AI-Powered Composable ERP: The Future of Agile Business Operations

AI-Powered Composable ERP: Supercharging Agility with LLMs

Composable ERP, which I helped define at Gartner, is like a modular toolkit, letting businesses snap together functions (finance, supply chain, or otherwise) as needs evolve. AI large language models (LLMs) act like a universal adapter, unifying disparate data without costly standardization. For instance, SAP’s Joule equips users with near-instant, natural-language-driven insights, enabling query-based access to key metrics and charts across SAP business data.

LLM-based integration frameworks have demonstrated dramatic reductions in integration workload. In one study, developers achieved up to 60–80% less integration code, plus significant performance gains, highlighting how LLMs can substantially simplify ERP connectivity.

Major ERP vendors are accelerating investment in multi-tenant SaaS and embedded AI capabilities. SAP’s Joule and Oracle Fusion Cloud ERP are examples of platforms embedding AI copilots to streamline HR, finance, and supply chain processes. These approaches rely on scalable SaaS infrastructure, a critical departure from the rigidity of legacy on-premises systems.

However, modernization remains fraught with challenges. According to Gartner, by 2027, more than 70% of recently implemented ERP initiatives will fail to fully meet their original business case goals, with up to 25% failing catastrophically. Furthermore, 75% of ERP strategies are not strongly aligned with the overall business strategy, leading to confusion and disappointing results. This underscores the risk of rushing into ERP transformation without a documented, value-driven strategy.

Composable ERP, enhanced by AI, offers a way forward, delivering adaptive, cross-functional agility that traditional monolithic systems cannot match.

AI Applications Across ERP Modules

AI transforms composable ERP, delivering measurable wins across all major modules. Here’s how it reshapes key areas:

1. Finance

AI-powered predictive analytics now transforms cash flow forecasting, reducing forecasting errors by 20% to 50%, delivering real-time insights, and significantly minimizing manual effort. It integrates data from diverse systems such as ERP and CRM, enabling dynamic scenario planning, early risk detection, and smarter decision-making. These innovations empower finance teams to maintain liquidity and respond proactively to market shifts. Meanwhile, according to 2025 IBM study , 70% of executives believe that AI agents will empower business professionals to explore deeper analytics and optimize operations in real time by 2026. Such agentic AI is fast becoming a strategic cornerstone for organizational agility and future-ready decision-making.

Estimated Efficiency Gains: 30-50%

2. Supply Chain

AI is revolutionizing supply chain operations through strategic automation and smarter decision-making. For example, organizations that prioritize AI investments in their supply chain operations enjoy significant performance advantages: they report up to 61% higher revenue growth than their peers.

Additionally, a leading U.S. logistics operator harnessed an AI-powered system to optimize warehouse picking, achieving a remarkable 30% boost in workforce productivity while reducing operational inefficiencies.

Estimated Efficiency Gains: 40-70%

3. Manufacturing

AI optimizes production yields and predictive maintenance. According to McKinsey, approximately 64% of organizations reported cost reductions in manufacturing, reflecting the financial impact of AI deployment in operational environments.

Epicor’s Forrester TEI study highlights that manufacturers using Epicor Kinetic achieved a 1.9% improvement in gross margins, driven by stronger operational efficiency and streamlined workflows.

Estimated Efficiency Gains: 40-60%

4. Procurement

AI is reshaping procurement by automating sourcing and streamlining decision-making. According to IBM, organizations already use AI across procurement functions, 60% in predictive analytics, 56% in accounts payable, and 55% in purchase order management. Early adopters report strong outcomes, including 20% productivity gains, 14% higher efficiency, and 11% improved profitability. AI also enables faster operations, such as onboarding suppliers up to 10× faster and cutting pricing analysis from days to minutes, making procurement more agile and value-driven.

Estimated Efficiency Gains: 20-50%

5. HCM

AI is transforming human capital management by automating HR workflows and improving process efficiency. In SAP SuccessFactors, Joule serves as an AI copilot that enables conversational, natural-language interactions, simplifying tasks such as data lookups, navigation, and transactional operations within HR systems.

According to a Forrester TEI study on Epicor Kinetic, including HR personnel, organizations achieved a 20% productivity improvement by Year 3 through streamlined operations and reduced manual labor.

Estimated Efficiency Gains: 20-40%

6. Warehousing

Amazon now deploys more than 750,000 robots alongside employees, taking on repetitive tasks to improve efficiency and safety. Its new Sequoia system, launched in Houston, enables inventory to be stored 75% faster and reduces order processing times by up to 25%, enhancing delivery speed and accuracy. More broadly, EY reports a 30% productivity boost from AI-enabled warehouse optimization, underscoring how intelligent automation is reshaping supply chain performance.

Estimated Efficiency Gains: 40-75%

AI’s power lies in its ability to unify data, making composable ERP a powerhouse. Key benefits include:

• Automation: Speeds up invoicing, screening, and inventory tasks.
• Cost Savings: Cuts expenses across finance, procurement, and warehousing.
• Real-Time Insights: LLMs enable data-driven decisions without standardization.
• Scalability: Adapts to business growth and market changes.