# Achievers: how AI models rank it, September 2026

HR AI Recommendation Index, September 2026 Edition. Named in 27 judge labels across 1 categories by 12 of 12 models. Page: https://hr-ai-index.com/vendors/achievers/

## Standing by category

| Category | Share | Rank | Negative rate | Labels |
|---|---|---|---|---|
| Recognition and rewards | 5% | 8 | 22% | 27 |

## What the models said for it

- "If I had to pick one default recommendation for a mid-sized B2B org, I'd choose Achievers" (Perplexity Sonar, Recognition and rewards)
- "### 1. Achievers (Best for Scaling Culture)" (MiniMax M2.5, Recognition and rewards)
- "Best for large-scale/global enterprise-style recognition: Workhuman or Achievers" (GPT-5.4 mini, Recognition and rewards)
- "If you need deep customization or complex goal alignment, Achievers is a top choice." (GLM 4.7 FlashX, Recognition and rewards)

## And against it

- "Limited points/rewards budgets: Frequent complaints (e.g., in Motivosity, Kudos, Achievers)... custom reports delayed in others like Achievers." (Grok 4.1 Fast, Recognition and rewards)
- "Scaling penalties, add-on modules, and professional service fees can significantly increase costs." (Mistral Small, Recognition and rewards)
- "Platforms like *O.C. Tanner* or *Achievers* require 1–3 months of custom implementation" (Gemini 3.5 Flash, Recognition and rewards)
- "without the complexity and cost of enterprise solutions like Achievers or O.C. Tanner" (Kimi K2, Recognition and rewards)

## Record

- Method: https://hr-ai-index.com/methodology/
- Raw judge labels and full responses: https://hr-ai-index.com/data/
- License: CC BY 4.0. Cite as HR AI Recommendation Index, September 2026 Edition, hr-ai-index.com.