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The author of this post, a user named /u/santanah8, has started compiling real-world AI implementations from various companies. The goal is to provide an unbiased view without any hype or speculation.
- The author found that Engineering and Finance are leading in the adoption of AI, with companies already seeing significant benefits such as speed gains.
- Logistics and manufacturing might appear slower at first glance, but the author believes these projects just take more time to show results.
- Three patterns are consistently observed: layered setups involving LLMs (large language models), orchestration tools, and application layers; end-to-end products where AI is integrated deeply into user interfaces without being explicitly stated as such; and hybrid approaches used by more mature organizations.
The author also notes that among the outcomes reported, speed gains are most common—about 14% of cases—and workforce reduction and revenue lift are much less frequent.
- Speed gains: The most prevalent outcome where AI implementations lead to faster processes or workflows.
- Workforce reduction: Less frequent but still observed, indicating that AI can help reduce the need for human labor in certain tasks.
- Revenue lift: Even rarer, with only a small percentage of cases reporting increases in revenue as a result of AI implementations.
The post concludes by asking readers to share their own observations and experiences regarding these findings. The author encourages engagement through the provided link to the full dataset of cases.
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# I’ve been documenting real AI implementations. Here is a list of findings, surprises and cases
The author of this post, a user named `/u/santanah8`, has started compiling real-world AI implementations from various companies. The goal is to provide an unbiased view without any hype or speculation.
## Observations
– **Engineering and Finance**: Leading in the adoption of AI, with significant benefits like speed gains.
– **Logistics and Manufacturing**: Might appear slower but are just taking more time to show results.
– **Three patterns consistently observed**:
– Layered setups involving LLMs (large language models), orchestration tools, and application layers.
– End-to-end products where AI is integrated deeply into user interfaces without being explicitly stated as such.
– Hybrid approaches used by more mature organizations.
## Outcomes
– **Speed gains**: The most prevalent outcome, with about 14% of cases reporting this benefit.
– **Workforce reduction**: Less frequent but still observed, indicating that AI can help reduce the need for human labor in certain tasks.
– **Revenue lift**: Even rarer, with only a small percentage of cases reporting increases in revenue as a result of AI implementations.
The post concludes by asking readers to share their own observations and experiences regarding these findings. The author encourages engagement through the provided link to the full dataset of cases.
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Originally published at reddit.com. Curated by AI Maestro.
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