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

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Text version: https://stenobird.com/podcast/data-futurology-leadership-and-strategy/242-tell-me-about-the-future-of-ai-here-be-dragons.md
Podcast: [Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science](https://stenobird.com/podcast/data-futurology-leadership-and-strategy)
Published: 2023-08-02T02:40:47+00:00
Episode link: https://podcasters.spotify.com/pod/show/datafuturology/episodes/242-Tell-me-about-the-future-of-AI-Here-Be-Dragons-e27l4hc
Audio file: https://anchor.fm/s/3fab060/podcast/play/74141676/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2023-7-2%2F341568232-44100-2-9cc2b21c0712f.mp3
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Duration seconds: 2226

## Resource

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.

## 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

## Topics

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

## 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.

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## Transcript

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