Decision-Dominant Logic: Reimagining Value Creation & Capture in the Algorithmic Age (Part 2)

Moving from Service-Dominant Logic to Decision-Dominant Logic to Improve your Competitiveness

Limitations of Current Logics in the Algorithmic Age

Both G-D and S-D logics offer valuable insights but also exhibit limitations when confronted with the realities of today's algorithm-driven, big-data environment. I want to highlight several gaps that motivate the need for a Decision-Dominant logic:

• Neglect of Decision-Centric Optimisation: Neither G-D nor S-D logic explicitly centres on decision processes as units of analysis. G-D logic focuses on outputs and transactions, assuming decisions are part of managerial routines but not theorised. S-D logic, while emphasising operant resources and interactions, largely treats decision-making implicitly (as something actors do using knowledge) rather than as a distinct source of value. In practice, modern firms derive advantage by systematically making better choices – e.g. identifying profitable customer segments, selecting which product features to release, or routing delivery trucks optimally. These decision optimisations often involve complex data analysis and algorithmic modelling. S-D logic’s vocabulary of “service-for-service exchange” and “co-creation” does not directly capture the idea that a firm’s analytical and decision-making capability could be a primary differentiator. As a result, phenomena like high-frequency algorithmic decisions – which are ubiquitous in tech firms – are somewhat “invisible” in S-D logic descriptions of value co-creation and do not explicitly address value capture. Decision-Dominant logic fills this gap by explicitly highlighting decision-making competence (especially AI-enabled) as a driver of value, surpassing the general notion of applying knowledge in service.

• Scale and Speed of Automation: S-D logic was formulated in a world where service exchanges, although interactive, were often synchronous and limited by human cognitive bandwidth and effort. Today’s algorithmic systems operate at scales and speeds far beyond human interaction. For example, Google’s search engine processes over 8.5 billion queries per day, each query triggering a series of decisions (ranking results, matching ads) in fractions of a second. Financial trading algorithms execute thousands of trades in milliseconds based on streaming data. These automated decisions occur with minimal direct human involvement in each transaction. S-D logic, with its emphasis on human-to-human or human-to-company interactions, does not explicitly account for the value created or captured by machine-to-machine or machine-to-environment interactions under human guidance. In other words, the locus of action in value creation and value capture has expanded to include autonomous AI agents making rapid microdecisions on behalf of firms and customers. A Decision-Dominant perspective recognises that such AI-agents are extensions of the firm’s decision-making capacity, essentially scaling up the service exchanges. It complements S-D logic by analysing how value co-creation can be mediated or magnified by autonomous AI-agents acting within the service ecosystem.