AI-Powered Application Portfolio Management: Using Intelligence to Optimise

Discover how AI and automation can transform application portfolio management—from automated discovery to intelligent recommendations for optimisation.

The Limits of Manual Portfolio Management

Why Manual Approaches Fall Short

Traditional application portfolio management relies heavily on manual data collection, spreadsheet-based analysis, and periodic reviews. This approach has significant limitations:

Data Collection Challenges:

• Manual inventories are always incomplete
• Usage data requires surveys (subjective and delayed)
• Cost data spread across multiple financial systems
• Integration mapping done through interviews
• Information becomes outdated quickly

Analysis Limitations:

• Spreadsheet analysis doesn't scale beyond 50-100 applications
• Scoring is subjective and inconsistent
• Trend detection requires comparing multiple point-in-time snapshots
• Cross-portfolio optimisation opportunities are easily missed
• Manual analysis can't process real-time data

How AI Transforms Portfolio Management

Automated Application Discovery

AI-Powered Discovery:

• Network traffic analysis using ML to identify SaaS usage patterns
• Expense report scanning to detect software purchases
• Email analysis to find vendor onboarding and renewal communications
• Browser extension monitoring to track web application usage
• API-based SSO and identity provider integration

Benefits:

• 95%+ discovery accuracy vs. 60-70% for manual inventory
• Real-time application landscape visibility
• Automatic detection of new shadow IT
• Continuous rather than periodic discovery

Intelligent Usage Analytics

AI-Enhanced Usage Tracking:

• Behavioural analysis of user interaction patterns
• Feature utilisation scoring beyond simple login tracking
• Productive vs. idle time differentiation
• Workflow completion analysis
• Cross-application usage correlation

Insights Generated:

• Applications with declining usage trends (retirement candidates)
• Users who would benefit from training (low feature utilisation)
• Peak usage patterns for license optimisation
• Overlapping usage across similar applications
• Department-level adoption variations

Predictive Cost Optimisation

AI-Driven Cost Analysis:

• License demand forecasting based on hiring and project plans
• Renewal price prediction based on market data
• Bundle optimisation recommendations
• Anomaly detection for unexpected cost spikes
• ROI prediction for migration and modernisation projects

Example Output: "Based on your usage trends, you can reduce your CRM licenses from 50 to 38 next quarter and save ₹1.2 lakh annually. Additionally, switching from monthly to annual billing on your project management tool and design software bundle would save ₹85,000."

Automated Scoring and Classification

AI-Assisted Assessment:

• Automatic business value scoring from usage patterns
• Technical health scoring from performance monitoring data
• Security risk scoring from vulnerability databases
• Vendor viability assessment from market intelligence
• Automated TIME classification with confidence levels

Advantages:

• Consistent scoring across the portfolio
• Real-time score updates as conditions change
• Objective data-driven classifications
• Reduced bias in decision-making

AI Tools for Portfolio Management

SaaS Management Platforms with AI

Enterprise Options:

• Zylo: AI-powered SaaS management and optimization
• Productiv: Application intelligence platform
• Torii: Automated SaaS lifecycle management
• Zluri: AI-driven SaaS management for mid-market
• Blissfully: SaaS management with workflow automation

SME-Friendly Options:

• Cledara: SaaS spend management with insights
• Intello: SaaS discovery and management
• Vendr: AI-powered SaaS buying and renewal
• NachoNacho: SaaS marketplace with management tools

Building Your Own AI-Assisted Analysis

Using Off-the-Shelf AI:

• Use ChatGPT/Claude to analyse exported usage data
• Build Power BI dashboards with AI-powered insights
• Create Google Sheets with AppScript for automated analysis
• Use Python with ML libraries for pattern detection
• Leverage Looker Studio with BigQuery for data analysis

Data Sources to Connect:

• SSO provider API (user and application data)
• Financial system API (cost data)
• Cloud provider APIs (infrastructure costs)
• Vendor APIs (usage and license data)
• Network monitoring tools (traffic data)

Implementing AI-Assisted Portfolio Management

Phase 1: Data Foundation (Month 1)

• Identify and connect data sources
• Establish automated data collection pipelines
• Clean and normalise historical data
• Define key metrics and scoring models
• Select or build analysis tools

Phase 2: Intelligence Layer (Month 2)

• Implement automated discovery and tracking
• Build scoring and classification models
• Create dashboards with AI-powered insights
• Set up anomaly detection and alerting
• Generate initial recommendations

Phase 3: Action and Automation (Month 3+)

• Automate routine optimisation actions
• Implement recommendation workflows
• Set up continuous monitoring and improvement
• Expand AI coverage to new data sources
• Refine models based on outcomes

Getting Started Without Enterprise Tools

Even without expensive AI platforms, SMEs can leverage intelligence:

• [ ] Export all application data to a single spreadsheet
• [ ] Use AI assistants to analyse patterns and suggest optimisation
• [ ] Set up Google Alerts for vendor news and pricing changes
• [ ] Create automated usage reports from key applications
• [ ] Build a simple scoring model and automate with formulas
• [ ] Use free tiers of SaaS management tools for initial discovery

AI-powered portfolio management isn't just for enterprises. By starting with readily available AI tools and building toward more sophisticated analysis, SMEs can gain insights that would be impossible through manual analysis alone.