LEFTURN PERSPECTIVE
There has never been a more powerful moment for finance, supply chain, and business leaders to lead with AI-powered data intelligence.
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Companies need intelligent systems—not just dashboards—to uncover actionable opportunities buried in their data.
With massive volumes of information spread across ERP, procure-to-pay, third-party, and carrier systems, finance and supply chain leaders require real-time access to insights that drive decisions. But legacy applications have become bottlenecks—slowed by customization, inaccessible formats, and even unknown data sources—making it harder to act with speed and precision.
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The solution begins with building a domain agent that understands your business.
By partnering with an independent third party to annotate and structure last-mile data, organizations can activate generative chat experiences that work across finance, supply chain, and business teams. These agents allow users to ask questions in natural language, receive contextual answers, and surface spend patterns and operational gaps—without needing to navigate complex systems.
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From this foundation, operational agents emerge—each tuned to a specific workflow.
Innovative leaders are now collaborating to deploy agents that support claims, replenishment, cost-to-serve, and more. These agents continuously learn, adapt, and provide feedback loops that empower the entire ecosystem to focus on what matters most: reducing costs, improving service, and accelerating transformation.
CLIENT LIMITATIONS
ECOSYSTEM LIMITATIONS
THE NEED
Today, the ability for enterprise clients to analyze, measure, and monitor information about supply chains to achieve cost reduction and operational efficiency is manual, labor intensive, and requires subject matter expertise. Each system and vendor have different acronyms and formats, dozens of networks and hundreds of offerings. Enterprises are challenged to be able to know what information to ask for, decode, and determine the best services to use. The result is that even the most well-trained employees stick to a few options. The majority of clients do nothing as they have limited resources, time, and willingness to tackle supply chain optimization.
Suppliers today face increasing pressure to deliver personalized support at scale—but many lack the tools to do so. According to research presented at PMI Global Congress, clients often cite suppliers as a key contributor to project failure. While the top reason was a lack of executive leadership, the next six were all tied to a vendor’s inability to integrate meaningfully into the client’s business. This reflects a broader challenge: traditional service models struggle to adapt to the nuanced needs of each enterprise.
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Agentic systems offer a new path forward. By deploying domain-specific AI agents—trained on client workflows, supplier data, and operational context—vendors can now embed intelligence directly into the customer experience. These agents don’t just answer questions; they learn, adapt, and collaborate. They help suppliers acculturate into the client’s business, proactively surface risks, and deliver insights that drive alignment and success. The shift from static service delivery to dynamic, AI-augmented engagement is not just a technological upgrade—it’s a strategic imperative.
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Solving these limitations starts with building your first domain agent—powered by annotated data and generative AI.
Rather than relying on fragmented dashboards or manual analysis, organizations can now partner with an independent third party to curate and annotate last-mile data across layered sources of record. This foundational agent is trained to understand your business context—finance, supply chain, procurement—and enables staff to ask natural-language questions from each line of business’s perspective. It’s not just about surfacing spend—it’s about unlocking the full capabilities hidden in your data.
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From this foundation, operational agents emerge—each tailored to a “day in the life” scenario. These agents don’t just analyze—they act. They identify inefficiencies, suggest optimizations, and continuously learn from feedback. The result is a shift from reactive reporting to proactive orchestration, where every team member has access to AI-powered insights embedded directly into their workflow.