Webinar Recap: The Agentic Enterprise — How AI Agents Are Redefining Decision-Making at Scale

Summary

In our Future.Now webinar, “The Agentic Enterprise: How AI Agents Are Redefining Decision-Making at Scale,” we explored how AI agents are moving beyond analysis to actively drive decisions and outcomes across the enterprise. As data volumes grow and business processes become more interconnected, traditional models that rely on human coordination across dashboards and systems struggle to keep pace. A new approach is emerging — one where AI can reason, decide, and act in context.

We also examined how agentic decision intelligence brings together data, models, and workflows into a unified system that operates continuously at scale. Real-world examples showed how AI agents move across the full decision lifecycle, from signal detection and analysis to execution and learning. The result is faster decisions, better alignment across functions, and measurable business impact.

Key Takeaways

• Decision intelligence has entered the mainstream.
  The recent introduction of Gartner’s inaugural Magic Quadrant for Decision Intelligence Platforms signals strong market momentum. Decision intelligence is now seen as strategic to the enterprise, with projections that by 2027, more than 50% of business decisions will be augmented by decision intelligence platforms. Clear evaluation frameworks and capability benchmarks are helping organizations move from experimentation to adoption.

• Generative AI was the beginning, not the end.
  The AI journey has evolved quickly, from early foundational models and pilots to copilots embedded in workflows. While copilots improved productivity, they still depended on humans to interpret insights and take action. The next step is agentic AI, where systems are designed not only to inform decisions, but to make and execute them within defined guardrails.

• Agentic AI operates across the full decision lifecycle.
  Agentic decision intelligence connects data ingestion, contextual analysis, recommendation generation, execution, and feedback loops into one continuous system. AI agents monitor signals, assess trade-offs, recommend actions, and trigger execution across systems. Each cycle strengthens performance through learning, creating a closed-loop process that improves over time.

• Scale is the defining advantage.
  Enterprise complexity makes manual coordination difficult. AI agents can evaluate thousands of variables simultaneously, manage trade-offs across supply chain, finance, and operations, and orchestrate actions across multiple systems. This enables organizations to scale decision-making in ways that human teams alone cannot sustain.
• Real-world impact is measurable and repeatable.
  Examples demonstrated how AI agents autonomously manage disruptions, rebalance supply and demand, optimize inventory, and coordinate financial and operational trade-offs. These use cases showed consistent improvements in responsiveness, efficiency, and business performance — delivered continuously rather than through one-time interventions.
• The path to value is practical and achievable.
  Deploying agentic decision intelligence does not require replacing existing systems. Instead, AI agents integrate with enterprise data and applications, orchestrating decisions across the landscape. Organizations can begin with targeted use cases and expand over time, building toward a fully agentic enterprise.

Speakers

	Pete Quimby, Regional Vice President of Sales, Aera Technology Pete brings over 25 years of experience in sales and organizational leadership across manufacturing, engineering, and information technology within the Fortune 250. Pete works closely with global enterprises to help them adopt decision intelligence as a strategic capability that drives measurable business outcomes.
	Suraj Ramalingam, Customer Success Leader, Aera Technology Suraj brings extensive experience in global supply chain management, having held senior roles at Salesforce and Procter & Gamble. At Aera, Suraj focuses on enabling customer success through technology-driven transformation, helping organizations operationalize AI-driven decision-making at scale.

Full Recording

Access the full webinar recording here.

Q&A

At the end of the presentation, we held a short session dedicated to answering attendees’ questions. Below are several of the key questions and answers.

Q: Where does the goal setting of the agent happen?

A: Goal setting happens during agent configuration. This is where the agent’s role, task objective, desired outcome, and completion criteria are defined. Context and guardrails are then added. The process is iterative. You simulate, review the output, and refine the tasks and guardrails until the desired behavior is achieved.

Q: Are there advanced cases where the agent reasons across different options beyond predefined checks?

A: Yes. Agents can evaluate multiple options, such as different expediting methods or what-if scenarios. They can also reason step by step, looping through tools and evaluations until they meet defined rules and guardrails.

Q: Looking ahead a few years, if agents are active at scale across operations, can Aera agents natively interact with other enterprise agents outside Aera? How would such interactions work in the future?

A: Aera supports bi-directional integration. Outputs from Aera agents can feed into other applications’ agents, and outputs from other systems can be used as inputs into Aera. Integrations are implemented on a case-by-case basis, and so far this approach has worked without issues.

Q: How does Aera compare with hyperscalers such as AWS when it comes to building agents?

A: Aera is purpose-built for decision intelligence and agentic capability. It offers a fuller end-to-end capability set, including human-out-of-the-loop and human-on-the-loop models, situational decisions, and learning from decisions. Hyperscalers were evaluated in Gartner research but were not ranked due to a lack of completeness of vision and execution in this category.

Q: How is this product different from Claude Cowork?

A: Aera is a decision automation platform built for enterprise business processes such as supply chain planning and execution. Claude Cowork, by contrast, is an LLM collaborator designed for conversations, summarization, and drafting. It supports knowledge work, but it is not a decision automation engine.

Q: What are the benefits compared to that outcome?

A: Aera produces actual business decisions and actions, such as generating purchase orders or operational plans, with governance controls and write-back into enterprise systems. It also provides traceable decision logic and built-in data lineage, so decisions are transparent and auditable by design.

Q: I understand how the agent is used to prioritize shipments. However, other software solutions perform shipment prioritization without using agents. What differentiates this approach?

A: The key difference is that Aera is an enterprise platform, not a point solution. Multiple agents can be built to address different challenges, such as expediting, order fulfillment, or demand and promotion synchronization. There is also cross-functional orchestration and sensing layered on top of other tools, supported by bi-directional integration across systems.

Q: How can these agents be incorporated into other SCPO software solutions?

A: Agents can be incorporated through bi-directional integration. Aera sits on top of other software capabilities and orchestrates decisions across functions, using outputs and inputs from multiple systems to coordinate execution.

Q: What types of roles within organizations are being upskilled to create skills or agents in Aera? Do they need to be IT professionals? Can business process or service professionals build them?

A: Typically, it is a collaboration between business and IT. However, the trend is shifting toward business users building skills and agents themselves. IT teams focus on integration and data pipelines, while business users configure agents and develop what-if models.

Q: Is there an additional charge for the Agent Factory? If a company already uses Aera, is access included or charged separately?

A: Agent Factory access is included for customers, and not a separate add-on fee. Costs align with standard SaaS drivers, such as the amount of data ingested and computed against.

Q: Can you show the material balance view across a time series?

A: Yes. A material balance time-series view can be added to a recommendation UI tab as part of the configurable output. It can be incorporated directly into the interface.

Q: Do the LLMs used understand supply chain data from ERP systems out of the box? Or are they customized LLMs trained to understand enterprise language?

A: The understanding primarily comes from the Decision Data Model (DDM). The DDM unifies and logically maps data from multiple enterprise systems, allowing the LLM to locate and reason over the relevant information. The structure of the model enables the LLM to work effectively with enterprise data.

Q: Is customer data shared with the LLM? Do you use private LLMs or hosted LLMs such as Gemini or OpenAI?

A: Customer data within a workspace is not shared externally. It remains in a logically independent environment. The LLM is used to perform reasoning and generate output, but the data itself is not shared back. Customers can use their own private LLMs, and many choose to leverage internal models. Hosted models such as OpenAI or Gemini can also be used, depending on preference.

Q: Is Aera Technology a no-code or low-code platform?

A: Aera is a low-code platform. With each release and improvement, the goal is to reduce the amount of code required even further.

Q: How does an agent team handle memory across agents to ensure robust decision making?

A: Memory is handled through the Decision Data Model, which stores decision context and outcomes. This feeds learning processes and confidence scoring.

Q: In the Gartner chart, many decision intelligence platform companies are listed. Is each platform specialized by area, process, or industry? How can we understand the differences among these platforms?

A: An RFP template can help evaluate each company’s criteria. Aera is the only supply chain-focused company positioned in the Leader quadrant, while two financial services companies are also included. Each platform has specific competencies and strengths. Although Aera has focused first on supply chain, it is not limited to it. The platform extends into procurement, marketing, operations, and go-to-market functions. Its extensible, composable architecture allows it to expand across industries.

Q: You mentioned Palantir and its ontology-driven architecture. How does Aera compare, given that ontology has not been explicitly mentioned?

A: Aera does not use the same terminology, but it implements a domain-aware decision model that includes business entities, rules, constraints, and workflows embedded within its decision framework. Its object model is optimized for decision automation rather than general-purpose data modeling.

Q: Is there a compute ceiling in the autonomous reasoning loop that prevents Aera from running an extensive number of loops?

A: Compute is bounded by configurable loop limits, defined by iteration thresholds, as well as by LLM usage costs when LLMs are used across multiple steps.