Webinar Recap: AI that Drives Real ROI — An Agentic Decision Intelligence Overview

Summary

In our Future.Now webinar, “AI that Drives Real ROI — An Agentic Decision Intelligence Overview,” we explored how enterprises can move from data-rich environments to truly decision-driven operations. As organizations gain access to more data than ever before, the focus is shifting toward how that data is translated into timely, coordinated action across supply chain, finance, and operations.

We presented a practical view of agentic decision intelligence and how it enables AI to do more than generate insights. By unifying data, models, and workflows, Aera makes it possible for AI to reason, recommend, and act within real business contexts. The result is a system that not only supports decisions, but actively drives them, delivering measurable outcomes at scale.

Key Takeaways

• More data creates more opportunity when decisions can scale with it.
  Organizations today operate with unprecedented volumes of data, creating the potential for faster and better decisions. However, value is only realized when that data is connected to action. It was shown that the ability to scale decisions, not just insights, is what allows enterprises to fully capitalize on their data investments.

• Agentic decision intelligence connects insight to action.
  Agentic decision intelligence was defined as a system that unifies data, analytics, and execution. Instead of stopping at dashboards or recommendations, it enables AI to take the next step by driving decisions forward. This creates a continuous loop where decisions are made, executed, and improved over time.

• AI agents operate within structured workflows to deliver outcomes.
  AI agents were shown to function within defined workflows, where they can reason through scenarios, evaluate options, and take action based on rules and guardrails. This structure ensures that decisions remain aligned with business goals while still allowing flexibility and adaptability in complex environments.

• Multi-agent orchestration enables coordinated decision-making.
  Rather than acting in isolation, multiple AI agents can collaborate across functions such as supply chain, finance, and operations. This orchestration allows decisions to be coordinated across systems and teams, reducing fragmentation and enabling more consistent, enterprise-wide outcomes.
• Decision automation drives measurable business impact.
  Examples demonstrated how organizations are using agentic decision intelligence to automate thousands of decisions at scale. This leads to improvements in efficiency, responsiveness, and overall performance. Measurable outcomes such as cost reduction, service level improvements, and faster response times highlight the tangible value of this approach.

Speakers

Ram Krishnan, SVP, Platform Product Marketing, Aera Technology Ram leads the GTM strategy for Aera Decision Cloud with over two decades of experience in enterprise, software, product strategy and customer success. Ram brings a deep understanding of how to align solutions with customer values. Under his leadership, Aera has earned a strong reputation for delivering measurable business outcomes and customer satisfaction.

Joe Derry, VP of Customer Success, Aera Technology Joe leads customer success at Aera Technology, bringing extensive experience in decision intelligence, process transformation, and applied AI. Joe previously held leadership roles at Western Governors University and Dell, spanning analytics, software engineering, and digital transformation. Joe is known for helping organizations turn AI ambition into measurable business results.

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: Technically, how does the system capture market signals and translate them into useful information for the organization? Is it through web scraping?

A: Aera captures market signals through its Decision Data Model, which ingests data from enterprise systems, external sources, and real-time streams. This is done using more than 200 connectors, along with patented crawlers and streaming pipelines, rather than relying on basic web scraping.

The data is then harmonized and enriched with context, such as demand, supply, and pricing signals. This allows AI engines to simulate outcomes and trigger decisions, enabling a continuous loop of sensing, reasoning, and action in real time.

Q: As a previous Unilever employee working with Aera for direct deliveries, I want to ask: What is more important for a business starting its AI journey today? Should it start with control tower operations and later move into developing skills?

A: It depends on organizational priorities. Starting with a control tower can help establish visibility, which is often a useful first step.

However, the greater value comes from moving into skill-based automation. Aera’s approach is to move beyond monitoring and begin deploying decision skills in high-impact use cases, such as inventory optimization, real-time demand sensing, and logistics optimization, where measurable value can be created.

Q: How do you incorporate forecast uncertainty into your Inventory Optimization Skill? And what drives the system’s decisions when choosing what’s best strategically for a company — improving service levels, minimizing costs, or balancing multiple objectives?

A: Aera incorporates forecast uncertainty by combining real-time data, historical patterns, simulation, optimization, and machine learning within its decision engines. As a result, inventory recommendations are evaluated across multiple scenarios rather than a single static forecast.

The strategic direction — whether to prioritize service levels, cost, waste, margin, or a combination — is defined by the company’s configured objectives, constraints, and policies within the decision model. Outcomes are then continuously measured and refined through Aera’s learning loop.