For much of modern economic history, work has been priced according to time. Hours, rates, utilisation, and capacity have served as the dominant units through which value is measured and exchanged. This model made sense in an industrial context, where output scaled predictably with labour input and efficiency was achieved by maximising throughput per unit of time. Factories, assembly lines, and early professional services were structured around this logic, and the tools used to manage work reflected it accordingly.
Yet this framing is increasingly misaligned with the reality of contemporary knowledge work. As automation, artificial intelligence, and globally distributed talent become embedded in everyday operations, the relationship between time spent and value produced weakens. Execution, once expensive and scarce, is becoming increasingly cheap and abundant. Scarcity now lies in judgement over effort.
This shift has profound implications for how work is organised, how value is priced, and how organisations should think about the software systems that underpin their operations.
In environments where software automates large portions of execution, the differentiating factors move upstream. The most valuable contributions are no longer found in performing tasks, but in defining which tasks matter, sequencing them correctly, and deciding which paths should not be pursued at all.
The transition is already visible but often misunderstood. Industrial metrics like time spent, resources allocated, and utilisation rates still dominate the management of technology initiatives. This only develops a growing mismatch between how value is created and how it is accounted for. The core problem with time-based thinking in a software-driven world is not simply economic inefficiency. If value is tied to hours, it rewards a team that takes longer to do something. However, the value of the product doesn’t change, just the time it takes to produce it.
When value is tied to hours, income is capped by design.
As a result, improvement becomes paradoxical: the more efficient a team becomes, the less time it has to bill. This penalises speed rather than rewards it, which creates the situation where a customer is getting the same product faster, but for cheaper. Penalising expertise trains the market to interpret capability as cost rather than leverage.
More subtly, time-based pricing encourages the optimisation of effort rather than outcomes. Rather than making decisions based on how meaningfully the work advances the organisation’s outcome, it becomes necessary to ‘fill’ as much work into a budget as possible. This only leads to over-engineering in all areas of the project, causing convoluted and unnecessary features that do little to improve the project’s overall capabilities.
Outcome-oriented thinking reverses this logic. Instead of asking how long a project will take and assigning a value to that time, it asks what the actual problem being solved is and how valuable a solution to that problem should be. Value is determined by the outcomes it causes for a company and in the way the risk, resilience, and clarity are affected in its foundations. Software is not just an expense justified by effort, and it never should have been. It’s an investment justified by the structural and behavioural changes that it enables.
At this level, the distinction between execution and judgement becomes unavoidable. As systems absorb more of the mechanical aspects of work, what remains is the responsibility for defining intent, interpreting signals, and deciding how trade-offs should be resolved. These are not tasks that scale linearly with time, nor can they be meaningfully priced by the hour. They are cognitive acts whose value lies in their consequences rather than their duration.
This reframes how work itself must be understood. In an outcome-oriented environment, value no longer emerges from effort expended, but rather from the uncertainty that is inherently reduced. The most meaningful contributions are those that remove ambiguity and downstream failure or reshape a system so that fewer decisions need to be made at all. These effects compound over time, yet they are decisive in determining whether an organisation becomes more coherent as it grows.
The persistence of time-based thinking, then, is not merely an outdated accounting practice but a conceptual lag. It reflects an attempt to apply industrial-era measures to post-industrial conditions. When execution was scarce, time was an appropriate proxy for value. When judgement is scarce, time becomes a misleading signal. Optimising around it incentivises the wrong behaviours, obscures real leverage, and systematically undervalues the work that matters most.
What is required is not the abandonment of measurement, but a recalibration of what is being measured. Outcomes, unlike hours, demand clarity of purpose. They force organisations to articulate what success actually means, which risks are worth paying to remove, and which forms of complexity are acceptable. In doing so, they shift attention away from activity and toward intent.
The transformation underway is not that work is disappearing, but that its centre of gravity is moving. As automation continues to erode the cost of execution, the differentiator becomes the quality of thinking embedded in systems and decisions. Those who continue to trade primarily in time will find themselves constrained by diminishing returns. Those who learn to price, design, and reward outcomes will operate under a different logic entirely.
The future of work, in this sense, is not about doing more in less time. It is about understanding what time no longer captures and building economic and organisational models that reflect that reality.
- Written and researched by Sean Veldboer, Consultant at CBOS.