Being a good buyer of HRIS in 2026

I didn’t want to write yet another article about AI and how it is “shaking things up” in 2026. But I find myself being asked the same questions again and again about the practical use cases of AI in HRIS and payroll.

So, I’ve put pen to (digital) paper to create a short, buyer-focused guide – grounded in vendor research, demos we’ve attended, and real conversations with HR and payroll leaders.

This is not about hype. It’s about helping you be a better buyer.

The market reality: “AI” in HRIS is now two different things

If you’re buying an HRIS in 2026, every vendor will demo “AI”. The real questions aren’t whether AI exists – they are:

• Is the AI embedded in the core workflows you’ll actually use?
  HR service delivery, manager actions, payroll-adjacent processes, reporting.

Many vendors talk about AI being “in the flow of work”. I take this to mean two things:

1. AI shows up where people already work (Slack, Teams, Outlook, etc.)
2. AI is embedded into existing workflows. For example, prompting an HR user to send onboarding information at the right moment, rather than being a standalone chatbot.

• Is it safe and compliant for HR use?

Before we connect our HRIS to OpenAI and upload payroll data, we should pause and ask: Where does our data go?

Is the vendor hosting their own AI capability, or brokering requests to a third party? And what does that mean for data retention, training, and risk?

• Will it scale across countries without becoming a governance nightmare?

UK organisations with overseas footprints quickly discover that AI behaves very differently once multiple entities, regulations and policies come into play.

In practice, most HRIS AI falls into two buckets:

1. Productivity AI (mature-ish, immediate value):
   Copilots, drafting and summarising, search, “ask your HR data”.
2. Execution AI (early, high promise, higher risk):
   Agentic workflows that do work across systems and processes, not just recommend actions.

Deloitte’s latest HR technology research aligns with what buyers are seeing: generative and agentic AI are becoming real product capabilities, but the gap between promise and scaled impact remains significant.

The direction of travel is clear. In this article, I want to be specific about where vendors are genuinely strong today – based on research, demos, and direct experience – and where caution is still needed.

What are the real AI use cases for HR teams?

There are dozens of potential AI capabilities being discussed, but the ones gaining real traction are those that:

• Support employee and manager engagement
• Reduce transactional and administrative effort
• Help HR teams analyse and interpret data more effectively

I once attended a meeting with a UK HRIS product manager who said, “We just need to have something AI in there so we can say we have it.” That is exactly what I’m trying to protect buyers from with the detail below.

The most common (and useful) AI use cases today

1) AI assistant / copilot (employee + manager + HR)

This is quickly becoming the default AI use case: natural-language answers, guided actions, and HR helpdesk deflection to reduce admin burden.

What good looks like:

• Role-based answers that respect HR permissions
• Responses tied to your policies and processes (not generic content)
• References back to source data (“show me how you got that”)
• Multi-entity, multi-country support (UK + overseas)

Common failure modes:

• Confident but wrong answers when data is incomplete or restricted
• Poor understanding of UK policy nuance (SSP, absence types, unionised contexts)
• Solutions that only work in “happy path” demos

Gartner has been very clear that responsible AI frameworks and trust are becoming prerequisites for HR technology adoption – not optional extras.

My own take: users are increasingly comfortable interacting with chat-based tools (many people now ask ChatGPT what they would previously have typed into Google). That expectation is now flowing into workplace systems.

2) Generative AI drafting & summarising inside workflows

Generative AI creates new content. For HR teams, this is often the fastest ROI category because it reduces admin effort without requiring perfect data.

Typical use cases include:

• Job descriptions, internal announcements, manager communications (similar to LinkedIn’s “Write with AI” feature)
• Performance feedback prompts and drafting
• Summarising notes, reviews, 1:1s, cases and tickets

There’s strong external evidence that generative AI can improve productivity, particularly for less experienced workers, although results vary and governance matters.

I sit on a product steering group for an HRIS vendor and am currently working with four other customers in an AI-focused user group. Performance management is one of the most interesting (and contentious) discussions.

Many of us are asking: If the manager uses AI to write the objective, and the employee uses AI to respond – is that productivity… or just reduced engagement?

I’ll let you ponder that one.

3) People analytics: “ask your HR data” + insight surfacing

This is where vendors start to separate, particularly in the 500–5,000 employee segment. Smaller and mid-market platforms often find this easier because their datasets are less complex.

There are two broad levels of capability:

• Level 1: Chat over dashboards
• Level 2: Explanation, anomaly detection, trends, narrative analysis

McKinsey’s research consistently highlights the challenge here: many pilots, far fewer deployments that scale and deliver sustained value.

4) Recruiting & talent AI

Recruitment AI often appears earlier than deeper workforce AI because the inputs are familiar: CVs, job descriptions, interview notes.

However, it also carries some of the highest risk – particularly around bias, transparency, and explainability – especially when used for screening or ranking candidates.

UK Government guidance on Responsible AI in Recruitment explicitly calls out risks such as discriminatory targeting and exclusion. Buyers should use this guidance as a lens when evaluating vendor claims and deciding how (or whether) to deploy these tools.

5) Workforce planning & forecasting

True workforce planning AI is typically strongest where:

• HR and payroll data are coherent
• Security and data lineage are robust
• Scenario modelling is a core product capability, not a bolt-on

I’ve seen some impressive examples where tools pull in third-party data such as ticket sales or weather patterns to predict demand, which then translates into staffing requirements. Clever stuff, but highly dependent on data quality and governance.

6) AI agents & automation (the “next wave”)

Vendors are starting to move from “assistant” to “agent” – systems that can execute workflows such as onboarding, changes, access provisioning, case resolution, and cross-system orchestration.

Buyer caution is essential. Treat agentic AI as roadmap value unless you can verify:

• A full audit trail (who or what triggered actions)
• Approval controls
• Separation of duties
• Rollback and exception handling

If you read my work regularly, you’ll know I’m a strong believer in the human in the loop: the human as the pilot, and AI as the co-pilot.

Vendor feature map (based on research in January 2026*)

*Worth calling out – the AI landscape is moving fast.

Key:
✅ available (clearly marketed) • 🛠️ announced / rolling out / limited availability • ❓ not clearly evidenced publicly

This table is intentionally conservative — I’ve only marked features as “available” where I can find strong public evidence. Buyers should always validate edition, UK packaging and enablement during demos and reference calls.

The regulatory lens: what HRIS buyers need to think about

Now that we understand the use cases and vendor positioning, it’s worth looking at the regulatory context.

The UK still doesn’t have a single “AI Act” equivalent. Instead, it has taken a principles-based approach, implemented through existing regulators and set out in the Government’s AI regulation white paper.

What matters most for HRIS buyers today:

• Data protection remains the hard edge.
  UK GDPR and ICO expectations still apply. The ICO’s AI and data protection guidance and risk toolkit are under review following the Data (Use and Access) Act, which came into force on 19 June 2025 – an important watch point for governance.
• Recruitment AI is a known risk area.
  UK Government guidance focuses heavily on bias, exclusion, and discriminatory targeting.
• Legislative signals are increasing.
  An Artificial Intelligence (Regulation) Bill is progressing through Parliament, even if it does not become law in its current form.

So what?

When selecting an HRIS, AI should be treated as requiring:

• Provable controls (audit logs, permissions, approvals)
• Bias and fairness considerations (especially in recruitment)
• Transparency from vendors on models, data usage, and retention

Final thought

HRIS AI is no longer about novelty. The real differentiator is whether AI:

• Reduces HR workload today,
• improves payroll-linked insight, and
• scales cleanly across borders –

– not how futuristic the demo sounds.

We’ve developed an AI Buyer’s Guide to support organisations considering new technology purchases. Read it here, or reach out if you’d like a conversation.

Here comes the AI Bit (I asked AI to write me a glossary of terms from this article in case it is too ‘tech talk’:

Glossary of AI & HRIS Terms