Listen to this blog

The Growing Need for Agentic AI for IT Spend Classification

Did you know? 74% of IT purchase orders lack proper cost category codes, leaving 157,000 transactions unclassified in a single CPG firm, forcing project managers to manually clean up spend data.

Now, imagine the scale of misclassified IT spend across hundreds of invoices, multiple vendors, and thousands of transactions—the financial impact is staggering.

IT Procurement Complexity is Increasing

Gone are the days when IT procurement was simply about buying hardware and software licenses. Today, organizations manage a vast ecosystem of technology investments, including:

Cloud services (AWS, Azure, Google Cloud)
SaaS subscriptions (Salesforce, Microsoft 365, Zoom)
IT consulting & professional services
Cybersecurity solutions & compliance software
Managed IT services & infrastructure outsourcing

As companies accelerate digital transformation, tracking, classifying, and optimizing IT spend has become a nightmare for procurement teams.

The Problem?

Traditional IT spend classification methods rely on:

Manual tagging and data entry— time-consuming, inconsistent, and prone to human error.
Rule-based classification systems— rigid frameworks that fail to adapt to rapidly evolving IT services.
Inconsistent spend descriptions— leading to misallocated budgets, inaccurate analytics, and poor supplier selection.

Why this Matters?

Without accurate IT spend classification, organizations:

Overpay for redundant services due to misclassified contracts.
Struggle to track IT costs across software licenses, cloud subscriptions, and consulting services.
Lack compliance visibility, increasing exposure to regulatory fines and non-compliance risks.

The AI-Powered Shift

Traditional methods simply can’t keep up with the sheer volume, complexity, and velocity of IT spending. Agentic AI changes the game by automating classification, learning from spend data, and reducing misclassification errors. Organizations leveraging AI can increase procurement efficiency, improve compliance, and drive cost savings—all while eliminating tedious manual work.
Download Whitepaper: Spend Data Classification: Making sense of Data

The Real Problem: Why Traditional IT Spend Classification Fails

Despite the rapid evolution of IT procurement, most organizations still rely on outdated classification methods. A leading industrial supplier needed 1,209 person-days to classify 145,000 product SKUs manually—a costly, time-consuming process that AI later reduced to just 20 days, saving $217,000.
For enterprises managing thousands of invoices across cloud services, SaaS, IT consulting, and cybersecurity, misclassification leads to overspending, compliance risks, and financial reporting inaccuracies.

1. Manual & Rule-Based Classification Is Slow and Error-Prone

Manual classification takes over 4 minutes per entry, making it impossible for procurement teams to scale. Worse, inconsistent tagging—where one service is labeled “cloud hosting” in one invoice and “IT infrastructure” in another—creates budget mismatches and unreliable analytics.
Companies processing thousands of IT invoices manually waste weeks, as procurement teams are forced to review and correct classification errors instead of focusing on strategic cost optimization.

2. Hidden Costs & Inaccurate Budgeting

Without standardized classification, organizations frequently overpay for redundant services. Untracked SaaS renewals, misclassified IT consulting fees, and duplicate cloud subscriptions result in millions lost to inefficiencies.
In one case, an AI-driven classification system helped an enterprise identify and eliminate redundant IT purchases, leading to $1.8 million in savings.

3. Poor Spend Visibility and Compliance Risks

Without real-time categorization, procurement teams struggle to monitor IT spend across different service types, leading to inaccurate financial reporting and compliance challenges.
Misclassified software licenses and cloud expenditures often violate contract terms and regulatory requirements, exposing organizations to GDPR, SOC 2, or ISO 27001 non-compliance risks.

Deutsche Telekom addressed this issue by leveraging AI-powered spend analytics to enhance supplier risk monitoring and ensure contract alignment.

4. Agenctic AI vs. Traditional IT Spend Classification: A Comparative Analysis

The table below highlights the key differences between traditional IT spend classification methods and AI-driven classification, showing why enterprises are increasingly shifting to Agentic AI for IT spend management.

Feature	Traditional Methods	AI-Driven Classification
Speed	4+ minutes per entry, weeks for large datasets	Processes thousands of transactions per second
Scalability	Requires manual rule updates, limited by human effort	Learns and adapts to new IT services dynamically
Accuracy	Prone to misclassification errors	90–99% accuracy with continuous improvement
Cost Efficiency	~$2 per classified item	AI reduces classification costs by up to 60%
Insights & Adaptability	Fixed rules, requires frequent adjustments	AI continuously refines classifications with real-time data

Companies can move beyond basic cost tracking toward a predictive, AI-enhanced procurement model that proactively prevents excess spending by leveraging these components.

Overcoming Implementation Challenges

While automating spend control delivers measurable business benefits, Gartner research highlights common challenges that enterprises must navigate:

Change Management Resistance: Employees accustomed to legacy processes may hesitate to embrace automation.
Upfront Technology Investment: Initial costs for AI-driven procurement tools can be perceived as prohibitive without a clear ROI strategy.
Data Integration Complexity: Connecting spend control systems to legacy ERP platforms requires a structured implementation roadmap.

Best Practices for a Smooth Transition

Below are some of the hurdles that Companies should proactively address to realize long-term cost efficiencies and improved financial governance:

Secure Stakeholder Buy-In: Clearly communicate the value proposition of automation to procurement teams, finance leaders, and IT stakeholders.
Adopt a Phased Implementation Approach: Start with high-impact areas (e.g., invoice processing, approvals) before scaling to full spend automation.
Leverage AI for Continuous Improvement: Utilize machine learning insights to refine spend control strategies over time.

Traditional manual and rule-based classification struggles to keep up with growing IT spend complexities, leading to misclassified expenses, budget discrepancies, and compliance risks.

AI-powered classification, on the other hand, automates categorization, eliminates human errors, and delivers real-time spend visibility, allowing enterprises to optimize procurement costs and improve financial accuracy.

5. How Agentic AI is Revolutionizing IT Spend Classification with Zycus

IT procurement is growing more complex, with thousands of transactions across SaaS, cloud, cybersecurity, and IT services flowing through procurement teams. Manually tracking and categorizing this spend is no longer sustainable.

Zycus’ Agentic AI, powered by Merlin AI, is transforming IT spend classification by eliminating human effort, enforcing compliance, and enabling predictive analytics. But AI isn’t just about automation—it’s about enhancing procurement intelligence and empowering teams with real-time insights to make smarter financial decisions.

Here’s how Zycus’ AI-powered capabilities deliver real impact:

AI-Driven Spend Categorization with Merlin AI

What it does: Uses Natural Language Processing (NLP) to automatically classify IT spend, recognizing cloud billing models, SaaS renewals, and consulting fees.
Why it matters: Procurement teams can interact with AI in Microsoft Teams—simply ask, “How much did we spend on cybersecurity last quarter?” and get an instant answer.

Zycus Tool: iAnalyze – AI-driven spend insights for real-time tracking and enrichment.

AI-Driven Compliance & Risk Mitigation

What it does: AI flags non-compliant IT expenses, ensuring alignment with regulatory standards like GDPR, SOC 2, and ISO 27001.
Why it matters: Prevents contract breaches and financial penalties by identifying vendor billing errors and unauthorized renewals.

Zycus Tool: GenAI for IT Spend Data Management– Automated invoice validation and policy enforcement.

Predictive Spend Analytics for Smarter Budgeting

What it does: AI forecasts future IT costs, identifying duplicate SaaS subscriptions, underutilized cloud services, and spend surges.
Why it matters: Helps procurement teams preemptively adjust budgets and renegotiate contracts before price hikes.

Zycus Tool: Merlin AI Studio – Autonomous spend optimization with AI-driven forecasting.

AI-Powered Vendor Benchmarking & Procurement Optimization

What it does: Compares IT vendors based on pricing models, contract terms, and performance data.
Why it matters: Enables data-backed vendor selection and better negotiations, reducing IT procurement costs.

Zycus Tool: Zycus Supplier Network (ZSN) – AI-driven benchmarking for smarter sourcing decisions.

Conclusion: AI is the Future of IT Spend Classification

Traditional IT spend classification fails to keep up with the complexity and scale of modern IT procurement. Manual errors, misclassified expenses, and compliance risks continue to cost organizations valuable time and money. Enterprises that have embraced AI-driven classification are already seeing measurable benefits—from significant cost savings to streamlined procurement workflows.

The Future of IT Spend Management is AI-Driven

AI ensures real-time classification, accurate spend tracking, and proactive risk mitigation—allowing procurement teams to focus on strategy instead of manual data cleanup. Businesses that adopt AI today will lead in efficiency, cost savings, and compliance excellence.

The question is no longer if AI should be part of IT spend classification but how soon organizations can leverage it to stay ahead. Book a demo!

Related Reads: