What We Heard at Transform About AI, Talent, and Decision-Making

AI is everywhere right now, but most organizations are still figuring out how to actually use it. Our team just got back from the Transform HR conference, and one theme kept coming up across every conversation. The gap is not in AI capability. It is in how systems, teams, and decisions connect.

We saw three perspectives play out repeatedly:

1. Builders are moving fast and experimenting
2. Operators are trying to execute and align
3. System thinkers are stepping back and asking how it all fits together.

That tension is where the real story is. Below is a walkthrough of the conversations that stood out, and what they mean for leaders right now.

The Builders: Speed, Experimentation, and the Push to Start

One of the most engaging sessions we attended was “The AI Game Show: CHROs Compete to Transform.”

Leaders like Danny Guillory and Brandon Sammut made something clear right away. AI is not something we are preparing for anymore. It is already showing up in how teams work every day.

Danny grounded the conversation in what this actually looks like inside organizations. He talked about the importance of not “AI washing” anything. Employees can tell when something is performative, and that breaks trust quickly. Transparency matters. What stood out most was how he framed leadership right now. The role is shifting from protecting teams from change to preparing them for it. Leaders cannot sit on the sidelines. They need to be involved, experimenting, and learning alongside their teams.

There is real pressure on HR teams to engage directly. That means getting into the work, testing things, building workflows, and figuring it out as you go.

Brandon added a perspective that tied it all together. The biggest barrier is not whether teams can use AI. It is whether they have the right context. Adoption happens faster when people are solving problems they already understand and care about. When AI is applied to something familiar, it clicks much more quickly.

Takeaway: Start small and focus on real workflows. The teams seeing traction are not rebuilding everything. They are identifying repetitive work, experimenting in context, and building trust through real use, not hype.

The Operators: HR and Finance Are Rewriting the Rules

In another session, “It’s Complicated (and That’s Okay): Rewriting the Rules of HR and Finance,” the conversation shifted from ideas to execution.

Liana Rodriguez and Susy Martins focused on what this actually looks like in practice. One point stood out right away. Headcount no longer equals capacity. With AI, one person can do more than before, and that is changing how teams think about workforce planning in a very real way.

Once you start looking at it this way, everything else starts to shift too. Planning becomes more continuous instead of something you revisit once or twice a year. Forecasting gets less predictable. Finance models have to adjust to keep up.

Liana also called out something that many teams feel but do not always say out loud. HR and Finance are often misaligned because they are speaking different languages. When HR starts to understand metrics like ARR, margins, and how the business defines value, the conversation becomes much more productive.

Susy brought it back to what actually moves things forward. You are never going to have perfect conditions. There will always be constraints. The key is to start with the problem you are trying to solve, and then figure out how technology, including AI, can help.

Takeaway: Be intentional about AI strategy. This is an AI FOMO moment, but the strongest teams are grounding decisions in business outcomes, aligning HR and Finance, and rethinking capacity beyond headcount.

The System Perspective: Connected Talent Systems in the AI Era

In “The CHRO’s Blueprint for Connected Talent Systems in the AI Era,” Stacey Harris and Arnaud Grunwald brought the conversation back to something foundational, the reality of today’s HR tech ecosystem.

The session drew on one of the largest HR systems surveys in the market, covering the full landscape. And the message was simple. Complexity is no longer the exception, it is the norm.

What became clear pretty quickly is that most organizations are not struggling because of a lack of tools. They are struggling because those tools do not connect. When systems are fragmented, adding AI does not solve the problem, it makes it more visible. Data gaps turn into decision risks, and small inconsistencies can scale into bigger issues like bias or flawed outputs.

At one point, the audience was asked if anyone was operating on a single HR system. Not a single hand went up. That moment captured the reality most teams are living in. Everyone is working across multiple systems, whether it is intentional or not.

Because of that, organizations are starting to rethink how they structure their tech stacks. Instead of constantly adding new tools, many are narrowing in on two or three core systems of record and building around them. The conversation is shifting from what should we buy next to how does this actually fit together.

Data is at the center of all of this. Before AI can deliver real value, the data behind it needs to be connected and reliable. Without that, even the most advanced tools are working off unstable inputs.

There is also a shift happening in ownership. Technology decisions are increasingly sitting with CIOs and IT teams, which means HR leaders need to be much more aligned with how systems are designed and managed. At the same time, workforce strategy itself is evolving. Some organizations are starting to think more deliberately about geographic distribution, building teams across regions to reduce risk tied to regulation or labor changes.

One of the more interesting reframes from the session was around AI itself. It is easy to think about it as a way to move faster, but it can also play a role in creating stability. It can bring consistency to processes, but only if the systems underneath are actually aligned.

Takeaway: Before accelerating with AI, organizations need to stabilize their systems. That means simplifying the ecosystem, anchoring around core platforms, and ensuring data is connected and trustworthy.

The System Reality: Context Is Still the Constraint

Something that kept coming up, both in sessions and in hallway conversations, was this idea that understanding your tech ecosystem is no longer optional. It is part of the job now.

A lot of leaders are trying to figure out how to bring in new AI tools without creating the same problems they have been dealing with for years. And the reality is, most systems today are still messy. Data is siloed. Information does not flow cleanly. Teams spend more time maintaining systems than actually using them.

AI does not solve that. If anything, it makes it more obvious.

When your data is fragmented, it is hard to trust what comes out the other side. And when your systems are unclear, every new layer just adds more complexity.

Stacey Harris put a really practical framework around this. Instead of continuing to add more tools, focus on a small number of systems of record across the employee lifecycle, and then build an insights layer that actually connects and activates the data. (which is EXACTLY what we do here at Illoominus!)

Takeaway: Treat your system landscape as a strategic asset. The organizations that move fastest with AI are the ones that first invest in clarity, integration, and data quality.

The Tension: Everyone Is Building, Ownership Is Less Clear

One of the biggest patterns we saw at Transform was just how much people are building right now. Teams are experimenting, creating solutions, and moving quickly. In many cases, they are ahead of where you might expect.

What is not getting talked about enough, though, is ownership.

We saw this play out in a conversation about a workforce planning solution someone had built. It was thoughtful and clearly solving a real problem. But then someone asked a simple question that shifted the tone completely. How are you going to maintain this?

That is when the real complexity started to surface. Questions around security and access. How updates would be handled. What other systems it depends on. Who steps in when something breaks.

It was a good reminder that building something is only the beginning. The long term responsibility that comes with it is often an afterthought, and that is where things can start to break down.

Takeaway: Speed without ownership creates risk. The most effective teams are thinking about maintenance, governance, and sustainability from the start, not as an afterthought.

What Does this Point To?

Across all of these conversations, a few themes connect everything. Old models are breaking, and one role no longer equals one unit of output.

Context is becoming the advantage. Teams that connect data and decisions move faster and make better choices. Systems matter more than tools. The strongest outcomes are coming from connected ecosystems, not more software.

Speed is increasing, but structure and ownership are still catching up.

Final Thought

AI is changing how decisions are made, but most systems were not designed for this reality.

Leaders are operating in an environment where capacity is flexible, systems are complex, data is fragmented, and expectations are evolving quickly.

The organizations that will lead are the ones that can connect data across systems, build capability within teams, design intentional tech ecosystems, and make decisions based on context instead of assumptions. AI creates opportunity, but it also raises the bar for how decisions are made and sustained over time.

About Illoominus:

Illoominus helps organizations modernize workforce planning by turning complex people data into clear, actionable insights. We design intuitive, self serve data experiences that empower leaders to explore information confidently and make informed decisions in real time. By combining thoughtful analytics with AI-driven capabilities, we equip teams with the tools they need to move from reactive reporting to proactive strategy.