Overview Leaderboard Methodology
Industry Report

Workforce Engagement Management

Solutions for forecasting, scheduling, quality monitoring, and performance management to optimize contact-center staffing and agent performance.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 275
Total responses: 1,400
Top Brand Overall?
NICE
95/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
NICE
86

Highest score on the LBA metric.

Authority Leader?
NICE
100

Highest score on the Authority metric.

TOM Leader?
NICE
100

Highest score on the TOM metric.

NICE is the default answer in AI responses for Workforce Engagement Management

When users ask ChatGPT, Claude, or Gemini about Workforce Engagement Management, NICE is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Verint is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 35 Workforce Engagement Management brands ranked

Ranked by overall AI Visibility Score (geometric mean of LBA, Authority, and TOM). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
NICE
nice.com
 86
100
100
95
2
Verint
verint.com
 70
73
91
77
3
Genesys
genesys.com
 82
66
76
75
4
Calabrio
calabrio.com
 74
59
55
62
5
Five9
five9.com
 75
17
42
37
6
UKG
ukg.com
 72
11
64
37
7
Talkdesk
talkdesk.com
 70
17
38
36
8
Playvox
playvox.com
 53
9
22
22
9
Aspect Workforce
aspect.com
 43
3
24
14
10
Amazon
amazon.com
 47
0
14
2
11
Zoom
zoom.us
 45
0
9
2
12
Alvaria
alvaria.com
 47
0
4
1
13
Assembled
assembled.com
 49
0
4
1
14
Intradiem
intradiem.com
 51
0
1
1
15
Klaus
klausapp.com
 36
0
0
1
16
MaestroQA
maestroqa.com
 57
0
0
1
17
Balto
balto.ai
 51
0
0
0
18
CallMiner
callminer.com
 62
0
0
0
19
ccMath
ccmath.com
 0
0
0
0
20
CommunityWFM
communitywfm.com
 20
0
0
0
21
Cresta
cresta.com
 60
0
0
0
22
DVSAnalytics
dvsanalytics.com
 32
0
0
0
23
Eleveo
eleveo.com
 30
0
0
0
24
Enthu.AI
enthu.ai
 19
0
0
0
25
EvaluAgent
evaluagent.com
 14
0
0
0
26
injixo
injixo.com
 41
0
0
0
27
I
InVision
invisionwfm.com
 13
0
0
0
28
Level AI
level.ai
 47
0
0
0
29
MiaRec
miarec.com
 33
0
0
0
30
Monet Software
monetsoftware.com
 19
0
0
0
31
Observe.AI
observe.ai
 63
0
0
0
32
Pipkins
pipkins.com
 12
0
0
0
33
QStory
qstory.ai
 22
0
0
0
34
Scorebuddy
scorebuddyqa.com
 47
0
0
0
35
Tymeshift
tymeshift.com
 46
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 275 shared Workforce Engagement Management prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Geometric mean of LBA, Authority and TOM: (LBA × Authority × TOM)^(1/3). Geometric mean is used so that any single weak metric pulls the overall score down, rather than being masked by strength elsewhere.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 275 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →