How do we redesign work to let humans and AI do what they do best?
Yes, artificial intelligence drives remarkable efficiency – but this isn’t always the unmitigated good that technology evangelists proclaim. As an experienced board director across diverse sectors, I’ve seen first-hand how the rush toward AI automation can sometimes undermine the very human capabilities that create sustainable competitive advantage. The critical question facing Australian boardrooms isn’t simply how quickly we can automate, but rather: How do we thoughtfully redesign work to let both humans and AI do what they do best?
This nuanced approach requires boards to move beyond binary thinking about technology adoption. The most successful organisations I’ve advised don’t frame decisions as “human versus machine” but instead ask: “What unique value do humans bring, and how can AI amplify rather than replace these capabilities?”
The Mixed Blessings of AI Efficiency
Across Australian industries, we’re witnessing AI’s transformative power – for better and sometimes worse:
Energy and Resource Sector
In the resource sector, for example, companies initially viewed AI primarily through a cost-reduction lens. However, many have since discovered that their social license to operate depends on being responsible employers in regional communities – leading to more thoughtful approaches that use technology to make human roles safer and more meaningful rather than eliminating them entirely.
AGL Energy’s AI-powered predictive maintenance systems have dramatically reduced equipment failures across their renewable portfolio. Yet their leadership discovered an unintended consequence: when maintenance crews were removed from regular inspection routes, they lost the contextual understanding and tacit knowledge that often-prevented problems AI couldn’t detect. Their solution? A hybrid model where AI identifies priority areas for human inspection rather than replacing the physical presence entirely – combining algorithmic efficiency with irreplaceable human judgment (AGL Energy, 2023).
Woodside Energy initially deployed their “Willow” AI platform to replace engineering analysis tasks, achieving impressive early efficiency gains. However, they soon discovered that completely removing humans from analytical processes created blind spots in their operational understanding. They’ve since redesigned workflows where AI handles data processing while engineering teams focus on interpreting anomalies and contextualising findings – improving both safety metrics and innovation capacity (Woodside Energy Annual Report, 2024).
Healthcare and Aged Care
In Australia’s aged care sector, where staffing shortages are endemic, the temptation to over-automate is particularly strong. Bupa initially implemented AI monitoring systems primarily as a cost-cutting measure, but quickly discovered that resident wellbeing suffered when human interaction decreased. Their revised approach uses AI to handle routine monitoring but deliberately preserves time for meaningful human connection. Care staff now spend less time on documentation and more on relationship-building conversations that technology simply cannot replace (Australian Aged Care Collaboration, 2024).
Royal Melbourne Hospital’s experience with AI diagnostic tools offers another instructive case. Their initial implementation of imaging analysis algorithms achieved accuracy rates that matched radiologists for certain conditions, prompting discussions about reducing specialist staffing. Yet they discovered that the highest value came not from replacement but redesign – radiologists now spend less time on routine screening and more time on complex cases and integrative analysis that combines multiple data sources in ways AI cannot (Royal Melbourne Hospital, 2024).
Private Equity
Australian private equity firms exemplify both the promise and pitfalls of AI adoption. BGH Capital initially implemented machine learning algorithms to replace junior analysts in due diligence processes. The efficiency gains were impressive – document analysis that once took weeks now completed in days. Yet they soon discovered something crucial was missing: the contextual questions and creative insights that often emerged when analysts immersed themselves in company data. Their redesigned approach now uses AI to handle initial data processing while human analysts focus on hypothesis generation, pattern recognition across disparate sources, and narrative development – skills that remain stubbornly resistant to automation (Australian Investment Council, 2024).
Research Sector
The CSIRO’s climate research division demonstrates perhaps the most balanced approach. Rather than viewing AI as primarily a cost-cutting tool, they’ve designed human-AI collaboration models where algorithms handle data processing at unprecedented scale while researchers focus on question formulation, experimental design, and interpretive analysis. This complementary approach has accelerated research timelines while expanding the scope of human scientific inquiry (CSIRO Annual Report, 2023).
Board Governance in the Age of Augmentation
For directors, navigating this territory requires new frameworks that move beyond simplistic efficiency metrics:
- Value-Centered Automation: Rather than asking “What can we automate?” boards should ask “Where does automation enhance our core value proposition?” Sometimes the answer involves preserving human roles precisely where the market rewards the distinctly human touch.
- Work Redesign Oversight: Implementation of AI systems should trigger automatic board-level review of how work processes are being reconceived, not merely automated. The boards I serve on now require explicit work redesign plans that articulate how human capabilities will be enhanced, not just replaced.
- Human Capital Development: As routine tasks shift to AI systems, boards must ensure proportionate investment in developing the uniquely human capabilities – creativity, empathy, ethical judgment, collaborative problem-solving – that will increasingly differentiate organisations.
- Cultural Integration: The most successful AI implementations I’ve observed are those where technology adoption is treated as a cultural transformation, not merely a technical one. Boards must ensure leaders are equipped to help teams navigate changing role boundaries and identity shifts.
The Board’s Critical Role
Australia’s position offers unique opportunities for leadership in human-AI integration. Our relatively high labour costs create strong incentives for automation, yet our strong emphasis on fair work practices and human wellbeing provides a counterbalance against excessive techno-optimism.
As directors, our responsibility extends beyond operational oversight to fundamental questions about organisational purpose and values. When evaluating AI implementation proposals, the boards I serve on now apply a simple but powerful framework: Does this approach enhance or diminish the distinctive human capabilities that ultimately drive sustainable value?
The most effective governance committees have established explicit “augmentation principles” that guide technology adoption. These principles acknowledge efficiency as important but insufficient – they require technology implementations to demonstrably enhance human potential rather than merely replace it.
Conclusion
The rush toward AI-driven efficiency brings both opportunity and risk for Australian organisations. The critical distinction I’ve observed between leaders and laggards isn’t the pace of technology adoption but rather the thoughtfulness with which they redesign work processes to capitalise on the complementary strengths of humans and machines.
In boardrooms where efficiency metrics dominate the conversation, AI implementations often destroy value even while appearing successful on conventional measures. Conversely, boards that foster nuanced conversations about the appropriate division of cognitive labour between humans and machines are creating organisations that are not only more efficient but more innovative, adaptive, and ultimately competitive.
The most successful organisations I advise view AI not as a replacement for human capability but as a catalyst for its evolution. They’re using automation to eliminate routine burdens while simultaneously expanding opportunities for the creativity, judgment, and interpersonal connection that remain uniquely human domains. Their strategic question isn’t “How quickly can we automate?” but rather “How thoughtfully can we augment?”
As Australian boards navigate this transformation, we have both the opportunity and responsibility to foster approaches that harness technological potential while preserving the human capabilities that ultimately give our organisations purpose and meaning. The future belongs not to those who automate most aggressively, but to those who most wisely reimagine the partnership between human potential and AI capability.
About Gary Morgan: Gary Morgan is an experienced board director, chief executive, consultant, and corporate advisor with extensive experience in strategy, innovation, and growth across various deep tech sectors including sustainable energy, health tech, aged care, and information security. He is a Fellow at the Governance Institute of Australia and serves on the Griffith University Industry Advisory Board for the ICT School. Gary has co-authored papers and reports published in entrepreneurship and medical journals.
Acknowledgment: This article was crafted with the assistance of AI technology.
References
AGL Energy. (2023). Sustainability Report 2023. https://www.agl.com.au/about-agl/investors/sustainability-performance?rmxt3r=vwt06z&srsltid=AfmBOorIME62wPoiTch54MR3qnMPuxe_bVGZSPakrhTzh8_L-0vHMAwg
Australian Investment Council. (2024). Private Capital Investment Report 2024.https://investmentcouncil.com.au/resource?resource=9
Bupa. (2024). Can healthcare set the standard for AI innovation? https://www.bupa.com/news-and-press/news-and-stories/2024/can-healthcare-set-the-standard-for-ai-innovation
CSIRO. (2023). Annual Report 2022-2023. https://www.csiro.au/en/about/corporate-governance/annual-reports/22-23-annual-report
Royal Melbourne Hospital. (2024). Artificial intelligence changes the way radiologists read scans. https://www.thermh.org.au/news/artificial-intelligence-changes-the-way-radiologists-read-scans
Woodside Energy. (2024). Annual Report 2023-2024. https://www.woodside.com/docs/default-source/investor-documents/major-reports-(static-pdfs)/2024-annual-report/annual-report-2024.pdf
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