AI has become the headline across the software industry, and at Dayforce, it’s becoming their foundation.

We recently had the opportunity to speak with David Lloyd, Chief AI Officer and SVP of Platform at Dayforce, and it became clear that AI isn’t just a feature for Dayforce. Dayforce is ensuring its platform is AI-first, and they’re doing it with enterprise grade governance, trustworthy data, and a clear vision of what AI should (and shouldn’t) do.

Dayforce is quietly rolling out capabilities at scale, backed by over 500 dedicated AI team members, strict compliance protocols, and a unified data model. 

A 500-Person AI Division Built for Execution

At most companies, AI is a side initiative. At Dayforce, it’s a core discipline, led by a 500+ person team embedded across the product lifecycle.

Lloyd’s group builds foundational AI capabilities, from recruiting and workforce planning to performance reviews and payroll compliance, all functions that are designed to scale. Their approach: build once, deploy everywhere. These shared components power multiple product features, allowing Dayforce to accelerate deliver while ensuring consistency and control.

“We’re not just asking ‘can we build this?’” Lloyd said. “We’re asking, ‘should we?’ and that’s backed by a full governance model.”

Every AI feature flows through this structure, from internal model reviews and risk assessments to third-party audits and jurisdiction-specific opt-ins. Execution isn’t just about speed, it’s about doing it responsibly, at scale.

Governance in Action: Saying No When It Matters

At Dayforce, AI governance is the front door every feature has to pass through.

Lloyd shared a recent example where a sales team brought in a third party call summarization tool for review. It seemed promising, until the team asked whether the tool secured explicit user consent in GDPR regions.

“They said it was on the roadmap,” Lloyd recalled. “So that was it. We shut it down in 20 seconds.”

The lesson? Compliance isn’t a retroactive patch. It’s a prerequisite.

From model training practices to deployment options, Dayforce operates under a strict AI by choice philosophy. Clients control whether and where AI is used. Every model is reviewed for compliance risk, and customer data is never used in training without clear, written consent.

“We never train our models on customer data without explicit contractual permission,” Lloyd emphasized. “That’s just non-negotiable.”

At Dayforce, trust is the entry requirement for AI

AI by Choice: Flexibility Built In

One of Dayforce’s most proactive design decisions is making every AI capability opt-in. Organizations aren’t locked into AI features by default, their clients have the opportunity to decide if, when, and where to enable them.

This flexibility extends beyond the company level. Features can be turned off in specific states, provinces, or countries to match local regulations or cultural attitudes toward AI. For example, recruiting automation that’s legal and accepted in Colorado might be restricted in New York, and Dayforce allows customers to configure usage accordingly.

It is a forward looking approach in a space where AI adoption philosophies vary widely. Some organizations want to embrace AI deeply; others are cautious or constrained by compliance. By building for both, Dayforce ensures their platform can adapt as attitudes, regulations, and business strategies evolve.

“We believe in AI by choice,” Lloyd said. “Our job is to empower customers to decide how AI fits into their operations, and to make that decision easy to implement.”

Models That Prioritize Precision, Not Promises

In most industries, 98% accuracy from AI is considered a win, but when you're dealing with payroll and employee data, anything less than 100% isn't just wrong, it's a liability.

That’s why Dayforce takes a dual-model approach engineered to minimize risk and maximize reliability.

• Customer-specific models are used for highly variable needs like staffing forecasts or workforce optimization, trained solely on each client’s operational data to ensure contextual accuracy.
  
  
• Generalized models, like their turnover prediction engine, are trained on large volumes of de-identified, aggregated data that are using over 30 distinct signals to generate accurate predictions without compromising privacy.

Even Dayforce’s generative AI follows strict accuracy controls. Their models are fully isolated, with prompts never leaving the Dayforce environment and outputs never feeding back into training. It’s retrieval augmented generation (RAG) deployed inside their infrastructure is purpose-built to deliver accurate, compliant, and auditable results.

Because in HCM, there’s no room for hallucinations, only answers you can trust.

Reimagining Implementation: The AI Assistant Takes the Lead

One of the most exciting future state use cases that we talked to Lloyd about was an AI-powered implementation assistant, capable of guiding new customers through the configuration journey.

Imagine this:

• A chatbot asks about your org’s structure, goals, and policies.
  
  
• It analyzes historical configuration files from your legacy system.
  
  
• It proposes best-fit setups based on your industry, size, and location.
  
  
• It simulates compliance impacts and payroll outputs.
  
  
• And it does all of this before a human consultant even steps in.

“This isn’t fantasy,” Lloyd said. “We’re already testing similar workflows for reading collective bargaining agreements and suggesting rule changes. Eventually, configuration becomes collaborative  with a human in the loop, working side-by-side with AI.”

What This Means for HR Teams

All of these advancements aren’t about replacing HR, they’re about removing busywork.

Want to understand what 10,000 employees said in your engagement survey? Dayforce’s AI will bucket those comments into 18 intent categories in minutes. Want to draft a new job description? Just tell it which skills you need to update, and the system will regenerate a compliant, role specific version for you to approve.

“I’d much rather be an editor than an author,” Lloyd said. “AI lets you focus on the insights and actions that matter, not the tedious steps that got you there.”

The impact? HR professionals shift from data processors to decision drivers. Analysts evolve into strategists. And teams finally get to do the work they’re meant to do.

Why Dayforce’s AI Has a Head Start

None of this would work without clean, unified data.

And that’s where Dayforce may have the strongest hand in the entire HCM space.

Unlike vendors who build by acquisition and rely on integrations between disparate systems, Dayforce fully absorbs every product it adds. The result is a single, consolidated data model across payroll, time, talent, benefits, and beyond.

“Everyone talks about garbage in, garbage out,” Lloyd noted. “But we don’t have the garbage problem.”

That core advantage of unified, trustworthy data is also what CEO David Ossip pointed to in his Q2 remarks:

“Our single database architecture is a strategic differentiator allowing customers to replace fragmented HCM systems with a unified Dayforce solution.

From an AI perspective, our single platform and single database is critical. Foundational models require well-formed, comprehensive data — and Dayforce is unique in delivering this across the entire HCM lifecycle.”

It is more than just a technical achievement, it’s the foundation for why Dayforce is able to move fast, deploy AI safely, and serve their clients with smart AI tools. 

The Bottom Line: Dayforce Isn’t Just Talking AI, They’re Defining How It Should Work

The entire HCM industry is watching the AI wave. Some are chasing it. Others are hedging.

Dayforce? They’re building it into their foundation, with security, compliance, and their customer trust at the core.

They’re not just checking the AI box. They’re redesigning what enterprise-grade AI in HR should look like.

And so far, that bet is positioning Dayforce to compete differently, with accuracy and trust at the core to their AI infrastructure.