Procurement has always been one of the oldest and yet critical business functions in an enterprise. It has witnessed numerous technological investments in terms of digital enhancement and transformation. However, the challenges that the Procurement function faced a few years ago still exist today, making it essential to use advanced spend analytics to generate better insights and decisions. AI-powered spend analytics outsourcing and consulting from Infosys BPM help procurement leaders unlock hidden value, improve compliance, and support strategic decision-making with trusted data and insights. To address these challenges, Infosys BPM has introduced TradeEdge, an enterprise-grade, AI-powered Spend analysis service that leverages spend data to help enterprises in their journey towards a holistic digital transformation.
The Infosys BPM Spend analysis service is designed to augment an enterprise’s procurement ecosystem with AI-enabled spend data management, spend analysis, and opportunity identification, as well as various leading domain services, to overcome business challenges.
Organizations typically engage Infosys BPM’s spend analytics services in high-impact scenarios such as:
In each case, the goal is to turn raw spend data into actionable intelligence that directly supports procurement strategy, supplier negotiations, and executive reporting.
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Case Study
Case Study
The following challenges and solutions reflect typical issues CPOs and procurement leaders face when scaling spend analytics outsourcing and consulting:
The core issue is fragmented, inconsistent data. An effective spend analytics program centralizes data from all source systems, applies ongoing cleansing and normalization, and maintains a governed “single source of truth” for suppliers, categories, and transactions. This allows procurement and finance to work from the same numbers and avoid manual reconciliation.
This usually happens when analytics stops at dashboards. Mature spend analytics turns patterns into prioritized opportunity pipelines such as consolidation, specification standardization, off-contract spend reduction, or risk signals and links them to sourcing events and category strategies. Stakeholders see not just what is happening, but what should be done next and in what order.
Manual rules and ad hoc taxonomies don’t scale. A more resilient approach combines machine learning-based classification with expert review of edge cases, using feedback loops to improve over time. This keeps classification accuracy high as the business changes, without needing to constantly grow internal analytics headcount.
Many programs focus only on value and miss non-financial dimensions. By integrating ESG attributes, risk scores, and policy rules into the spend model, analytics can highlight suppliers, categories, or regions that are misaligned with ESG or compliance requirements. Procurement can then use this insight to inform sourcing decisions, supplier development, and contract clauses.
The solution is to define KPIs and value tracking that link analytics-driven actions to outcomes such as savings, cost avoidance, compliance uplift, risk reduction, and working capital benefits. Regular reporting that connects specific initiatives to these metrics makes it clear how spend analytics is supporting strategic procurement and enterprise performance.
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