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

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