01.

Executive Summary

02.

What’s changed: How AI is reshaping HR and benefits

03.

What’s agentic AI and why does it matter in benefits

04.

How successful teams are using AI today

05.

Five practical ways to get started with AI

06.

AI literacy for your HR and Benefits team

CHAPTER

Table of Contents

1%

The no-nonsense
guide to AI in benefits

A practical guide for Reward and Benefits leaders. Cut through the hype, understand real use cases, and implement AI with measurable impact and minimal risk.
Summarise

01

Executive Summary

AI is changing benefits. Here’s what that means for global Reward leaders.

If you run Rewards and Benefits in a large, distributed organisation, AI is already on your radar. Maybe you’ve trialled a chatbot or used it to speed up translations. But turning isolated experiments into everyday impact is a different story.

You want to reduce admin, improve employee experience, stay compliant, control costs, and prove results. But you’re managing complexity across countries, payroll systems, brokers, and platforms that don’t always speak to each other.

That’s where AI can help. This guide cuts through the noise and shows how to fold AI into your benefits strategy in ways that are:

Secure — governance, human oversight, and privacy baked in

Measurable — metrics your CFO, CISO, and CHRO care about

Scalable & cost‑efficient — across regions, languages, and workforce types

We’ve built this for teams like yours: Global, fast-moving, resource-stretched, and ready to get started. No fluff. No jargon. Just the tools to make AI real in your rewards strategy.

02

What’s changed: How AI is reshaping HR and benefits

AI is changing how companies handle everything from enrolments and communications to budget and plan design. Some teams are charging ahead. Others are cautious. Most are somewhere in the middle: interested, but unsure where to start.

Until recently, AI in benefits meant chatbots and static FAQs. Now AI can take action: it updates records, flags issues, reconciles data, and routes tasks, based on what the employee or team is trying to do. It’s not just answering questions; it’s helping you execute.

For global Reward leaders, that unlocks faster policy changes, cleaner payroll reconciliations, and fewer admin errors, at scale across countries and systems.

38%

of HR leaders have already explored or implemented AI to boost efficiency according to Gartner (2024) ↗. Rewards and Benefits teams are no exception.

The next step for AI? Intelligent Agents (Agentic AI) that manage entire workflows end-to-end, improving over time with minimal human oversight, but with governance and guardrails built in. Here's what those agents might help with:

Engagement Orchestrator

Takes an engagement goal and drafts targeted campaigns—email, push, in-app, blogs—for you to review and launch

Invoice Reconciler

Spots invoice mismatches by cross-checking payroll data, saving hours of manual reconciliation.

Claims/Reimbursement Processor

Auto-approves eligible claims using policy logic—reduces wait times and manual work.

Form Prefiller

Fills in expense forms by pulling data from receipts—no typing, fewer errors.

Renewal Negotiator

Uses claims and market data to negotiate better benefit deals without lifting a finger.

Compliance Advisor

Keeps you compliant by tracking regulation changes and updating policies automatically.

“We can now make much better predictions based on someone’s role, age, or benefit engagement history, about what might be most relevant to them. And use that to remove manual admin, reduce errors and improve outcomes. You stay in control, but the system runs itself.”

— Sebastian Fallert, CEO & Co-founder at Ben

03

What’s agentic AI and why does it matter in benefits?

Agentic AI refers to artificial intelligence that can take action on your behalf, not just answer questions.

Unlike traditional chatbots, agentic AI understands your goal, plans the steps, and carries them out autonomously. You set the intent. It handles the execution.

In benefits, this has huge potential. Managing benefits programmes is fiddly, manual, and prone to errors. And as every Rewards and Benefits leader knows, there’s no such thing as a small mistake. One payroll slip or reporting error can trigger costly problems fast.

Too often your team ends up bogged down in admin instead of focusing on the strategic work that really makes a difference. These tasks might seem simple, but they’re repetitive, time-consuming, and demand expertise, making them surprisingly easy to get wrong.

This is where agentic AI can help. It’s ideal for high-effort, low-strategic-value tasks:

Prepping enrolments

Flagging claim issues

Summarising policy updates

Or even drafting communications

Most tools keep you in control with a human-in-the-loop (HITL) model, so nothing goes out without your sign-off. That means fewer errors, faster cycles, and more time for your team to focus on strategy, not admin.

Types of AI: What’s the difference?

Type

Chatbot / Co-pilot

Simple AI agents

Autonomous AI agents
(also known as agentic AI)

What is does

Gives answers, follows scripts

Takes actions based on goals or user intent

Makes decisions, improves itself, runs full workflows

How it works

Reactive, based on
workflows or pre-set rules

Can interpret intent and collaborate to get things done

Works independently - can learn, adapt, and act without needing human intervention

Examples
of prompts

What’s the deadline?

Choose plan

Need help picking a plan?

It looks like you're enrolling a new dependent. Want to continue?

04

How successful teams are using AI today

We spoke to industry experts and reward leaders at enterprise companies, and found that there are

Three key use cases when it comes to bringing AI into benefits:

Benefits administration

Automating repetitive tasks, surfacing errors, and syncing data.

Enhanced employee experience

Increasing visibility, relevance and understanding of benefits.

Strategic benefits analytics and design

Understanding benefits program performance, benchmarking, forecasting, and optimising benefits design and program spend.

But nearly every one of them stressed that only thoughtful, secure rollouts, which prioritise data privacy, win trust. Getting this right will be critical to building and maintaining employee trust with AI.

“It helped us rapidly scale implementation. Four years to get to 15 countries and then 27 countries in one year. And it came down to leveraging gen AI. … For example, the fact-finding process from six months to six hours.”

— Mark Kelly, Global Health, Wellbeing & Benefits Leader

05

Five practical ways to get started with AI

01.

Using AI to simplify benefits administration

AI doesn’t just reduce admin, it helps you fix the real blockers: manual hand-offs, messy data, and inconsistent processes. If your HRIS, payroll, and provider systems don’t talk to each other, even basic tasks can create risk. Here’s how to tackle that.

1.  Start with your data

Before AI can help, your data needs to be clean and connected.

Map your flow

From HRIS to payroll to provider —
where are the weak links?

Find the leaks

Identify the top 3 manual hand-offs
(CSV uploads, dual entry, copy-
paste fixes)

Assign owners

Each automation pilot needs
someone to track fixes and flag risks

“We once onboarded 200 people and only 68% made it through without manual fixes—until we digitised the missing API link.”

— John Whitaker, Reward Leader, Workday

2. Make automation explainable

If AI changes something, like adjusting a payroll deduction, your team needs to see why.

A good system won’t just update the numbers. It will generate a plain-language summary (e.g. “Dependent added 28 April — premium increased by £15”), link to the source record, and suggest the appropriate back-date if needed.

Aim to strike a balance between brevity and depth. Over-simplifying (“Premium changed”) will frustrate your power users; whereas over-detailed logs might overwhelm.

Start with a one-line summary. Add optional drill-down links to the raw data, policy rules, or full change history so people can go deeper only when they need to.

Better still, AI can help sift those logs for you, highlighting what’s important and flagging anomalies, so your team doesn’t have to dig.

65%

of HR leaders say AI is most valuable when it doesn’t just act, but explains its reasoning

“When discrepancies arise, our AI hands you a narrative, not just a corrected spreadsheet, so you truly understand the ‘why.’”

— Thorsten, VP Implementation at Ben

3. Use a smart governance model

You should always trial and test the AI Solution with a small group of pilot users to make sure it’s accurate. And bear in mind that not every task should be fully automated. Use a traffic-light mode:

What it covers
Who reviews it

Green Level

Routine tasks (e.g. enrolment
confirmations)

Amber Level

Medium-risk actions
(e.g. backdate requests)

Red Level

Medium-risk actions
(e.g. backdate requests)

Green Level

AI only

Amber Level

Human Review

Red Level

Human Only

Know the limits – limitations and guardrails

Garbage in, garbage out

AI can’t fix bad HRIS data; it will repeat the error at speed

No policy overrides

Agents must follow insurer wording and local law; final approval stays with HR/Legal.

Edge‑case blind spots

Rare scenarios can confuse the model; have a human fallback.

Bias risk

Outputs reflect training data; schedule quarterly fairness audits.

Data consent

Over‑personalisation feels creepy; start with opt‑in data and clear opt‑outs.

If any of these red‑flags fire, route to human review immediately.

“We handle ~40% of support queries with AI; anything off-script is immediately routed to our HR helpdesk—no blind spots.”

— Felipe Morales, Product Lead at Ben

Prompts to try:

Identify top 3 recurring data mismatches between payroll and HRIS

Summarise all recent provider policy changes impacting premiums

02.

Using AI to make benefits simpler, smarter and more personal with AI simplify benefits administration

Most employees don’t think about benefits, until they really need them. And when that moment comes they want quick answers they can trust.

From surfacing the right features at the right time to translating policies in real time, AI can make benefits feel more useful, personal, and easy to engage with.

Here’s how AI can help your employees navigate their plans with less effort and more clarity:

63%

of companies said employee engagement was their number one benefits priority in 2024

50%

of employees are actively looking for new jobs in 2025

86%

would be more likely to leave a job if it did not support their well-being

25%

don’t know who their pension provider is (Drewberry, 2024)

66%

Value benefits more when they understand them (Metlife, 2023)

Step 1: Help employees find the value

Whether your teams are in Munich, Manila or Manchester, employees just want fast, clear answers — they want to know “Do I have dental?” or “Can I see a therapist?”

AI can surface that instantly, parsing every mention of “dental” or “therapist” across legal pages and policy documents then presenting them as clickable tiles or acting as a digital on-demand educator.

“One employee discovered he had a £300 dental voucher he never knew existed—simply by exploring our ‘Features’ view.”

— Samuel Carter, Head of Customer Solutions at Ben

Automate support with an AI chat widget

Embed an AI chat widget to answer FAQs instantly. Roughly 70% of common supportqueries can be resolved automatically, boosting engagement and reducing help-desk load.

Prompts to try:

List all benefits related to mental health available in Spain

Highlight features relevant to first-time parents in Germany

Step 2: Maximise personalisation without the “creep factor”

Employee benefits are deeply personal

This means that, while too little personalisation can feel generic, too much can feel intrusive.

By tapping into HRIS data and personal preferences (like tenure, dependants, or past claims) AI can offer tailored benefit recommendations to each employee. That might mean using broader data points, like age, to suggest pension contribution ladders, or office proximity to highlight cycle-to-work schemes.

It can be as simple as an onboarding quiz about employees“ interests like Fitness, Family, or Finance, helping the AI understand their priorities — no sensitive data needed.

Smart behavioural nudges then spotlight underused perks in real time

“Did you know your plan rewards you for 10,000 steps?”

“We saw you welcomed a baby 2 months ago — would you like to enrol in family cover?”

“Did you know you can save X amount on a gym membership through your PMI?”

Smart behavioural nudges spotlight underused perks in real time

These “Did you know?” moments transform static plans into relevant living experiences,which boost employee engagement with your benefits.

[AI] definitely will help with things like engagement, usage and employees feeling more connected to the benefits they have on offer. It would be good to log in and see, “You went to the gym, did you know you have a gym membership?” The challenge is how do you get around the creep factor? You could surface features, that’s a great place where AI could proactively, personally alert me and be like, “Did you know?‥”

— Kaitlyn Knopp, Co-founder at Pequity, former Compensation Specialist (ex Google)

Start with opt-in data (role, location, life stage)

Earn trust before using behavioural inputs.

Step 3: Get the right message to the right people

Let’s face it, most benefits communications get ignored. AI can change that by targeting communications by work pattern, time zone, and preferred channel.

Example:

“Message on-site parents in London after 6pm via Slack”
or “Email remote contractors at 9am in their local time zone”

With personalised notifications, you can set specific rules. Built-in translation means everyone receives messages in their language too.

Personalised, well-timed nudges are what turn a static plan into a benefits experience.  With the right guardrails, AI can help every employee see the value in what you offer, and actually use it.

Prompts to try:

Send OE reminders to shift workers in French via SMS at 8am local time

Highlight features relevant to first-time parents in Germany

“We've been using AI on our benefits website so that it can be translated in real time into other languages and we’ve seen some really good engagement from this”.

— John Whitaker, Global Head of Reward at Workday

03.

Designing better benefits with AI

If you want to stand out as a top-tier benefits provider, average isn’t good enough. It takes thoughtful design — and ongoing analysis — to build an offering that truly resonates.

If you're managing global plan design, AI can surface usage trends by region, spot coverage gaps, or flag underused local benefits, without waiting for your annual review cycle.

for

51%

of companies, employee appreciation of their benefits is just average (Source: Ben)

1. Example of renewal negotiator setup screen

2. Stress-test your benefits budget

Traditional budgeting relies on last year’s numbers plus a rough buffer. AI lets you move from guesswork to precision. Use it to:

Run “what-if” scenarios:

“If premiums rise 4% next year, how will that affect our H1 2026 spend?”

Get early warnings on cost spikes:

“We spotted a 12% jump in dental claims this quarter — time to review coverage”

Model impact of new benefits

“If we add enhanced fertility support, how many employees are likely to use it — and at what cost?”

“Right now, you only get your renewal rates six weeks before the deadline. That’s too late. AI lets you see trends early and act ahead of time”.

— Carl Chapman, VP Benefit Design & Partnerships at Ben

3. Optimise your benefits mix

Use AI to find what’s missing, what’s underused, and what could be improved. Tools to try:

Real-time benchmarking

Instead of waiting three years for a benchmarking report, ask AI:  “What are FMCG companies in the UK offering in their wellness package?” With access to an anonymised client portfolio of thousands of clients (depending on your broker), AI can generate tailored benchmarks — product features, ESG credentials, even user-experience scores — so you’ll know where you are over- or under-investing.

Gap analysis

Pull from employee pulse surveys, platform Q&A logs, and enrolment data to spot missing benefits or surface trends.

Optimisation engines

Test dozens of benefit combinations against defined KPIs (cost per engagement, NPS uplift) and surface the combination that maximises ROI.

Segment-level analysis

Understand uptake within relevant cohorts
(e.g. 25–34 in urban areas) instead of flat averages

“Low utilisation isn’t always bad. For example, assisted reproduction might be used by few — but for them, it’s life-changing.”

— Nikki Stones, VP of Marketing at Ben

4. Build insight-led workflows

To turn data into action, you need the right mix of tech and guardrails:

Keep humans in the driver’s seat:

AI suggests, you approve.
Especially for high-impact choices.

Pilot, measure, adjust:

Every rollout should be tracked against clear KPIs (cost, usage, satisfaction)

Embed privacy from the start

(see section 4.4)

Link your data: HRIS, claims platforms,
and surveys into one up-to-date system.

When everything shares the same format and refresh schedule, your AI insights stay accurate and reliable.

Ask questions in plain English:

“Which teams saw a spike in absenteeism this month?” and get instant, easy-to-read answers — no digging through spreadsheets or waiting on static reports.

Prompts to try:

Forecast uptake and budget impact of extending assisted reproduction support to all staff.

Compare wellness benefits offered by top-performing companies in our sector

“I still have someone review AI’s work because only a person can apply the empathy and conscientiousness our employees deserve.”

— Kaitlyn Knopp, Co-founder at Pequity, former Compensation Specialist (ex Google)

04.

Build trust and stay compliant with AI in benefits

Using AI in benefits means handling personal,  and often sensitive, employee data. That comes with real risk. But with the right systems in place, you can move fast, stay compliant, and earn trust across Legal, IT, and the C-suite.

This section gives you a practical framework to govern AI securely at scale — from scoping and cleansing your data to vetting vendors and staying ahead of global regulations like

1. Scope only the data you need

Start with a data-scope workshop before you build anything. Ask: What fields does the AI actually need to complete this task?

Examples:

Yes

Employee ID, benefit tier, location (city level)

No

Full name, contact details, full date of birth, home address, passport number

By reducing the amount of data you need to feed the AI, you practice the principle of “data minimisation”, a key tenet of the GDPR. It also avoids scope creep later on.

2. Clean your pipelines

Most errors come from mismatched, bad, or manual data flows, not the AI itself.

Check your HRIS data against insurer feeds and claims systems to spot mismatches early

Automate simple transforms (CSV → API) before moving to deeper integrations

Log who owns each data source and how often it refreshes

3. Vet vendors thoroughly

Every vendor handling employee data should meet enterprise-grade security standards:

What to ask for:

Security Certification such as: Cyber Essentials Plus, SOC 2 Type II, or ISO 27001

Data residency options: Where is the data being hosted/processed? Can data stay in-region?

Deletion policy: Does the system delete data after each request, and can they prove it?

Usage policy: Will your data be used for training AI, or other unauthorised use cases? (It shouldn’t be.)

Ethics and alignment: Is the usage of AI aligned with your organisation's ethical values? Are there any high-risk transfers or processing occurring?

Accessibility compliance: e.g. UK WCAG or Section 508 (US)

“Ask vendors: ‘How do you delete my data after each request?’ If they can’t prove it, don’t onboard them.”

— Sebastian Fallert, CEO & Co-founder at Ben

4. Design for global AI and data compliance

With new AI-specific laws like the EU AI Act joining established data regulations like GDPR and HIPAA, global benefits teams need to design systems that meet evolving standards — from data minimisation to risk classification and human oversight.

This table shows some of the key frameworks shaping AI and data compliance across your global footprint — and what to build in from day one.

Global AI & Data Privacy Regulations for Benefits Teams (as at 2025)

Region

EU / UK

EU / UK

United States

Regulation

GDPR

EU AI Act (from 2025–2026)

HIPAA (health data) CCPA / CPRA (California)

What to Build In

  • Collect only the minimum data needed (data minimisation)

  • Define a clear lawful basis for processing

  • Enable right to access, correction, and deletion

  • Maintain audit logs and human review for sensitive use cases

  • Classify risk for each AI use case (minimal, limited, high)

  • Prohibit banned practices (e.g. social scoring)

  • For high-risk AI: document risk management, enable human oversight, register use cases

  • Disclose AI usage in plain language to employees

  • Classify risk for each AI use case (minimal, limited, high)

  • Prohibit banned practices (e.g. social scoring)

  • For high-risk AI: document risk management, enable human oversight, register use cases

  • Disclose AI usage in plain language to employees

Best practice design choices across all regions

Minimise the data you’re using for your AI use-case:

  • Use age ranges (25–34, 35–44) instead of exact birth dates

  • Group by city or region, not postcode

  • Use broad job level and department rather than an exact title

Get clear consent via opt-in for nudges, AI chat, and personalisation

Offer transparent and simple opt-outs of any AI-driven features

“You don’t need someone’s date of birth to answer a question about their allowance balance.”

— Patryk Grzelak, Engineering Lead, Ben

5. Run regular AI health checks

Make AI risk reviews part of your quarterly rhythm. Include HR, IT, Legal, and brokers to review:

Audit logs

Keep records of what the AI did so you can trace issues later.

Drift or hallucinations

Check if the AI starts giving off-track or made-up answers.

Feedback loops

Collect feedback use it to make improvements.

Regulatory changes in each operating region

Stay on top of new laws where you operate to avoid compliance risks.

6. Keep a human in the loop

Even the best AI needs review.

Route high-risk outputs (e.g. legal, eligibility, escalation triggers) to a human for sign-off

Build thumbs-up/down feedback into the user interface of your benefits hub to catch issues in real time.

Utilise the feedback to adjust the system continuously, ensuring alignment and safety

Trust isn’t a line in your privacy policy, it’s how your systems actually behave. If you're operating across borders, your AI governance needs to be global from day one.

05.

Getting stakeholder buy-in for AI in benefits

You don’t need a perfect strategy to get started. But you do need a plan that shows clear savings, built-in risk controls, and direct links to company goals.

This section gives you five practical steps to help you build a case that resonates with your CFO, CISO, and executive sponsors:

1. Define success metrics and baseline costs

What to do:

Map current admin spend: FTE hours, error fixes, support tickets.

Quantify friction e.g. average time to resolve an enrolment issue, monthly helpdesk volume.

Set pilot KPIs

e.g. 30% fewer manual tasks, 20% faster policy updates.

“Our translation pilot cut non-English support tickets by 40%, saving the work of one full-time employee in global HR.”

— Carl Chapman, VP Benefit Design

2. Estimate quick-win ROI

What to do:

Estimate time savings: automated hours × loaded HR cost

Assess error reduction: cost of mis-enrolment or compliance breach and project the drop with AI reconciliation.

Don’t ignore soft ROI

NPS, plan uptake, retention, absenteeism.

Pilot

Time Saved

Error Cost Avoided

Soft ROI (est.)

Translation & Summaries

120 hrs/mth

£5K/mth

↑10 pt NPS

Automated Forecasts

80 hrs/qtr

£12K per cycle

↑5% plan uptake

3. Mitigate risk with built-in controls

What to do:

Use the Build trust and stay compliant with AI in benefits section (4) to show you’ve locked down data scope, vendor security, and privacy

Apply traffic-light governance (see Use a smart governance model 1.2)

Always include human sign-off and real-time feedback loops, especially when piloting the technology

4. Secure your funding ask

What to do:

Build a 1-pager or short deck with:

  • Problem: “Manual enrolment errors cost us £100K/year.”

  • Solution: 2–3 quick-win pilots with expected savings

  • Cost: Tooling and implementation support

  • Projected payback: ROI in 4–6 months

Pre-empt objections: Share your data scope and vendor vetting plans

Link it to company goals like productivity, cost efficiency, and employee well-being

Run small tests before asking for full rollout

5.  Plan a 30/60/90-day pilot

Days 1–30

Pick pilot, audit data, sign vendor SLAs

Clean data map, signed contracts

Days 31–60

Launch pilot  (e.g. translation, reconciliation), embed human-in-loop

Pilot report: hours saved, errors cut

Days 61–90

Expand to second use case (e.g. forecasting)

ROI summary deck, Phase 2 roadmap

Make AI in benefits a strategic win

By measuring current costs, showing clear savings, and building in strong risk controls, you can turn AI from a buzzword into a board-backed initiative. The result: measurable ROI, aligned with company goals, and delivered with managed risk.

Before you scale: Six essential questions every Reward & Benefits leader should ask

You’ve seen what’s possible. Before you commit budget, pick vendors or go live across countries, get clarity on these six questions.

Where will AI make the biggest difference?

Look across your benefits lifecycle,  enrolment, claims, comms, payroll, and invoicing. Where are people spending time fixing errors, chasing answers, or duplicating work? That’s where to start.

Is your data up to the job?

No AI can do its job if the data’s a mess. Are your HRIS, payroll and provider feeds clean, consistent and connected — or are you still wrangling spreadsheets and patchy APIs?

How will you prove it’s working?

To keep momentum (and funding), you need proof. What will success look like — fewer admin hours, fewer errors, happier employees? And can you track it?

Can you scale it without breaking everything?

Pilots are easy. Scaling’s harder. Do you have a roadmap to roll this out across countries and teams without creating operational chaos?

How will your teams work with AI, not around it?

What training, roles and ways of working need to shift so that your teams see AI as a support act, not a threat?

Are you set up to use AI responsibly?

Can you explain how decisions are made? Can you catch and fix bias? Do you know how data will be stored, secured and deleted? If regulators asked tomorrow, would you be ready?

06

AI literacy for your Rewards and Benefits teams

For most Rewards and Benefits teams, AI isn’t second nature yet — and that’s okay.

To roll out AI successfully, your team needs a shared understanding of the basics: how AI works, when to trust it, and how to know when something’s off.

Here’s a simple guide you can use to upskill your team.

AI literacy cheat sheet: 10 terms to know

Term

What It Means

Why It Matters

Agentic AI

AI that can plan and complete tasks independently, not just respond to prompts.

Drives automation for enrolments, reporting, or forecasting. Think of it as a proactive assistant.

HITL (Human-in-the-Loop)

A model where humans review or approve AI outputs before final action.

Essential in HR/benefits to prevent errors, bias, or policy misfires.

Confidence Score

A percentage showing how sure the AI is about its answer or decision.

Helps you know when to trust the output vs. when to double-check.

Hallucination

When AI generates an incorrect or made-up response.

Especially risky in employee-facing comms — needs flagging and review.

Prompt

The instruction or question you give the AI.

Better prompts = better results.
Be specific!

Fine-Tuning

Training an AI on your organisation’s data to improve accuracy.

Useful if you're using AI for forecasting, benchmarking, or custom insights.

Model

The engine that powers the AI — how it “thinks”.

Different models have different strengths and risk profiles.

Token

A piece of language the AI uses to process text. One word = ~1.3 tokens.

Important for understanding why some outputs get cut off.

Bias

When AI repeats or amplifies assumptions from its training data.

Must be mitigated in hiring, salary data, or sensitive nudges.

Explainability

The AI’s ability to show why it made a decision.

Critical for trust in HR/benefits workflows.

Download PDF

How to use this in your team

Run a 30-minute AI 101 lunch-and-learn using the cheat sheet.

Create simple escalation rules: “If confidence <80%, escalate to benefits lead.”

Encourage curiosity — build a safe space for the team to experiment and ask questions.

By building basic AI fluency, you’ll empower your team to cut repetitive tasks, reduce error rates, and give them more time for strategic work.

AI vendor evaluation scorecard

Use this scorecard to compare tools side-by-side and choose with confidence.

Evaluation Criteria

What to Look For

Score (1–5)

Security & Compliance

SOC 2 Type II or ISO 27001; GDPR, HIPAA, LGPD alignment

Data Privacy

Data deletion after use; opt-outs; not using your data to train models

Explainability

AI can show how it made a decision; includes audit trails

Human-in-the-loop (HITL)

Supports confidence thresholds, manual review triggers, or sign-off steps

Model Accuracy

Demonstrated low error rates on HR/benefits tasks; referenceable examples

Configuration & Integration

Integrates with HRIS, payroll, broker feeds; flexible data schema

Accessibility & Inclusion

WCAG 2.1 or equivalent; multi-language support; inclusive design

Hosting & Localisation

Option for EU/US/APAC data residency; compliance with regional hosting rules

Vendor Transparency

Clear roadmap visibility; accountable contact; transparent pricing and SLAs

Customer Success &
Support

Dedicated success lead; onboarding and support plans; measurable implementation ROI

Download PDF

If you’re leading rewards and benefits in a global organisation and want to move from pilot to practice, let’s talk.

Whether you're just starting out or already experimenting with AI, we're always up for a conversation about what’s possible (and what’s practical). You can get in touch here ↗.