Fred Laluyaux, Co-Founder and CEO of Aera Technology – Interview Series

Fred Laluyaux, Co-Founder, President and CEO of Aera Technology, is a seasoned enterprise software executive who has built and led companies at the intersection of analytics, automation, and decision-making. Before founding Aera, he served as CEO of Anaplan and held multiple senior leadership roles at SAP, spanning finance, performance management, risk, sales, and corporate development. Earlier in his career, he worked in executive positions at Business Objects and ALG Software, and founded Transcribe Technologies, giving him decades of experience scaling global software organizations and translating complex data into business outcomes.

Aera Technology develops AI-driven decision intelligence software designed to help large enterprises operate with greater speed and precision. The company’s platform continuously analyzes data from across the organization and its external environment, turning insights into recommended and automated actions in real time. By focusing on decisions rather than dashboards, Aera aims to help organizations move from reactive analysis to proactive, continuously improving operations.

You’ve founded and led multiple enterprise software companies, from your early days building Transcribe Technologies to running Anaplan and now co- founding Aera Technology. What problem did you see in large enterprises that convinced you decision intelligence needed to exist as its own category, and why was 2017 the right moment to build Aera?

I’ve been working on this problem for more than a decade — long before Aera existed. Back in 2010, while I was at SAP, I wrote a paper about what I believed would become the biggest challenge for large enterprises: making and executing decisions fast enough to keep pace with the digitization of the economy.

Three forces were colliding — volume, complexity, and speed. Decisions were moving to a much finer grain, closer to the point of impact, but enterprises were still structured as deep pyramids of people, tools, and processes that simply couldn’t scale.

The real question became: how do you bring the power of technology down to the transactional level? Not just insights or dashboards, but machines executing decisions, continuously learning from all the decisions made, and with humans in control.

As for 2017, we were early. The market wasn’t fully ready, and neither were we. That’s the nature of a startup: you start with a clear vision and build early so you’re ready when the market matures. In Aera’s case, it took a few years. And COVID-19 didn’t help. But it’s been fascinating to see that our core vision has remained true to its initial formulation while both the platform and the market evolved to the point where Aera is now leading the decision intelligence category and working with some of the world’s largest organizations.

There’s a lot of discussion today around AI agents, but you’ve been clear that insights alone are not enough. How do you explain the difference between analytics, AI assisted recommendations, and true decision intelligence to CIOs who are trying to cut through the noise?

Traditional analytics and business intelligence tools tell you what happened. AI can help predict what might happen. AI-assisted recommendations suggest options but they still rely on humans to decide and act.

Decision intelligence moves beyond static dashboards or one-off recommendations. It operates as a continuous learning loop to accelerate and improve decisions — using data, analytics, AI, and automation to evaluate tradeoffs, simulate scenarios, and execute and monitor actions in real time, aligned to business goals.

While AI can help teams predict demand or optimize workflows, decision intelligence determines how to act on those insights. It balances cost, risk, service levels, and operational constraints across the enterprise at scale.

Aera is often described as enabling the self-driving enterprise. In practical terms, what does that actually look like inside a large organization, and which decisions are realistically ready for this level of automation today?

When we talk about the self-driving enterprise, this isn’t autonomy without control. From day one, our vision was to move from people making and executing decisions supported by machines, to machines executing decisions guided by people — with clear intent, constraints, and accountability.

In practice, Aera operates as a decision agent. It continuously understands data, detects triggers, evaluates tradeoffs, recommends actions, and executes decisions directly in enterprise systems. Using Aera, humans don’t manage dashboards; they govern decisions, often through a simple agree-or-disagree interaction.

The decisions ready for this level of automation today are high-volume and repeatable — inventory rebalancing, purchase order prioritization, parameter changes — where speed matters and manual coordination creates the most inefficiency.

You’ve worked closely with global enterprises across supply chain, finance, and operations. Where are CIOs seeing the fastest and most tangible returns from decision intelligence, whether in working capital, service levels, or waste reduction?

CIOs see the fastest, most tangible returns from decision intelligence where decisions are high-volume, repeatable, and constrained by cost, capacity, or service tradeoffs. In supply chain and operations, this often includes inventory rebalancing, purchase-order prioritization, and logistics. This is where automated execution at scale drives measurable gains in working capital, service levels, and waste reduction.

For example, a global life sciences company uses decision intelligence to continuously monitor demand and adjust purchase orders — automatically requesting supplier cancellations or reductions, validating responses, and confirming changes. This capability is delivering more than millions in annualized waste reduction, while also reducing truck miles and associated greenhouse gas (GHG) emissions.

Many companies already struggle to operationalize AI models at scale. What are the most common blockers you see when organizations try to move from insight generation to automated decision execution?

Challenges often emerge when teams start by experimenting with standalone AI tools. They may automate a single workflow but struggle to operationalize decisions consistently across the business. Without a composable, purpose-built decision platform, these efforts are difficult to govern, scale, or adapt as conditions change.

Another common blocker is a lack of clarity around where decision-making is breaking down. Companies invest in AI and prediction but don’t pinpoint why inventory builds, forecasts miss, or logistics underperform. Fragmented visibility across decisions compounds the problem.

Teams that succeed start with a clear, high-impact use case where tradeoffs are understood, build trust through recommendations and execution, and automate gradually. From there, they can scale as decisions continuously adapt and improve over time.

Agentic AI is becoming a buzzword across the industry. How do you see agents fitting into decision intelligence platforms, and where do you think enterprises need to be cautious about autonomy versus human oversight?

In decision intelligence, agents add the most value when they’re embedded in a supervised decision system — not operating in isolation. With the Aera Decision Cloud platform, agents work as coordinated teams, each contributing a specific capability: simulating scenarios; integrating real-time signals; validating feasibility; assessing financial impact; and executing actions — all orchestrated around a single decision.

Where enterprises need to be cautious is autonomy without governance. In practice, agentic decisions are always guided by people. Human teams set the parameters and goals, monitor performance, test assumptions, and manage data quality from a control-room environment. The system can run continuously, but humans govern how decisions evolve. That balance is what makes agentic AI scalable, trustworthy, and safe in the enterprise.

Trust is critical when decisions affect revenue, customers, or compliance. How does Aera ensure decisions are explainable, auditable, and defensible, especially in regulated environments?

Trust starts with transparency. For every decision, Aera captures the full context — the data used, the recommendation, the logic behind it, the decision taken, and the outcome. As the system runs and refreshes, it monitors and measures the decisions outcomes to continually improve decision-making.

We call this auto-decision learning. Based on the decision performance, Aera calculates confidence scores for recommendations — explaining root causes, tradeoffs, and expected impact. A user might see a recommendation with a clear rationale and a 92% confidence level.

This approach is autonomous but supervised. Through the platform’s Decision Intelligence Network, which serves as a centralized control room, users have complete visibility across decisions, actions, and outcomes. They can monitor performance, test assumptions, manage data quality, and adjust logic over time.

Based on your conversations with CIOs, how is the role of humans evolving as decision intelligence systems mature, and what skills become more important as machines take on more operational decisions?

As decision intelligence matures, the role of humans doesn’t disappear — it moves up the value chain. We’re seeing a shift from people manually executing decisions to people designing, governing, and improving decisions.

In many consumer packaged goods companies, traditional planner roles are already evolving into decision analysts that are focused on monitoring outcomes, understanding tradeoffs, and improving decision logic over time. Alongside them, decision architects define intent, constraints, and guardrails that guide how machines act.

The most important skills become judgment, systems-level thinking, and the ability to frame the right decisions. Humans stay firmly in control, governing how decisions are made by machines but not every individual action.

Gartner’s first Magic Quadrant for Decision Intelligence Platforms signals that this category is entering the mainstream. What capabilities do you believe will separate leading vendors from laggards over the next few years?

Having been named a Leader in the inaugural Gartner Magic Quadrant for Decision Intelligence Platforms, we see leadership defined by strong execution and the ability to deliver comprehensive, composable capabilities across the full decision lifecycle. In Gartner’s companion Critical Capabilities research, Aera was also recognized for its performance across key decision use cases — including decision analysis, decision engineering, decision science, and decision stewardship — assessing how well platforms can model, operationalize, govern, and continuously improve decisions at enterprise scale.

We believe that leading vendors will also be distinguished by how effectively they integrate advanced AI techniques, including generative and agentic AI, into supervised, enterprise-ready decision systems. This requires purpose-built platforms that are composable, accessible to the business through low-code and natural language interfaces, and governed at scale to meet security and regulatory requirements. Ultimately, the strongest vendors will embed decision intelligence as an operating layer that continuously learns and improves, not just another application teams have to manage.

For organizations that recognize the gap between insights and action, how does Aera’s platform help them close that loop in practice, and what does a successful first deployment typically look like for a CIO looking to drive measurable business impact?

Closing the gap between insight and action starts by operationalizing decisions in day-to-day operations. Aera’s platform enables CIOs to treat decisions as continuous processes: monitoring outcomes; testing tradeoffs; and improving performance over time. This is often anchored in a decision center of excellence, virtual or physical, where teams govern and refine how decisions are made and executed.

Aera unifies data, analytics, business rules, AI, and automation in a single composable platform to power decisions that flow from insight through execution and learning. Its composable architecture allows IT to maintain oversight and security, while enabling business teams to define, adapt, and evolve decision flows. As outcomes are captured, decisions continuously improve and free teams to focus on judgment, strategy, and exceptions.

A successful first deployment often proves measurable results on one high-impact decision use case in 10-12 weeks, executing and continuously improving decisions end to end. This creates a repeatable blueprint for enterprise scale.

Thank you for the great interview, readers who wish to learn more should visit Aera Technology.