Landscape Architects

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Landscape Architects (ISCO-08 2162) score an average of 0.37 on a 0–1 exposure scale — more exposed than about 70% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Minimal band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Compiling and analysing site and community data about geographical and ecological features, landforms, soils, vegetation, site hydrology, visual characteristics and human-made structures, to formulate land use and development recommendations, feasibility studies and environmental impact statements;”

Scores 0.47 on the 2025 scale. The task of compiling and analyzing site and community data related to geographical, ecological, and human-made features for land use and development recommendations involves both technical and strategic elements. Generative AI can significantly aid in data compilation, pattern recognition, and generating initial reports or recommendations using existing datasets. This aligns with some of the semantically similar tasks, such as "Developing reclamation projects for degraded areas" (which scored 0.4) and "Conducting research in hydrogeology and engineering geology" (scored at 0.3), where AI assists in preliminary analysis yet requires human judgment for nuanced decision-making. Like these tasks, the present task requires contextual understanding, site-specific conditions assessment, and regulatory compliance, which still necessitate human expertise. The ability of AI to support data-driven components enhances efficiency but does not negate the need for human oversight, strategy, and decision-making. Thus, considering the potential for AI assistance primarily in data handling but recognizing the complexities of strategic planning and compliance, the adjusted score reflects this balance in a high-income country with good technology infrastructure.

Moving fastest, 2023 → 2025

“Identifying and finding best solutions for problems regarding function and quality of exterior environments and making necessary designs, drawings and plans;”

Model capability on this task changed by +0.11 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2162, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Landscape Architects

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Landscape Architects sit at the 70th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Landscape Architects rank in the 70th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Compiling and analysing site and community data about geographical and ecological features, landforms, soils, vegetation, site hydrology, visual characteristics and human-made structures, to formulate land use and development recommendations, feasibility studies and environmental impact statements;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Landscape Architects". https://singulariki.com/gradient/2162-landscape-architects.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.