"AI does not simply replace labor; rather, it frees up resources through productivity gains and reallocates them to higher-value roles."
The key to AI adoption is not technology deployment, but the systematic reconstruction of organizational capability: technology without strategic anchoring is a 'ship without a rudder,' and strategy without organizational fit
01 The Adoption Gap of Generative AI
The adoption gap of generative AI refers to the gap between 'AI's commercialization capability' and 'the enterprise's organizational absorptive capability.'
As generative AI technology penetrates from 'early adopters' to 'early majority,' the industry faces the contradiction of technological maturity versus lagging adoption: on the technology side
This gap between 'technology being available' and 'the organization being able to use it' is essentially a lack of the enterprise's organizational absorptive capability. Goldman Sachs' moves in 2025 became a model for crossing the gap
02 Goldman Sachs 3.0's 'Gap-Crossing Practice'
The adoption gap of generative AI refers to the gap between 'AI's commercialization capability' and 'the enterprise's organizational absorptive capability.' How can an enterprise cross the gap? Goldman
In 2025, Goldman Sachs' CEO David Solomon, President John Waldron, and CFO Denis Coleman jointly signed the 'OneGS 3
Structured workforce adjustment: from 'replacement' to 'restructuring'
Goldman Sachs' workforce moves revolve around 'optimize — add — transform': in the second quarter of 2025 it had already cut a net 700 people, which belongs to 'annual routine
Organizational structure adaptation: breaking down 'departmental silos'
To solve the collaboration difficulty of AI adoption, Goldman Sachs formed cross-functional teams that hold regular sync meetings to unify data standards and improve AI adoption efficiency; at the same time it established
Cultural mechanism safeguard: tolerating 'reasonable trial and error'
Goldman Sachs established a mechanism of 'small-scale pilot — data iteration — rollout,' clarified the consensus that 'efficiency gains from AI feed back into the business,' and within a controllable
Goldman Sachs' AI adoption practice has two core characteristics: first, 'business orientation' — all adjustments revolve around solving real pain points, not showing off technology; second, 'full participation
03 Crossing the Gap: Strategic Guidance, Organizational Execution, Cultural Safeguard
Judging from Goldman Sachs' 'OneGS 3.0' AI transformation practice, reconstructing organizational capability is not a single move, but a closed loop of 'strategic anchoring, organizational fit, cultural support
Strategic anchoring and calibration: small entry points leverage large value
Many enterprises treat AI as a 'brand label' and fall into the trap of 'AI for the sake of AI.' Although executives acknowledge AI's importance, yet
Organizational fit and reconstruction: cross-boundary collaboration boosts efficiency
Departmental barriers are a fatal obstacle to AI adoption. Due to insufficient cross-department collaboration, the cognitive gap between the technical team and the business team on 'success criteria' reaches..., data standards
Cultural support cultivation: tolerance for trial and error promotes acceptance
The demand for 'zero errors' stalls many enterprises' AI projects. Establishing a 'trial-and-error tolerance' mechanism can effectively reduce innovation resistance: pilot first in a small scope, allow
Data governance consolidation: standards and circulation unlock value
Data quality is the foundation of AI adoption, yet it is often overlooked. Unified data standards are the basis. For example, manufacturing enterprises need to define core metrics such as 'fault' and 'anomaly
Change management implementation: sustained push ensures results
Executives need to provide resource guarantees; Goldman Sachs allocated a dedicated budget for 'OneGS 3.0' to ensure the transformation never lacks funding. In addition, a 'gradual pilot' approach
04 Lessons from the Case: Reconstructing Organizational Capability Is the Best Answer
The adoption gap of generative AI was never a technology gap, but an organizational-capability gap. Goldman Sachs' practice proves that technology without strategic anchoring is a 'ship without a rudder
For traditional enterprises, crossing the gap requires clarifying the priority of actions: first anchor the core pain-point scenarios, then form cross-functional teams, and finally advance adoption through trial-and-error iteration. At the same time
Only when an enterprise shifts the focus of its AI transformation from 'technology deployment' to 'organizational capability reconstruction' can it achieve 'efficiency gains and business growth' in the AI age
