Predictive Intelligence for ServiceNow

Predictive Intelligence uses machine learning to automate categorization, routing, and recommendations.

PI Capabilities

Key Tables

Classification (ES5)

Configure Classification Solution

// Create classification solution (ES5 ONLY!)
// Note: Usually done via UI, shown for understanding

var solution = new GlideRecord('ml_solution');
solution.initialize();

solution.setValue('name', 'Incident Category Classifier');
solution.setValue('label', 'Incident Category Classifier');
solution.setValue('table', 'incident');
solution.setValue('active', true);

// Capability type
solution.setValue('capability', 'classification');

// Target field to predict
solution.setValue('target_field', 'category');

// Input fields for training
solution.setValue('input_fields', 'short_description,description');

solution.insert();

Get Classification Prediction

// Get classification prediction for record (ES5 ONLY!)
function getClassificationPrediction(tableName, recordSysId, solutionName) {
    var predictor = new sn_ml.ClassificationPredictor(solutionName);

    var gr = new GlideRecord(tableName);
    if (!gr.get(recordSysId)) {
        return null;
    }

    try {
        var result = predictor.predict(gr);

        return {
            predicted_value: result.getPredictedValue(),
            confidence: result.getConfidence(),
            top_predictions: result.getTopPredictions(5)
        };
    } catch (e) {
        gs.error('Prediction failed: ' + e.message);
        return null;
    }
}

Apply Prediction to Record

// Auto-apply classification prediction (ES5 ONLY!)
// Business Rule: before, insert, incident

(function executeRule(current, previous) {
    // Skip if already categorized
    if (current.category) {
        return;
    }

    var solutionName = 'incident_category_classifier';

    try {
        var predictor = new sn_ml.ClassificationPredictor(solutionName);
        var result = predictor.predict(current);

        // Only apply if confidence is high enough
        if (result.getConfidence() >= 0.8) {
            current.category = result.getPredictedValue();
            current.work_notes = 'Category auto-assigned by Predictive Intelligence ' +
                                '(Confidence: ' + Math.round(result.getConfidence() * 100) + '%)';
        }
    } catch (e) {
        gs.warn('Classification prediction failed: ' + e.message);
    }
})(current, previous);

Similarity (ES5)

Find Similar Records

// Find similar incidents (ES5 ONLY!)
function findSimilarIncidents(incidentSysId, maxResults) {
    maxResults = maxResults || 5;

    var incident = new GlideRecord('incident');
    if (!incident.get(incidentSysId)) {
        return [];
    }

    try {
        var similarity = new sn_ml.SimilarityPredictor('incident_similarity');
        var results = similarity.findSimilar(incident, maxResults);

        var similar = [];
        for (var i = 0; i < results.length; i++) {
            var match = results[i];
            similar.push({
                sys_id: match.getRecordSysId(),
                similarity_score: match.getSimilarityScore(),
                record: match.getRecord()
            });
        }

        return similar;
    } catch (e) {
        gs.error('Similarity search failed: ' + e.message);
        return [];
    }
}

Similar Record Widget

// Widget Server Script for similar records (ES5 ONLY!)
(function() {
    if (!input || !input.table || !input.sys_id) {
        data.similar = [];
        return;
    }

    var solutionName = input.table + '_similarity';

    try {
        var gr = new GlideRecord(input.table);
        if (!gr.get(input.sys_id)) {
            data.similar = [];
            return;
        }

        var similarity = new sn_ml.SimilarityPredictor(solutionName);
        var results = similarity.findSimilar(gr, 5);

        data.similar = [];
        for (var i = 0; i < results.length; i++) {
            var match = results[i];
            var record = match.getRecord();

            data.similar.push({
                sys_id: match.getRecordSysId(),
                score: Math.round(match.getSimilarityScore() * 100),
                number: record.getValue('number'),
                short_description: record.getValue('short_description'),
                state: record.state.getDisplayValue()
            });
        }
    } catch (e) {
        data.error = 'Similarity search unavailable';
        data.similar = [];
    }
})();

Clustering (ES5)

Get Cluster Assignment

// Get cluster for record (ES5 ONLY!)
function getClusterAssignment(tableName, recordSysId, solutionName) {
    var gr = new GlideRecord(tableName);
    if (!gr.get(recordSysId)) {
        return null;
    }

    try {
        var clustering = new sn_ml.ClusteringPredictor(solutionName);
        var result = clustering.predict(gr);

        return {
            cluster_id: result.getClusterId(),
            cluster_label: result.getClusterLabel(),
            confidence: result.getConfidence()
        };
    } catch (e) {
        gs.error('Clustering failed: ' + e.message);
        return null;
    }
}

Analyze Clusters

// Get cluster statistics (ES5 ONLY!)
function getClusterStats(solutionName) {
    var stats = [];

    var cluster = new GlideRecord('ml_cluster');
    cluster.addQuery('solution.name', solutionName);
    cluster.query();

    while (cluster.next()) {
        stats.push({
            cluster_id: cluster.getValue('cluster_id'),
            label: cluster.getValue('label'),
            size: parseInt(cluster.getValue('record_count'), 10),
            keywords: cluster.getValue('keywords')
        });
    }

    return stats;
}

Training Models (ES5)

Trigger Model Training

// Trigger retraining of ML solution (ES5 ONLY!)
function retrainSolution(solutionName) {
    var solution = new GlideRecord('ml_solution');
    if (!solution.get('name', solutionName)) {
        gs.error('Solution not found: ' + solutionName);
        return false;
    }

    try {
        // Queue training job
        var trainer = new sn_ml.MLTrainer();
        trainer.train(solution.getUniqueValue());

        gs.info('Training queued for solution: ' + solutionName);
        return true;
    } catch (e) {
        gs.error('Training failed: ' + e.message);
        return false;
    }
}

Check Training Status

// Check model training status (ES5 ONLY!)
function getTrainingStatus(solutionName) {
    var model = new GlideRecord('ml_model');
    model.addQuery('solution.name', solutionName);
    model.orderByDesc('sys_created_on');
    model.setLimit(1);
    model.query();

    if (model.next()) {
        return {
            model_id: model.getUniqueValue(),
            status: model.getValue('state'),
            accuracy: model.getValue('accuracy'),
            trained_on: model.getValue('sys_created_on'),
            record_count: model.getValue('training_record_count')
        };
    }

    return null;
}

Prediction Results (ES5)

Store Prediction Feedback

// Record prediction feedback for model improvement (ES5 ONLY!)
function recordPredictionFeedback(predictionSysId, wasCorrect, actualValue) {
    var prediction = new GlideRecord('ml_prediction_result');
    if (!prediction.get(predictionSysId)) {
        return false;
    }

    prediction.setValue('feedback', wasCorrect ? 'correct' : 'incorrect');
    prediction.setValue('actual_value', actualValue);
    prediction.setValue('feedback_date', new GlideDateTime());
    prediction.setValue('feedback_user', gs.getUserID());

    prediction.update();

    return true;
}

Analyze Prediction Accuracy

// Get prediction accuracy stats (ES5 ONLY!)
function getPredictionAccuracy(solutionName, days) {
    days = days || 30;

    var startDate = new GlideDateTime();
    startDate.addDaysLocalTime(-days);

    var ga = new GlideAggregate('ml_prediction_result');
    ga.addQuery('solution.name', solutionName);
    ga.addQuery('sys_created_on', '>=', startDate);
    ga.addNotNullQuery('feedback');
    ga.addAggregate('COUNT');
    ga.groupBy('feedback');
    ga.query();

    var stats = { correct: 0, incorrect: 0 };

    while (ga.next()) {
        var feedback = ga.getValue('feedback');
        var count = parseInt(ga.getAggregate('COUNT'), 10);
        stats[feedback] = count;
    }

    var total = stats.correct + stats.incorrect;
    stats.accuracy = total > 0 ? Math.round((stats.correct / total) * 100) : 0;
    stats.total = total;

    return stats;
}

MCP Tool Integration

Available Tools

Example Workflow

// 1. Query ML solutions
await snow_query_table({
    table: 'ml_solution',
    query: 'active=true',
    fields: 'name,table,capability,target_field'
});

// 2. Check model status
await snow_query_table({
    table: 'ml_model',
    query: 'solution.active=true',
    fields: 'solution,state,accuracy,sys_created_on'
});

// 3. Test prediction
await snow_execute_script_with_output({
    script: `
        var result = getClassificationPrediction('incident', 'inc_sys_id', 'incident_classifier');
        gs.info(JSON.stringify(result));
    `
});

Best Practices

1. Quality Data – Clean training data is essential
2. Feature Selection – Choose relevant input fields
3. Confidence Thresholds – Only apply high-confidence predictions
4. Feedback Loop – Collect user feedback
5. Regular Retraining – Update models periodically
6. Monitor Accuracy – Track prediction performance
7. Fallback – Have manual process when prediction fails
8. ES5 Only – No modern JavaScript syntax