AI strategy for organisations
Get the structure right.
AI is changing the underlying economics of how work is organised. Capturing the full value of AI means going beyond augmentation and automation to re-engineering your organisation.
What the research tells us
AI adoption follows a J-curve. Firms experiment, workers learn, but the impact on productivity is modest at first—because the existing organisation of work was not designed around AI. Then comes deeper change: firms re-engineer processes and production structures. This is when the large productivity gains, and the large disruptions, arrive.
At the firm level, this plays out through creative destruction. Successful adopters gain market share; slow movers lose it. New entrants built around AI from the start can rapidly displace incumbents.
While the dynamics at the industry or economy level are very hard to predict, a lot more can be said about how AI can be deployed at the task level today. This is the starting point for evaluating how AI impacts your organisation.
Trewick's methodology draws on underlying analysis from Treehouse Consultancy, a sibling company. For a deeper examination of the research, read about the broader framework.
Our approach
We step below the level of jobs to look at the underlying tasks a firm needs to undertake. Tasks have been bundled into jobs historically to trade off the benefits of specialisation against the costs of coordinating across separate roles. AI not only automates and augments tasks, but changes the economics of this trade-off in a way that means we need to re-think how work is structured.
We use a structured interview process—delivered via an AI assistant—that maps which tasks employees perform, how those tasks depend on one another, who does them, and which systems they interact with. This builds a detailed network graph of the tasks in your organisation: not an org chart, but a picture of how work actually gets done.
Four channels of opportunity
Task automation
AI performs a task entirely, removing it from the human workload.
Task augmentation
AI assists with a task, raising quality or speed while the human remains in the loop.
Coordination compression
AI reduces the cost of splitting interdependent tasks across people, enabling tighter specialisation.
Skill-requirement reduction
AI lowers the skill threshold for certain tasks, changing which roles make economic sense.
While these channels may save time at the task level, turning that into productivity gains requires re-organising tasks at the job level. This in turn requires understanding the dependencies between tasks—which is where the task graph comes into its own.
Understanding risk
Not all opportunities are equal. Automating a peripheral task might be a safe quick win with modest productivity gains. Automating a coordination hub could be transformative—but runs the risk of serious disruption if it goes wrong.
Understanding the structural position of each task is essential to sequencing adoption well. For a fuller explanation and a worked example, see the detailed methodology.
What you get
An assessment of where you could use AI in your organisation and guidance on how to get started:
- A task graph mapping of how your work is currently structured
- Opportunity identification across automation, augmentation, coordination and skill compression channels
- A risk assessment and adoption sequencing recommendations
- An implementation roadmap tailored to your priorities