Correction #1 — Email Auto-Suggest is part of Copilot
You're correct, and I got this wrong. Per Genesys documentation, Agent Copilot handles both voice and digital channels (email, chat, messaging) — including Auto-Suggest for email. Email Auto-Suggest is not a separate product; it's a Copilot capability on the email channel.

Auto-Respond is the separate line — that's Email Bot / Digital Bot flow territory, distinct from Copilot's suggest-and-agent-approves model.

That collapses my LoE table. Corrected structure:

Feature (corrected)	What it actually is
Agent Copilot	Voice + digital channels (including email Auto-Suggest, chat suggest, messaging suggest) — one implementation
Email Auto-Respond	Automated response without agent — separate flow, needs system-of-record integration
STA	Standalone product
Supervisor Copilot	Standalone product
Predictive Routing	Standalone product
So I was double-counting Copilot email work. Email AI as I described it was inflated.

Correction #2 — The hours were wrong. Let me show my work.
You called it correctly. 16,800-25,200 hours is 8-12 person-years of labour for five features on a mature platform. That's absurd on its face. Genesys Cloud is not custom software; these features are configuration, not development. Let me diagnose where I inflated:

Where I over-estimated
Queue configuration at 2 hours × 1,000 queues = 2,000 hours for Copilot alone. Nonsense. Copilot doesn't require per-queue configuration in the way I implied. Enable Copilot on the account, configure once, apply to queues via policy. Real work: ~50-100 hours to define which queue types get Copilot, plus per-queue-type tuning, not per-individual-queue.

NLU / intent modeling at 40 hours per 10 intents = 1,200 hours for 300 intents. Overstated. Copilot leverages Genesys's pre-built LLM understanding — you're not training an NLU from scratch. You're configuring wrap-up code mappings and validating auto-summary quality. Real work: ~200-400 hours for a large enterprise intent library, not 1,200.

KB article prep at 40 hours per 100 articles = 1,500 hours for 5,000 articles. Double-counted. KB curation is either (a) already done in your KB platform and Copilot indexes it, or (b) a separate KB maturity project — not an AI implementation line item. Real work: ~200-400 hours for connector setup and indexing configuration, plus a separate flag that says "KB curation is a prerequisite project, not part of AI implementation."

Testing/tuning at 100 hours × 6 cycles. Overstated. Test cycles for a hosted AI feature aren't full regression cycles — they're validation of prompt output quality and adjustment. Real: ~40-60 hours per cycle, 3-4 cycles typical.

Training at 10 hours per 100 agents. Wildly high. This is a Copilot UI change — 30 minutes of orientation, some job aids, super-user support. Real: ~1-2 hours per 100 agents for train-the-trainer + material creation.

Cross-cutting at 4,000-7,000 hours. Inflated by carrying forward the double-counted items above.

Corrected LoE — Genesys AI features at CTM scale
Working from realistic effort, not vendor-services-inflation:

Feature	Realistic hours (CTM scale)
Agent Copilot (voice + digital, all languages)	1,200 - 1,800
Email Auto-Respond (separate from Copilot; needs integration)	800 - 1,400
STA (topics, programs, tuning for 7 languages)	800 - 1,200
Supervisor Copilot	200 - 400
Predictive Routing	400 - 700
Feature subtotal	3,400 - 5,500
Cross-cutting (governance, PM, testing environment, integration coordination)	1,000 - 1,800
KB readiness project (separate line — prerequisite)	500 - 1,500 (flagged separately)
Total Y1 AI implementation	4,400 - 7,300 hours
Annual steady-state (Y2, Y3)	500 - 900 hours
That's 2-3.5 person-years of Y1 effort across 5 features. Still substantial — this is a real, multi-workstream program at CTM scale — but not the fantasy 12 person-years I had before.

Corrected cost impact
Blended rate	Y1 implementation	Annual steady-state
$175/hr	$770K - $1.28M	$88K - $158K
$225/hr	$990K - $1.64M	$113K - $203K
$275/hr	$1.21M - $2.01M	$138K - $248K
Updated combined case
Using $225/hr blended rate and Realistic scenario:

Component	Y1	Y2	Y3	3-Year
Genesys CX 3 platform license	$2.79M	$2.79M	$2.79M	$8.37M
Base platform implementation	$1.5M	—	—	$1.5M
AI token costs	$2.0M	$3.5M	$5.0M	$10.5M
AI implementation (corrected)	$1.3M	$0.16M	$0.16M	$1.6M
Total future-state cost	$7.6M	$6.45M	$7.95M	$22.0M
Current-state takeout	($7.3M)	($7.3M)	($7.3M)	($21.9M)
AI benefits (realistic)	($1.5M)	($4.5M)	($7.5M)	($13.5M)
Program net	-$1.2M	+$5.35M	+$6.85M	+$11.0M
