An insight has gradually revealed itself as this series has developed.
When I started writing From Inertia to Immersion, I thought the central problem was largely one of representation. Our institutions build maps of the world to represent it in a form they can understand, through accounts, indicators, models, disciplines and reporting systems. They then, quite reasonably, use those maps to make decisions. The trouble starts when the map leaves something important out.
That still seems true. But after working through the river, GDP, the university and, most recently, stewardship, I am no longer sure it goes far enough, because in each case the institution actually knew quite a lot already.
The Murray-Darling was not an information vacuum. Governments had hydrological data, allocations, ecological monitoring, operating rules and decades of accumulated expertise. Nobody needed to discover that rivers were living systems affected by flow, temperature, extraction and time. Yet millions of fish could still die in a river being actively measured and managed.
GDP presents an even stranger version of the same problem. Its shortcomings as a proxy for national wellbeing are hardly an underground insight and have been discussed almost since national income accounting began. We have spent decades developing better ways to describe social progress, environmental condition, wealth, wellbeing and natural capital. Despite this, GDP continues to sit at the centre of economic life.
Then came the university. Here was an institution containing ecologists, economists, engineers, lawyers, anthropologists, data scientists and public-policy scholars who could all be working on different pieces of the same problem without ever meeting. Yet the academy struggled to turn that extraordinary concentration of knowledge into genuinely integrated choices. Again, lack of knowledge didn’t seem to explain very much.
For a while I thought the answer might simply be silos. Bring the disciplines together, create the cross-faculty centre, run the workshop, put different perspectives around the same table. Those things are useful, but they don’t quite solve it either. A room full of intelligent people can leave with a much richer understanding of a problem and still face exactly the same institutional machinery the week after. Budgets are still allocated in the same way. Projects still have sponsors, risk sits somewhere else, ethics has its process, procurement has another, data has an owner and somebody eventually has to sign something. The conversation may have changed but the decision hasn’t.
There is a live argument running at the moment, across several publications I follow, that institutions need to become far better at learning. That innovation management and compliance systems are necessary but insufficient, and what is really required is a continuing capacity to understand together and act together. I agree with the diagnosis. What strikes me is where these arguments tend to stop. They describe the intellectual and social conditions for collective learning in considerable detail, then name the thing that converts what a group has learned into an institutional choice and leave it undefined. The conversion gets treated as something that will follow naturally once the learning is good enough. My experience suggests it does not follow at all.
That distinction became harder to ignore while writing about stewardship. A community can be consulted extensively. Its knowledge can be respectfully documented. People can listen carefully, change their language, acknowledge Country, establish advisory groups and genuinely value the relationship. But there is still a fairly brutal test hiding underneath all of that. Can the community say no, and if they do, will it change anything?
That question shifts the issue away from whether knowledge has been heard towards whether it has standing. Does what was learned in formulating the decision have any bearing on the choice being made?
I think that may be the thread running through all of these essays. Not simply what the institution knows, but what the decision is actually required to answer to. There is a difference.
A Treasury may have an excellent wellbeing framework, but if political and fiscal choices still answer overwhelmingly to growth, revenue and expenditure, the broader account remains informative rather than operative. A Traditional Owner may bring knowledge essential to the success of a project, but if the agreement gives that knowledge no authority over what proceeds, participation and power have been split with agency falling through the gaps.
These are not failures of measurement but of connection between knowledge and consequence.
That is why moving next into integrated decision processes in enterprises feels less like changing sectors and more like arriving at the place where the problem becomes unusually visible. Businesses are, after all, very good at making certain information consequential.
A credit limit matters because something happens when it is breached. A hurdle rate matters because an investment does not proceed if it fails. A covenant matters because it changes what a borrower can do. A risk appetite matters, at least in principle, because it marks the point beyond which exposure should trigger action. A budget matters because it confers permission.
These are not just measures. They are attached to decisions.
Over the past decade companies have become vastly better at measuring things that were once barely visible in corporate reporting. Emissions, water, biodiversity, workforce conditions, supply-chain exposure, social impacts, climate scenarios and nature dependencies. The reporting architecture has become more sophisticated, more standardised and increasingly subject to assurance. That is progress, though a harder question still remains open. Can a company become very good at seeing these things without any of it reaching the decisions themselves?
Imagine a business with an exemplary sustainability report. Its disclosures are complete, its methodologies defensible, its climate and nature sections assured. Somewhere in the supply chain, however, sits a supplier whose production depends on a catchment sliding towards ecological failure. The company may know about the water risk. Its sustainability team may have mapped it. The information may even appear in a report.
But does it change supplier selection, alter inventory strategy, affect the value placed on the relationship or move an investment threshold? Does somebody own the exposure? At what point, exactly, does the condition of that living system acquire enough institutional weight to change a commercial choice?
That seems to me a much more interesting question than whether the company has disclosed a risk exposure. It also points towards something I have been circling for a while in Emerdigm’s work. Perhaps the real challenge is not to keep adding information to the corporate map. Perhaps it is to change what the organisation can see at the moment a choice is made.
That takes us into uncomfortable territory quite quickly. Traditional accounting gives businesses an extraordinarily powerful language for some forms of value and almost nothing for others. A degraded wetland may be economically vital to an asset and absent from its balance sheet. A resilient workforce may take years to build and appear primarily as a cost. A supplier relationship may depend on ecological conditions several steps removed from the contractual boundary. A decision can therefore look perfectly rational inside the accounting frame while quietly consuming the conditions that make the economics possible.
None of that means we should solve the problem by putting a dollar value on everything. I am increasingly wary of that solution as well. If the only way nature can enter a decision is by pretending to be financial capital, we have probably misunderstood the problem and simply found a better answer to the wrong question. The more useful task is to make dependencies, trade-offs and consequences legible enough to influence the choice without flattening everything into a single unit or ledger entry.
That is where the next part of the series begins.
The next essay, Beyond ROI, looks at what happens when the balance sheet is treated not as a complete picture of value, but as one very particular view of it. It asks what changes when natural, human and social capital are understood not as worthy additions to a sustainability report, but as part of the productive system on which financial performance depends.
From there, Making Living Systems Legible tackles the harder practical problem. How do you bring something as dynamic, relational and place-specific as a living system into an institutional decision without destroying the very qualities that made it important?
The final essay returns to immersion itself, because none of this is ultimately solved by a better spreadsheet. It requires people who can move between different kinds of evidence, recognise dependencies before they become failures, work across boundaries and remain curious when the system refuses to behave as expected.
Looking back over the series so far, I think that is the shift I had been trying to describe without quite having the language for it. We have spent enormous effort improving what institutions know. The next challenge is harder.
Making what matters difficult for the decision to ignore.