Webinar Recap: Agentic Decision Intelligence — Achieving 10x Decision Velocity with AI Agents and Agent Teams

Summary

In our Future.Now webinar, “Agentic Decision Intelligence: Achieving 10x Decision Speed with AI Agents and Agent Teams,” we explored how enterprises are moving beyond traditional decision automation toward a new model powered by autonomous AI agents. The session focused on how agentic decision intelligence extends the value of decision intelligence by introducing agents that reason, collaborate, and act independently across complex decision workflows.

We examined how AI agents and agent teams operate across the full decision lifecycle, from building decision logic to executing actions at scale. Through real-world examples and a live demonstration, the webinar showed how organizations are achieving dramatic gains in speed by using agents to analyze options, evaluate trade-offs, coordinate across systems, and continuously learn from outcomes. The result is a more adaptive, scalable, and resilient approach to enterprise decision-making that keeps pace with the volatility on today’s business landscape.

Key Takeaways

• Agentic decision intelligence represents a new phase of enterprise decision-making.
  Decision intelligence already connects data, analytics, and actions in real time. Agentic AI builds on this foundation by adding autonomous agents that reason, collaborate, and act independently. This shift enables decisions to move faster and adapt dynamically as conditions change, without relying on constant human intervention.
• AI agents accelerate decisions by working across the entire decision lifecycle.
  Rather than supporting isolated steps, agents operate end to end. They help design decision logic, evaluate scenarios, recommend actions, and execute changes across systems. By covering the full lifecycle, decision speed increases while consistency and quality are maintained at enterprise scale.
• Agent teams unlock collective intelligence for complex business problems.
  Single agents handle focused tasks, while agent teams collaborate to solve broader challenges. Each agent brings specialized capabilities, allowing teams to evaluate trade-offs, coordinate actions, and converge on optimal decisions more quickly than traditional rule-based or manual approaches.
• Decision speed improves dramatically when agents are easy to build and deploy.
  Agents and agent teams can be created in minutes using simple prompts. This lowers the barrier to entry and shortens time to value. As a result, organizations can move from experimentation to production far faster than with traditional development-heavy approaches.
• Reusable decision functions enable scalable and reliable execution.
  Agents rely on shared, reusable functions that encode business logic and constraints. This ensures decisions remain consistent, auditable, and enterprise-ready, even as agents operate autonomously across different processes and systems.
• Real-world deployments have shown that agentic decision intelligence can scale well beyond isolated use cases.
  Drawing on its work with clients across industries, EY has successfully deployed more than 100 decision skills using agentic decision intelligence. This milestone reflects not just technical scalability, but growing organizational confidence in autonomous decision-making. As skills mature and expand, teams are able to move faster, standardize decision logic, and continuously build on proven results across the enterprise.

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.

Harrison Wickman, Aera US Alliance Executive, EY Harrison leads the global EY–Aera Technology Alliance and plays a central role in shaping EY’s decision intelligence practice. He brings deep experience across end-to-end supply chain operations, with a growing focus on applying decision intelligence, data, AI, and human insight to enable faster, more informed decision-making at enterprise 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: How quickly can we see results using Aera’s agentic decision intelligence, and what does the implementation timeline typically look like?

A: The timeline depends on the specific use case and the complexity of the skill being deployed. In some cases, organizations have achieved value in as little as three weeks. For simpler, more straightforward skills, most implementations begin seeing value within an eight- to twelve-week timeframe, with ongoing iteration to expand and deepen that value.

As additional agentic capabilities are introduced, these timelines continue to shrink. Most programs now see meaningful results within about two months. This process includes more than just technology. It covers bringing the right data together, assembling and enabling the team, and focusing on the most impactful value drivers. The emphasis is on delivering stable, high-quality decisions that consistently drive business value.

Q: How is agentic decision intelligence different from copilots and workflow automation?

A: This distinction is important. Copilots are task-focused assistants designed to help with specific activities such as writing, coding, or data analysis. They boost individual productivity but remain limited to defined tasks and operate alongside humans in the moment. Workflow automation, on the other hand, follows predefined rules and sequences. While effective for repeatable processes, it cannot adapt to new situations or reason through complexity.

Agentic decision intelligence goes further. AI agents can autonomously reason, decide, act, and learn. They combine advanced reasoning with collective intelligence from agent teams and continuously improve based on outcomes. Decisions can be made independently when appropriate, while still allowing for human oversight on critical choices.

A simple way to think about it is this: workflow automation is like a vending machine following fixed steps, copilots are like personal assistants helping with specific tasks, and AI agents are like trusted colleagues who analyze situations, collaborate, and execute decisions at enterprise scale.

Q: How do you ensure AI agents make decisions that align with company policies and risk tolerance?

A: Governance and alignment are foundational to agentic decision intelligence. The first step is assigning agent functions to agents. These functions convert existing business logic and decision processes into reusable, executable components that draw from a pre-approved function library, ensuring that decisions align with company policies.

Next, agents are tested before being published. Their behavior is simulated and debugged to validate how they interact with functions and logic. Agents are then embedded directly into decision workflows using Aera’s Process Builder, which controls how decisions are executed and orchestrated.

Finally, agents continuously learn from feedback. When policies evolve or edge cases emerge, agent behavior can be refined. This approach ensures autonomous execution remains fully governed and aligned across the enterprise.

Q: Can these agents integrate with existing systems like ERP, CRM, or legacy platforms?

A: Yes. Aera integrates seamlessly with existing systems and data sources through more than 200 prebuilt enterprise connectors. Agentic capabilities are built on the same integration foundation as the core platform, enhancing existing functionality rather than introducing a separate product.

Aera continuously pulls data from enterprise systems and external sources into the Decision Data Model. This model unifies structured data from systems like ERP and CRM with unstructured data from sources such as PDFs and emails. Agents use this continuously refreshed data to make real-time decisions.

Through agent functions, agents can also access analytics, optimization logic, machine learning models, scenario planning, and graph reasoning. This allows organizations to enhance their current technology investments, with agents operating across the existing ecosystem through a single, unified platform.

Q: How do you fetch data that is updated daily or in real time, and can system-based and non-system-based data be combined?

A: Yes, both structured and unstructured data can be combined. Many implementations work with data that is refreshed within minutes, and in some cases, in near real time. This is determined during the design phase, where teams define how data should be captured and incorporated into the model.

Existing IT processes are also taken into account. For example, data lakes can be accessed, though there may be limits on larger data pulls. The approach depends on the nature of the dataset, but there has been strong success integrating live, unstructured data into the Decision Data Model to support effective decision-making.

Q: Could an agent automatically decide which large language model to use for decision intelligence?

A: This question gets to the broader evolution of agentic systems. It asks whether the system will move beyond prompting toward a model where a business goal, KPI, or OKR can be provided and the system determines how to achieve it.

From a technical perspective, everything is programmatic and accessible through APIs, so this is feasible. The platform is moving toward this capability incrementally. The long-term direction is true goal-seeking behavior, where agents can interpret objectives and determine the best path forward. This evolution is already underway.

Q: How can users train themselves on the platform, and are there reliable learning resources available?

A: Comprehensive documentation and an enablement certification program are available. Training is delivered digitally, and many clients also take advantage of implementation partner training programs. In addition, Aera Technology provides a learning platform that supports ongoing education as part of a broader enablement program.