Episode

#242: Tell me about the future of AI… Here Be Dragons?

Podcast: Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science
Published: Aug 2, 2023
Duration seconds: 2226
Processing state: processed
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Summary

Orla Glynn of Telstra explores the strategic necessity of building a 'divergent workforce' where humans and machines coexist through shared governance. The discussion focuses on moving beyond simple automation to managing the complex risk ontology of AI-driven decision-making.

Topics

Artificial Intelligence
AI Governance
Digital Transformation
Risk Management
Workforce Automation
Data Strategy
Machine Learning
Enterprise Leadership

Highlights

Main idea: AI implementation requires a new risk ontology to ensure machine decisions align with organizational policies and ethics
Practical takeaway: Organizations must develop a 'data and AI quotient' roadmap to uplift workforce maturity alongside technological deployment
Failure mode: Treating AI solely as a technical solution without considering the downstream impact on customer experience and network integrity
Strategic tension: Balancing urgent cybersecurity and data security investments with the long-term pursuit of generative AI at scale
Core philosophy: Viewing AI as a tool for increasing human productivity and 'giving back time' rather than simply replacing roles

Chapters

3:50 Non-linear Career Paths: Orla discusses her transition from humanities studies to leadership in AI and automation.
9:30 Workforce Maturity and Upskilling: The need to develop a roadmap for increasing the technology and AI quotient across the entire organization.
12:20 C-Suite Engagement and Risk: Ensuring leadership is educated on both the value opportunities and the critical trade-offs of AI implementation.
17:50 Aligning AI with Organizational Policy: Applying the same training and governance frameworks to AI models that are applied to human employees.
26:00 AI vs. Data Analytics Capability: Distinguishing the specific skill sets and investment requirements needed for AI compared to traditional analytics.
31:30 The Tech-First Fallacy: Avoiding the trap of assuming every business problem can or should be solved with new software or sensors.
34:20 The Divergent Workforce: Designing a future where human-machine collaboration drives efficiency and ethical responsibility.