CTM Business case update

studies/202512_GenesysCX/ctm-token-calculator/docs/ctm_ai_labour_estimate.md

@@ -0,0 +1,198 @@
+The Genesys ROI documents claim 5 AI feature benefit categories:
+
+Agent Copilot (voice + digital handle time + ACW)
+Email AI (Auto-Respond + Auto-Suggest)
+Speech & Text Analytics
+Supervisor Copilot (AI Translate, AI Summary, Admin)
+Predictive Routing
+None of these are turn-key. Each requires configuration, tuning, and enablement effort. The original case has zero implementation cost. 
+
+Framework — four LoE dimensions per feature
+Every AI feature carries four kinds of effort:
+
+Dimension	What it is	Scales with
+Fixed setup	One-time base configuration — instance creation, settings, rules, permissions, security	Roughly constant per feature
+Variable configuration	Per-scope effort — per queue, per language, per intent, per wrap-up code, per KB article	Multipliers × unit count
+Iterative tuning	Test → measure → adjust cycles. Non-negotiable for AI features. Typical: 3-6 cycles before production stability	Complexity of the feature
+Enablement & change	Agent/supervisor training, adoption support, communications, super-user network	Headcount + geography
+Plus a steady-state annual line that everyone forgets:
+
+Steady-state	What it is
+Annual optimization	Retraining, KB refresh, drift correction, model tuning as customer behavior shifts
+Per-feature LoE — Genesys-claimed feature set
+Below is my working LoE structure for the calculator. Hours are for a typical medium-complexity implementation. CTM-specific amplifiers follow in the next section.
+
+1. Agent Copilot
+Per the Genesys documentation you shared, the setup dimensions are: create the Copilot instance, configure settings, configure NLU (intents), configure rules, configure queues, configure per-language variants, wrap-up code configuration, AI Studio for custom summaries, testing, permissions, KB integration for answer highlighting.
+
+Activity	Unit	Hours per unit	Notes
+Base Copilot instance setup	Fixed	80-120	Per language variant (one instance per language)
+Settings, rules, permissions config	Fixed	40-60	
+NLU / intent modeling	Per 10 intents	30-50	Includes utterance generation, training, validation
+Wrap-up code mapping	Per 20 wrap-ups	8-12	Includes utterance training per code
+Queue configuration	Per queue	1-3	Critical CTM scaling factor
+Custom summary templates (AI Studio)	Per template	20-40	If custom summaries wanted
+Knowledge base article preparation	Per 100 articles	20-40	Only if KB used for answer highlighting — separate from KB creation
+Testing / tuning cycles	Per cycle	80-120	Plan for 4-6 cycles Y1
+Agent training	Per 100 agents	8-12	Blended live/self-paced
+Supervisor / admin enablement	Per site	16-24	
+Typical medium implementation (10 queues, 1 language, 100 intents, 100 wrap-ups, 500 agents, 4 tuning cycles, 500 KB articles): ~1,500 hours.
+
+2. Email AI (Auto-Suggest + Auto-Respond)
+Activity	Unit	Hours per unit
+Base Email AI setup	Fixed	60-100
+Intent library for email	Per 10 intents	40-60 (higher than voice — more text nuance)
+Response template library (Auto-Suggest)	Per 20 templates	30-50
+Auto-Respond flow design	Per flow	60-100 (business rules, escalation logic, guardrails)
+Integration to systems of record for response	Per integration	80-200
+Testing / tuning cycles	Per cycle	100-160
+Agent training on suggested/edit vs. auto	Per 100 agents	6-10
+Typical medium implementation: ~1,200-1,800 hours.
+
+Critical note: Auto-Respond at any meaningful rate requires integration to case/order/account data — doesn't work without the ESB. Auto-Suggest is more forgiving. This shapes phasing.
+
+3. Speech & Text Analytics
+Activity	Unit	Hours per unit
+STA topic/program setup	Fixed	80-120
+Program per language	Per language	60-100
+Topic library — compliance	Per 20 topics	20-30
+Topic library — CX / operational	Per 20 topics	20-30
+Category / phrase library tuning	Per cycle	60-100 (plan for 3-5 cycles)
+Dashboard / report configuration	Per dashboard	20-30
+Supervisor enablement	Per site	8-16
+Typical medium implementation: ~600-1,000 hours.
+
+4. Supervisor Copilot
+Activity	Unit	Hours per unit
+Supervisor Copilot instance & settings	Fixed	40-60
+AI Translate configuration per language pair	Per pair	8-16
+AI Summary insight configuration	Fixed	40-60
+Alerting rules & thresholds	Per rule set	20-40
+Supervisor training	Per 10 supervisors	8-16
+Typical medium implementation: ~300-500 hours.
+
+5. Predictive Routing
+Activity	Unit	Hours per unit
+PR model configuration	Fixed	60-100
+Data source setup and validation	Fixed	40-80
+Per-queue optimization	Per queue	2-4
+Baseline measurement & A/B	Per cycle	80-120 (plan 2-3 cycles)
+Model retraining automation	Fixed	20-40
+Typical medium implementation: ~500-800 hours.
+
+Cross-cutting activities (allocate across features)
+These are the ones that get missed and blow budgets:
+
+Activity	Unit	Hours
+KB curation & prep (source-of-truth for Copilot, Email AI, and STA)	Per 100 articles	40-80
+KB governance setup (versioning, ownership, refresh cadence)	Fixed	100-200
+AI governance framework (drift detection, model versioning, escalation paths)	Fixed	120-200
+Data pipeline / integration to systems of record	Per SoR	200-500
+Testing environment setup	Fixed	80-160
+Program management overhead	Per month program duration	40-80
+Regulatory / compliance review for AI features	Per feature	20-60
+CTM-specific amplifiers
+Now the ugly part. Every parameter above gets multiplied at CTM scale:
+
+Parameter	Typical medium	CTM
+Tails	10-50	1,000+ (6-10× amplifier on queue-configuration line items)
+Languages	1-3	7+ (English, French, Spanish, German, Mandarin, Cantonese, Japanese)
+Sites	1-3	9 (change management overhead compounds)
+Agent count	100-500	~1,900 (training scales linearly)
+Regions	1	4 (NAM, EMEA, AUZ, APAC) — program management overhead compounds
+Systems-of-record integration	1-2 pre-built	0 today, ESB Nov 2026+
+KB maturity	Unknown	Unknown — flag as major risk
+Amplifier math for Agent Copilot at CTM scale
+Using the LoE table above at CTM parameters, mid-range hours:
+
+Activity	CTM units	Hours
+Base Copilot instance × 7 languages	7	700
+Settings/rules/permissions	1	50
+NLU/intent modeling — 300 intents (large enterprise)	30	1,200
+Wrap-up codes — 500 codes	25	250
+Queue configuration — 1,000 queues at 2 hrs each	1,000	2,000
+Custom summary templates — 15 templates	15	450
+KB article preparation — 5,000 articles	50	1,500
+Testing/tuning — 6 cycles	6	600
+Agent training — 1,900 agents	19	190
+Supervisor enablement — 9 sites	9	180
+Agent Copilot subtotal		~7,100 hours
+Full CTM AI implementation LoE
+Feature	Estimated hours
+Agent Copilot	6,500 - 8,500
+Email AI (Auto-Suggest + Auto-Respond)	3,000 - 4,500
+Speech & Text Analytics	1,500 - 2,500
+Supervisor Copilot	600 - 900
+Predictive Routing	1,200 - 1,800
+Feature subtotal	12,800 - 18,200
+Cross-cutting (KB, governance, PM, integration)	4,000 - 7,000
+Total Y1 implementation LoE	16,800 - 25,200 hours
+Translating to dollars
+I don't know your PS rate, but for context using industry-standard blended rates:
+
+Blended rate	Y1 implementation cost range
+$175/hr (offshore-heavy blend)	$2.9M - $4.4M
+$225/hr (typical NTT DATA blended)	$3.8 million - $5.7 million
+$275/hr (onshore-heavy specialist)	$4.6M - $6.9M
+Plus annual steady-state at 15-20% of implementation = $430K - $1.4M/yr recurring for ongoing optimization, tuning, KB refresh, model retraining.
+
+What this does to the case
+Adding implementation costs to the model:
+
+Component	Y1	Y2	Y3	3-Year
+Platform license	$2.79M	$2.79M	$2.79M	$8.37M
+AI token costs (Realistic)	$2.0M	$3.5M	$5.0M	$10.5M
+AI implementation LoE (new)	$3.8 million-5.7 million	$0.6M-1.1M	$0.6M-1.1M	$5.0M-7.9M
+Legacy platform takeouts	($2.0M)	($2.0M)	($2.0M)	($6.0M)
+Realistic AI benefits	($1.5M)	($4.5M)	($7.5M)	($13.5M)
+NET	+$5.1M to +$7.0M	+$0.4M to +$0.9M	-$1.1M to -$1.6M	+$4.4M to +$6.3M
+In the current model, program is net-negative $4-6M over 3 years even in Realistic scenario. Y1 is the ugly year because implementation cost front-loads. Y3 is when benefits catch up — barely.
+
+And that's using Genesys's own claimed benefits, unadjusted. If we apply the realistic haircuts we discussed earlier (Y1 benefit realization at 30-50%), the picture gets worse.
+
+Calculator amendment
+Add to the spec:
+
+New dataclass in inputs.py
+@dataclass
+class ImplementationEffort:
+    feature: str
+    fixed_setup_hours: float
+    per_queue_hours: float
+    per_language_hours: float
+    per_100_intents_hours: float
+    per_20_wrapups_hours: float
+    per_100_kb_articles_hours: float
+    tuning_cycles: int
+    hours_per_tuning_cycle: float
+    per_100_agents_training_hours: float
+    per_site_enablement_hours: float
+
+@dataclass
+class ImplementationCosts:
+    ps_blended_rate: float          # $/hr — user-configurable
+    steady_state_pct_annual: float  # default 0.175 (17.5%)
+    ramp_phasing: dict[str, float]  # feature -> % of hours in Y1 vs Y2
+New module tokencalc/implementation.py
+def calculate_feature_loe(effort: ImplementationEffort,
+                           sites: list[SiteInput],
+                           feature_scope: FeatureScope,
+                           complexity_params: ComplexityParams
+                           ) -> pd.DataFrame:
+    """Returns hours breakdown by activity for one feature."""
+
+def calculate_implementation_cost(loe_by_feature: dict,
+                                    ps_rate: float,
+                                    phasing: dict
+                                    ) -> pd.DataFrame:
+    """Distributes hours across Y1/Y2/Y3 with steady-state tail."""
+New Streamlit page: "Implementation Effort"
+LoE inputs per feature (adjustable, defaults from table above)
+Complexity multipliers (queue count, language count, KB size — pulled from Inputs page)
+PS rate slider
+Steady-state % slider
+Output table: hours × rate by feature, by year
+Warning: "Implementation cost was NOT in the original Genesys business case."
+Smell-test rule to add
+If total AI implementation cost < 15% of AI benefit claim, flag as under-modeled. Industry benchmark: 20-40% of Y1 benefit claim goes to implementation.
+

studies/202512_GenesysCX/ctm-token-calculator/docs/ctm_ai_labour_estimate_V2.md

@@ -0,0 +1,64 @@
+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

studies/202512_GenesysCX/ctm-token-calculator/tests/test_cost_model.py

@@ -108,6 +108,50 @@ def test_consumption_tokens_rounded_up_monthly():
     assert df.iloc[0]["annual_cost"] == pytest.approx(3 * 12 * 1.0)
 
 
+def test_predictive_routing_consumption():
+    """1,700 calls/mo ÷ 17 per token = 100 tokens/mo → $1,200/yr (year 2, no ramp)."""
+    site = SiteInput(
+        "Tiny", "US", agents=5, supervisors=0,
+        voice_volume_monthly=1_700, email_volume_monthly=0,
+        chat_volume_monthly=0, sms_volume_monthly=0,
+        voice_aht_seconds=300, email_aht_seconds=600,
+        chat_aht_seconds=480, voice_acw_seconds=60,
+        fully_loaded_agent_cost_annual=65_000,
+        fully_loaded_supervisor_cost_annual=95_000,
+    )
+    df = calculate_consumption_ai_cost(
+        [site], FeatureScope("Predictive Routing", ["Tiny"]),
+        DEFAULT_METERS["Predictive Routing"], "realistic", DEFAULT_PRICING, year=2,
+    )
+    assert df.iloc[0]["tokens_monthly"] == 100
+    assert df.iloc[0]["annual_cost"] == pytest.approx(1_200)
+
+
+def test_predictive_routing_eligibility_and_total_cost():
+    """eligibility_pct halves the routed volume; total_cost handles the scope."""
+    site = SiteInput(
+        "Tiny", "US", agents=5, supervisors=0,
+        voice_volume_monthly=1_700, email_volume_monthly=0,
+        chat_volume_monthly=0, sms_volume_monthly=0,
+        voice_aht_seconds=300, email_aht_seconds=600,
+        chat_aht_seconds=480, voice_acw_seconds=60,
+        fully_loaded_agent_cost_annual=65_000,
+        fully_loaded_supervisor_cost_annual=95_000,
+    )
+    scope = FeatureScope("Predictive Routing", ["Tiny"], eligibility_pct=0.5)
+    df = calculate_consumption_ai_cost(
+        [site], scope, DEFAULT_METERS["Predictive Routing"], "realistic",
+        DEFAULT_PRICING, year=2,
+    )
+    assert df.iloc[0]["tokens_monthly"] == 50
+    total = calculate_total_cost(
+        [site], [scope], DEFAULT_METERS, DEFAULT_PRICING, "realistic", 2,
+        include_platform=False,
+    )
+    pr_row = total[total["cost_line"] == "Predictive Routing"].iloc[0]
+    assert pr_row["annual_cost"] == pytest.approx(50 * 12 * 1.0)
+
+
 def test_regional_pricing_not_hardcoded():
     pricing = dict(DEFAULT_PRICING)
     from tokencalc.meters import TokenPricing

studies/202512_GenesysCX/ctm-token-calculator/tokencalc/cost_model.py

@@ -170,6 +170,11 @@ def _monthly_units(site: SiteInput, feature: str, scope: FeatureScope,
         # can be used to scope to a subset of interactions (e.g. non-English only).
         eligibility = scope.eligibility_pct if scope.eligibility_pct is not None else 1.0
         return site.voice_volume_monthly * eligibility  # translations
+    if feature == "Predictive Routing":
+        # Every predictively-routed voice interaction consumes the PR meter;
+        # eligibility_pct scopes to the share of volume on PR-enabled queues.
+        eligibility = scope.eligibility_pct if scope.eligibility_pct is not None else 1.0
+        return site.voice_volume_monthly * eligibility  # routed interactions
     raise KeyError(f"No consumption-volume mapping for feature {feature!r}")