Australian universities are simultaneously under pressure to be more agile and more industry-engaged. They are failing at both - not because their people lack ambition, but because the systems those people work inside actively prevent the behaviour institutions claim to want. Agility is not a mindset problem. Industry engagement is not a culture problem. Both are structural problems, and until they are treated as such, universities will keep expecting miracles from people working inside machines built for a different era.
The word “agility” has become an institutional buzz word, invoked in strategies, repeated in leadership messaging, and often ignored in operational reality. The gap between those two registers is where universities are getting into trouble.
Genuine agility means the operational capacity to mobilise in response to a changing environment. Not a leadership statement. Not a set of values. The actual ability to sense a shift - a funding signal, a geopolitical disruption, a competitor move - and move the institution toward it, quickly. By that definition, most Australian universities are not agile, and the size of the institution is largely irrelevant. Smaller universities carry the same weight of compliance, risk aversion, and legacy systems as their larger counterparts. The assumption that smaller means nimbler rarely survives contact with reality.
The blockers are well understood by anyone who has worked inside the system. Fragmented infrastructure that doesn’t communicate. Legacy processes built by people solving yesterday’s problems, now calcified into institutional habit. Overlapping responsibilities across siloed teams with unclear boundaries. And, presiding over all of it, a governance architecture that routes decisions through a maze of subcommittees, working groups, academic senates, and councils before anything can move. That structure was not designed for speed. It was designed for accountability and shared governance, which are legitimate values — but they create a system where the distance between an idea and a decision is measured in months, not days.
What makes this particularly difficult is that many of these constraints are intrinsic to what universities are. The governance complexity is not an accident. The perfectionism is not irrational in a sector where public trust depends on rigour. The challenge, then, is not to dismantle these things but to create protected space alongside them where different rules apply.
The same structural logic plays out in how universities approach industry engagement. The sector says it wants deeper, more productive partnerships with industry. The data suggests something different is actually happening.
Funding trajectories are telling. Over the past decade, the growth in industry and government co-investment in R&D — Category 2 and 3 funding — has significantly outpaced competitive grant funding. The money is moving toward partnership. But the academic career system has not followed. Publications, competitive grant success, and supervision loads remain the primary metrics against which most academics are assessed and promoted. An academic who spends two years building a trust-based industry relationship, negotiating IP agreements, and co-designing a research program is working toward an outcome that the institution’s performance framework may barely register.
This is not a subtle problem. It is a direct contradiction between stated strategy and structural incentive. And it has consequences beyond the individual academic. Industry partners notice. Some are stepping back from university engagement entirely, choosing instead to invest in internal R&D, work with private research organisations, or simply act as fast followers rather than co-innovators. When universities signal openness to partnership without backing that signal with meaningful structural change, industry interprets it correctly: as lip service.
The academics who are doing industry engagement are largely doing it despite the system. They are absorbing the friction — the workload, the uncertainty, the gap between what they are doing and what their performance framework rewards — through goodwill and personal commitment. That is not a sustainable model, and institutions that depend on it are carrying a quiet risk they may not have formally acknowledged.
Both agility and industry engagement tend to be treated as culture problems. Leaders invest in workshops, run change programs, and encourage their teams to think differently. These efforts are not worthless, but they are insufficient when the underlying structures remain unchanged.
Culture follows structure. If an academic is assessed on publications, they will prioritise publications. If a decision requires approval from five committees, people will avoid initiating decisions that require approval from five committees. The culture that emerges from a system reflects its incentives. Trying to change the culture without changing the incentives is hoping that goodwill will do the work of institutional design.
The perfectionism that runs through university culture compounds this. Universities hold themselves to high standards across research, teaching, and public engagement — rightly so. But when that standard is applied uniformly, it eliminates the experimental space that both agility and industry partnership require. A partnership that takes two years to bear fruit looks like failure at the eighteen-month mark. A process redesign that doesn’t work perfectly on the first iteration looks like failure too. Without formal protection for that kind of exploratory work, institutions default to what they can measure, defend, and optimise: the traditional metrics.
The good news is that the changes required are structural, which means they are designable. They require will and leadership, not transformation programs.
On agility, the key move is decision architecture reform. Rather than routing all decisions through existing governance pathways, institutions can establish clear ownership boundaries and empowered decision pods for specific domains — operational improvement, technology adoption, process redesign — with appropriate guardrails rather than full committee oversight. This is not deregulation. It is distributing authority to people who have the context to use it. Alongside this, institutions need to formally identify what they will stop doing. Legacy processes, reporting that produces no useful output, activity that made sense in a previous era: the explicit decision to say no to these things frees capacity that operational agility requires.
On industry engagement, the structural moves are equally clear. Audit the academic promotion framework and ask honestly whether industry engagement appears as a core metric or as a vague desirable. Consider creating differentiated academic career pathways — a traditional research track, a hybrid track, and an industry-focused track — with distinct performance indicators for each. CRM-style relationship metrics (active partnerships, proposals sent, contracts engaged, frequency of industry contact) are not soft measures; they are meaningful business data, and treating them as such sends a signal about what the institution genuinely values. Critically, involve the academics already doing industry engagement in designing the new framework. They know where the friction is. They have already made the career sacrifices. They are the best possible co-designers.
In both domains, the experimental bucket matters. Not everything needs to meet the full standard of institutional perfectionism. Carving out a formal space — call it 10% of operational activity, or a defined innovation pathway for academic careers — where the rules are different, where failure is expected and processed rather than hidden, changes what people are willing to attempt. The goal is not to lower standards. It is to apply the right standards to the right kinds of work.
Universities seeking genuine agility should begin with decision architecture, not culture. Identify two or three operational domains where authority can be clearly owned and decisions made without full governance escalation. Pair this with an explicit list of things the institution will stop doing — this is as important as deciding what to start.
On industry engagement, the single most powerful signal a university can send is a formal change to its academic performance framework. Even one policy amendment — adding a defined metric for industry engagement to a promotion criteria — communicates seriousness in a way that no strategy document can. Pick one lever and pull it.
Institutions should formally identify and engage the academics already doing industry partnership work. These people are currently absorbing institutional friction through goodwill. Understanding what that costs them, and acknowledging it explicitly, is both a risk management step and an act of good faith. Invite them to co-design the framework that replaces the one they are working around.
Finally, resist the reflex to treat both agility and industry engagement as culture change programs. Culture is downstream of structure. Fix the structures — the governance, the incentives, the performance frameworks, the decision rights — and the culture will follow. Do it the other way around, and you will be running the same workshops in five years.
This article draws on discussions from Episodes 3 and 4 of the Inside Research Strategy Podcast, featuring conversations on institutional agility and academic incentive structures. Both episodes are available wherever you listen to podcasts.
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