible AI and quantum hold? Uli Homann, Corporate Vice President at Microsoft, joins today to dive into some huge topics in the industry. With over 35 years at Microsoft, Uli shares invaluable insights into AI, big data, and the future of technology. Topics covered include Microsoft's AI strategy, the paradigm shift in AI, responsible AI, the potential of quantum computing, and the impact of AI on industry and society. Uli also emphasizes the importance of a responsible AI charter, organizational readiness, and the integration of AI into business processes to achieve tangible outcomes. Listeners will gain a deeper understanding of AI's transformative power and practical advice on implementing AI effectively.

Takeaways:

• Define Your AI Charter: Before going "big" with AI, establish a clear public charter outlining what your company will and will not do with AI.
• Prioritize Business Needs Over Platform Building: Focus on building applications (apps) with AI that add immediate business value, rather than spending excessive time building an AI platform from scratch.
• Establish Independent Escalation Paths for AI Concerns: Create organizational structures that allow employees to raise concerns about uncomfortable AI use scenarios without fear of reprisal from their direct management.
• Address Data Readiness and Security: Prioritize getting your data in order, ensuring you have the right, sufficient, and accessible data, and implementing robust security models for AI capabilities.
• Understand When to Use AI vs. Traditional Code: Use AI for tasks where the outcome is unclear and requires reasoning or plan-building (e.g., trip planning, marketing plans), but stick to traditional code for fixed, deterministic, and repetitive processes.
• Anticipate the Rise of Physical AI and Humanoids in Manufacturing: Be aware of the increasing role of robotics and humanoids in factories, driven by AI, to address labor shortages and dangerous conditions.
• Shift Human Roles from Operator to Supervisor in Industrial Settings: As AI becomes the "operator" for tasks like configuring refineries, humans will transition to supervisory roles, focusing on security, safety, and optimization.

Quote of the Show:

• “ Without the people, AI won't be able to do anything for quite some time, if ever.” - Uli Homann

Links:

• LinkedIn: https://www.linkedin.com/in/ulrichhomann/
• Website: https://www.microsoft.com/en-us/

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“You need human-in-the-loop activities cause AI is still not that good yet.” Joining the podcast today is Doug Shannon, a two-time Top 25 thought leader and member of Forbes Technology Council to discuss the future he envisions with AI, emphasizing responsible data sharing and the educational approach in AI innovation. He explores the concepts of establishing AI centers of enablement, integrating diverse teams, and the importance of human oversight in AI-driven environments. Doug shares his insights into balancing automation with human oversight, the significance of transparency and clarity in deploying AI, and the critical role of ontology and taxonomy in understanding and leveraging data. The discussion also covers the Magi system, a framework Doug proposes for ensuring compliance and trust in AI systems, and the ethical considerations enterprises must address when integrating AI solutions. The episode culminates in rapid-fire questions about AI collaboration and Doug's vision for his legacy in intelligent automation.

Takeaways:

• Embrace Collaboration: Actively collaborate within your organization and with external partners to better understand and utilize AI. As AI technology evolves, collaborative efforts can accelerate learning and implementation.
• Foster a Culture of Knowledge Sharing: Encourage a culture where information and best practices are freely shared among team members. This can drive greater innovation and improvements in AI-related projects.
• Balance Human and AI Roles: Develop strategies to balance automation with human oversight, particularly in high-risk and highly regulated industries. Feeding AI with quality data and maintaining human context in the decision loop can ensure better outcomes.
• Focus on Ethical Considerations: Ensure AI deployments comply with regulatory standards and ethical guidelines. Intellectual property and data management are important areas to monitor closely for compliance and protection.
• Utilize AI Thought Leadership: Engage with thought leaders and industry experts to gain insights and stay updated with the latest trends and best practices in AI and automation.
• Plan for Autonomous Systems: Begin developing strategies for integrating multi-agent systems and autonomous enterprise models. Prioritize initiatives that create centers of enablement within the organization for greater effectiveness.

Quote of the Show:

• “The hope is that we learn and we dive in to use AI because if you don't use it, you don't really have a voice. And if you haven't used it, you don't really know what you're up against.” - Doug Shannon

Links:

• LinkedIn: https://www.linkedin.com/in/doug-shannon/
• Newsletter: https://www.linkedin.com/newsletters/brewing-insights-7124563088439197696/
• Podcast: https://youtube.com/playlist?list=PLivd8WaRu-XnT0kiQTmTAXQ2e5gloVLvp&feature=shared

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AI solution factories vs agent factories: what’s the difference and what should you know about them? Today, Iridius’s AI Technical Fellow, Spencer Bentley, and Chief Technology Officer, Peter Larsen, join to explore how solution factories address broader business applications and multiple users, while agent factories focus on individual, narrowly scoped AI agents. Key topics include the reduction of base coding requirements, operational efficiencies, scalability, compliance, security, and the potential impact of AI deployment in businesses. They also highlight the importance of proper risk management, leveraging standards and regulations, and the value of comprehensive AI solutions for enterprise-grade applications. Finally, the conversation touches on the future direction of AI and the importance of cultural readiness within organizations for adopting AI innovations.

Takeaways:

• Embrace AI Innovation: Enterprises should start experimenting with AI technologies to stay competitive. Adopt a structured approach like a solution factory to reduce risks and improve implementation efficiency.
• Invest in Specialized Agents: Focus on creating agents targeted at specific tasks instead of overloading one agent with multiple functions.
• Prepare for Compliance: Develop a clear understanding of AI-related regulations and standards, and ensure your AI systems are compliant by integrating these from the start.
• Foster a Supportive Culture: Assess and work on the organizational culture to ensure it is supportive of AI adoption. Engage and empower employees by clearly communicating the value and purpose behind AI initiatives.
• Measure and Mitigate Risks: Explicitly model and manage risks at every stage of AI development. Use dynamic entitlements and sandboxing to control and minimize potential issues.
• Strategic Alignment: Align AI initiatives with broader business goals to ensure cohesive and strategic deployment. Regularly review and adjust AI strategies to remain agile and competitive in evolving markets.

Quote of the Show:

• “ Any company that's getting involved in AI at any level is doing the right thing.” - Spencer Bentley

Links:

• LinkedIn: https://www.linkedin.com/in/phillip-swan/
• LinkedIn: https://www.linkedin.com/in/alistaln/
• LinkedIn: https://www.linkedin.com/in/spencer-bentley-ai/
• LinkedIn: https://www.linkedin.com/in/peterskjottlarsen/
• Website: https://iridius.ai/

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Why do so many companies struggle with moving beyond AI pilot programs into production? Cat Valverde, Founder of Enterprise AI Solutions, joins The Agentic Insider to provide real, actionable advice on how to ethically and equitably implement AI in your business. Cat shares her unique background and motivation for focusing on ethical AI, driven by her father's telemarketing practices. The discussion covers the complexities of AI implementation in enterprises, the importance of strategy to scale, and the challenges of gaining trust in the rapidly evolving AI landscape. She emphasizes the need for ethical and equitable AI use, suggesting that enterprises should consider upskilling employees and focusing on long-term goals rather than short-term metrics. The episode concludes with rapid-fire questions and insights into Cat's personal interests and upcoming projects.

Takeaways:

• Prepare for AI Integration by Building Trust: Building trust is a key factor for adopting AI. Ensure transparent communication and demonstrate reliability to gain buy-in from all stakeholders.
• Upskill Existing Employees: Rather than resorting to layoffs, invest in upskilling your current workforce to handle new AI technologies. This approach can provide long-term benefits and stability.
• Evaluate AI Use Cases Carefully: Don't jump on AI trends just because others are doing it. Critically assess whether a particular AI solution addresses a real problem in your business.
• Measure Time to Value: Focus on leading indicators rather than lagging ones. Track how quickly a new AI implementation can deliver tangible value to your business.
• Pilot Programs and Feedback: When running AI pilots, frequently review and assess their progress. Abandon unsuccessful pilots early to minimize wasted resources.
• Collaboration and Discernment in AI: Encourage collaboration between human intelligence and AI. Ensure that staff members are trained to critically evaluate AI outputs rather than taking them at face value.
• Adopt a Long-Term Perspective: For AI implementation, set and review long-term goals rather than focusing exclusively on quarterly targets.

Quote of the Show:

• “Scaling is important, profits are important, that's what business is. But finding a way that it can also be ethically and equitably implemented is the name of the game for everything that we do.” - Cat Valverde

Links:

• LinkedIn: https://www.linkedin.com/in/catvalverde/
• Website: https://www.eais.io/
• RVSP for our next webinar: enterpriseai.events
• Subscribe to the daily briefing: enterpriseaisolutions.io

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• YouTube: https://www.youtube.com/@iridiusAI

What if we've already lost control? Dr. Mark Brady, Deputy Chief Data Officer of TRMC at KBR Inc., unveils the two AI singularities that could redefine humanity's future. The episode dives into the significance of safe AI, exploring the six laws of safe AI and various facets of AI safety, such as the importance of safety objective functions and the challenges posed by AI evolution and machine learning. The conversation also touches on the applications of AgTech AI in defense and government, and the current capabilities and limitations of autonomous AI agents. To close, Mark shares personal hobbies and offers career advice for aspiring data scientists and future chief data officers.

Takeaways:

• Understanding AI Singularities: Familiarize yourself with the concept of AI singularities. Recognize the symptoms and implications of these singularities on society and technology.
• Safety Objective Functions: Implement safety objective functions in AI systems to ensure they prioritize human safety over other objectives.
• Tracking Data Lineage: Recognize the challenges in data lineage and strive to improve transparency and understanding of how data flows through systems.
• AI Creating AI: Ensure that any AI creating other AI systems includes its own safety objective functions in its creations. This prevents the propagation of unsafe AI systems.
• Human and AI Coexistence: Consider the potential future coexistence of different intelligences, such as biological, machine, augmented biological, and biological augmented machine, and how they might interact and compete.
• Large Language Models: Keep abreast of the advancements in large language models and their applications. Understand their current limitations and potential for future development.

Quote of the Show:

• “If AI is smarter than we are, how are we gonna be able to compete with it? How are we going to be able to make sure something's not going awry? We need AI to help us.” - Mark Brady

Links:

• LinkedIn: https://www.linkedin.com/in/mark-brady-18274321b/
• Website: https://www.kbr.com/en
• Book Link: https://a.co/d/gtODJjR

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“Data is the lifeblood of AI agents.” Is your data ready for the future of AI? Phillip and Alistair welcome William McKnight, President of McKnight Consulting Group and a leading global influencer in big data, data centers, cloud, and analytics, on the podcast today. William shares his insights on preparing enterprises for AI adoption, the importance of data accessibility, and the critical role of thoughtful data architecture. He also discusses the evolving role of data scientists, the AI-driven transformation in various industries, and the significance of strong leadership in managing AI implementations. The episode concludes with practical advice on maximizing ROI from AI investments and preparing for the future landscape of AI agents.

Takeaways:

• Develop a Strong Data Strategy: Ensure all your data is accessible, well-performing, and governed. Consider adopting a data mesh approach to balance centralization and decentralization.
• Commit to AI Agents: Begin experimenting with AI agents now rather than waiting. Pick initial use cases that are simple yet impactful and scalable for future projects.
• Emphasize Organizational Change Management: Successful AI implementation often requires significant organizational change and strong leadership. Be prepared to tackle change management as a critical component of AI projects.
• Focus on Data Maturity: Assess your organization's data maturity by evaluating data performance, understanding, and governance. Implement an information management action plan to improve analytical capabilities and enterprise data architecture.
• Embrace New Roles: Consider emerging roles like prompt engineer, AI ethics manager, AI explainer, and advanced data architects to support AI initiatives. Encourage upskilling and reskilling of current team members to fill these roles.
• Explore Diverse Use Cases: Investigate and apply AI in diverse areas such as drug discovery, predictive maintenance, fraud detection, personalized marketing, and healthcare.
• Prepare for Integration Challenges: Anticipate and address data integration challenges, especially in environments with multiple legacy systems.

Quote of the Show:

• “Data is the lifeblood of AI agents.” - William McKnight

Links:

• LinkedIn: https://www.linkedin.com/in/wmcknight/
• Website: https://mcknightcg.com/
• Book Link: https://www.amazon.com/stores/William-McKnight/author/B002QLXASK?ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true

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Will AI lead to a manufacturing resurgence in North America? According to Vlad Romanov, Founder and Manufacturing Executive of Solis PLC, that is the hope he has. He discusses the loss of manufacturing advantages in recent years and the potential role of technology, including AI and robotics, in enhancing competitiveness. The conversation covers topics such as the impact of AI on operational technologies, workforce dynamics, supply chain communications, and Industry 4.0 initiatives. Vlad also shares his thoughts on the importance of training, change management, and the need for real-world AI application case studies. The episode concludes with Vlad giving career advice for aspiring professionals in the manufacturing sector and sharing a glimpse into his interests.

Takeaways:

• Explore AI Tools: Start experimenting with AI tools to improve different aspects of your business, such as sales, operations, marketing, and manufacturing efficiency. Consider running small, fast sprints and MVPs (Minimum Viable Products) to test out different solutions.
• Focus on Sales and Marketing: Improve your sales and marketing strategies to get more orders through the door. Utilize AI to better understand customer needs, enhance marketing efforts, and streamline sales processes.
• Invest in Education: Invest time in learning and understanding both the technical and business sides of manufacturing. Consider upskilling in AI, programming, and data analysis through available online resources and AI models.
• Leverage Data: Ensure you are collecting and utilizing data effectively. This includes data from various sources, such as machine operations, sales, customer feedback, and more. Apply AI to analyze this data for better decision-making.
• Address Obsolete Systems: Assess your current equipment and systems, especially if they are obsolete. Look for ways to modernize and integrate new technologies that can improve efficiency and data collection.
• Share and Learn: Encourage the sharing of case studies and successful implementations of AI in manufacturing. Learning from the experiences of others can help improve your own AI deployment strategies.

Quote of the Show:

• “ I think AI is gonna drastically change every single area, and if we're not up to speed on it, we're simply not gonna be able to win on that front.” - Vlad Romanov

Links:

• LinkedIn: https://www.linkedin.com/in/vladromanov/
• Website: https://www.solisplc.com/
• Podcast: https://www.manufacturinghub.live/

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