The Problem We Keep Missing
The language of transformation is changing. For decades, leaders have spoken in terms of cost, efficiency and headcount. But the real productivity loss lies in the hidden layers: conflicting policies, duplicated accountabilities, and interdependencies that nobody sees until something breaks.
Leaders sense the drag but can’t locate it. Employees feel it as bureaucracy. Investors discover it too late, when value evaporates in post-merger integration.
These frictions don’t just waste time: they reduce quality, stifle innovation, and keep people from doing work that feels meaningful.
But as organisations become more digitised and as AI enables new forms of classification and relational understanding, a new way forward is possible: a shift in how we understand the work of organisations and the work of people inside them.
What Research Tells Us
This isn’t a new problem. Before the current wave of AI, researchers were already grappling with how to represent organisations as complex systems. The model of an organisation as a system didn’t fully take hold at the time, but in today’s AI era it feels strangely urgent again.
Thomas et al showed that organisations can be represented as evolving systems, built from the digital artefacts we already have: job descriptions, org charts, process flows, and policies. The insight is not the digital twin concept, but the principle: if we connect these fragments, we can model the consequences of change before acting, rather than after the fact.1
Luo’s work on redundancy and innovation performance reminds us that duplication can also be resilience and not always waste. Too little slack leaves organisations brittle; too much overlap leads to stagnation. The right balance, governed well, can fuel innovation. Any system we design to surface redundancies must be careful to distinguish the harmful from the strategic.2
Rauch asks the crucial question: where do humans fit? Models and AI can observe, analyse, and recommend. But people still decide what matters, what to prioritise, what gives work meaning. The value is not in replacing human judgment, but in removing systemic noise so people can focus on higher value, more human centric work.3
These ideas have had varying levels of traction over recent years, but now with AI capable of reading and linking policy clauses, roles, and processes at scale, they are actionable.
From Efficiency to Clarity
The future of transformation lies in shifting the question. Not “where can we cut?” but “where do contradictions and dependencies hold us back?”. Not “how lean can we run?” but “how do we keep the right kind of slack?”
This is more than process improvement. It’s about building a capability that:
Integrates structural data (roles, processes, policies).
Surfaces conflicts, redundancies, and hidden dependencies.
Distinguishes strategic slack from harmful duplication.
Guides leaders toward interventions that improve alignment and resilience.
Why This Matters Now
Across industries, productivity growth has slowed to a crawl. In Australia, labour productivity grew by just 0.2 % in the year to June 2025.4 At the same time, surveys show that while around one in five firms report productivity gains from AI, more than three-quarters are adopting it without a roadmap.5
That combination is telling: the tools are available, but the ability to use them to address systemic friction is lagging. The result is an opportunity gap between what organisations deliver today and what could be achieved if contradictions, redundancies, and hidden dependencies were surfaced and managed.
The national conversation on productivity has been stuck on costs for too long. Cutting deeper hasn’t moved the dial. The real shift will come from capabilities that make invisible contradictions visible, highlight where redundancies help or hinder, and show leaders where dependencies block resilience.
The Human Future of Work
The goal is not automation for its own sake. Productivity without quality is wasted effort. Efficiency without human meaning is burnout.
The organisations that thrive will be those that use new tools not just to reduce effort, but to clarify their systems — freeing people to focus on work that is creative, relational, and purposeful.
This is not about replacing judgement, it is about restoring it. Removing contradictions and clutter that keep executives firefighting, employees disillusioned, and investors guessing. Creating space for leaders and teams to do the work only they can do.
This is the line between “efficiency projects” and true transformation. And it is the space I am working in: building the capability to map, test, and align the hidden layers of organisations so leaders and investors can see not just cost, but upside.
1 Thomas et al. (2019). Thomas, L. D. W., Autio, E., & Gann, D. M. (2019). Building an organizational digital twin. Business Horizons, 63(6), 725–736.
2 Luo et al. (2021). Luo, J., Chen, Y., & Chen, H. (2021). Organizational Redundancy, Corporate Governance and Innovation Performance: Evidence from Chinese Technology Firms. Journal of Business Research, 124, 150–163.
3 Rauch et al. (2023). Rauch, E., Dallasega, P., Matt, D. T., & Mattsson, S. (2023). Digital Twin: Where do humans fit in? Procedia CIRP, 121, 1085–1090.
4 Productivity Commission, Productivity Insights Bulletin 2025 – Labour productivity, June 2025.
5 Decidr, National AI Readiness Index 2025 (survey of 1,042 Australian SMEs).