Verusen Launches Explainability AI Agent; Accelerates Context-Driven MRO Optimization for Enterprises
Industry’s first Explainability AI Agent empowers enterprise teams with transparency, trust, and control across the MRO supply chain
ATLANTA, May 15, 2025 (GLOBE NEWSWIRE) -- Verusen, the industry leader driving AI for MRO (maintenance, repair, and operations) supply chain and inventory optimization, today launched its groundbreaking Explainability AI Agent for data and context-driven material and inventory optimization. This first-of-its-kind capability delivers unprecedented transparency into Verusen’s stocking policy recommendations, enabling procurement, operations, and supply chain teams to trust, understand, and confidently act on AI-driven insights, accelerating smarter execution and enterprise-wide alignment.
Verusen is Transforming MRO with Purpose-Built AI Agents
Verusen’s Material Graph — the world’s largest MRO materials knowledge base — has ingested over 41 million unique SKUs, $12 billion in annual inventory and spend, and all associated transactional POs. This powerful platform redefines how asset-intensive enterprises manage critical materials inventory, procurement, and risk across their global MRO supply chains.
By integrating Large Language Models (LLMs), Machine Learning, and Natural Language Processing technologies, Verusen transforms manual, disconnected inventory management practices into streamlined, context-rich optimization strategies—empowering teams to make smarter decisions faster while reducing risk and operational costs.
Explainability AI Agent: Turning the Black Box into a Trusted Engine
Humans need to be able to understand and trust AI reasoning processes and have insights into the decisioning. According to global management consultant firm McKinsey & Company, by shedding some light on the complexity of so-called black-box AI algorithms, explainability can increase trust and engagement among those who use AI tools. This is an essential step as AI initiatives make the difficult journey from early use case deployments to scaled, enterprise-wide adoption.
Enterprises adopting AI for MRO management often struggle with the "black box" problem—trusting recommendations without understanding the logic behind them. Verusen’s Explainability AI Agent eliminates this barrier by providing clear, concise insights into every recommendation’s rationale, supported by a powerful feedback loop that continuously learns and adapts based on user interactions.
Unlike traditional AI platforms—or even today’s general-purpose generative AI tools—Verusen’s Explainability Agent is task-driven to deliver clarifications and explanations to users. It examines model inputs, outputs, and logic to surface tailored insights directly within the platform, ensuring every decision is rooted in transparency and context.
“Too often, enterprise AI is a black box—something you’re expected to trust without question,” said Ross Sonnabend, Chief Product Officer at Verusen. “Our Explainability agent changes that. We’re explaining the ‘why’ behind the recommendations and decisions in a language our users can understand, proving that responsible AI can be powerful and transparent.”
Responsible AI Built for Enterprise Scale
Verusen’s Explainability AI Agent is part of the company’s broader commitment to responsible AI, ensuring that solutions are secure, accountable, and enterprise-ready from day one. Key pillars of Verusen’s responsible AI design include:
- No exposure of Customer data to third-party LLMs
- Built-in Explainability, not bolted-on as an afterthought
- User-in-the-loop feedback models that improve recommendations over time
“By giving stakeholders across procurement, operations, and supply chain full transparency into AI decision-making, Verusen is not just accelerating AI adoption, but building a new standard for trust, collaboration, and measurable success in materials management,” said the Director of Supply Chain for a Fortune 500, pharmaceutical manufacturing company.
The Future of MRO Optimization Is Understandable, Actionable, and Trusted
At a time when enterprise leaders are overwhelmed by an avalanche of AI tools—and often unsure of which to trust—Verusen is focused on delivering a purpose-built solution that solves real problems with responsibility at the core.
“AI doesn’t have to be mysterious,” continued Sonnabend. “With Verusen, it’s understandable, actionable, and, most importantly, yours to trust. Explainability isn’t just a feature. It’s a philosophy. We believe users deserve to understand the technology they rely on. We believe trust is earned, not assumed.”
To learn more about Verusen’s Explainability AI Agent and the future of MRO optimization, visit https://verusen.com/ai-agent-explainability-for-mro-inventory-optimization/
About Verusen
Verusen is a leading AI MRO supply chain optimization SaaS provider focused on helping global, asset-intensive enterprises streamline their supply and materials management strategy. Verusen uses advanced data science and artificial intelligence capabilities to harmonize disparate MRO data across multiple enterprise systems to provide complex supply chains with true visibility for supply and inventory planning and procurement intelligence. This helps organizations reduce risk, optimize working capital, and ensure production uptime to meet customer needs. The result is a foundation organizations can trust to fuel digital transformation and support supply chain maturity initiatives. Headquartered in Atlanta, Verusen has been named to the Inc. 5000 fastest-growing private companies list and one of Georgia’s Top 10 Innovative Technology Companies. Visit verusen.com for more information, or follow us on X/Twitter at @Verusen_AI and LinkedIn.
PR Contact:
Greg Cross
PenVine for Verusen
greg@penvine.com
+1 925 413 5327
A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/ea384983-ac2e-4a0a-91e8-670a5e627f2e
Verusen launches Explainability AI Agent
Verusen launches groundbreaking Explainability AI Agent for data and context-driven material and inventory optimization.
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