diff --git a/frontend/components/tabs/DimensionAnalysisTab.tsx b/frontend/components/tabs/DimensionAnalysisTab.tsx
index 04a09f9..07b4165 100644
--- a/frontend/components/tabs/DimensionAnalysisTab.tsx
+++ b/frontend/components/tabs/DimensionAnalysisTab.tsx
@@ -1,4 +1,5 @@
 import React from 'react';
+import { useTranslation } from 'react-i18next';
 import { motion } from 'framer-motion';
 import { ChevronRight, TrendingUp, TrendingDown, Minus, AlertTriangle, Lightbulb, DollarSign, Clock } from 'lucide-react';
 import type { AnalysisData, DimensionAnalysis, Finding, Recommendation, HeatmapDataPoint } from '../../types';
@@ -42,6 +43,7 @@ function generateCausalAnalysis(
   dimension: DimensionAnalysis,
   heatmapData: HeatmapDataPoint[],
   economicModel: { currentAnnualCost: number },
+  t: (key: string, options?: any) => string,
   staticConfig?: { cost_per_hour: number },
   dateRange?: { min: string; max: string }
 ): CausalAnalysisExtended[] {
@@ -129,28 +131,29 @@ function generateCausalAnalysis(
         // Estimar ahorro con solución Copilot (25-30% reducción AHT)
         const copilotSavings = Math.round(ahtExcessCost * 0.28);
 
-        // Causa basada en AHT elevado
-        const cause = 'Agentes dedican tiempo excesivo a búsqueda manual de información, navegación entre sistemas y tareas repetitivas.';
+        const ahtFormatted = `${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')}`;
 
         analyses.push({
-          finding: `AHT elevado: P50 ${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')} (benchmark: 5:00)`,
-          probableCause: cause,
+          finding: t('dimensionAnalysis.operationalEfficiency.highAHTFinding', { aht: ahtFormatted }),
+          probableCause: t('dimensionAnalysis.operationalEfficiency.highAHTCause'),
           economicImpact: ahtExcessCost,
           impactFormula: `${excessHours.toLocaleString()}h × €${HOURLY_COST}/h`,
           timeSavings: `${excessHours.toLocaleString()} horas/año en exceso de AHT`,
-          recommendation: `Desplegar Copilot IA para agentes: (1) Auto-búsqueda en KB; (2) Sugerencias contextuales en tiempo real; (3) Scripts guiados para casos frecuentes. Reducción esperada: 20-30% AHT. Ahorro: ${formatCurrency(copilotSavings)}/año.`,
+          recommendation: t('dimensionAnalysis.operationalEfficiency.highAHTRecommendation', { savings: formatCurrency(copilotSavings) }),
           severity: p50Aht > 420 ? 'critical' : 'warning',
           hasRealData: true
         });
       } else {
         // AHT dentro de benchmark - mostrar estado positivo
+        const ahtFormatted = `${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')}`;
+
         analyses.push({
-          finding: `AHT dentro de benchmark: P50 ${Math.floor(p50Aht / 60)}:${String(Math.round(p50Aht) % 60).padStart(2, '0')} (benchmark: 5:00)`,
-          probableCause: 'Tiempos de gestión eficientes. Procesos operativos optimizados.',
+          finding: t('dimensionAnalysis.operationalEfficiency.goodAHTFinding', { aht: ahtFormatted }),
+          probableCause: t('dimensionAnalysis.operationalEfficiency.goodAHTCause'),
           economicImpact: 0,
-          impactFormula: 'Sin exceso de coste por AHT',
-          timeSavings: 'Operación eficiente',
-          recommendation: 'Mantener nivel actual. Considerar Copilot para mejora continua y reducción adicional de tiempos en casos complejos.',
+          impactFormula: t('dimensionAnalysis.operationalEfficiency.goodAHTImpact'),
+          timeSavings: t('dimensionAnalysis.operationalEfficiency.goodAHTTimeSavings'),
+          recommendation: t('dimensionAnalysis.operationalEfficiency.goodAHTRecommendation'),
           severity: 'info',
           hasRealData: true
         });
@@ -176,30 +179,42 @@ function generateCausalAnalysis(
       let effCause = '';
       if (avgFCR < 70) {
         effCause = skillsLowFCR.length > 0
-          ? `Alta tasa de transferencias (${avgTransferRate.toFixed(0)}%) indica falta de herramientas o autoridad. Crítico en ${skillsLowFCR.slice(0, 2).map(s => s.skill).join(', ')}.`
-          : `Transferencias elevadas (${avgTransferRate.toFixed(0)}%): agentes sin información contextual o sin autoridad para resolver.`;
+          ? t('dimensionAnalysis.effectiveness.criticalCause', {
+              transfer: avgTransferRate.toFixed(0),
+              skills: skillsLowFCR.slice(0, 2).map(s => s.skill).join(', ')
+            })
+          : t('dimensionAnalysis.effectiveness.criticalCauseGeneric', { transfer: avgTransferRate.toFixed(0) });
       } else if (avgFCR < 85) {
-        effCause = `Transferencias del ${avgTransferRate.toFixed(0)}% indican oportunidad de mejora con asistencia IA para casos complejos.`;
+        effCause = t('dimensionAnalysis.effectiveness.warningCause', { transfer: avgTransferRate.toFixed(0) });
       } else {
-        effCause = `FCR Técnico en nivel óptimo. Transferencias del ${avgTransferRate.toFixed(0)}% principalmente en casos que requieren escalación legítima.`;
+        effCause = t('dimensionAnalysis.effectiveness.goodCause', { transfer: avgTransferRate.toFixed(0) });
       }
 
       // Construir recomendación
       let effRecommendation = '';
       if (avgFCR < 70) {
-        effRecommendation = `Desplegar Knowledge Copilot con búsqueda inteligente en KB + Guided Resolution Copilot para casos complejos. Objetivo: FCR >85%. Potencial ahorro: ${formatCurrency(potentialSavingsEff)}/año.`;
+        effRecommendation = t('dimensionAnalysis.effectiveness.criticalRecommendation', { savings: formatCurrency(potentialSavingsEff) });
       } else if (avgFCR < 85) {
-        effRecommendation = `Implementar Copilot de asistencia en tiempo real: sugerencias contextuales + conexión con expertos virtuales para reducir transferencias. Objetivo: FCR >90%.`;
+        effRecommendation = t('dimensionAnalysis.effectiveness.warningRecommendation');
       } else {
-        effRecommendation = `Mantener nivel actual. Considerar IA para análisis de transferencias legítimas y optimización de enrutamiento predictivo.`;
+        effRecommendation = t('dimensionAnalysis.effectiveness.goodRecommendation');
       }
 
       analyses.push({
-        finding: `FCR Técnico: ${avgFCR.toFixed(0)}% | Transferencias: ${avgTransferRate.toFixed(0)}% (benchmark: FCR >85%, Transfer <10%)`,
+        finding: t('dimensionAnalysis.effectiveness.finding', {
+          fcr: avgFCR.toFixed(0),
+          transfer: avgTransferRate.toFixed(0)
+        }),
         probableCause: effCause,
         economicImpact: transferCostTotal,
-        impactFormula: `${transferCount.toLocaleString()} transferencias/año × €${CPI_TCO}/int × 50% coste adicional`,
-        timeSavings: `${transferCount.toLocaleString()} transferencias/año (${avgTransferRate.toFixed(0)}% del volumen)`,
+        impactFormula: t('dimensionAnalysis.effectiveness.impactFormula', {
+          count: transferCount.toLocaleString(),
+          cpi: CPI_TCO
+        }),
+        timeSavings: t('dimensionAnalysis.effectiveness.timeSavings', {
+          count: transferCount.toLocaleString(),
+          pct: avgTransferRate.toFixed(0)
+        }),
         recommendation: effRecommendation,
         severity: effSeverity,
         hasRealData: true
@@ -215,12 +230,25 @@ function generateCausalAnalysis(
         const deflectionPotential = Math.round(annualTopSkillVolume * CPI_TCO * 0.20);
         const interactionsDeflectable = Math.round(annualTopSkillVolume * 0.20);
         analyses.push({
-          finding: `Concentración de volumen: ${topSkill.skill} representa ${topSkillPct.toFixed(0)}% del total`,
-          probableCause: `Alta concentración en un skill indica consultas repetitivas con potencial de automatización.`,
+          finding: t('dimensionAnalysis.volumetry.concentrationFinding', {
+            skill: topSkill.skill,
+            pct: topSkillPct.toFixed(0)
+          }),
+          probableCause: t('dimensionAnalysis.volumetry.concentrationCause'),
           economicImpact: deflectionPotential,
-          impactFormula: `${topSkill.volume.toLocaleString()} int × anualización × €${CPI_TCO} × 20% deflexión potencial`,
-          timeSavings: `${annualTopSkillVolume.toLocaleString()} interacciones/año en ${topSkill.skill} (${interactionsDeflectable.toLocaleString()} automatizables)`,
-          recommendation: `Analizar tipologías de ${topSkill.skill} para deflexión a autoservicio o agente virtual. Potencial: ${formatCurrency(deflectionPotential)}/año.`,
+          impactFormula: t('dimensionAnalysis.volumetry.impactFormula', {
+            volume: topSkill.volume.toLocaleString(),
+            cpi: CPI_TCO
+          }),
+          timeSavings: t('dimensionAnalysis.volumetry.timeSavings', {
+            volume: annualTopSkillVolume.toLocaleString(),
+            skill: topSkill.skill,
+            deflectable: interactionsDeflectable.toLocaleString()
+          }),
+          recommendation: t('dimensionAnalysis.volumetry.concentrationRecommendation', {
+            skill: topSkill.skill,
+            savings: formatCurrency(deflectionPotential)
+          }),
           severity: 'info',
           hasRealData: true
         });
@@ -242,28 +270,34 @@ function generateCausalAnalysis(
 
         // Causa dinámica basada en nivel de variabilidad
         const cvCause = avgCVAHT > 125
-          ? 'Dispersión extrema en tiempos de atención impide planificación efectiva de recursos. Probable falta de scripts o procesos estandarizados.'
-          : 'Variabilidad moderada en tiempos indica oportunidad de estandarización para mejorar planificación WFM.';
+          ? t('dimensionAnalysis.complexity.highCVCauseCritical')
+          : t('dimensionAnalysis.complexity.highCVCauseWarning');
 
         analyses.push({
-          finding: `CV AHT elevado: ${avgCVAHT.toFixed(0)}% (benchmark: <${cvBenchmark}%)`,
+          finding: t('dimensionAnalysis.complexity.highCVFinding', {
+            cv: avgCVAHT.toFixed(0),
+            benchmark: cvBenchmark
+          }),
           probableCause: cvCause,
           economicImpact: staffingCost,
-          impactFormula: `~3% del coste operativo por ineficiencia de staffing`,
-          timeSavings: `~${staffingHours.toLocaleString()} horas/año en sobre/subdimensionamiento`,
-          recommendation: `Implementar scripts guiados por IA que estandaricen la atención. Reducción esperada: -50% variabilidad. Ahorro: ${formatCurrency(standardizationSavings)}/año.`,
+          impactFormula: t('dimensionAnalysis.complexity.highCVImpactFormula'),
+          timeSavings: t('dimensionAnalysis.complexity.highCVTimeSavings', { hours: staffingHours.toLocaleString() }),
+          recommendation: t('dimensionAnalysis.complexity.highCVRecommendation', { savings: formatCurrency(standardizationSavings) }),
           severity: cvSeverity,
           hasRealData: true
         });
       } else {
         // CV AHT dentro de benchmark - mostrar estado positivo
         analyses.push({
-          finding: `CV AHT dentro de benchmark: ${avgCVAHT.toFixed(0)}% (benchmark: <${cvBenchmark}%)`,
-          probableCause: 'Tiempos de atención consistentes. Buena estandarización de procesos.',
+          finding: t('dimensionAnalysis.complexity.goodCVFinding', {
+            cv: avgCVAHT.toFixed(0),
+            benchmark: cvBenchmark
+          }),
+          probableCause: t('dimensionAnalysis.complexity.goodCVCause'),
           economicImpact: 0,
-          impactFormula: 'Sin impacto por variabilidad',
-          timeSavings: 'Planificación WFM eficiente',
-          recommendation: 'Mantener nivel actual. Analizar casos atípicos para identificar oportunidades de mejora continua.',
+          impactFormula: t('dimensionAnalysis.complexity.goodCVImpactFormula'),
+          timeSavings: t('dimensionAnalysis.complexity.goodCVTimeSavings'),
+          recommendation: t('dimensionAnalysis.complexity.goodCVRecommendation'),
           severity: 'info',
           hasRealData: true
         });
@@ -277,12 +311,16 @@ function generateCausalAnalysis(
         const holdCost = Math.round(excessHoldHours * HOURLY_COST);
         const searchCopilotSavings = Math.round(holdCost * 0.60);
         analyses.push({
-          finding: `Hold time elevado: ${avgHoldTime.toFixed(0)}s promedio (benchmark: <30s)`,
-          probableCause: 'Agentes ponen cliente en espera para buscar información. Sistemas no presentan datos de forma contextual.',
+          finding: t('dimensionAnalysis.complexity.holdTimeFinding', { holdTime: avgHoldTime.toFixed(0) }),
+          probableCause: t('dimensionAnalysis.complexity.holdTimeCause'),
           economicImpact: holdCost,
-          impactFormula: `Exceso ${Math.round(excessHold)}s × ${totalVolume.toLocaleString()} int × anualización × €${HOURLY_COST}/h`,
-          timeSavings: `${excessHoldHours.toLocaleString()} horas/año de cliente en espera`,
-          recommendation: `Desplegar vista 360° con contexto automático: historial, productos y acciones sugeridas visibles al contestar. Reducción esperada: -60% hold time. Ahorro: ${formatCurrency(searchCopilotSavings)}/año.`,
+          impactFormula: t('dimensionAnalysis.complexity.holdTimeImpactFormula', {
+            excess: Math.round(excessHold),
+            volume: totalVolume.toLocaleString(),
+            cost: HOURLY_COST
+          }),
+          timeSavings: t('dimensionAnalysis.complexity.holdTimeTimeSavings', { hours: excessHoldHours.toLocaleString() }),
+          recommendation: t('dimensionAnalysis.complexity.holdTimeRecommendation', { savings: formatCurrency(searchCopilotSavings) }),
           severity: avgHoldTime > 60 ? 'critical' : 'warning',
           hasRealData: true
         });
@@ -297,12 +335,12 @@ function generateCausalAnalysis(
           const customersAtRisk = Math.round(annualVolumeCsat * 0.02);
           const churnRisk = Math.round(customersAtRisk * 50);
           analyses.push({
-            finding: `CSAT por debajo del objetivo: ${avgCSAT.toFixed(0)}% (benchmark: >80%)`,
-            probableCause: 'Clientes insatisfechos por esperas, falta de resolución o experiencia de atención deficiente.',
+            finding: t('dimensionAnalysis.satisfaction.lowCSATFinding', { csat: avgCSAT.toFixed(0) }),
+            probableCause: t('dimensionAnalysis.satisfaction.lowCSATCause'),
             economicImpact: churnRisk,
-            impactFormula: `${totalVolume.toLocaleString()} clientes × anualización × 2% riesgo churn × €50 valor`,
-            timeSavings: `${customersAtRisk.toLocaleString()} clientes/año en riesgo de fuga`,
-            recommendation: `Implementar programa VoC: encuestas post-contacto + análisis de causas raíz + acción correctiva en 48h. Objetivo: CSAT >80%.`,
+            impactFormula: t('dimensionAnalysis.satisfaction.lowCSATImpactFormula', { volume: totalVolume.toLocaleString() }),
+            timeSavings: t('dimensionAnalysis.satisfaction.lowCSATTimeSavings', { customers: customersAtRisk.toLocaleString() }),
+            recommendation: t('dimensionAnalysis.satisfaction.lowCSATRecommendation'),
             severity: avgCSAT < 50 ? 'critical' : 'warning',
             hasRealData: true
           });
@@ -319,12 +357,22 @@ function generateCausalAnalysis(
         const potentialSavings = Math.round(annualVolumeCpi * excessCPI);
         const excessHours = Math.round(potentialSavings / HOURLY_COST);
         analyses.push({
-          finding: `CPI por encima del benchmark: €${CPI.toFixed(2)} (objetivo: €${CPI_TCO})`,
-          probableCause: 'Coste por interacción elevado por AHT alto, baja ocupación o estructura de costes ineficiente.',
+          finding: t('dimensionAnalysis.economy.highCPIFinding', {
+            cpi: CPI.toFixed(2),
+            target: CPI_TCO
+          }),
+          probableCause: t('dimensionAnalysis.economy.highCPICause'),
           economicImpact: potentialSavings,
-          impactFormula: `${totalVolume.toLocaleString()} int × anualización × €${excessCPI.toFixed(2)} exceso CPI`,
-          timeSavings: `€${excessCPI.toFixed(2)} exceso/int × ${annualVolumeCpi.toLocaleString()} int = ${excessHours.toLocaleString()}h equivalentes`,
-          recommendation: `Optimizar mix de canales + reducir AHT con automatización + revisar modelo de staffing. Objetivo: CPI <€${CPI_TCO}.`,
+          impactFormula: t('dimensionAnalysis.economy.highCPIImpactFormula', {
+            volume: totalVolume.toLocaleString(),
+            excess: excessCPI.toFixed(2)
+          }),
+          timeSavings: t('dimensionAnalysis.economy.highCPITimeSavings', {
+            excess: excessCPI.toFixed(2),
+            volume: annualVolumeCpi.toLocaleString(),
+            hours: excessHours.toLocaleString()
+          }),
+          recommendation: t('dimensionAnalysis.economy.highCPIRecommendation', { target: CPI_TCO }),
           severity: CPI > 5 ? 'critical' : 'warning',
           hasRealData: true
         });
@@ -347,13 +395,15 @@ function DimensionCard({
   findings,
   recommendations,
   causalAnalyses,
-  delay = 0
+  delay = 0,
+  t
 }: {
   dimension: DimensionAnalysis;
   findings: Finding[];
   recommendations: Recommendation[];
   causalAnalyses: CausalAnalysisExtended[];
   delay?: number;
+  t: (key: string, options?: any) => string;
 }) {
   const Icon = dimension.icon;
 
@@ -365,11 +415,11 @@ function DimensionCard({
   };
 
   const getScoreLabel = (score: number): string => {
-    if (score < 0) return 'N/A';
-    if (score >= 80) return 'Óptimo';
-    if (score >= 60) return 'Aceptable';
-    if (score >= 40) return 'Mejorable';
-    return 'Crítico';
+    if (score < 0) return t('common.na');
+    if (score >= 80) return t('common.optimal');
+    if (score >= 60) return t('common.acceptable');
+    if (score >= 40) return t('common.improvable');
+    return t('common.critical');
   };
 
   const getSeverityConfig = (severity: string) => {
@@ -410,13 +460,13 @@ function DimensionCard({
           </div>
           <div className="text-right">
             <Badge
-              label={dimension.score >= 0 ? `${dimension.score} ${getScoreLabel(dimension.score)}` : '— N/A'}
+              label={dimension.score >= 0 ? `${dimension.score} ${getScoreLabel(dimension.score)}` : `— ${t('common.na')}`}
               variant={scoreVariant}
               size="md"
             />
             {totalImpact > 0 && (
               <p className="text-xs text-red-600 font-medium mt-1">
-                Impacto: {formatCurrency(totalImpact)}
+                {t('dimensionAnalysis.impact')} {formatCurrency(totalImpact)}
               </p>
             )}
           </div>
@@ -459,7 +509,7 @@ function DimensionCard({
           <div className="p-3 bg-gray-50 rounded-lg border border-gray-200">
             <p className="text-sm text-gray-500 italic flex items-center gap-2">
               <Minus className="w-4 h-4" />
-              Sin datos disponibles para esta dimensión.
+              {t('dimensionAnalysis.noDataAvailable')}
             </p>
           </div>
         </div>
@@ -469,7 +519,7 @@ function DimensionCard({
       {dimension.score >= 0 && causalAnalyses.length > 0 && (
         <div className="p-4 space-y-3">
           <h4 className="text-xs font-semibold text-gray-500 uppercase tracking-wider">
-            Hallazgo Clave
+            {t('dimensionAnalysis.keyFinding')}
           </h4>
           {causalAnalyses.map((analysis, idx) => {
             const config = getSeverityConfig(analysis.severity);
@@ -485,7 +535,7 @@ function DimensionCard({
 
                 {/* Causa probable */}
                 <div className="ml-6 mb-2">
-                  <p className="text-xs text-gray-500 font-medium mb-0.5">Causa probable:</p>
+                  <p className="text-xs text-gray-500 font-medium mb-0.5">{t('dimensionAnalysis.probableCause')}</p>
                   <p className="text-xs text-gray-700">{analysis.probableCause}</p>
                 </div>
 
@@ -498,7 +548,7 @@ function DimensionCard({
                   <span className="text-xs font-bold text-red-600">
                     {formatCurrency(analysis.economicImpact)}
                   </span>
-                  <span className="text-xs text-gray-500">impacto anual (coste del problema)</span>
+                  <span className="text-xs text-gray-500">{t('dimensionAnalysis.annualImpact')}</span>
                   <span className="text-xs text-gray-400">i</span>
                 </div>
 
@@ -527,7 +577,7 @@ function DimensionCard({
       {dimension.score >= 0 && causalAnalyses.length === 0 && findings.length > 0 && (
         <div className="p-4">
           <h4 className="text-xs font-semibold text-gray-500 uppercase tracking-wider mb-2">
-            Hallazgos Clave
+            {t('dimensionAnalysis.keyFindings')}
           </h4>
           <ul className="space-y-2">
             {findings.slice(0, 3).map((finding, idx) => (
@@ -550,7 +600,7 @@ function DimensionCard({
           <div className={cn('p-3 rounded-lg border', STATUS_CLASSES.success.bg, STATUS_CLASSES.success.border)}>
             <p className={cn('text-sm flex items-center gap-2', STATUS_CLASSES.success.text)}>
               <ChevronRight className="w-4 h-4" />
-              Métricas dentro de rangos aceptables. Sin hallazgos críticos.
+              {t('dimensionAnalysis.withinAcceptable')}
             </p>
           </div>
         </div>
@@ -561,7 +611,7 @@ function DimensionCard({
         <div className="px-4 pb-4">
           <div className="p-3 bg-blue-50 rounded-lg border border-blue-100">
             <div className="flex items-start gap-2">
-              <span className="text-xs font-semibold text-blue-600">Recomendación:</span>
+              <span className="text-xs font-semibold text-blue-600">{t('dimensionAnalysis.recommendation')}</span>
               <span className="text-xs text-gray-600">{recommendations[0].text}</span>
             </div>
           </div>
@@ -574,6 +624,8 @@ function DimensionCard({
 // ========== v3.16: COMPONENTE PRINCIPAL ==========
 
 export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
+  const { t } = useTranslation();
+
   // DEBUG: Verificar CPI en dimensión vs heatmapData
   const economyDim = data.dimensions.find(d =>
     d.id === 'economy_costs' || d.name === 'economy_costs' ||
@@ -609,7 +661,7 @@ export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
 
   // Generar hallazgo clave para cada dimensión
   const getCausalAnalysisForDimension = (dimension: DimensionAnalysis) =>
-    generateCausalAnalysis(dimension, data.heatmapData, data.economicModel, data.staticConfig, data.dateRange);
+    generateCausalAnalysis(dimension, data.heatmapData, data.economicModel, t, data.staticConfig, data.dateRange);
 
   // Calcular impacto total de todas las dimensiones con datos
   const impactoTotal = coreDimensions
@@ -627,10 +679,10 @@ export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
     <div className="space-y-6">
       {/* v3.16: Header simplificado - solo título y subtítulo */}
       <div className="mb-2">
-        <h2 className="text-lg font-bold text-gray-900">Diagnóstico por Dimensión</h2>
+        <h2 className="text-lg font-bold text-gray-900">{t('dimensionAnalysis.title')}</h2>
         <p className="text-sm text-gray-500">
-          {coreDimensions.length} dimensiones analizadas
-          {sinDatos.length > 0 && ` (${sinDatos.length} sin datos)`}
+          {t('dimensionAnalysis.dimensionsAnalyzed', { count: coreDimensions.length })}
+          {sinDatos.length > 0 && ` ${t('dimensionAnalysis.noData', { count: sinDatos.length })}`}
         </p>
       </div>
 
@@ -644,6 +696,7 @@ export function DimensionAnalysisTab({ data }: DimensionAnalysisTabProps) {
             recommendations={getRecommendationsForDimension(dimension.id)}
             causalAnalyses={getCausalAnalysisForDimension(dimension)}
             delay={idx * 0.05}
+            t={t}
           />
         ))}
       </div>
diff --git a/frontend/components/tabs/ExecutiveSummaryTab.tsx b/frontend/components/tabs/ExecutiveSummaryTab.tsx
index 6c5b27c..ee7f8c7 100644
--- a/frontend/components/tabs/ExecutiveSummaryTab.tsx
+++ b/frontend/components/tabs/ExecutiveSummaryTab.tsx
@@ -1,4 +1,5 @@
 import React from 'react';
+import { useTranslation } from 'react-i18next';
 import { TrendingUp, TrendingDown, AlertTriangle, CheckCircle, Target, Activity, Clock, PhoneForwarded, Users, Bot, ChevronRight, BarChart3, Cpu, Map, Zap, Calendar } from 'lucide-react';
 import type { AnalysisData, Finding, DrilldownDataPoint, HeatmapDataPoint } from '../../types';
 import type { TabId } from '../DashboardHeader';
@@ -146,7 +147,7 @@ interface Hallazgo {
   metrica?: string;
 }
 
-function generarHallazgos(data: AnalysisData): Hallazgo[] {
+function generarHallazgos(data: AnalysisData, t: any): Hallazgo[] {
   const hallazgos: Hallazgo[] = [];
   const allQueues = data.drilldownData?.flatMap(s => s.originalQueues) || [];
   const totalVolume = allQueues.reduce((s, q) => s + q.volume, 0);
@@ -163,7 +164,7 @@ function generarHallazgos(data: AnalysisData): Hallazgo[] {
     const pctVolumen = (colasAltaVariabilidad.reduce((s, q) => s + q.volume, 0) / totalVolume) * 100;
     hallazgos.push({
       tipo: 'critico',
-      texto: `${colasAltaVariabilidad.length} colas con variabilidad crítica (CV >100%) representan ${pctVolumen.toFixed(0)}% del volumen`,
+      texto: t('executiveSummary.highVariabilityQueues', { count: colasAltaVariabilidad.length, pct: pctVolumen.toFixed(0) }),
       metrica: 'CV AHT'
     });
   }
@@ -173,7 +174,7 @@ function generarHallazgos(data: AnalysisData): Hallazgo[] {
   if (colasAltoTransfer.length > 0) {
     hallazgos.push({
       tipo: 'warning',
-      texto: `${colasAltoTransfer.length} colas con tasa de transferencia >25% - posible problema de routing o formación`,
+      texto: t('executiveSummary.highTransferQueues', { count: colasAltoTransfer.length }),
       metrica: 'Transfer'
     });
   }
@@ -183,7 +184,7 @@ function generarHallazgos(data: AnalysisData): Hallazgo[] {
   if (colasBajoFCR.length > 0) {
     hallazgos.push({
       tipo: 'warning',
-      texto: `${colasBajoFCR.length} colas con FCR <50% - clientes requieren múltiples contactos`,
+      texto: t('executiveSummary.lowFCRQueues', { count: colasBajoFCR.length }),
       metrica: 'FCR'
     });
   }
@@ -192,7 +193,7 @@ function generarHallazgos(data: AnalysisData): Hallazgo[] {
   if (avgAHT > 400) {
     hallazgos.push({
       tipo: 'warning',
-      texto: `AHT promedio de ${Math.round(avgAHT)}s supera el benchmark de industria (380s)`,
+      texto: t('executiveSummary.ahtAboveBenchmark', { aht: Math.round(avgAHT) }),
       metrica: 'AHT'
     });
   }
@@ -203,7 +204,7 @@ function generarHallazgos(data: AnalysisData): Hallazgo[] {
     const pctHuman = (colasHumanOnly.reduce((s, q) => s + q.volume, 0) / totalVolume) * 100;
     hallazgos.push({
       tipo: 'info',
-      texto: `${colasHumanOnly.length} colas (${pctHuman.toFixed(0)}% volumen) requieren intervención humana completa`,
+      texto: t('executiveSummary.humanOnlyQueues', { count: colasHumanOnly.length, pct: pctHuman.toFixed(0) }),
       metrica: 'Tier'
     });
   }
@@ -213,8 +214,8 @@ function generarHallazgos(data: AnalysisData): Hallazgo[] {
   if (colasAutomate.length > 0) {
     hallazgos.push({
       tipo: 'info',
-      texto: `${colasAutomate.length} colas listas para automatización con potencial de ahorro significativo`,
-      metrica: 'Oportunidad'
+      texto: t('executiveSummary.automateReadyQueues', { count: colasAutomate.length }),
+      metrica: t('executiveSummary.opportunity')
     });
   }
 
@@ -222,7 +223,8 @@ function generarHallazgos(data: AnalysisData): Hallazgo[] {
 }
 
 function PrincipalesHallazgos({ data }: { data: AnalysisData }) {
-  const hallazgos = generarHallazgos(data);
+  const { t } = useTranslation();
+  const hallazgos = generarHallazgos(data, t);
 
   if (hallazgos.length === 0) return null;
 
@@ -240,7 +242,7 @@ function PrincipalesHallazgos({ data }: { data: AnalysisData }) {
 
   return (
     <Card>
-      <h3 className="font-semibold text-gray-900 mb-3">Principales Hallazgos</h3>
+      <h3 className="font-semibold text-gray-900 mb-3">{t('executiveSummary.title')}</h3>
       <div className="space-y-2">
         {hallazgos.map((h, idx) => (
           <div key={idx} className={cn('flex items-start gap-2 p-2 rounded-lg border', getClase(h.tipo))}>
@@ -265,6 +267,7 @@ function PrincipalesHallazgos({ data }: { data: AnalysisData }) {
 // ============================================
 
 function CabeceraPeriodo({ data }: { data: AnalysisData }) {
+  const { t } = useTranslation();
   const totalInteractions = data.heatmapData.reduce((sum, h) => sum + h.volume, 0);
 
   // Contar colas únicas (original_queue_id) desde drilldownData
@@ -278,7 +281,7 @@ function CabeceraPeriodo({ data }: { data: AnalysisData }) {
   // Formatear fechas del periodo
   const formatPeriodo = () => {
     if (!data.dateRange?.min || !data.dateRange?.max) {
-      return 'Periodo no especificado';
+      return t('executiveSummary.periodNotSpecified');
     }
     const formatDate = (dateStr: string) => {
       try {
@@ -295,13 +298,13 @@ function CabeceraPeriodo({ data }: { data: AnalysisData }) {
     <div className="flex flex-col sm:flex-row items-start sm:items-center justify-between gap-2 sm:gap-4 py-3 px-3 sm:px-4 bg-gray-50 rounded-lg border border-gray-200">
       <div className="flex items-center gap-2 text-gray-600">
         <Calendar className="w-4 h-4 flex-shrink-0" />
-        <span className="text-xs sm:text-sm font-medium">Periodo:</span>
+        <span className="text-xs sm:text-sm font-medium">{t('executiveSummary.period')}</span>
         <span className="text-xs sm:text-sm">{formatPeriodo()}</span>
       </div>
       <div className="flex flex-wrap items-center gap-2 sm:gap-4 md:gap-6 text-xs sm:text-sm text-gray-500">
-        <span><strong>{formatNumber(totalInteractions)}</strong> int.</span>
-        <span><strong>{uniqueQueues}</strong> colas</span>
-        <span><strong>{numLineasNegocio}</strong> LN</span>
+        <span><strong>{formatNumber(totalInteractions)}</strong> {t('executiveSummary.interactions')}</span>
+        <span><strong>{uniqueQueues}</strong> {t('executiveSummary.queues')}</span>
+        <span><strong>{numLineasNegocio}</strong> {t('executiveSummary.businessLines')}</span>
       </div>
     </div>
   );
@@ -323,10 +326,12 @@ function HeadlineEjecutivo({
   resolucionScore: number;
   satisfaccionScore: number;
 }) {
+  const { t } = useTranslation();
+
   const getStatusLabel = (score: number): string => {
-    if (score >= 80) return 'Óptimo';
-    if (score >= 60) return 'Aceptable';
-    return 'Crítico';
+    if (score >= 80) return t('common.optimal');
+    if (score >= 60) return t('common.acceptable');
+    return t('common.critical');
   };
 
   const getStatusVariant = (score: number): 'success' | 'warning' | 'critical' => {
@@ -340,16 +345,10 @@ function HeadlineEjecutivo({
       {/* Título principal */}
       <div className="mb-3 sm:mb-4">
         <h1 className="text-lg sm:text-xl md:text-2xl font-light mb-1">
-          Tu operación procesa{' '}
-          <span className="font-bold text-white">{formatNumber(totalInteracciones)}</span>{' '}
-          interacciones
+          {t('executiveSummary.yourOperation', { total: formatNumber(totalInteracciones) })}
         </h1>
         <p className="text-sm sm:text-lg text-gray-300">
-          con oportunidad de{' '}
-          <span className="font-bold text-emerald-400">
-            {formatCurrency(oportunidadTotal)}
-          </span>{' '}
-          en optimización
+          {t('executiveSummary.withOpportunity', { amount: formatCurrency(oportunidadTotal) })}
         </p>
       </div>
 
@@ -361,7 +360,7 @@ function HeadlineEjecutivo({
         )}>
           <Clock className={cn('w-4 h-4', STATUS_CLASSES[getStatusVariant(eficienciaScore)].text)} />
           <span className={cn('text-sm font-medium', STATUS_CLASSES[getStatusVariant(eficienciaScore)].text)}>
-            Eficiencia: {getStatusLabel(eficienciaScore)}
+            {t('executiveSummary.efficiency')} {getStatusLabel(eficienciaScore)}
           </span>
         </div>
         <div className={cn(
@@ -370,7 +369,7 @@ function HeadlineEjecutivo({
         )}>
           <CheckCircle className={cn('w-4 h-4', STATUS_CLASSES[getStatusVariant(resolucionScore)].text)} />
           <span className={cn('text-sm font-medium', STATUS_CLASSES[getStatusVariant(resolucionScore)].text)}>
-            Resolución: {getStatusLabel(resolucionScore)}
+            {t('executiveSummary.resolution')} {getStatusLabel(resolucionScore)}
           </span>
         </div>
         <div className={cn(
@@ -379,7 +378,7 @@ function HeadlineEjecutivo({
         )}>
           <Users className={cn('w-4 h-4', STATUS_CLASSES[getStatusVariant(satisfaccionScore)].text)} />
           <span className={cn('text-sm font-medium', STATUS_CLASSES[getStatusVariant(satisfaccionScore)].text)}>
-            Satisfacción: {getStatusLabel(satisfaccionScore)}
+            {t('executiveSummary.satisfaction')} {getStatusLabel(satisfaccionScore)}
           </span>
         </div>
       </div>
@@ -390,6 +389,7 @@ function HeadlineEjecutivo({
 // v7.0: Unified KPI + Benchmark Card Component
 // Combines KeyMetricsCard + BenchmarkTable into single 3x2 card grid
 function UnifiedKPIBenchmark({ heatmapData }: { heatmapData: HeatmapDataPoint[] }) {
+  const { t } = useTranslation();
   const [selectedIndustry, setSelectedIndustry] = React.useState<IndustryKey>('aerolineas');
   const benchmarks = BENCHMARKS_INDUSTRIA[selectedIndustry];
 
@@ -442,11 +442,11 @@ function UnifiedKPIBenchmark({ heatmapData }: { heatmapData: HeatmapDataPoint[]
 
   // Calculate percentile position
   const getPercentileBadge = (percentile: number): { label: string; color: string } => {
-    if (percentile >= 90) return { label: 'Top 10%', color: 'bg-emerald-500 text-white' };
-    if (percentile >= 75) return { label: 'Top 25%', color: 'bg-emerald-100 text-emerald-700' };
-    if (percentile >= 50) return { label: 'Promedio', color: 'bg-amber-100 text-amber-700' };
-    if (percentile >= 25) return { label: 'Bajo Avg', color: 'bg-orange-100 text-orange-700' };
-    return { label: 'Bottom 25%', color: 'bg-red-100 text-red-700' };
+    if (percentile >= 90) return { label: t('executiveSummary.top10'), color: 'bg-emerald-500 text-white' };
+    if (percentile >= 75) return { label: t('executiveSummary.top25'), color: 'bg-emerald-100 text-emerald-700' };
+    if (percentile >= 50) return { label: t('executiveSummary.average'), color: 'bg-amber-100 text-amber-700' };
+    if (percentile >= 25) return { label: t('executiveSummary.belowAvg'), color: 'bg-orange-100 text-orange-700' };
+    return { label: t('executiveSummary.bottom25'), color: 'bg-red-100 text-red-700' };
   };
 
   // Calculate GAP vs P50 - positive is better, negative is worse
@@ -504,11 +504,11 @@ function UnifiedKPIBenchmark({ heatmapData }: { heatmapData: HeatmapDataPoint[]
 
   // Get insight text based on percentile position
   const getInsightText = (percentile: number, bench: BenchmarkMetric): string => {
-    if (percentile >= 90) return `Superas al 90% del mercado`;
-    if (percentile >= 75) return `Mejor que 3 de cada 4 empresas`;
-    if (percentile >= 50) return `En línea con la mediana del sector`;
-    if (percentile >= 25) return `Por debajo de la media del mercado`;
-    return `Área crítica de mejora`;
+    if (percentile >= 90) return t('executiveSummary.surpasses90');
+    if (percentile >= 75) return t('executiveSummary.betterThan75');
+    if (percentile >= 50) return t('executiveSummary.alignedWithMedian');
+    if (percentile >= 25) return t('executiveSummary.belowAverage');
+    return t('executiveSummary.criticalArea');
   };
 
   // Format benchmark value for display
@@ -522,79 +522,89 @@ function UnifiedKPIBenchmark({ heatmapData }: { heatmapData: HeatmapDataPoint[]
   // FCR Real context: métrica más estricta que incluye recontactos 7 días
   const fcrRealDiff = operacion.fcrTecnico - operacion.fcrReal;
   const fcrRealContext = fcrRealDiff > 0
-    ? `${Math.round(fcrRealDiff)}pp de recontactos 7d`
+    ? `${Math.round(fcrRealDiff)}pp ${t('executiveSummary.recontacts7d')}`
     : null;
 
   // AHT Total context: diferencia entre AHT limpio y AHT con todas las filas
   const ahtTotalDiff = operacion.ahtTotal - operacion.aht;
   const ahtTotalContext = Math.abs(ahtTotalDiff) > 1
-    ? `${ahtTotalDiff > 0 ? '+' : ''}${Math.round(ahtTotalDiff)}s vs AHT limpio`
+    ? `${ahtTotalDiff > 0 ? '+' : ''}${Math.round(ahtTotalDiff)}s ${t('executiveSummary.vsCleanAht')}`
     : null;
 
   const metricsData = [
     {
       id: 'aht',
-      label: 'AHT',
+      label: t('executiveSummary.aht'),
       valor: operacion.aht,
       display: `${Math.floor(operacion.aht / 60)}:${String(Math.round(operacion.aht) % 60).padStart(2, '0')}`,
       subDisplay: `(${Math.round(operacion.aht)}s)`,
       bench: benchmarks.metricas.aht,
-      tooltip: 'Tiempo medio de gestión (solo interacciones válidas)',
+      tooltip: t('executiveSummary.ahtTooltip'),
       // AHT Total integrado como métrica secundaria
       secondaryMetric: {
-        label: 'AHT Total',
+        label: t('executiveSummary.ahtTotal'),
         value: `${Math.floor(operacion.ahtTotal / 60)}:${String(Math.round(operacion.ahtTotal) % 60).padStart(2, '0')} (${Math.round(operacion.ahtTotal)}s)`,
         note: ahtTotalContext,
-        tooltip: 'Incluye todas las filas (noise, zombie, abandon) - solo informativo',
-        description: 'Incluye noise, zombie y abandonos — solo informativo'
+        tooltip: t('executiveSummary.ahtTotalTooltip'),
+        description: t('executiveSummary.ahtTotalDesc')
       }
     },
     {
       id: 'fcr_tecnico',
-      label: 'FCR',
+      label: t('executiveSummary.fcr'),
       valor: operacion.fcrTecnico,
       display: `${Math.round(operacion.fcrTecnico)}%`,
       subDisplay: null,
       bench: benchmarks.metricas.fcr,
-      tooltip: 'First Contact Resolution - comparable con benchmarks de industria',
+      tooltip: t('executiveSummary.fcrTooltip'),
       // FCR Real integrado como métrica secundaria
       secondaryMetric: {
-        label: 'FCR Ajustado',
+        label: t('executiveSummary.fcrAdjusted'),
         value: `${Math.round(operacion.fcrReal)}%`,
         note: fcrRealContext,
-        tooltip: 'Excluye recontactos en 7 días (métrica más estricta)',
-        description: 'Incluye filtro de recontactos 7d — métrica interna más estricta'
+        tooltip: t('executiveSummary.fcrAdjustedTooltip'),
+        description: t('executiveSummary.fcrAdjustedDesc')
       }
     },
     {
       id: 'abandono',
-      label: 'ABANDONO',
+      label: t('executiveSummary.abandonment'),
       valor: operacion.abandono,
       display: `${operacion.abandono.toFixed(1)}%`,
       subDisplay: null,
       bench: benchmarks.metricas.abandono,
-      tooltip: 'Tasa de abandono',
+      tooltip: t('executiveSummary.abandonmentTooltip'),
       secondaryMetric: null
     },
     {
       id: 'cpi',
-      label: 'COSTE/INTERAC.',
+      label: t('executiveSummary.costPerInteraction'),
       valor: operacion.cpi,
       display: `€${operacion.cpi.toFixed(2)}`,
       subDisplay: null,
       bench: benchmarks.metricas.cpi,
-      tooltip: 'Coste por interacción',
+      tooltip: t('executiveSummary.cpiTooltip'),
       secondaryMetric: null
     }
   ];
 
+  // Map industry keys to translation keys
+  const industryNameMap: Record<IndustryKey, string> = {
+    aerolineas: t('industries.airlines'),
+    telecomunicaciones: t('industries.telco'),
+    banca: t('industries.banking'),
+    utilities: t('industries.utilities'),
+    retail: t('industries.retail'),
+    general: t('industries.crossIndustry')
+  };
+
   return (
     <Card>
       {/* Header with industry selector */}
       <div className="flex flex-col sm:flex-row sm:items-center sm:justify-between gap-2 mb-3">
         <div>
-          <h3 className="font-semibold text-gray-900">Indicadores vs Industria</h3>
-          <p className="text-xs text-gray-500">Fuente: {benchmarks.fuente}</p>
+          <h3 className="font-semibold text-gray-900">{t('executiveSummary.indicators')}</h3>
+          <p className="text-xs text-gray-500">{t('benchmark.source', { source: benchmarks.fuente })}</p>
         </div>
         <select
           value={selectedIndustry}
@@ -602,7 +612,7 @@ function UnifiedKPIBenchmark({ heatmapData }: { heatmapData: HeatmapDataPoint[]
           className="text-sm border border-gray-300 rounded-md px-2 py-1 bg-white w-full sm:w-auto"
         >
           {Object.entries(BENCHMARKS_INDUSTRIA).map(([key, val]) => (
-            <option key={key} value={key}>{val.nombre}</option>
+            <option key={key} value={key}>{industryNameMap[key as IndustryKey]}</option>
           ))}
         </select>
       </div>
@@ -700,15 +710,15 @@ function UnifiedKPIBenchmark({ heatmapData }: { heatmapData: HeatmapDataPoint[]
               {/* Benchmark Reference Values */}
               <div className="grid grid-cols-3 gap-1 text-center mb-2 py-1.5 bg-white/50 rounded">
                 <div>
-                  <div className="text-[9px] text-gray-400">Bajo</div>
+                  <div className="text-[9px] text-gray-400">{t('executiveSummary.benchmarkLow')}</div>
                   <div className="text-[10px] font-medium text-gray-600">{formatBenchValue(m.bench.p25, m.bench.unidad)}</div>
                 </div>
                 <div className="border-x border-gray-200">
-                  <div className="text-[9px] text-gray-400">Mediana</div>
+                  <div className="text-[9px] text-gray-400">{t('executiveSummary.benchmarkMedian')}</div>
                   <div className="text-[10px] font-semibold text-gray-700">{formatBenchValue(m.bench.p50, m.bench.unidad)}</div>
                 </div>
                 <div>
-                  <div className="text-[9px] text-gray-400">Top</div>
+                  <div className="text-[9px] text-gray-400">{t('executiveSummary.benchmarkTop')}</div>
                   <div className="text-[10px] font-medium text-emerald-600">{formatBenchValue(m.bench.p90, m.bench.unidad)}</div>
                 </div>
               </div>
@@ -744,6 +754,8 @@ function HealthScoreDetailed({
   avgAbandonmentRate: number; // Tasa de abandono (%)
   avgTransferRate: number;    // Tasa de transferencia (%)
 }) {
+  const { t } = useTranslation();
+
   const getScoreColor = (s: number): string => {
     if (s >= 80) return COLORS.status.success;
     if (s >= 60) return COLORS.status.warning;
@@ -751,10 +763,10 @@ function HealthScoreDetailed({
   };
 
   const getScoreLabel = (s: number): string => {
-    if (s >= 80) return 'Excelente';
-    if (s >= 60) return 'Bueno';
-    if (s >= 40) return 'Regular';
-    return 'Crítico';
+    if (s >= 80) return t('executiveSummary.excellent');
+    if (s >= 60) return t('executiveSummary.good');
+    if (s >= 40) return t('executiveSummary.regular');
+    return t('common.critical');
   };
 
   const color = getScoreColor(score);
@@ -815,35 +827,35 @@ function HealthScoreDetailed({
   // Nueva ponderación: FCR 35%, Abandono 30%, CSAT Proxy 20%, AHT 15%
   const factors = [
     {
-      name: 'FCR Técnico',
+      name: t('executiveSummary.fcrTechnical'),
       weight: '35%',
       score: Math.round(fcrScore),
       status: getFactorStatus(fcrScore),
-      insight: fcrScore >= 80 ? 'Óptimo' : fcrScore >= 50 ? 'En P50' : 'Bajo P90',
+      insight: fcrScore >= 80 ? t('common.optimal') : fcrScore >= 50 ? t('executiveSummary.atP50') : t('executiveSummary.lowP90'),
       rawValue: `${avgFCR.toFixed(0)}%`
     },
     {
-      name: 'Accesibilidad',
+      name: t('executiveSummary.accessibility'),
       weight: '30%',
       score: Math.round(abandonoScore),
       status: getFactorStatus(abandonoScore),
-      insight: abandonoScore >= 80 ? 'Bajo' : abandonoScore >= 50 ? 'Moderado' : 'Crítico',
+      insight: abandonoScore >= 80 ? t('common.low') : abandonoScore >= 50 ? t('executiveSummary.moderate') : t('common.critical'),
       rawValue: `${avgAbandonmentRate.toFixed(1)}% aband.`
     },
     {
-      name: 'CSAT Proxy',
+      name: t('executiveSummary.csatProxy'),
       weight: '20%',
       score: Math.round(csatProxyScore),
       status: getFactorStatus(csatProxyScore),
-      insight: csatProxyScore >= 80 ? 'Óptimo' : csatProxyScore >= 50 ? 'Mejorable' : 'Bajo',
+      insight: csatProxyScore >= 80 ? t('common.optimal') : csatProxyScore >= 50 ? t('common.improvable') : t('common.low'),
       rawValue: '(FCR+Aband.)'
     },
     {
-      name: 'Eficiencia',
+      name: t('executiveSummary.efficiencyMetric'),
       weight: '15%',
       score: Math.round(ahtScore),
       status: getFactorStatus(ahtScore),
-      insight: ahtScore >= 80 ? 'Rápido' : ahtScore >= 50 ? 'En rango' : 'Lento',
+      insight: ahtScore >= 80 ? t('executiveSummary.fast') : ahtScore >= 50 ? t('executiveSummary.inRange') : t('executiveSummary.slow'),
       rawValue: `${Math.floor(avgAHT / 60)}:${String(Math.round(avgAHT) % 60).padStart(2, '0')}`
     }
   ];
@@ -896,9 +908,9 @@ function HealthScoreDetailed({
 
         {/* Breakdown */}
         <div className="flex-1 min-w-0">
-          <h3 className="font-semibold text-gray-900 mb-2">Health Score</h3>
+          <h3 className="font-semibold text-gray-900 mb-2">{t('executiveSummary.healthScore')}</h3>
           <p className="text-[10px] text-gray-400 mb-2">
-            Benchmarks: FCR P10=85%, Aband. P10=3%, AHT P10=240s
+            {t('executiveSummary.healthScoreBenchmark')}
           </p>
 
           <div className="space-y-2">
@@ -923,7 +935,7 @@ function HealthScoreDetailed({
           {/* Nota de cálculo */}
           <div className="mt-3 pt-2 border-t border-gray-100">
             <p className="text-[9px] text-gray-400 text-center">
-              Score = FCR×35% + Accesibilidad×30% + CSAT Proxy×20% + Eficiencia×15%
+              {t('executiveSummary.healthScoreFormula')}
             </p>
           </div>
         </div>
@@ -934,6 +946,7 @@ function HealthScoreDetailed({
 
 // v3.16: Potencial de Automatización - Sin gauge confuso, solo distribución clara
 function AgenticReadinessScore({ data }: { data: AnalysisData }) {
+  const { t } = useTranslation();
   const allQueues = data.drilldownData?.flatMap(skill => skill.originalQueues) || [];
   const totalQueueVolume = allQueues.reduce((sum, q) => sum + q.volume, 0);
 
@@ -962,17 +975,17 @@ function AgenticReadinessScore({ data }: { data: AnalysisData }) {
 
   // Datos de tiers con descripción clara
   const tiers = [
-    { key: 'AUTOMATE', label: 'AUTOMATE', bgColor: 'bg-emerald-500', desc: 'Bot autónomo' },
-    { key: 'ASSIST', label: 'ASSIST', bgColor: 'bg-cyan-500', desc: 'Bot + agente' },
-    { key: 'AUGMENT', label: 'AUGMENT', bgColor: 'bg-amber-500', desc: 'Agente asistido' },
-    { key: 'HUMAN-ONLY', label: 'HUMAN', bgColor: 'bg-gray-400', desc: 'Solo humano' }
+    { key: 'AUTOMATE', label: t('executiveSummary.automate'), bgColor: 'bg-emerald-500', desc: t('executiveSummary.autonomousBot') },
+    { key: 'ASSIST', label: t('executiveSummary.assist'), bgColor: 'bg-cyan-500', desc: t('executiveSummary.botPlusAgent') },
+    { key: 'AUGMENT', label: t('executiveSummary.augment'), bgColor: 'bg-amber-500', desc: t('executiveSummary.assistedAgent') },
+    { key: 'HUMAN-ONLY', label: t('executiveSummary.human'), bgColor: 'bg-gray-400', desc: t('executiveSummary.humanOnly') }
   ];
 
   return (
     <Card>
       <div className="flex items-center gap-2 mb-4">
         <Bot className="w-5 h-5 text-blue-600" />
-        <h3 className="font-semibold text-gray-900">Potencial de Automatización</h3>
+        <h3 className="font-semibold text-gray-900">{t('executiveSummary.automationPotential')}</h3>
       </div>
 
       {/* Distribución por tier */}
@@ -996,7 +1009,7 @@ function AgenticReadinessScore({ data }: { data: AnalysisData }) {
               <div className="w-14 text-right">
                 <div className="text-sm font-semibold text-gray-700">{Math.round(pct)}%</div>
               </div>
-              <div className="w-16 text-xs text-gray-400 text-right">{count} colas</div>
+              <div className="w-16 text-xs text-gray-400 text-right">{count} {t('executiveSummary.queuesLabel')}</div>
             </div>
           );
         })}
@@ -1007,15 +1020,15 @@ function AgenticReadinessScore({ data }: { data: AnalysisData }) {
         <div className="grid grid-cols-2 gap-3 text-center">
           <div className="p-2 bg-emerald-50 rounded-lg">
             <p className="text-lg font-bold text-emerald-700">{Math.round(tierPcts.AUTOMATE)}%</p>
-            <p className="text-[10px] text-emerald-600">Automatización completa</p>
+            <p className="text-[10px] text-emerald-600">{t('executiveSummary.fullAutomation')}</p>
           </div>
           <div className="p-2 bg-cyan-50 rounded-lg">
             <p className="text-lg font-bold text-cyan-700">{Math.round(tierPcts.AUTOMATE + tierPcts.ASSIST)}%</p>
-            <p className="text-[10px] text-cyan-600">Con asistencia IA</p>
+            <p className="text-[10px] text-cyan-600">{t('executiveSummary.withAIAssistance')}</p>
           </div>
         </div>
         <p className="text-[10px] text-gray-400 text-center mt-2">
-          Basado en {formatNumber(totalQueueVolume)} interacciones analizadas
+          {t('executiveSummary.basedOnInteractions', { total: formatNumber(totalQueueVolume) })}
         </p>
       </div>
     </Card>
@@ -1094,29 +1107,31 @@ function TopOpportunities({ findings, opportunities }: {
 
 // v3.15: Economic Summary Compact
 function EconomicSummary({ economicModel }: { economicModel: AnalysisData['economicModel'] }) {
+  const { t } = useTranslation();
+
   return (
     <Card padding="md">
-      <h3 className="font-semibold text-gray-900 mb-3">Impacto Económico</h3>
+      <h3 className="font-semibold text-gray-900 mb-3">{t('executiveSummary.economicImpact')}</h3>
 
       <div className="grid grid-cols-2 gap-3 mb-3">
         <Stat
           value={formatCurrency(economicModel.currentAnnualCost)}
-          label="Coste Anual"
+          label={t('executiveSummary.annualCost')}
         />
         <Stat
           value={formatCurrency(economicModel.annualSavings)}
-          label="Ahorro Potencial"
+          label={t('executiveSummary.potentialSavings')}
           status="success"
         />
       </div>
 
       <div className="flex items-center justify-between p-2.5 bg-blue-50 rounded-lg">
         <div>
-          <p className="text-xs text-blue-600">ROI 3 años</p>
+          <p className="text-xs text-blue-600">{t('executiveSummary.roi3Years')}</p>
           <p className="text-lg font-bold text-blue-600">{economicModel.roi3yr}%</p>
         </div>
         <div className="text-right">
-          <p className="text-xs text-gray-500">Payback</p>
+          <p className="text-xs text-gray-500">{t('executiveSummary.payback')}</p>
           <p className="text-lg font-bold text-gray-700">{economicModel.paybackMonths}m</p>
         </div>
       </div>
@@ -1125,6 +1140,8 @@ function EconomicSummary({ economicModel }: { economicModel: AnalysisData['econo
 }
 
 export function ExecutiveSummaryTab({ data, onTabChange }: ExecutiveSummaryTabProps) {
+  const { t } = useTranslation();
+
   // Métricas básicas - VOLUME-WEIGHTED para consistencia con calculateHealthScore()
   const totalInteractions = data.heatmapData.reduce((sum, h) => sum + h.volume, 0);
 
@@ -1204,7 +1221,7 @@ export function ExecutiveSummaryTab({ data, onTabChange }: ExecutiveSummaryTabPr
       {onTabChange && (
         <div className="bg-gray-50 rounded-lg p-4">
           <p className="text-xs font-medium text-gray-500 uppercase tracking-wider mb-3">
-            Explorar análisis detallado
+            {t('executiveSummary.exploreDetailed')}
           </p>
 
           <div className="grid grid-cols-1 md:grid-cols-3 gap-3">
@@ -1218,12 +1235,12 @@ export function ExecutiveSummaryTab({ data, onTabChange }: ExecutiveSummaryTabPr
               </div>
               <div className="flex-1 min-w-0">
                 <div className="flex items-center gap-2">
-                  <span className="font-medium text-gray-700 text-sm">Dimensiones</span>
+                  <span className="font-medium text-gray-700 text-sm">{t('executiveSummary.dimensionsTab')}</span>
                   {dimensionesConProblemas > 0 && (
-                    <Badge label={`${dimensionesConProblemas} críticas`} variant="warning" size="sm" />
+                    <Badge label={`${dimensionesConProblemas} ${t('executiveSummary.criticalQueues')}`} variant="warning" size="sm" />
                   )}
                 </div>
-                <p className="text-xs text-gray-400">Eficiencia, resolución, satisfacción</p>
+                <p className="text-xs text-gray-400">{t('executiveSummary.dimensionsDesc')}</p>
               </div>
               <ChevronRight className="w-4 h-4 text-gray-300 group-hover:text-gray-500 group-hover:translate-x-0.5 transition-all" />
             </button>
@@ -1238,12 +1255,12 @@ export function ExecutiveSummaryTab({ data, onTabChange }: ExecutiveSummaryTabPr
               </div>
               <div className="flex-1 min-w-0">
                 <div className="flex items-center gap-2">
-                  <span className="font-medium text-gray-700 text-sm">Agentic Readiness</span>
+                  <span className="font-medium text-gray-700 text-sm">{t('executiveSummary.agenticReadinessTab')}</span>
                   {colasAutomate.length > 0 && (
-                    <Badge label={`${colasAutomate.length} listas`} variant="success" size="sm" />
+                    <Badge label={`${colasAutomate.length} ${t('executiveSummary.readyQueues')}`} variant="success" size="sm" />
                   )}
                 </div>
-                <p className="text-xs text-gray-400">Colas elegibles para automatización</p>
+                <p className="text-xs text-gray-400">{t('executiveSummary.agenticReadinessDesc')}</p>
               </div>
               <ChevronRight className="w-4 h-4 text-gray-300 group-hover:text-gray-500 group-hover:translate-x-0.5 transition-all" />
             </button>
@@ -1258,11 +1275,11 @@ export function ExecutiveSummaryTab({ data, onTabChange }: ExecutiveSummaryTabPr
               </div>
               <div className="flex-1 min-w-0">
                 <div className="flex items-center gap-2">
-                  <span className="font-medium text-gray-700 text-sm">Plan de Acción</span>
-                  <Badge label="Prioridad" variant="critical" size="sm" />
+                  <span className="font-medium text-gray-700 text-sm">{t('executiveSummary.actionPlan')}</span>
+                  <Badge label={t('executiveSummary.priority')} variant="critical" size="sm" />
                 </div>
                 <p className="text-xs text-gray-400">
-                  {ahorroTotal > 0 ? `Potencial: ${formatCurrency(ahorroTotal)}/año` : 'Roadmap de implementación'}
+                  {ahorroTotal > 0 ? t('executiveSummary.potentialPerYear', { amount: formatCurrency(ahorroTotal) }) : t('executiveSummary.roadmapImplementation')}
                 </p>
               </div>
               <ChevronRight className="w-4 h-4 text-gray-300 group-hover:text-gray-500 group-hover:translate-x-0.5 transition-all" />
diff --git a/frontend/locales/en.json b/frontend/locales/en.json
index 98c4e2b..da164ff 100644
--- a/frontend/locales/en.json
+++ b/frontend/locales/en.json
@@ -246,7 +246,38 @@
     "betterThan75": "Better than 3 out of 4 companies",
     "alignedWithMedian": "Aligned with sector median",
     "belowAverage": "Below market average",
-    "criticalArea": "Critical improvement area"
+    "criticalArea": "Critical improvement area",
+    "opportunity": "Opportunity",
+    "top10": "Top 10%",
+    "top25": "Top 25%",
+    "average": "Average",
+    "belowAvg": "Below Avg",
+    "bottom25": "Bottom 25%",
+    "benchmarkLow": "Low",
+    "benchmarkMedian": "Median",
+    "benchmarkTop": "Top",
+    "excellent": "Excellent",
+    "good": "Good",
+    "regular": "Fair",
+    "aht": "AHT",
+    "fcr": "FCR",
+    "abandonment": "ABANDONMENT",
+    "costPerInteraction": "COST/INTERAC.",
+    "ahtTotal": "Total AHT",
+    "fcrAdjusted": "Adjusted FCR",
+    "ahtTooltip": "Average handling time (valid interactions only)",
+    "ahtTotalTooltip": "Includes all rows (noise, zombie, abandon) - informational only",
+    "ahtTotalDesc": "Includes noise, zombie and abandons — informational only",
+    "fcrTooltip": "First Contact Resolution - comparable with industry benchmarks",
+    "fcrAdjustedTooltip": "Excludes recontacts in 7 days (stricter metric)",
+    "fcrAdjustedDesc": "Includes 7-day recontact filter — stricter internal metric",
+    "abandonmentTooltip": "Abandonment rate",
+    "cpiTooltip": "Cost per interaction",
+    "recontacts7d": "of 7-day recontacts",
+    "vsCleanAht": "vs clean AHT",
+    "queuesLabel": "queues",
+    "readyQueues": "ready",
+    "criticalQueues": "critical"
   },
   "industries": {
     "airlines": "Airlines",
@@ -263,7 +294,72 @@
     "keyFinding": "Key Finding",
     "keyFindings": "Key Findings",
     "noDataAvailable": "No data available for this dimension.",
-    "withinAcceptable": "Metrics within acceptable ranges. No critical findings."
+    "withinAcceptable": "Metrics within acceptable ranges. No critical findings.",
+    "impact": "Impact:",
+    "probableCause": "Probable cause:",
+    "annualImpact": "annual impact (problem cost)",
+    "recommendation": "Recommendation:",
+    "operationalEfficiency": {
+      "highAHTFinding": "High AHT: P50 {{aht}} (benchmark: 5:00)",
+      "highAHTCause": "Agents spend excessive time on manual information search, system navigation and repetitive tasks.",
+      "highAHTRecommendation": "Deploy AI Copilot for agents: (1) Auto-search in KB; (2) Contextual suggestions in real-time; (3) Guided scripts for frequent cases. Expected reduction: 20-30% AHT. Savings: {{savings}}/year.",
+      "goodAHTFinding": "AHT within benchmark: P50 {{aht}} (benchmark: 5:00)",
+      "goodAHTCause": "Efficient handling times. Optimized operational processes.",
+      "goodAHTImpact": "No excess cost from AHT",
+      "goodAHTTimeSavings": "Efficient operation",
+      "goodAHTRecommendation": "Maintain current level. Consider Copilot for continuous improvement and additional time reduction on complex cases."
+    },
+    "effectiveness": {
+      "finding": "Technical FCR: {{fcr}}% | Transfers: {{transfer}}% (benchmark: FCR >85%, Transfer <10%)",
+      "criticalCause": "High transfer rate ({{transfer}}%) indicates lack of tools or authority. Critical in {{skills}}.",
+      "criticalCauseGeneric": "High transfers ({{transfer}}%): agents without contextual information or authority to resolve.",
+      "warningCause": "{{transfer}}% transfers indicate opportunity for improvement with AI assistance for complex cases.",
+      "goodCause": "Technical FCR at optimal level. {{transfer}}% transfers mainly in cases requiring legitimate escalation.",
+      "criticalRecommendation": "Deploy Knowledge Copilot with smart KB search + Guided Resolution Copilot for complex cases. Target: FCR >85%. Potential savings: {{savings}}/year.",
+      "warningRecommendation": "Implement real-time assistance Copilot: contextual suggestions + virtual expert connection to reduce transfers. Target: FCR >90%.",
+      "goodRecommendation": "Maintain current level. Consider AI for legitimate transfer analysis and predictive routing optimization.",
+      "impactFormula": "{{count}} transfers/year × €{{cpi}}/int × 50% additional cost",
+      "timeSavings": "{{count}} transfers/year ({{pct}}% of volume)"
+    },
+    "volumetry": {
+      "concentrationFinding": "Volume concentration: {{skill}} represents {{pct}}% of total",
+      "concentrationCause": "High concentration in one skill indicates repetitive queries with automation potential.",
+      "concentrationRecommendation": "Analyze {{skill}} typologies for deflection to self-service or virtual agent. Potential: {{savings}}/year.",
+      "impactFormula": "{{volume}} int × annualization × €{{cpi}} × 20% deflection potential",
+      "timeSavings": "{{volume}} interactions/year in {{skill}} ({{deflectable}} automatable)"
+    },
+    "complexity": {
+      "highCVFinding": "High CV AHT: {{cv}}% (benchmark: <{{benchmark}}%)",
+      "highCVCauseCritical": "Extreme dispersion in handling times prevents effective resource planning. Likely lack of scripts or standardized processes.",
+      "highCVCauseWarning": "Moderate time variability indicates opportunity for standardization to improve WFM planning.",
+      "highCVImpactFormula": "~3% of operational cost due to staffing inefficiency",
+      "highCVTimeSavings": "~{{hours}} hours/year in over/under staffing",
+      "highCVRecommendation": "Implement AI-guided scripts to standardize service. Expected reduction: -50% variability. Savings: {{savings}}/year.",
+      "goodCVFinding": "CV AHT within benchmark: {{cv}}% (benchmark: <{{benchmark}}%)",
+      "goodCVCause": "Consistent handling times. Good process standardization.",
+      "goodCVImpactFormula": "No impact from variability",
+      "goodCVTimeSavings": "Efficient WFM planning",
+      "goodCVRecommendation": "Maintain current level. Analyze edge cases to identify opportunities for continuous improvement.",
+      "holdTimeFinding": "High hold time: {{holdTime}}s average (benchmark: <30s)",
+      "holdTimeCause": "Agents put customer on hold to search for information. Systems don't present data contextually.",
+      "holdTimeImpactFormula": "Excess {{excess}}s × {{volume}} int × annualization × €{{cost}}/h",
+      "holdTimeTimeSavings": "{{hours}} hours/year of customer on hold",
+      "holdTimeRecommendation": "Deploy 360° view with automatic context: history, products and suggested actions visible when answering. Expected reduction: -60% hold time. Savings: {{savings}}/year."
+    },
+    "satisfaction": {
+      "lowCSATFinding": "CSAT below target: {{csat}}% (benchmark: >80%)",
+      "lowCSATCause": "Dissatisfied customers due to waiting, lack of resolution or poor service experience.",
+      "lowCSATImpactFormula": "{{volume}} customers × annualization × 2% churn risk × €50 value",
+      "lowCSATTimeSavings": "{{customers}} customers/year at risk of churn",
+      "lowCSATRecommendation": "Implement VoC program: post-contact surveys + root cause analysis + corrective action in 48h. Target: CSAT >80%."
+    },
+    "economy": {
+      "highCPIFinding": "CPI above benchmark: €{{cpi}} (target: €{{target}})",
+      "highCPICause": "High cost per interaction due to high AHT, low occupancy or inefficient cost structure.",
+      "highCPIImpactFormula": "{{volume}} int × annualization × €{{excess}} excess CPI",
+      "highCPITimeSavings": "€{{excess}} excess/int × {{volume}} int = {{hours}} equivalents",
+      "highCPIRecommendation": "Optimize channel mix + reduce AHT with automation + review staffing model. Target: CPI <€{{target}}."
+    }
   },
   "roadmap": {
     "wave1": "Wave 1: AUTOMATE",
@@ -394,6 +490,184 @@
       "augmentDesc": "Require prior optimization: standardize processes, reduce variability (Score 3.5-5.5)",
       "humanOnly": "HUMAN-ONLY Queues",
       "humanOnlyDesc": "Not suitable for automation: insufficient volume, low data quality or extreme complexity"
+    },
+    "tiers": {
+      "automate": "Automate",
+      "assist": "Assist",
+      "optimize": "Optimize",
+      "human": "Human"
+    },
+    "tierLabels": {
+      "automateFull": "Full AI",
+      "assistCopilot": "Copilot",
+      "augmentTools": "Tools",
+      "humanManual": "Manual"
+    },
+    "status": {
+      "high": "High",
+      "medium": "Medium",
+      "low": "Low",
+      "critical": "Critical",
+      "readyForAutomation": "Ready for automation",
+      "moderatePotential": "Moderate potential",
+      "requiresOptimization": "Requires optimization",
+      "notReady": "Not ready"
+    },
+    "table": {
+      "queues": "queues",
+      "queue": "queue",
+      "queueId": "Queue (ID)",
+      "queueOriginalId": "Queue (original_queue_id)",
+      "skill": "Skill",
+      "businessUnit": "Business Unit (Skill)",
+      "strategicSkill": "Strategic Queue Skill",
+      "volume": "Volume",
+      "volumePerMonth": "int/month",
+      "ahtAvg": "Avg AHT",
+      "cvAvg": "Avg CV",
+      "savingsPotential": "Potential Savings",
+      "dominantTier": "Dom. Tier",
+      "transfer": "Transfer",
+      "redFlags": "Red Flags",
+      "savingsPerMonth": "Savings/month",
+      "cost": "Cost:",
+      "savings": "Savings:",
+      "total": "TOTAL",
+      "clickToExpand": "Click on a skill to see individual queue details",
+      "clickToExpandReason": "Click on a reason to see affected queues. Prioritize actions by impacted volume.",
+      "showing": "Showing {{shown}} of {{total}} queues",
+      "reason": "Reason / Red Flag",
+      "recommendedAction": "Recommended Action",
+      "score": "Score",
+      "int": "int",
+      "perYear": "/year",
+      "perMonth": "/month"
+    },
+    "summary": {
+      "volumeAutomatable": "Automatable Volume",
+      "tierAutoAssist": "(Tier AUTOMATE + ASSIST)",
+      "interactions": "interactions",
+      "queuesAnalyzed": "queues analyzed",
+      "interpretation": "Interpretation:",
+      "interpretationText": "The {{pct}}% represents automatable interaction volume (AUTOMATE + ASSIST). Only {{queuePct}}% of queues ({{count}} of {{total}}) are AUTOMATE, but they concentrate {{volumePct}}% of total volume. This indicates few high-volume automatable queues - opportunity concentrated in high-impact Quick Wins.",
+      "inSkills": "in {{count}} skills",
+      "groupedBy": "grouped by {{count}} reasons",
+      "requiresIntervention": "These queues require intervention before considering automation"
+    },
+    "filters": {
+      "tier": "Tier:",
+      "all": "All",
+      "minSavings": "Min savings:",
+      "minVolume": "Min volume:",
+      "activeFilters": "Active filters:",
+      "of": "of"
+    },
+    "opportunityMap": {
+      "title": "Opportunity Map",
+      "subtitle": "Size = Volume · Color = Tier · Position = Score vs TCO Savings",
+      "quickWins": "QUICK WINS",
+      "highPotential": "HIGH POTENTIAL",
+      "develop": "DEVELOP",
+      "easyImpl": "EASY IMPL.",
+      "backlog": "BACKLOG",
+      "colorTier": "COLOR = TIER",
+      "sizeVolume": "SIZE = VOLUME",
+      "visibleSavings": "VISIBLE SAVINGS",
+      "agenticScore": "Agentic Score",
+      "annualTcoSavings": "Annual TCO Savings",
+      "noQueuesMatch": "No queues match the selected filters",
+      "clickForDetail": "Click for details",
+      "quickWinCandidate": "Quick Win Candidate",
+      "highPotentialCopilot": "High Potential with Copilot",
+      "requiresStandardization": "Requires prior standardization",
+      "matureProcesses": "Score ≥7.5 indicates mature processes ready for full automation.",
+      "benefitsAI": "Score 5.5-7.5 benefits from AI assistance (Copilot) to elevate to Tier 1.",
+      "needsWork": "Score <5.5 requires prior standardization work before automating."
+    },
+    "classification": {
+      "title": "CLASSIFICATION BY SKILL",
+      "titleByTier": "CLASSIFICATION BY AUTOMATION TIER",
+      "subtitle": "Skills with queues classified as AUTOMATE (score ≥ 7.5, CV ≤ 75%, transfer ≤ 20%)",
+      "distribution": "Queue Distribution by Tier",
+      "action": "Action",
+      "auto": "AUTO",
+      "assist": "ASSIST",
+      "augm": "AUGM",
+      "human": "HUMAN",
+      "waveBot": "→ Wave 4: Full Bot",
+      "waveCopilot": "→ Wave 3: Copilot",
+      "waveTools": "→ Wave 2: Tools",
+      "waveFoundation": "→ Wave 1: Foundation",
+      "quickWins": "Quick Wins:",
+      "attention": "Attention:",
+      "volumeT1T2": "Vol in T1+T2:",
+      "volumeT4": "Vol in T4:",
+      "prioritizeWave1": "→ prioritize in Wave 1",
+      "balancedDistribution": "Balanced distribution across tiers. Review individual queues for prioritization.",
+      "hasT1T2Volume": "have >60% volume in T1+T2",
+      "hasHumanVolume": "has {{pct}}% in HUMAN",
+      "analysisPerSkill": "Analysis per Skill",
+      "skillsHaveAutomate": "of {{total}} skills have at least one AUTOMATE tier queue",
+      "seeIndividualQueues": "Click on a skill to see individual queues with score breakdown"
+    },
+    "globalFactors": {
+      "title": "Score Factors (Global Operation Level)",
+      "note": "NOTE:",
+      "noteText": "These factors are global averages. Per-queue scoring uses these same factors calculated individually for each queue.",
+      "factor": "Factor",
+      "weight": "Weight",
+      "realMetric": "Actual Metric",
+      "status": "Status",
+      "globalScore": "GLOBAL SCORE",
+      "insight": "The global score ({{score}}) reflects the complete operation. However, {{pct}}% of volume is in individual queues that DO meet automation criteria."
+    },
+    "nextSteps": {
+      "title": "NEXT STEPS → ROADMAP",
+      "basedOnAnalysis": "BASED ON THIS ANALYSIS:",
+      "immediateQuickWins": "IMMEDIATE QUICK WINS (without Wave 1)",
+      "queuesAutomate": "AUTOMATE queues",
+      "interactionsPerMonth": "interactions/month",
+      "potentialSavings": "Potential savings:",
+      "containment": "containment",
+      "skills": "Skills:",
+      "alignedWave4": "→ Aligned with Wave 4 of Roadmap. Can be implemented in parallel to Wave 1.",
+      "waveFoundation": "WAVE 1-3: FOUNDATION → ASSIST ({{count}} queues)",
+      "tierAssist": "in ASSIST tier",
+      "focusWave1": "Wave 1 Focus:",
+      "reduceTransfer": "Reduce transfer in",
+      "potentialCopilot": "Potential with Copilot:",
+      "deflection": "deflection",
+      "requiresWave1": "→ Requires Wave 1 (Foundation) to enable Copilot in Wave 3",
+      "seeRoadmap": "See Roadmap tab for detailed plan",
+      "perInt": "/int"
+    },
+    "humanOnlyReasons": {
+      "title": "HUMAN-ONLY Queues",
+      "subtitle": "Not suitable for automation: insufficient volume, low data quality or extreme complexity",
+      "volumeTotal": "Total volume:"
+    },
+    "redFlagsActions": {
+      "noSpecificFlags": "No Specific Red Flags",
+      "noFlagsDesc": "Queues that don't meet automation criteria",
+      "reviewManually": "Review manually",
+      "standardizeProcesses": "Standardize processes and scripts",
+      "simplifyFlow": "Simplify flow, train agents",
+      "consolidate": "Consolidate with similar queues",
+      "improveDataCapture": "Improve data capture"
+    },
+    "factorsExtended": {
+      "volumeMethodology": "Score = normalized log10(Volume). >5000 → 10, <100 → 2",
+      "volumeBenchmark": "Positive ROI requires >500/month",
+      "volumeGood": "High volume justifies investment",
+      "volumeBad": "Consider shared solutions",
+      "roiPotential": "ROI Potential",
+      "roiDesc": "Expected economic return",
+      "roiMethodology": "Score based on total annual cost. >€500K → 10",
+      "roiBenchmark": "ROI >150% at 12 months",
+      "roiGood": "Solid business case",
+      "roiBad": "Marginal ROI, evaluate other benefits",
+      "resolution": "Resolution",
+      "dataQuality": "Data Quality"
     }
   },
   "economicModel": {
@@ -549,4 +823,4 @@
       "december": "December"
     }
   }
-}
+}
\ No newline at end of file
diff --git a/frontend/locales/es.json b/frontend/locales/es.json
index f0778cc..9f7cb42 100644
--- a/frontend/locales/es.json
+++ b/frontend/locales/es.json
@@ -246,7 +246,38 @@
     "betterThan75": "Mejor que 3 de cada 4 empresas",
     "alignedWithMedian": "En línea con la mediana del sector",
     "belowAverage": "Por debajo de la media del mercado",
-    "criticalArea": "Área crítica de mejora"
+    "criticalArea": "Área crítica de mejora",
+    "opportunity": "Oportunidad",
+    "top10": "Top 10%",
+    "top25": "Top 25%",
+    "average": "Promedio",
+    "belowAvg": "Bajo Avg",
+    "bottom25": "Bottom 25%",
+    "benchmarkLow": "Bajo",
+    "benchmarkMedian": "Mediana",
+    "benchmarkTop": "Top",
+    "excellent": "Excelente",
+    "good": "Bueno",
+    "regular": "Regular",
+    "aht": "AHT",
+    "fcr": "FCR",
+    "abandonment": "ABANDONO",
+    "costPerInteraction": "COSTE/INTERAC.",
+    "ahtTotal": "AHT Total",
+    "fcrAdjusted": "FCR Ajustado",
+    "ahtTooltip": "Tiempo medio de gestión (solo interacciones válidas)",
+    "ahtTotalTooltip": "Incluye todas las filas (noise, zombie, abandon) - solo informativo",
+    "ahtTotalDesc": "Incluye noise, zombie y abandonos — solo informativo",
+    "fcrTooltip": "First Contact Resolution - comparable con benchmarks de industria",
+    "fcrAdjustedTooltip": "Excluye recontactos en 7 días (métrica más estricta)",
+    "fcrAdjustedDesc": "Incluye filtro de recontactos 7d — métrica interna más estricta",
+    "abandonmentTooltip": "Tasa de abandono",
+    "cpiTooltip": "Coste por interacción",
+    "recontacts7d": "de recontactos 7d",
+    "vsCleanAht": "vs AHT limpio",
+    "queuesLabel": "colas",
+    "readyQueues": "listas",
+    "criticalQueues": "críticas"
   },
   "industries": {
     "airlines": "Aerolíneas",
@@ -263,7 +294,72 @@
     "keyFinding": "Hallazgo Clave",
     "keyFindings": "Hallazgos Clave",
     "noDataAvailable": "Sin datos disponibles para esta dimensión.",
-    "withinAcceptable": "Métricas dentro de rangos aceptables. Sin hallazgos críticos."
+    "withinAcceptable": "Métricas dentro de rangos aceptables. Sin hallazgos críticos.",
+    "impact": "Impacto:",
+    "probableCause": "Causa probable:",
+    "annualImpact": "impacto anual (coste del problema)",
+    "recommendation": "Recomendación:",
+    "operationalEfficiency": {
+      "highAHTFinding": "AHT elevado: P50 {{aht}} (benchmark: 5:00)",
+      "highAHTCause": "Agentes dedican tiempo excesivo a búsqueda manual de información, navegación entre sistemas y tareas repetitivas.",
+      "highAHTRecommendation": "Desplegar Copilot IA para agentes: (1) Auto-búsqueda en KB; (2) Sugerencias contextuales en tiempo real; (3) Scripts guiados para casos frecuentes. Reducción esperada: 20-30% AHT. Ahorro: {{savings}}/año.",
+      "goodAHTFinding": "AHT dentro de benchmark: P50 {{aht}} (benchmark: 5:00)",
+      "goodAHTCause": "Tiempos de gestión eficientes. Procesos operativos optimizados.",
+      "goodAHTImpact": "Sin exceso de coste por AHT",
+      "goodAHTTimeSavings": "Operación eficiente",
+      "goodAHTRecommendation": "Mantener nivel actual. Considerar Copilot para mejora continua y reducción adicional de tiempos en casos complejos."
+    },
+    "effectiveness": {
+      "finding": "FCR Técnico: {{fcr}}% | Transferencias: {{transfer}}% (benchmark: FCR >85%, Transfer <10%)",
+      "criticalCause": "Alta tasa de transferencias ({{transfer}}%) indica falta de herramientas o autoridad. Crítico en {{skills}}.",
+      "criticalCauseGeneric": "Transferencias elevadas ({{transfer}}%): agentes sin información contextual o sin autoridad para resolver.",
+      "warningCause": "Transferencias del {{transfer}}% indican oportunidad de mejora con asistencia IA para casos complejos.",
+      "goodCause": "FCR Técnico en nivel óptimo. Transferencias del {{transfer}}% principalmente en casos que requieren escalación legítima.",
+      "criticalRecommendation": "Desplegar Knowledge Copilot con búsqueda inteligente en KB + Guided Resolution Copilot para casos complejos. Objetivo: FCR >85%. Potencial ahorro: {{savings}}/año.",
+      "warningRecommendation": "Implementar Copilot de asistencia en tiempo real: sugerencias contextuales + conexión con expertos virtuales para reducir transferencias. Objetivo: FCR >90%.",
+      "goodRecommendation": "Mantener nivel actual. Considerar IA para análisis de transferencias legítimas y optimización de enrutamiento predictivo.",
+      "impactFormula": "{{count}} transferencias/año × €{{cpi}}/int × 50% coste adicional",
+      "timeSavings": "{{count}} transferencias/año ({{pct}}% del volumen)"
+    },
+    "volumetry": {
+      "concentrationFinding": "Concentración de volumen: {{skill}} representa {{pct}}% del total",
+      "concentrationCause": "Alta concentración en un skill indica consultas repetitivas con potencial de automatización.",
+      "concentrationRecommendation": "Analizar tipologías de {{skill}} para deflexión a autoservicio o agente virtual. Potencial: {{savings}}/año.",
+      "impactFormula": "{{volume}} int × anualización × €{{cpi}} × 20% deflexión potencial",
+      "timeSavings": "{{volume}} interacciones/año en {{skill}} ({{deflectable}} automatizables)"
+    },
+    "complexity": {
+      "highCVFinding": "CV AHT elevado: {{cv}}% (benchmark: <{{benchmark}}%)",
+      "highCVCauseCritical": "Dispersión extrema en tiempos de atención impide planificación efectiva de recursos. Probable falta de scripts o procesos estandarizados.",
+      "highCVCauseWarning": "Variabilidad moderada en tiempos indica oportunidad de estandarización para mejorar planificación WFM.",
+      "highCVImpactFormula": "~3% del coste operativo por ineficiencia de staffing",
+      "highCVTimeSavings": "~{{hours}} horas/año en sobre/subdimensionamiento",
+      "highCVRecommendation": "Implementar scripts guiados por IA que estandaricen la atención. Reducción esperada: -50% variabilidad. Ahorro: {{savings}}/año.",
+      "goodCVFinding": "CV AHT dentro de benchmark: {{cv}}% (benchmark: <{{benchmark}}%)",
+      "goodCVCause": "Tiempos de atención consistentes. Buena estandarización de procesos.",
+      "goodCVImpactFormula": "Sin impacto por variabilidad",
+      "goodCVTimeSavings": "Planificación WFM eficiente",
+      "goodCVRecommendation": "Mantener nivel actual. Analizar casos atípicos para identificar oportunidades de mejora continua.",
+      "holdTimeFinding": "Hold time elevado: {{holdTime}}s promedio (benchmark: <30s)",
+      "holdTimeCause": "Agentes ponen cliente en espera para buscar información. Sistemas no presentan datos de forma contextual.",
+      "holdTimeImpactFormula": "Exceso {{excess}}s × {{volume}} int × anualización × €{{cost}}/h",
+      "holdTimeTimeSavings": "{{hours}} horas/año de cliente en espera",
+      "holdTimeRecommendation": "Desplegar vista 360° con contexto automático: historial, productos y acciones sugeridas visibles al contestar. Reducción esperada: -60% hold time. Ahorro: {{savings}}/año."
+    },
+    "satisfaction": {
+      "lowCSATFinding": "CSAT por debajo del objetivo: {{csat}}% (benchmark: >80%)",
+      "lowCSATCause": "Clientes insatisfechos por esperas, falta de resolución o experiencia de atención deficiente.",
+      "lowCSATImpactFormula": "{{volume}} clientes × anualización × 2% riesgo churn × €50 valor",
+      "lowCSATTimeSavings": "{{customers}} clientes/año en riesgo de fuga",
+      "lowCSATRecommendation": "Implementar programa VoC: encuestas post-contacto + análisis de causas raíz + acción correctiva en 48h. Objetivo: CSAT >80%."
+    },
+    "economy": {
+      "highCPIFinding": "CPI por encima del benchmark: €{{cpi}} (objetivo: €{{target}})",
+      "highCPICause": "Coste por interacción elevado por AHT alto, baja ocupación o estructura de costes ineficiente.",
+      "highCPIImpactFormula": "{{volume}} int × anualización × €{{excess}} exceso CPI",
+      "highCPITimeSavings": "€{{excess}} exceso/int × {{volume}} int = {{hours}} equivalentes",
+      "highCPIRecommendation": "Optimizar mix de canales + reducir AHT con automatización + revisar modelo de staffing. Objetivo: CPI <€{{target}}."
+    }
   },
   "roadmap": {
     "wave1": "Wave 1: AUTOMATE",
@@ -394,6 +490,184 @@
       "augmentDesc": "Requieren optimización previa: estandarizar procesos, reducir variabilidad (Score 3.5-5.5)",
       "humanOnly": "Colas HUMAN-ONLY",
       "humanOnlyDesc": "No aptas para automatización: volumen insuficiente, datos de baja calidad o complejidad extrema"
+    },
+    "tiers": {
+      "automate": "Automatizar",
+      "assist": "Asistir",
+      "optimize": "Optimizar",
+      "human": "Humano"
+    },
+    "tierLabels": {
+      "automateFull": "Full IA",
+      "assistCopilot": "Copilot",
+      "augmentTools": "Tools",
+      "humanManual": "Manual"
+    },
+    "status": {
+      "high": "Alto",
+      "medium": "Medio",
+      "low": "Bajo",
+      "critical": "Crítico",
+      "readyForAutomation": "Listo para automatización",
+      "moderatePotential": "Potencial moderado",
+      "requiresOptimization": "Requiere optimización",
+      "notReady": "No preparado"
+    },
+    "table": {
+      "queues": "colas",
+      "queue": "cola",
+      "queueId": "Cola (ID)",
+      "queueOriginalId": "Cola (original_queue_id)",
+      "skill": "Skill",
+      "businessUnit": "Business Unit (Skill)",
+      "strategicSkill": "Queue Skill (Estratégico)",
+      "volume": "Volumen",
+      "volumePerMonth": "int/mes",
+      "ahtAvg": "AHT Prom.",
+      "cvAvg": "CV Prom.",
+      "savingsPotential": "Ahorro Potencial",
+      "dominantTier": "Tier Dom.",
+      "transfer": "Transfer",
+      "redFlags": "Red Flags",
+      "savingsPerMonth": "Ahorro/mes",
+      "cost": "Coste:",
+      "savings": "Ahorro:",
+      "total": "TOTAL",
+      "clickToExpand": "Click en un skill para ver el detalle de colas individuales",
+      "clickToExpandReason": "Click en una razón para ver las colas afectadas. Priorizar acciones según volumen impactado.",
+      "showing": "Mostrando {{shown}} de {{total}} colas",
+      "reason": "Razón / Red Flag",
+      "recommendedAction": "Acción Recomendada",
+      "score": "Score",
+      "int": "int",
+      "perYear": "/año",
+      "perMonth": "/mes"
+    },
+    "summary": {
+      "volumeAutomatable": "Volumen Automatizable",
+      "tierAutoAssist": "(Tier AUTOMATE + ASSIST)",
+      "interactions": "interacciones",
+      "queuesAnalyzed": "colas analizadas",
+      "interpretation": "Interpretación:",
+      "interpretationText": "El {{pct}}% representa el volumen de interacciones automatizables (AUTOMATE + ASSIST). Solo el {{queuePct}}% de las colas ({{count}} de {{total}}) son AUTOMATE, pero concentran {{volumePct}}% del volumen total. Esto indica pocas colas de alto volumen automatizables - oportunidad concentrada en Quick Wins de alto impacto.",
+      "inSkills": "en {{count}} skills",
+      "groupedBy": "agrupadas por {{count}} razones",
+      "requiresIntervention": "Estas colas requieren intervención antes de considerar automatización"
+    },
+    "filters": {
+      "tier": "Tier:",
+      "all": "Todos",
+      "minSavings": "Ahorro mín:",
+      "minVolume": "Volumen mín:",
+      "activeFilters": "Filtros activos:",
+      "of": "de"
+    },
+    "opportunityMap": {
+      "title": "Mapa de Oportunidades",
+      "subtitle": "Tamaño = Volumen · Color = Tier · Posición = Score vs Ahorro TCO",
+      "quickWins": "QUICK WINS",
+      "highPotential": "ALTO POTENCIAL",
+      "develop": "DESARROLLAR",
+      "easyImpl": "FÁCIL IMPL.",
+      "backlog": "BACKLOG",
+      "colorTier": "COLOR = TIER",
+      "sizeVolume": "TAMAÑO = VOLUMEN",
+      "visibleSavings": "AHORRO VISIBLE",
+      "agenticScore": "Agentic Score",
+      "annualTcoSavings": "Ahorro TCO Anual",
+      "noQueuesMatch": "No hay colas que cumplan los filtros seleccionados",
+      "clickForDetail": "Click para ver detalle",
+      "quickWinCandidate": "Candidato a Quick Win",
+      "highPotentialCopilot": "Alto Potencial con Copilot",
+      "requiresStandardization": "Requiere estandarización previa",
+      "matureProcesses": "Score ≥7.5 indica procesos maduros listos para automatización completa.",
+      "benefitsAI": "Score 5.5-7.5 se beneficia de asistencia IA (Copilot) para elevar a Tier 1.",
+      "needsWork": "Score <5.5 requiere trabajo previo de estandarización antes de automatizar."
+    },
+    "classification": {
+      "title": "CLASIFICACIÓN POR SKILL",
+      "titleByTier": "CLASIFICACIÓN POR TIER DE AUTOMATIZACIÓN",
+      "subtitle": "Skills con colas clasificadas como AUTOMATE (score ≥ 7.5, CV ≤ 75%, transfer ≤ 20%)",
+      "distribution": "Distribución Colas por Tier",
+      "action": "Acción",
+      "auto": "AUTO",
+      "assist": "ASIST",
+      "augm": "AUGM",
+      "human": "HUMAN",
+      "waveBot": "→ Wave 4: Bot Full",
+      "waveCopilot": "→ Wave 3: Copilot",
+      "waveTools": "→ Wave 2: Tools",
+      "waveFoundation": "→ Wave 1: Foundation",
+      "quickWins": "Quick Wins:",
+      "attention": "Atención:",
+      "volumeT1T2": "Vol en T1+T2:",
+      "volumeT4": "Vol en T4:",
+      "prioritizeWave1": "→ priorizar en Wave 1",
+      "balancedDistribution": "Distribución equilibrada entre tiers. Revisar colas individuales para priorización.",
+      "hasT1T2Volume": "tienen >60% volumen en T1+T2",
+      "hasHumanVolume": "tiene {{pct}}% en HUMAN",
+      "analysisPerSkill": "Análisis por Skill",
+      "skillsHaveAutomate": "de {{total}} skills tienen al menos una cola tier AUTOMATE",
+      "seeIndividualQueues": "Haz clic en un skill para ver las colas individuales con desglose de score"
+    },
+    "globalFactors": {
+      "title": "Factores del Score (Nivel Operación Global)",
+      "note": "NOTA:",
+      "noteText": "Estos factores son promedios globales. El scoring por cola usa estos mismos factores calculados individualmente para cada cola.",
+      "factor": "Factor",
+      "weight": "Peso",
+      "realMetric": "Métrica Real",
+      "status": "Status",
+      "globalScore": "SCORE GLOBAL",
+      "insight": "El score global ({{score}}) refleja la operación completa. Sin embargo, {{pct}}% del volumen está en colas individuales que SÍ cumplen criterios de automatización."
+    },
+    "nextSteps": {
+      "title": "PRÓXIMOS PASOS → ROADMAP",
+      "basedOnAnalysis": "BASADO EN ESTE ANÁLISIS:",
+      "immediateQuickWins": "QUICK WINS INMEDIATOS (sin Wave 1)",
+      "queuesAutomate": "colas AUTOMATE",
+      "interactionsPerMonth": "interacciones/mes",
+      "potentialSavings": "Ahorro potencial:",
+      "containment": "contención",
+      "skills": "Skills:",
+      "alignedWave4": "→ Alineado con Wave 4 del Roadmap. Pueden implementarse en paralelo a Wave 1.",
+      "waveFoundation": "WAVE 1-3: FOUNDATION → ASSIST ({{count}} colas)",
+      "tierAssist": "en tier ASSIST",
+      "focusWave1": "Foco Wave 1:",
+      "reduceTransfer": "Reducir transfer en",
+      "potentialCopilot": "Potencial con Copilot:",
+      "deflection": "deflection",
+      "requiresWave1": "→ Requiere Wave 1 (Foundation) para habilitar Copilot en Wave 3",
+      "seeRoadmap": "Ver pestaña Roadmap para plan detallado",
+      "perInt": "/int"
+    },
+    "humanOnlyReasons": {
+      "title": "Colas HUMAN-ONLY",
+      "subtitle": "No aptas para automatización: volumen insuficiente, datos de baja calidad o complejidad extrema",
+      "volumeTotal": "Volumen total:"
+    },
+    "redFlagsActions": {
+      "noSpecificFlags": "Sin Red Flags específicos",
+      "noFlagsDesc": "Colas que no cumplen criterios de automatización",
+      "reviewManually": "Revisar manualmente",
+      "standardizeProcesses": "Estandarizar procesos y scripts",
+      "simplifyFlow": "Simplificar flujo, capacitar agentes",
+      "consolidate": "Consolidar con colas similares",
+      "improveDataCapture": "Mejorar captura de datos"
+    },
+    "factorsExtended": {
+      "volumeMethodology": "Score = log10(Volumen) normalizado. >5000 → 10, <100 → 2",
+      "volumeBenchmark": "ROI positivo requiere >500/mes",
+      "volumeGood": "Alto volumen justifica inversión",
+      "volumeBad": "Considerar soluciones compartidas",
+      "roiPotential": "ROI Potencial",
+      "roiDesc": "Retorno económico esperado",
+      "roiMethodology": "Score basado en coste anual total. >€500K → 10",
+      "roiBenchmark": "ROI >150% a 12 meses",
+      "roiGood": "Caso de negocio sólido",
+      "roiBad": "ROI marginal, evaluar otros beneficios",
+      "resolution": "Resolutividad",
+      "dataQuality": "Calidad Datos"
     }
   },
   "economicModel": {
@@ -549,4 +823,4 @@
       "december": "Diciembre"
     }
   }
-}
+}
\ No newline at end of file