Synthetic Work: The New Production Logic of Digital Value Creation

This shift can be described as Synthetic Work: a production logic in which AI systems are not merely tools, but productive agents within work processes. For companies, this doesn’t just mean efficiency gains – it means a structural change in roles, decision paths, and quality standards.

The central question, therefore, isn’t whether AI replaces work, but how value creation is being reorganized.

Automation isn’t really a new phenomenon. Optimizing industrial processes through machines has been a reality for decades. In the digital realm, rule-based automation also emerged early on—from scripts to workflow engines.

Synthetic Work takes this a step further: it’s no longer just about executing clearly defined procedures, but about generating new content, structures, or solutions. Generative AI models produce drafts, simulate scenarios, synthesize data, write code, or develop visual concepts.

Digital value creation is shifting from linear execution to orchestrated generation.

In traditional knowledge work, one idea long held true: quality comes from meticulous refinement. And while diligence is still an important part of any high-quality deliverable, synthetic work processes increasingly follow a different principle: quality emerges through deliberate iteration.

Instead of refining a single concept over weeks, dedicated teams use generative large language models (LLMs) to produce multiple lo-fi variants in a short time. These are then evaluated in a structured way—combined, discarded, or systematically developed further.

The production logic is changing:

That requires new competencies. What matters isn’t technical depth alone, but the ability to structure complex decision ecologies.

Synthetic Work enables a dramatic acceleration of creative and analytical processes.

This includes:

For companies, this means:
Value creation is limited less by production capacity than by decision-making capability.

Where speed increases, the risk of surface-level plausibility also grows. Synthetically generated results often appear coherent and professional. But plausibility doesn’t replace validation. Without clear criteria, Synthetic Work can lead to a flood of convincing yet strategically irrelevant outputs.

Typical risks include:

This is where it becomes clear: Synthetic Work requires robust, well-structured governance.

The more work depends on synthetic outputs, the more important the question of responsibility becomes.

Respectively, companies need clear guardrails:

The bottom line: Synthetic Work doesn’t run itself. It’s an organizational question.

The transformation of professional roles is already well underway. Under the growing influence of algorithmic actors, this shift is accelerating and spreading further.

The key competencies are shifting: it’s no longer about perfect execution—clear judgment is now at the center. This also means a move away from pure production toward a stronger emphasis on steering processes.

Reflective capacity is the core resource of digital value creation.

Not every organization benefits equally from Synthetic Work. It requires a functioning infrastructure to make technological innovations truly effective. Anyone aiming to use Synthetic Work successfully must first engage (critically) with their own standards and requirements.

Synthetic Work is particularly effective:

It is less suitable where context sensitivity and tacit experiential knowledge dominate. For example, if highly specialized regulatory questions play a central role without a solid training-data foundation, Synthetic Work may not be the first choice.

The key question, therefore, is: Where do synthetic production and synthetic data generate real insight—and where is it merely about speed?

In the long run, Synthetic Work will not be an optional add-on, but a durable part of clearly defined digital production standards.

The competitive advantage, however, lies not in simply using AI, but in the ability to embed synthetic processes strategically:

Companies that view Synthetic Work merely as an efficiency lever underestimate the transformative potential inherent in this way of working. Those who understand it as a new production logic, by contrast, actively shape the next level of digital value creation.

Synthetic Work marks a shift from manual production to orchestrated generation. AI becomes a productive component of work processes, but it cannot replace strategic thinking.

In the future, digital value creation will emerge precisely where organizations learn to steer synthetic options intelligently instead of adopting them uncritically – where synthetic data makes it possible to power high-performing models.

The decisive capability isn’t being able to generate as much content as possible. It’s knowing which generated possibilities are worth pursuing consistently.

Anyone who wants to implement Synthetic Work in a structured way should talk less about tools. It’s more about processes, roles, and the corresponding decision architectures.