AI Tools for the Administration of Gift and Reward Card Programs

In the swiftly evolving landscape of eCommerce, we're innovating our gift and reward card programs to harness the power of AI, reducing manual administrative burdens and enhancing operational efficiency. Our initiatives focus on creating AI-driven tools that automate and optimize various aspects of gift card administration, from reconciliation to inventory management. Below, we outline several tools under development, aimed at revolutionizing the management of gift and reward card systems.

AI-Enhanced Reconciliation and Activation Automation

We are developing an AI-powered reconciliation system that automates the process of matching gift card transactions with sales records. Utilizing machine learning algorithms, these tools will automatically identify discrepancies between sales data and gift card activations, enabling quicker and more accurate balancing of accounts. This system is set to drastically reduce the time spent on manual checks, proposing corrections and reconciling errors in near real-time.

AI-Powered Reconciliation Service

Our .NET reconciliation service uses ML.NET to automatically match transactions and identify discrepancies:

using Microsoft.ML;
using Microsoft.ML.Data;
using System.Text.Json;

namespace eGifter.Administration.Services
{
    public class ReconciliationData
    {
        [LoadColumn(0)] public float TransactionAmount { get; set; }
        [LoadColumn(1)] public float ExpectedAmount { get; set; }
        [LoadColumn(2)] public float TimeDifference { get; set; }
        [LoadColumn(3)] public float LocationMatch { get; set; }
        [LoadColumn(4)] public float DeviceMatch { get; set; }
        [LoadColumn(5)] public float UserPatternMatch { get; set; }
        [LoadColumn(6)] public float IsReconciled { get; set; }
    }

    public class ReconciliationPrediction
    {
        [VectorType(2)]
        public float[] Prediction { get; set; }
    }

    public class ReconciliationService : IReconciliationService
    {
        private readonly MLContext _mlContext;
        private readonly ITransformer _model;
        private readonly PredictionEngine<ReconciliationData, ReconciliationPrediction> _predictionEngine;
        private readonly ILogger<ReconciliationService> _logger;
        private readonly ITransactionRepository _transactionRepo;
        private readonly ISalesRepository _salesRepo;

        public ReconciliationService(
            ILogger<ReconciliationService> logger,
            ITransactionRepository transactionRepo,
            ISalesRepository salesRepo)
        {
            _mlContext = new MLContext(seed: 42);
            _logger = logger;
            _transactionRepo = transactionRepo;
            _salesRepo = salesRepo;
            
            // Load pre-trained reconciliation model
            _model = _mlContext.Model.Load("Models/reconciliation_model.zip", out var _);
            _predictionEngine = _mlContext.Model.CreatePredictionEngine<ReconciliationData, ReconciliationPrediction>(_model);
        }

        public async Task<ReconciliationResult> ReconcileTransactionsAsync(DateTime startDate, DateTime endDate)
        {
            var transactions = await _transactionRepo.GetTransactionsAsync(startDate, endDate);
            var salesRecords = await _salesRepo.GetSalesAsync(startDate, endDate);
            
            var reconciliationItems = new List<ReconciliationItem>();
            var discrepancies = new List<Discrepancy>();

            foreach (var transaction in transactions)
            {
                var matchingSales = await FindMatchingSales(transaction, salesRecords);
                
                if (matchingSales.Any())
                {
                    var bestMatch = await SelectBestMatch(transaction, matchingSales);
                    var reconciliationData = CreateReconciliationData(transaction, bestMatch);
                    var prediction = _predictionEngine.Predict(reconciliationData);
                    
                    var confidence = prediction.Prediction[0];
                    var isReconciled = confidence > 0.8f;

                    if (isReconciled)
                    {
                        reconciliationItems.Add(new ReconciliationItem
                        {
                            TransactionId = transaction.Id,
                            SalesId = bestMatch.Id,
                            Confidence = confidence,
                            Status = ReconciliationStatus.Matched,
                            Timestamp = DateTime.UtcNow
                        });
                    }
                    else
                    {
                        discrepancies.Add(new Discrepancy
                        {
                            TransactionId = transaction.Id,
                            ExpectedAmount = bestMatch.Amount,
                            ActualAmount = transaction.Amount,
                            Confidence = confidence,
                            SuggestedAction = await GenerateCorrectionSuggestion(transaction, bestMatch),
                            Timestamp = DateTime.UtcNow
                        });
                    }
                }
                else
                {
                    discrepancies.Add(new Discrepancy
                    {
                        TransactionId = transaction.Id,
                        ExpectedAmount = 0,
                        ActualAmount = transaction.Amount,
                        Confidence = 0,
                        SuggestedAction = "No matching sales record found",
                        Timestamp = DateTime.UtcNow
                    });
                }
            }

            var result = new ReconciliationResult
            {
                StartDate = startDate,
                EndDate = endDate,
                TotalTransactions = transactions.Count,
                ReconciledCount = reconciliationItems.Count,
                DiscrepancyCount = discrepancies.Count,
                ReconciliationItems = reconciliationItems,
                Discrepancies = discrepancies,
                Accuracy = CalculateAccuracy(reconciliationItems, discrepancies)
            };

            await SaveReconciliationResultAsync(result);
            return result;
        }

        private async Task<List<SalesRecord>> FindMatchingSales(Transaction transaction, List<SalesRecord> salesRecords)
        {
            return salesRecords
                .Where(s => Math.Abs(s.Amount - transaction.Amount) < 0.01m)
                .Where(s => Math.Abs((s.Timestamp - transaction.Timestamp).TotalMinutes) < 30)
                .Where(s => s.LocationId == transaction.LocationId)
                .ToList();
        }

        private async Task<SalesRecord> SelectBestMatch(Transaction transaction, List<SalesRecord> salesRecords)
        {
            // Use AI to select the best match based on multiple factors
            var bestMatch = salesRecords
                .Select(s => new
                {
                    SalesRecord = s,
                    Score = CalculateMatchScore(transaction, s)
                })
                .OrderByDescending(x => x.Score)
                .First();

            return bestMatch.SalesRecord;
        }

        private float CalculateMatchScore(Transaction transaction, SalesRecord salesRecord)
        {
            var timeScore = 1.0f - Math.Min(Math.Abs((salesRecord.Timestamp - transaction.Timestamp).TotalMinutes) / 30.0f, 1.0f);
            var amountScore = 1.0f - Math.Min(Math.Abs((float)(salesRecord.Amount - transaction.Amount)) / 10.0f, 1.0f);
            var locationScore = salesRecord.LocationId == transaction.LocationId ? 1.0f : 0.0f;
            
            return (timeScore * 0.4f) + (amountScore * 0.4f) + (locationScore * 0.2f);
        }

        private ReconciliationData CreateReconciliationData(Transaction transaction, SalesRecord salesRecord)
        {
            return new ReconciliationData
            {
                TransactionAmount = (float)transaction.Amount,
                ExpectedAmount = (float)salesRecord.Amount,
                TimeDifference = (float)Math.Abs((salesRecord.Timestamp - transaction.Timestamp).TotalMinutes),
                LocationMatch = salesRecord.LocationId == transaction.LocationId ? 1.0f : 0.0f,
                DeviceMatch = await CalculateDeviceMatch(transaction, salesRecord),
                UserPatternMatch = await CalculateUserPatternMatch(transaction, salesRecord)
            };
        }

        private async Task<string> GenerateCorrectionSuggestion(Transaction transaction, SalesRecord salesRecord)
        {
            var difference = Math.Abs(transaction.Amount - salesRecord.Amount);
            
            if (difference < 0.01m)
            {
                return "Minor rounding difference - likely acceptable";
            }
            else if (difference < 1.00m)
            {
                return "Small discrepancy - review for tax or fee differences";
            }
            else
            {
                return "Significant discrepancy - manual review required";
            }
        }

        private float CalculateAccuracy(List<ReconciliationItem> reconciled, List<Discrepancy> discrepancies)
        {
            var total = reconciled.Count + discrepancies.Count;
            return total > 0 ? (float)reconciled.Count / total * 100 : 0;
        }
    }
}

Mermaid Diagram for Process Flow:

Inventory Optimization with Predictive Analytics

For managing physical gift card inventories at sale points, we are leveraging AI to provide insights into sales trends and optimize stock levels. Our systems deploy predictive analytics based on historical sales data, considering seasonal patterns and retail week variations to forecast demand accurately. This empowers retailers to optimize their inventory, reducing overstocking and stockouts and ensuring just-in-time replenishment.

Inventory Management Dashboard with Predictive Analytics

Our Vue.js dashboard provides real-time inventory insights and AI-powered demand forecasting:

<template>
  <div class="inventory-management-dashboard">
    <div class="inventory-overview">
      <h3>Inventory Overview</h3>
      <div class="metrics-grid">
        <div class="metric">
          <span class="metric-value">{{ inventoryMetrics.totalCards }}</span>
          <span class="metric-label">Total Cards</span>
        </div>
        <div class="metric">
          <span class="metric-value">{{ inventoryMetrics.lowStock }}</span>
          <span class="metric-label">Low Stock Items</span>
        </div>
        <div class="metric">
          <span class="metric-value">{{ inventoryMetrics.reorderAlerts }}</span>
          <span class="metric-label">Reorder Alerts</span>
        </div>
        <div class="metric">
          <span class="metric-value">{{ inventoryMetrics.forecastAccuracy }}%</span>
          <span class="metric-label">Forecast Accuracy</span>
        </div>
      </div>
    </div>

    <div class="inventory-items">
      <h3>Inventory Items</h3>
      <div class="filters">
        <input v-model="searchTerm" placeholder="Search cards..." class="search-input" />
        <select v-model="selectedCategory" class="category-filter">
          <option value="">All Categories</option>
          <option value="restaurant">Restaurant</option>
          <option value="retail">Retail</option>
          <option value="entertainment">Entertainment</option>
        </select>
        <select v-model="stockFilter" class="stock-filter">
          <option value="">All Stock Levels</option>
          <option value="low">Low Stock</option>
          <option value="normal">Normal Stock</option>
          <option value="high">High Stock</option>
        </select>
      </div>

      <div class="items-table">
        <table>
          <thead>
            <tr>
              <th>Card Name</th>
              <th>Category</th>
              <th>Current Stock</th>
              <th>AI Forecast</th>
              <th>Reorder Point</th>
              <th>Status</th>
              <th>Actions</th>
            </tr>
          </thead>
          <tbody>
            <tr v-for="item in filteredItems" :key="item.id" 
                :class="getRowClass(item)">
              <td>{{ item.name }}</td>
              <td>{{ item.category }}</td>
              <td>
                <span :class="getStockClass(item.currentStock, item.reorderPoint)">
                  {{ item.currentStock }}
                </span>
              </td>
              <td>
                <div class="forecast-info">
                  <span class="forecast-value">{{ item.aiForecast }}</span>
                  <span class="forecast-confidence" 
                        :class="getConfidenceClass(item.forecastConfidence)">
                    {{ (item.forecastConfidence * 100).toFixed(0) }}%
                  </span>
                </div>
              </td>
              <td>{{ item.reorderPoint }}</td>
              <td>
                <span :class="`status-${item.status}`">
                  {{ item.status }}
                </span>
              </td>
              <td>
                <button @click="viewDetails(item.id)" class="btn-details">
                  Details
                </button>
                <button v-if="item.status === 'low_stock'" 
                        @click="createOrder(item.id)" 
                        class="btn-order">
                  Order
                </button>
              </td>
            </tr>
          </tbody>
        </table>
      </div>
    </div>

    <div class="predictive-analytics">
      <h3>Predictive Analytics</h3>
      <div class="analytics-charts">
        <div class="chart-container">
          <h4>Demand Forecast (Next 30 Days)</h4>
          <canvas ref="demandChart" width="400" height="200"></canvas>
        </div>
        <div class="chart-container">
          <h4>Seasonal Trends</h4>
          <canvas ref="seasonalChart" width="400" height="200"></canvas>
        </div>
      </div>
    </div>
  </div>
</template>

<script>
import { ref, computed, onMounted, watch } from 'vue'
import { Chart, registerables } from 'chart.js'
import { useInventoryManagement } from '@/composables/inventoryManagement'

Chart.register(...registerables)

export default {
  name: 'InventoryManagementDashboard',
  setup() {
    const { 
      inventoryItems, 
      inventoryMetrics, 
      demandForecast, 
      seasonalTrends,
      viewDetails, 
      createOrder,
      startMonitoring 
    } = useInventoryManagement()

    const searchTerm = ref('')
    const selectedCategory = ref('')
    const stockFilter = ref('')
    const demandChart = ref(null)
    const seasonalChart = ref(null)

    const filteredItems = computed(() => {
      let filtered = inventoryItems.value

      if (searchTerm.value) {
        filtered = filtered.filter(item => 
          item.name.toLowerCase().includes(searchTerm.value.toLowerCase())
        )
      }

      if (selectedCategory.value) {
        filtered = filtered.filter(item => item.category === selectedCategory.value)
      }

      if (stockFilter.value) {
        filtered = filtered.filter(item => {
          const stockLevel = getStockLevel(item.currentStock, item.reorderPoint)
          return stockLevel === stockFilter.value
        })
      }

      return filtered
    })

    const getStockLevel = (currentStock, reorderPoint) => {
      if (currentStock <= reorderPoint * 0.5) return 'low'
      if (currentStock <= reorderPoint) return 'normal'
      return 'high'
    }

    const getRowClass = (item) => {
      const stockLevel = getStockLevel(item.currentStock, item.reorderPoint)
      return `stock-${stockLevel}`
    }

    const getStockClass = (currentStock, reorderPoint) => {
      if (currentStock <= reorderPoint * 0.5) return 'stock-critical'
      if (currentStock <= reorderPoint) return 'stock-low'
      return 'stock-normal'
    }

    const getConfidenceClass = (confidence) => {
      if (confidence > 0.8) return 'confidence-high'
      if (confidence > 0.6) return 'confidence-medium'
      return 'confidence-low'
    }

    const initializeCharts = () => {
      // Demand Forecast Chart
      const demandCtx = demandChart.value.getContext('2d')
      new Chart(demandCtx, {
        type: 'line',
        data: {
          labels: demandForecast.value.labels,
          datasets: [{
            label: 'Predicted Demand',
            data: demandForecast.value.data,
            borderColor: 'rgb(75, 192, 192)',
            backgroundColor: 'rgba(75, 192, 192, 0.2)',
            tension: 0.1
          }]
        },
        options: {
          responsive: true,
          plugins: {
            title: {
              display: true,
              text: 'AI-Powered Demand Forecast'
            }
          }
        }
      })

      // Seasonal Trends Chart
      const seasonalCtx = seasonalChart.value.getContext('2d')
      new Chart(seasonalCtx, {
        type: 'bar',
        data: {
          labels: seasonalTrends.value.labels,
          datasets: [{
            label: 'Seasonal Demand',
            data: seasonalTrends.value.data,
            backgroundColor: 'rgba(54, 162, 235, 0.8)'
          }]
        },
        options: {
          responsive: true,
          plugins: {
            title: {
              display: true,
              text: 'Seasonal Patterns'
            }
          }
        }
      })
    }

    onMounted(() => {
      startMonitoring()
      initializeCharts()
    })

    return {
      inventoryItems,
      inventoryMetrics,
      searchTerm,
      selectedCategory,
      stockFilter,
      filteredItems,
      demandChart,
      seasonalChart,
      viewDetails,
      createOrder,
      getRowClass,
      getStockClass,
      getConfidenceClass
    }
  }
}
</script>

The corresponding composable handles AI-powered inventory predictions:

// composables/inventoryManagement.js
import { ref, reactive } from 'vue'
import { useWebSocket } from '@/composables/websocket'

export function useInventoryManagement() {
  const inventoryItems = ref([])
  const inventoryMetrics = reactive({
    totalCards: 0,
    lowStock: 0,
    reorderAlerts: 0,
    forecastAccuracy: 0
  })
  
  const demandForecast = ref({
    labels: [],
    data: []
  })
  
  const seasonalTrends = ref({
    labels: [],
    data: []
  })

  const { connect, disconnect, subscribe } = useWebSocket()

  const startMonitoring = () => {
    connect('wss://api.egifter.com/inventory-management')
    
    subscribe('inventory-update', (items) => {
      inventoryItems.value = items
      updateMetrics()
    })

    subscribe('inventory-metrics', (metrics) => {
      Object.assign(inventoryMetrics, metrics)
    })

    subscribe('demand-forecast', (forecast) => {
      demandForecast.value = forecast
    })

    subscribe('seasonal-trends', (trends) => {
      seasonalTrends.value = trends
    })
  }

  const updateMetrics = () => {
    inventoryMetrics.totalCards = inventoryItems.value.length
    inventoryMetrics.lowStock = inventoryItems.value.filter(item => 
      item.status === 'low_stock'
    ).length
    inventoryMetrics.reorderAlerts = inventoryItems.value.filter(item => 
      item.status === 'reorder_alert'
    ).length
  }

  const viewDetails = async (itemId) => {
    const response = await fetch(`/api/inventory/${itemId}/details`)
    const details = await response.json()
    
    // Show detailed modal with AI insights
    showDetailsModal(details)
  }

  const createOrder = async (itemId) => {
    const response = await fetch('/api/inventory/orders', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ 
        itemId, 
        quantity: await calculateOptimalOrderQuantity(itemId)
      })
    })

    if (response.ok) {
      // Update inventory status
      const item = inventoryItems.value.find(i => i.id === itemId)
      if (item) {
        item.status = 'order_placed'
      }
    }
  }

  const calculateOptimalOrderQuantity = async (itemId) => {
    const response = await fetch(`/api/inventory/${itemId}/optimal-quantity`)
    const result = await response.json()
    return result.optimalQuantity
  }

  const showDetailsModal = (details) => {
    // Implementation for showing detailed AI insights modal
    console.log('Showing details for:', details)
  }

  return {
    inventoryItems,
    inventoryMetrics,
    demandForecast,
    seasonalTrends,
    startMonitoring,
    viewDetails,
    createOrder
  }
}

Automated Store Replenishment

AI tools are being crafted to monitor store-level inventory and automatically trigger replenishment alerts or orders. By continuously analyzing sales velocity and rate of depletion at the SKU level, these solutions support efficient stock management and minimize manual intervention in ordering processes. The algorithm is designed to alert administrators only when necessary, thereby optimizing response time and reducing the cognitive load on store managers.

AI-Driven Activation and Cancellation Processes

An integral part of our administration is streamlining the activation and cancellation of gift cards. By employing AI technologies, we aim to automate these processes based on trigger events (e.g., point-of-sale activation or online customer request). The system will independently validate request legitimacy, execute the necessary actions in milliseconds, and log the transaction securely for audit purposes.

Administrative Task Optimization

To handle mundane administrative tasks, our AI-driven automation tools will include features such as:

AI-Powered Task Scheduler Service

Our .NET service autonomously plans and schedules administrative tasks using AI prioritization:

using Microsoft.ML;
using Microsoft.ML.Data;
using Quartz;
using System.Text.Json;

namespace eGifter.Administration.Services
{
    public class TaskData
    {
        [LoadColumn(0)] public float Priority { get; set; }
        [LoadColumn(1)] public float Urgency { get; set; }
        [LoadColumn(2)] public float Complexity { get; set; }
        [LoadColumn(3)] public float BusinessImpact { get; set; }
        [LoadColumn(4)] public float ResourceAvailability { get; set; }
        [LoadColumn(5)] public float DeadlinePressure { get; set; }
        [LoadColumn(6)] public float OptimalSchedule { get; set; }
    }

    public class TaskPrediction
    {
        [VectorType(3)]
        public float[] Prediction { get; set; }
    }

    public class AdministrativeTask
    {
        public Guid Id { get; set; }
        public string Name { get; set; }
        public string Description { get; set; }
        public TaskType Type { get; set; }
        public TaskPriority Priority { get; set; }
        public DateTime CreatedAt { get; set; }
        public DateTime? DueDate { get; set; }
        public DateTime? ScheduledFor { get; set; }
        public TaskStatus Status { get; set; }
        public float AiPriorityScore { get; set; }
        public List<string> Dependencies { get; set; } = new();
        public Dictionary<string, object> Metadata { get; set; } = new();
    }

    public class AITaskSchedulerService : IJob, ITaskSchedulerService
    {
        private readonly MLContext _mlContext;
        private readonly ITransformer _model;
        private readonly PredictionEngine<TaskData, TaskPrediction> _predictionEngine;
        private readonly ILogger<AITaskSchedulerService> _logger;
        private readonly ISchedulerFactory _schedulerFactory;
        private readonly ITaskRepository _taskRepo;
        private readonly INotificationService _notificationService;

        public AITaskSchedulerService(
            ILogger<AITaskSchedulerService> logger,
            ISchedulerFactory schedulerFactory,
            ITaskRepository taskRepo,
            INotificationService notificationService)
        {
            _mlContext = new MLContext(seed: 42);
            _logger = logger;
            _schedulerFactory = schedulerFactory;
            _taskRepo = taskRepo;
            _notificationService = notificationService;
            
            // Load pre-trained task prioritization model
            _model = _mlContext.Model.Load("Models/task_prioritization_model.zip", out var _);
            _predictionEngine = _mlContext.Model.CreatePredictionEngine<TaskData, TaskPrediction>(_model);
        }

        public async Task Execute(IJobExecutionContext context)
        {
            await ProcessTaskQueueAsync();
        }

        public async Task ProcessTaskQueueAsync()
        {
            var pendingTasks = await _taskRepo.GetPendingTasksAsync();
            var prioritizedTasks = await PrioritizeTasksAsync(pendingTasks);
            var scheduledTasks = await ScheduleTasksAsync(prioritizedTasks);
            
            await UpdateTaskScheduleAsync(scheduledTasks);
            await SendSmartNotificationsAsync(scheduledTasks);
        }

        private async Task<List<AdministrativeTask>> PrioritizeTasksAsync(List<AdministrativeTask> tasks)
        {
            var prioritizedTasks = new List<AdministrativeTask>();

            foreach (var task in tasks)
            {
                var taskData = CreateTaskData(task);
                var prediction = _predictionEngine.Predict(taskData);
                
                task.AiPriorityScore = prediction.Prediction[0];
                prioritizedTasks.Add(task);
            }

            return prioritizedTasks
                .OrderByDescending(t => t.AiPriorityScore)
                .ThenBy(t => t.DueDate)
                .ToList();
        }

        private TaskData CreateTaskData(AdministrativeTask task)
        {
            var urgency = CalculateUrgency(task);
            var complexity = CalculateComplexity(task);
            var businessImpact = CalculateBusinessImpact(task);
            var resourceAvailability = await CalculateResourceAvailability(task);
            var deadlinePressure = CalculateDeadlinePressure(task);

            return new TaskData
            {
                Priority = (float)task.Priority,
                Urgency = urgency,
                Complexity = complexity,
                BusinessImpact = businessImpact,
                ResourceAvailability = resourceAvailability,
                DeadlinePressure = deadlinePressure
            };
        }

        private float CalculateUrgency(AdministrativeTask task)
        {
            if (!task.DueDate.HasValue) return 0.5f;
            
            var timeUntilDue = task.DueDate.Value - DateTime.UtcNow;
            var daysUntilDue = (float)timeUntilDue.TotalDays;
            
            if (daysUntilDue <= 0) return 1.0f;
            if (daysUntilDue <= 1) return 0.9f;
            if (daysUntilDue <= 3) return 0.7f;
            if (daysUntilDue <= 7) return 0.5f;
            
            return 0.3f;
        }

        private float CalculateComplexity(AdministrativeTask task)
        {
            return task.Type switch
            {
                TaskType.Reconciliation => 0.8f,
                TaskType.InventoryCheck => 0.4f,
                TaskType.ReportGeneration => 0.6f,
                TaskType.SystemMaintenance => 0.9f,
                TaskType.CustomerSupport => 0.5f,
                _ => 0.5f
            };
        }

        private float CalculateBusinessImpact(AdministrativeTask task)
        {
            return task.Type switch
            {
                TaskType.Reconciliation => 0.9f,
                TaskType.InventoryCheck => 0.7f,
                TaskType.ReportGeneration => 0.6f,
                TaskType.SystemMaintenance => 0.8f,
                TaskType.CustomerSupport => 0.8f,
                _ => 0.5f
            };
        }

        private async Task<float> CalculateResourceAvailability(AdministrativeTask task)
        {
            var availableResources = await GetAvailableResourcesAsync();
            var requiredResources = GetRequiredResources(task);
            
            var availabilityScore = 0.0f;
            foreach (var resource in requiredResources)
            {
                if (availableResources.Contains(resource))
                {
                    availabilityScore += 1.0f;
                }
            }
            
            return availabilityScore / requiredResources.Count;
        }

        private float CalculateDeadlinePressure(AdministrativeTask task)
        {
            if (!task.DueDate.HasValue) return 0.0f;
            
            var timeUntilDue = task.DueDate.Value - DateTime.UtcNow;
            var hoursUntilDue = (float)timeUntilDue.TotalHours;
            
            if (hoursUntilDue <= 0) return 1.0f;
            if (hoursUntilDue <= 2) return 0.9f;
            if (hoursUntilDue <= 24) return 0.7f;
            if (hoursUntilDue <= 72) return 0.5f;
            
            return 0.2f;
        }

        private async Task<List<AdministrativeTask>> ScheduleTasksAsync(List<AdministrativeTask> tasks)
        {
            var scheduledTasks = new List<AdministrativeTask>();
            var currentTime = DateTime.UtcNow;
            var availableSlots = await GetAvailableTimeSlotsAsync();

            foreach (var task in tasks)
            {
                var optimalSlot = await FindOptimalTimeSlot(task, availableSlots);
                
                if (optimalSlot.HasValue)
                {
                    task.ScheduledFor = optimalSlot.Value;
                    scheduledTasks.Add(task);
                    
                    // Reserve the time slot
                    await ReserveTimeSlotAsync(optimalSlot.Value, task.EstimatedDuration);
                }
                else
                {
                    // Schedule for next available slot
                    task.ScheduledFor = currentTime.AddHours(1);
                    scheduledTasks.Add(task);
                }
            }

            return scheduledTasks;
        }

        private async Task<DateTime?> FindOptimalTimeSlot(AdministrativeTask task, List<TimeSlot> availableSlots)
        {
            var taskData = CreateTaskData(task);
            var prediction = _predictionEngine.Predict(taskData);
            var optimalHour = prediction.Prediction[1] * 24; // Convert to hour of day
            
            var optimalSlots = availableSlots
                .Where(slot => Math.Abs(slot.StartTime.Hour - optimalHour) < 2)
                .OrderBy(slot => Math.Abs(slot.StartTime.Hour - optimalHour))
                .ToList();

            return optimalSlots.FirstOrDefault()?.StartTime;
        }

        private async Task SendSmartNotificationsAsync(List<AdministrativeTask> tasks)
        {
            var highPriorityTasks = tasks
                .Where(t => t.AiPriorityScore > 0.8f)
                .ToList();

            foreach (var task in highPriorityTasks)
            {
                var notification = new SmartNotification
                {
                    TaskId = task.Id,
                    TaskName = task.Name,
                    Priority = task.AiPriorityScore,
                    ScheduledFor = task.ScheduledFor,
                    RecommendedAction = await GenerateRecommendedAction(task),
                    Urgency = CalculateUrgency(task) > 0.8f ? "High" : "Medium"
                };

                await _notificationService.SendNotificationAsync(notification);
            }
        }

        private async Task<string> GenerateRecommendedAction(AdministrativeTask task)
        {
            var urgency = CalculateUrgency(task);
            var complexity = CalculateComplexity(task);

            if (urgency > 0.9f && complexity < 0.5f)
            {
                return "Execute immediately - low complexity, high urgency";
            }
            else if (urgency > 0.7f && complexity > 0.7f)
            {
                return "Review and delegate - high complexity, moderate urgency";
            }
            else if (task.Dependencies.Any())
            {
                return "Check dependencies first before proceeding";
            }
            else
            {
                return "Proceed as scheduled";
            }
        }
    }
}

• AI Task Scheduler: A tool that autonomously plans and schedules recurring administrative tasks based on priority and deadline, reducing human intervention.
• Smart Notifications: An AI-driven alert mechanism that informs administrators of pending tasks or exceptional situations requiring human oversight.

By integrating these AI-based tools, we are setting a new standard in the industry for how gift and reward card programs can be managed more efficiently and accurately, allowing administrators to focus on strategic growth rather than repetitive tasks. This strategic enhancement in our platform will not only reduce labor costs but will also elevate the customer experience by ensuring seamless and timely service.