Integrating AI/ML with Adobe Experience Manager: The Future of Content

// AI/ML Service Architecture for AEM
interface AIMLServiceArchitecture {
  // Data Layer
  dataIngestion: {
    userBehaviorTracking: UserBehaviorService;
    contentAnalytics: ContentAnalyticsService;
    performanceMetrics: PerformanceMonitoringService;
  };
  
  // Processing Layer
  mlPipeline: {
    dataPreprocessing: DataPreprocessingService;
    modelTraining: ModelTrainingService;
    inferenceEngine: InferenceService;
  };
  
  // Integration Layer
  aemIntegration: {
    personalizationEngine: PersonalizationService;
    contentOptimization: ContentOptimizationService;
    workflowAutomation: WorkflowAutomationService;
  };
  
  // Delivery Layer
  experienceDelivery: {
    realTimePersonalization: RTPersonalizationService;
    contentRecommendations: RecommendationService;
    adaptiveOptimization: OptimizationService;
  };
}

// Example implementation
class AEMAIMLService implements AIMLServiceArchitecture {
  constructor(
    private aemInstance: AEMInstance,
    private mlPlatform: MLPlatform,
    private config: AIMLConfig
  ) {}
  
  async initializeAIServices(): Promise<void> {
    // Initialize data collection
    await this.setupUserTracking();
    await this.setupContentAnalytics();
    
    // Initialize ML models
    await this.loadPersonalizationModels();
    await this.loadContentOptimizationModels();
    
    // Setup real-time processing
    await this.initializeRealTimePersonalization();
  }
  
  async personalizeContent(
    user: UserContext, 
    content: ContentRequest
  ): Promise<PersonalizedContent> {
    // Analyze user context
    const userProfile = await this.analyzeUserContext(user);
    
    // Generate personalized content
    const personalizedContent = await this.mlPlatform.generatePersonalization({
      userProfile,
      contentContext: content,
      businessRules: this.config.personalizationRules
    });
    
    // Track interaction for learning
    await this.recordPersonalizationInteraction(user, content, personalizedContent);
    
    return personalizedContent;
  }
}

@Component(service = PersonalizationService.class)
@Designate(ocd = PersonalizationService.Config.class)
public class AIPersonalizationService implements PersonalizationService {
    
    @ObjectClassDefinition(name = "AI Personalization Service Configuration")
    public @interface Config {
        @AttributeDefinition(name = "ML Model Endpoint")
        String mlModelEndpoint() default "https://ml-api.enterprise.com/personalization";
        
        @AttributeDefinition(name = "Real-time Processing Enabled")
        boolean realTimeEnabled() default true;
        
        @AttributeDefinition(name = "Fallback Strategy")
        String fallbackStrategy() default "DEFAULT_CONTENT";
    }
    
    @Reference
    private UserProfileService userProfileService;
    
    @Reference
    private MLModelService mlModelService;
    
    @Reference
    private ContentService contentService;
    
    private Config config;
    
    @Activate
    protected void activate(Config config) {
        this.config = config;
        initializeMLModels();
    }
    
    @Override
    public PersonalizedContent personalizeContent(
            SlingHttpServletRequest request, 
            Resource contentResource) {
        
        try {
            // Extract user context
            UserContext userContext = extractUserContext(request);
            
            // Analyze content context
            ContentContext contentContext = analyzeContentContext(contentResource);
            
            // Generate personalization
            PersonalizationResult result = generatePersonalization(userContext, contentContext);
            
            // Apply personalization
            PersonalizedContent personalizedContent = applyPersonalization(
                contentResource, result);
            
            // Record interaction for learning
            recordPersonalizationInteraction(userContext, contentContext, result);
            
            return personalizedContent;
            
        } catch (Exception e) {
            LOG.error("Personalization failed, falling back to default content", e);
            return createFallbackContent(contentResource);
        }
    }
    
    private UserContext extractUserContext(SlingHttpServletRequest request) {
        UserProfile profile = userProfileService.getUserProfile(request);
        
        return UserContext.builder()
            .userId(profile.getUserId())
            .segments(profile.getSegments())
            .behaviorHistory(profile.getBehaviorHistory())
            .preferences(profile.getPreferences())
            .sessionData(extractSessionData(request))
            .deviceContext(extractDeviceContext(request))
            .geographicContext(extractGeographicContext(request))
            .temporalContext(extractTemporalContext(request))
            .build();
    }
    
    private ContentContext analyzeContentContext(Resource contentResource) {
        ValueMap properties = contentResource.getValueMap();
        
        // Extract content features
        ContentFeatures features = ContentFeatures.builder()
            .contentType(properties.get("sling:resourceType", String.class))
            .category(properties.get("category", String.class))
            .tags(Arrays.asList(properties.get("cq:tags", String[].class)))
            .publishDate(properties.get("cq:lastModified", Calendar.class))
            .author(properties.get("jcr:createdBy", String.class))
            .build();
        
        // Analyze content performance
        ContentPerformance performance = analyzeContentPerformance(contentResource);
        
        return ContentContext.builder()
            .resourcePath(contentResource.getPath())
            .features(features)
            .performance(performance)
            .relatedContent(findRelatedContent(contentResource))
            .build();
    }
    
    private PersonalizationResult generatePersonalization(
            UserContext userContext, 
            ContentContext contentContext) {
        
        // Prepare ML model input
        MLModelInput input = MLModelInput.builder()
            .userFeatures(extractUserFeatures(userContext))
            .contentFeatures(extractContentFeatures(contentContext))
            .contextFeatures(extractContextFeatures(userContext, contentContext))
            .build();
        
        // Call ML model for predictions
        MLModelOutput output = mlModelService.predict(
            "content-personalization-v2", input);
        
        // Process predictions
        return PersonalizationResult.builder()
            .personalizedVariant(output.getRecommendedVariant())
            .confidenceScore(output.getConfidenceScore())
            .recommendations(output.getContentRecommendations())
            .optimizations(output.getContentOptimizations())
            .build();
    }
    
    private PersonalizedContent applyPersonalization(
            Resource contentResource, 
            PersonalizationResult result) {
        
        PersonalizedContent.Builder builder = PersonalizedContent.builder()
            .originalResource(contentResource);
        
        // Apply content variations
        if (result.getPersonalizedVariant() != null) {
            builder.variant(result.getPersonalizedVariant());
        }
        
        // Apply dynamic content modifications
        if (result.getOptimizations() != null) {
            builder.optimizations(result.getOptimizations());
        }
        
        // Add personalized recommendations
        if (result.getRecommendations() != null && !result.getRecommendations().isEmpty()) {
            List<Resource> recommendedResources = result.getRecommendations().stream()
                .map(contentService::getContentResource)
                .filter(Objects::nonNull)
                .collect(Collectors.toList());
            
            builder.recommendations(recommendedResources);
        }
        
        return builder.build();
    }
    
    private void recordPersonalizationInteraction(
            UserContext userContext, 
            ContentContext contentContext,
            PersonalizationResult result) {
        
        // Record interaction for model improvement
        PersonalizationInteraction interaction = PersonalizationInteraction.builder()
            .userId(userContext.getUserId())
            .contentPath(contentContext.getResourcePath())
            .variant(result.getPersonalizedVariant())
            .confidenceScore(result.getConfidenceScore())
            .timestamp(Instant.now())
            .build();
        
        // Async recording to avoid impacting response time
        CompletableFuture.runAsync(() -> {
            try {
                mlModelService.recordInteraction("content-personalization-v2", interaction);
            } catch (Exception e) {
                LOG.warn("Failed to record personalization interaction", e);
            }
        });
    }
}

@Component(service = {AssetProcessor.class, EventHandler.class})
@Property(name = EventConstants.EVENT_TOPIC, value = DamConstants.EVENT_TOPIC)
public class AIAssetProcessorService implements AssetProcessor, EventHandler {
    
    @Reference
    private ComputerVisionService computerVisionService;
    
    @Reference
    private AssetOptimizationService optimizationService;
    
    @Reference
    private BrandComplianceService brandComplianceService;
    
    @Override
    public void handleEvent(Event event) {
        String assetPath = (String) event.getProperty(DamConstants.EVENT_PROPERTY_ASSET_PATH);
        
        if (isProcessableAsset(assetPath)) {
            CompletableFuture.runAsync(() -> processAsset(assetPath));
        }
    }
    
    private void processAsset(String assetPath) {
        try (ResourceResolver resolver = getServiceResourceResolver()) {
            Resource assetResource = resolver.getResource(assetPath);
            Asset asset = assetResource.adaptTo(Asset.class);
            
            if (asset == null) {
                LOG.warn("Could not adapt resource to Asset: {}", assetPath);
                return;
            }
            
            // Computer vision analysis
            VisionAnalysisResult visionResult = analyzeAssetWithAI(asset);
            
            // Apply intelligent tagging
            applyIntelligentTags(asset, visionResult);
            
            // Generate optimized renditions
            generateOptimizedRenditions(asset, visionResult);
            
            // Brand compliance check
            performBrandComplianceCheck(asset, visionResult);
            
            // Update asset metadata
            updateAssetMetadata(asset, visionResult);
            
            LOG.info("AI processing completed for asset: {}", assetPath);
            
        } catch (Exception e) {
            LOG.error("AI asset processing failed for: {}", assetPath, e);
        }
    }
    
    private VisionAnalysisResult analyzeAssetWithAI(Asset asset) {
        try {
            // Get original rendition
            Rendition originalRendition = asset.getOriginal();
            InputStream imageStream = originalRendition.getBinary().getStream();
            
            // Perform computer vision analysis
            VisionAnalysisRequest request = VisionAnalysisRequest.builder()
                .imageData(imageStream)
                .analysisTypes(Arrays.asList(
                    AnalysisType.OBJECT_DETECTION,
                    AnalysisType.SCENE_CLASSIFICATION,
                    AnalysisType.TEXT_EXTRACTION,
                    AnalysisType.FACE_DETECTION,
                    AnalysisType.COLOR_ANALYSIS,
                    AnalysisType.COMPOSITION_ANALYSIS
                ))
                .confidenceThreshold(0.7f)
                .build();
            
            return computerVisionService.analyzeImage(request);
            
        } catch (Exception e) {
            LOG.error("Computer vision analysis failed", e);
            return VisionAnalysisResult.empty();
        }
    }
    
    private void applyIntelligentTags(Asset asset, VisionAnalysisResult visionResult) {
        Set<String> intelligentTags = new HashSet<>();
        
        // Add object detection tags
        visionResult.getDetectedObjects().stream()
            .filter(obj -> obj.getConfidence() > 0.8f)
            .map(DetectedObject::getLabel)
            .forEach(intelligentTags::add);
        
        // Add scene classification tags
        visionResult.getSceneClassifications().stream()
            .filter(scene -> scene.getConfidence() > 0.7f)
            .map(SceneClassification::getLabel)
            .forEach(intelligentTags::add);
        
        // Add color-based tags
        visionResult.getDominantColors().stream()
            .map(color -> "color-" + color.getName().toLowerCase())
            .forEach(intelligentTags::add);
        
        // Add composition tags
        CompositionAnalysis composition = visionResult.getCompositionAnalysis();
        if (composition != null) {
            intelligentTags.add("composition-" + composition.getStyle().toLowerCase());
            intelligentTags.add("orientation-" + composition.getOrientation().toLowerCase());
        }
        
        // Apply tags to asset
        try {
            ModifiableValueMap metadata = asset.adaptTo(ModifiableValueMap.class);
            if (metadata != null) {
                String[] existingTags = metadata.get("cq:tags", String[].class);
                Set<String> allTags = new HashSet<>();
                
                if (existingTags != null) {
                    Collections.addAll(allTags, existingTags);
                }
                
                // Add AI-generated tags with namespace
                intelligentTags.stream()
                    .map(tag -> "ai-generated:" + tag)
                    .forEach(allTags::add);
                
                metadata.put("cq:tags", allTags.toArray(new String[0]));
                metadata.put("dam:aiProcessedDate", Calendar.getInstance());
                metadata.put("dam:aiConfidenceScore", visionResult.getOverallConfidence());
                
                asset.getResource().getResourceResolver().commit();
            }
        } catch (Exception e) {
            LOG.error("Failed to apply intelligent tags to asset", e);
        }
    }
    
    private void generateOptimizedRenditions(Asset asset, VisionAnalysisResult visionResult) {
        try {
            // Determine optimal crop areas using composition analysis
            CompositionAnalysis composition = visionResult.getCompositionAnalysis();
            List<CropArea> optimalCrops = calculateOptimalCrops(composition);
            
            // Generate smart crops for different aspect ratios
            for (CropArea cropArea : optimalCrops) {
                String renditionName = String.format("smart-crop-%s.jpg", cropArea.getAspectRatio());
                
                optimizationService.generateSmartCrop(
                    asset,
                    cropArea,
                    renditionName,
                    OptimizationSettings.builder()
                        .quality(85)
                        .format("JPEG")
                        .progressive(true)
                        .build()
                );
            }
            
            // Generate format-optimized renditions
            generateWebPRenditions(asset);
            generateAVIFRenditions(asset);
            
        } catch (Exception e) {
            LOG.error("Failed to generate optimized renditions", e);
        }
    }
    
    private void performBrandComplianceCheck(Asset asset, VisionAnalysisResult visionResult) {
        try {
            BrandComplianceResult complianceResult = brandComplianceService.checkCompliance(
                asset, visionResult);
            
            ModifiableValueMap metadata = asset.adaptTo(ModifiableValueMap.class);
            if (metadata != null) {
                metadata.put("dam:brandCompliant", complianceResult.isCompliant());
                metadata.put("dam:complianceScore", complianceResult.getScore());
                
                if (!complianceResult.isCompliant()) {
                    metadata.put("dam:complianceIssues", 
                        complianceResult.getIssues().toArray(new String[0]));
                }
                
                asset.getResource().getResourceResolver().commit();
            }
            
            // Trigger workflow if non-compliant
            if (!complianceResult.isCompliant()) {
                triggerComplianceReviewWorkflow(asset, complianceResult);
            }
            
        } catch (Exception e) {
            LOG.error("Brand compliance check failed", e);
        }
    }
}

// Content Performance Prediction API
class ContentPerformancePredictionService {
  private mlModel: MLModel;
  private featureExtractor: FeatureExtractor;
  
  constructor(
    private aemService: AEMService,
    private analyticsService: AnalyticsService
  ) {
    this.mlModel = new MLModel('content-performance-predictor-v3');
    this.featureExtractor = new FeatureExtractor();
  }
  
  async predictContentPerformance(contentItem: ContentItem): Promise<PerformancePrediction> {
    // Extract content features
    const contentFeatures = await this.extractContentFeatures(contentItem);
    
    // Extract contextual features
    const contextFeatures = await this.extractContextualFeatures(contentItem);
    
    // Extract historical performance patterns
    const historicalFeatures = await this.extractHistoricalFeatures(contentItem);
    
    // Combine all features
    const allFeatures = {
      ...contentFeatures,
      ...contextFeatures,
      ...historicalFeatures
    };
    
    // Make prediction
    const prediction = await this.mlModel.predict(allFeatures);
    
    return {
      expectedEngagement: prediction.engagementScore,
      predictedReach: prediction.reachEstimate,
      conversionProbability: prediction.conversionRate,
      optimalPublishTime: prediction.optimalTiming,
      audienceMatch: prediction.audienceAlignment,
      confidenceInterval: prediction.confidence,
      optimizationSuggestions: await this.generateOptimizationSuggestions(prediction)
    };
  }
  
  async generateOptimizationSuggestions(
    prediction: MLPrediction
  ): Promise<OptimizationSuggestion[]> {
    const suggestions: OptimizationSuggestion[] = [];
    
    // Timing optimization
    if (prediction.timingScore < 0.7) {
      suggestions.push({
        type: 'timing',
        priority: 'high',
        description: 'Consider publishing during peak audience hours',
        expectedImprovement: '+15% engagement',
        implementation: {
          suggestedTime: prediction.optimalTiming,
          reasoning: 'Based on historical audience activity patterns'
        }
      });
    }
    
    // Content optimization
    if (prediction.contentScore < 0.8) {
      const contentAnalysis = await this.analyzeContentGaps(prediction);
      suggestions.push({
        type: 'content',
        priority: 'medium',
        description: 'Content could be enhanced for better performance',
        expectedImprovement: '+10% engagement',
        implementation: {
          suggestions: contentAnalysis.improvements,
          examples: contentAnalysis.examples
        }
      });
    }
    
    // Audience targeting
    if (prediction.audienceScore < 0.75) {
      suggestions.push({
        type: 'targeting',
        priority: 'high',
        description: 'Content targeting could be refined',
        expectedImprovement: '+20% conversion',
        implementation: {
          targetSegments: prediction.optimalAudience,
          personalizationOpportunities: prediction.personalizationSuggestions
        }
      });
    }
    
    return suggestions;
  }
  
  async monitorAndLearn(contentItem: ContentItem, actualPerformance: PerformanceMetrics): Promise<void> {
    // Compare predictions with actual performance
    const prediction = await this.getPreviousPrediction(contentItem.id);
    
    if (prediction) {
      const accuracy = this.calculatePredictionAccuracy(prediction, actualPerformance);
      
      // Update model with actual results
      await this.mlModel.updateWithFeedback({
        features: prediction.inputFeatures,
        predictedOutcome: prediction.prediction,
        actualOutcome: actualPerformance,
        accuracy: accuracy
      });
      
      // Retrain model if accuracy drops below threshold
      if (accuracy < 0.85) {
        await this.scheduleModelRetraining();
      }
    }
  }
}