Michael Atalla, CMO UiPath
The automation industry has spent the past year racing to pursue AI agents. UiPath’s new Chief Marketing Officer Michael Atalla pushes back on what that rush implies – not because agents lack value, but because he believes the destination is being misread. In his view, enterprises aren’t moving through a temporary hybrid phase on the way to full autonomy. They are settling into hybrid as the long-term operating model: deterministic workflows where certainty matters, autonomous systems where ambiguity can’t be avoided.
In our first conversation since joining UiPath in August 2025, Atalla tells me:
The hybrid approach, both the hybrid infrastructures and hybrid technology relative to automation, is permanent. That’s where we are today and it’s where we will always be.
His parallel is cloud adoption. A decade ago, the industry confidently predicts a wholesale shift to public cloud. What actually emerges is messier: hybrid and multi-cloud shaped by regulation, latency, sunk infrastructure, and risk. Automation, he argues, follows the same trajectory – not as compromise, but as reality once AI meets enterprise constraints.
Risk-heavy deployments, not cautious pilots
Atalla says what surprises him most in his early customer conversations isn’t that organizations are experimenting with AI, but that the most meaningful results are appearing in environments many would consider high stakes. Pointing to CSL Behring’s use of UiPath to support plasma processing for medical treatments, he says:
The customers that were having the most success were the ones that were actually taking the most risk with their AI implementations. I thought I’d be dealing with smaller stories and little examples. I didn’t know the customers would be ready for that yet.
The comment sits in contrast against the dominant narrative that enterprises ease into autonomy through low-risk pilots before scaling. A more grounded interpretation is that success correlates less with boldness and more with problem selection. Organizations tackling consequential workflows are forced to build governance, validation, and orchestration from the outset. That discipline may matter more than the AI itself.
What UiPath thinks an agentic enterprise actually needs
When customers tell UiPath they “need agents,” Atalla reframes the discussion. Agents alone, he argues, don’t solve enterprise problems. Operating models do. He outlines four elements UiPath is aligning around:
Core automation remains both robotic and agentic. This isn’t a defence of legacy Robotic Process Automation (RPA). Deterministic workflows still anchor processes where repeatability and auditability matter.
Orchestration connects end-to-end flows that include humans as well as systems. Without that connective layer, autonomy fragments quickly into isolated tasks.
Testing becomes what Atalla calls a “first-party participant.” In agent-driven environments, validation isn’t downstream Quality Assurance (QA) – it functions as part of the control mechanism that builds trust before agents act on live data.
Developer tooling underpins everything, spanning traditional automation builders and modern application developers. An operating model that only specialists can implement never scales.
The symmetry is strategic. UiPath positions itself less as an agent vendor and more as the layer that makes mixed autonomy deployable and governable at scale. Whether buyers see that as differentiation or simply baseline capability remains open.
Governance as the gating factor
Security and oversight surface repeatedly in Atalla’s answers, shaped by his background in infrastructure and cybersecurity. In one example he elaborates:
The risks associated with both the way AI is exposed to data – critical sensitive corporate data – and the things AI can then do with that data… it’s quite a cocktail.
UiPath argues its enterprise history gives it credibility in visibility, auditability, and policy enforcement across robotic and agentic layers. That matters because once agents trigger transactions or modify records, governance stops being compliance overhead and becomes operational infrastructure. Trust here is procedural rather than emotional. Autonomy has to be observed, tested, and failure modes contained.
Verticalization as pattern extraction, not reinvention
UiPath’s move toward industry-specific solutions follows directly from customer deployments, as Atalla explains:
Because we have insurance companies, medical records companies, manufacturing, retail and supply chain companies solving these problems with our platform, we know we can deliver packaged products ready out of the box.
The strategy focuses on productizing what customers already prove is possible. That lowers deployment friction but raises a different challenge: translating organization-specific workflows into repeatable software without flattening their complexity.
The appeal is clarity. As Atalla puts it:
Sometimes they don’t need a Swiss Army Knife. They just need a spoon.
The ecosystem layer
Atalla frames UiPath’s ambition as ecosystem participation rather than stack dominance. He references Apple’s incorporation of third-party model capabilities as evidence that even the largest players accept multi-vendor AI as inevitable:
If Apple licenses Gemini, we’re in an ecosystem forever. It feels like a time shift.
The argument is straightforward – enterprises won’t standardize on a single model or vendor. UiPath’s role becomes orchestrating across that fragmentation, with the aim of being middleware by design.
That stance carries risk. If foundation models commoditize, integration becomes power. If differentiation remains concentrated at the model layer, control shifts upward and platforms below risk becoming invisible plumbing.
My take
The automation market still talks about agents as if autonomy is the destination and hybrid is merely a waypoint. Atalla’s argument flips that logic. What’s emerging in enterprise environments looks less like a transition phase and more like a steady pattern shaped by operational constraints. Deterministic workflows, human oversight, orchestration, testing, and agents aren’t competing approaches – rather, they’re interdependent layers. The scaffolding around autonomy increasingly is the product.
UiPath accepts it may never own the flashiest layer of the AI stack. Instead, it aims to own the reliability layer that allows heterogeneous systems to function safely together. History suggests hybrid architectures persist longer than markets predict. The tension is narrative versus reality. Buyers reward simplicity even when underlying systems are complex. UiPath believes that that enterprises will prioritize operational trust over architectural purity.
Cloud taught us hybrid wasn’t a halfway house – it was equilibrium. Automation appears to be landing in the same pragmatic, stubbornly unglamorous place.
