Archives September 2026

Employee experience isn’t a perk or a programme.

It’s what work feels like for your people, day after day.

HR teams know how much the employee experience matters.

The problem for many HR teams is finding enough time to shape it when admin and process work take up so much of the week.

Sage’s HR and Payroll Leaders’ Report, based on research with 1,000 HR and people professionals across the UK, Ireland, and South Africa, shows the scale of the challenge.

61% of HR leaders spend 10 or more hours a week on administration and paperwork, compared with just 30% who spend that long supporting employees.

What’s more, 89% say they face challenges with resource and capacity.

Despite these challenges, HR is still making time for culture: 67% already spend significant time creating an inclusive, engaging workplace.

However, what’s in short supply is the capacity to do more.

The good news is that some of that time can be reclaimed.

And you don’t need to overhaul your entire HR function to start.

In this article, you’ll learn what employee experience is, why it matters to business performance, what’s getting in the way, and how to build a practical plan for improving it.

Here’s what we’ll cover:

What is employee experience?

Employee experience is everything a person encounters throughout their time working for you.

It starts during the recruitment process, before they’re even technically an employee, and continues through the day-to-day experience of working for your organisation.

How they’re onboarded, whether their pay arrives correctly, how easily they can book leave, and how their manager responds when they raise a problem all contribute to that experience.

In turn, their experience shapes their engagement.

If people feel valued and able to do their jobs without unnecessary friction, they’re more likely to feel engaged.

Those everyday operational moments matter more than you might think.

88% of HR leaders say accurate and timely payroll processes are critical to employee satisfaction and trust.

A payslip with an error, or a form that takes 4 emails to process, can affect how someone feels about working for you just as much as a wellbeing programme.

That’s why employee experience can’t be separated from HR’s day-to-day work.

HR touches almost every stage of the employee journey, giving it a major influence over how your culture feels in practice.

Why employee experience matters to your business?

The HR and Payroll Leaders’ Report puts it plainly: when people thrive, business thrives.

Employee experience affects much more than how people feel about coming to work.

It can influence some of the issues HR and business leaders are already trying to solve:

• Retention: 66% of HR leaders say attracting and retaining people with critical skills is a current challenge. Given the current organisational skills gap, creating an experience that gives good people reasons to stay has become even more important.
• Wellbeing: 65% cite low morale, burnout, or employee wellbeing issues as a current challenge. A great employee experience can help you address the conditions that contribute to those problems.
• Trust and satisfaction: 88% say accurate and timely payroll processes are a critical driver of employee satisfaction and trust. Employee experience isn’t just about culture initiatives. Basic processes matter too.
• Business value: 87% agree that when HR and payroll work well together, it creates measurable value for the business.

Employee experience has a clear business impact.

The challenge for HR is translating that impact into terms leadership recognises.

Why HR is struggling to deliver a better employee experience

The biggest barrier is capacity.

77% of HR leaders spend a lot of time maintaining accurate records, contracts, and policies, while 75% say the same about compliance.

And 89% face challenges with resource and capacity.

That pressure is clear when you look at how HR spends its week.

69% of HR leaders spend 10 or more hours a week on processes, while 61% spend that long on administration and paperwork.

Just 30% spend 10 or more hours supporting employees.

Ask where they’d like to spend their time and the picture changes.

58% would like to spend 10 or more hours a week on strategy, 50% on technology and AI, and 37% supporting employees.

By comparison, just 31% want to spend that much time on processes and 24% on admin.

The pattern is clear: HR leaders want to shift more of their time away from process and admin work and towards strategy, technology, and people.

That matters for employee experience.

When operational work consumes so much capacity, there’s less room for the work that directly shapes it, from stronger onboarding to meaningful check-ins and spotting signs of disengagement early.

The pressure affects HR teams too. HR workload challenges are now the norm, according to our research: 71% say their workload has increased compared with last year, while 52% feel a sense of burnout in their current role.

How to improve employee experience

The first step in any successful employee experience strategy is freeing up more time to focus on your people.

1.  Automate repetitive admin

2.  Streamline routine processes

3.  Build manager capability

4.  Make engagement part of the routine

Every practice holds information that people would rather not lose control of.

Payroll data, bank details, unfiled figures, disputes with HMRC, and plans that have not been announced yet… Accountancy and bookkeeping are more burdened with personally identifiable data than nearly any other profession.

So it is no surprise that data security tops the list of AI implementation challenges. In the AccountingWEB report State of the nation: AI in accountancy and bookkeeping, produced in association with Sage, 62% of respondents named it, putting it ahead of a lack of skilled personnel at 48%.

The encouraging part is that this is a procedural problem with a procedural answer.

Work through the checklist below and data security stops being the thing blocking your AI decision. It becomes the thing that makes your decision defensible.

Here’s what we discuss:

Why data security tops the list of AI barriers

At 62%, data security is the most frequently cited implementation challenge in the profession. It sits ahead of the skills shortage and ahead of cost.

There is a nuance worth noticing, though.

When the same research asked respondents for their single biggest concern about the rise of AI, a lack of accuracy and hallucinations came first at 29%, with data security second at around 22%.

Both findings are true and they measure different things. Security is the barrier accountants name when they think about implementation. Accuracy is the fear that keeps them up at night.

That distinction is useful, because the two risks have different remedies.

Accuracy is managed through review processes and human sign-off. Security is managed through tool selection, contracts, and daily habits.

This guide covers the second.

The underlying obligation has not changed. Your duty of confidentiality contains no technology exemption, and UK GDPR applies to personal data whether it sits in a filing cabinet or a chat window.

What accounting data should never enter a general LLM

Start with a clear prohibited list, written down and shared with everyone.

As a baseline, none of the following should be pasted into a general consumer AI tool:

• Client names alongside any financial detail
• National Insurance numbers, UTRs, bank details, and payroll records
• Anything covered by legal professional privilege or a specific confidentiality undertaking
• Unpublished results, valuations, or price-sensitive information
• HMRC correspondence containing client identifiers
• Anything you would hesitate to email to a third party you had never met

The habit that makes this workable is redaction before pasting.

Ask about the scenario rather than the client. A question on the VAT treatment of a mixed supply does not need your client’s name, turnover, or reference numbers to be answered well.

This matters more than it might seem, because of where external tools are actually being used.

In the research, around half of those using AI outside their core accounting software use it to research tax rules or other technical knowledge, and just under half use it to draft client emails and content.

Both of those uses invite people to paste in real client facts without thinking about it.

Premium and non-training settings, explained

Free consumer tiers commonly use your inputs to improve their models by default.

Paid business and enterprise tiers usually do not, and they typically add retention controls, data residency options, and administrator visibility.

Establish all of the following before anyone in your practice types anything sensitive:

• Is the training exclusion contractual, or a toggle in settings that a user could switch back?
• How long are inputs retained, and can you set that to zero or near zero?
• Where is data processed and stored, and can you demand a specific region, e.g. EU vs US?
• Is there a data processing agreement, and does it name every sub-processor?
• Which underlying model providers sit beneath the tool, and what are their terms?
• Are conversations subject to human review for safety or quality purposes?

Get those answers from the contract rather than the FAQ page.

A marketing page can be rewritten overnight without anyone telling you. A data processing agreement cannot.

Two routes into AI carry different risk profiles, and most practices end up using both.

Embedded AI lives inside software you already use and have already assessed. Client data stays within a system you have contracted for, and the supplier is one you have a relationship with.

The trade-off is narrower capability, since the features are built for defined accounting tasks. Around two-thirds of accountants believe their core software already includes AI of this kind.

External tools are general models and standalone products. The capability is broader and often better at open-ended work, but you are adding a new processor, a new data flow, and a new contract to your risk register.

A workable rule of thumb: use embedded tools for anything that touches client records, and reserve external tools for generic drafting, structuring, and thinking, with identifiers stripped before anything is pasted.

When a task genuinely needs an external tool and real client data, that is the moment for a proper procurement conversation rather than a personal subscription paid for on someone’s card.

Here are a good set of questions to ask before trying-out the software, rather than after it.

• Who is the legal entity behind the product, where is it based, and how long has it operated?
• Is there a data processing agreement, and does it list sub-processors by name?
• Are your inputs used for training, and will they confirm that in writing?
• Where is data stored and processed, and is there a UK or EU option?
• What are the retention and deletion policies, and can you export and delete everything on demand?
• Do they hold SOC 2 Type II, ISO 27001, or Cyber Essentials Plus?
• Does the product support single sign-on and role-based access?
• What happens to your data if the company is acquired or ceases trading?
• Has there been a security incident, and how was it disclosed?

Two further steps that practices routinely skip: Tell your professional indemnity insurer what you are planning, because some policies now ask about AI use at renewal. And check whether your engagement letters need a clause added before you begin rather than after.

Documenting your approach for clients and regulators

A written policy protects you twice.

It gives your team a clear line to work to, and it gives you something to show when a client, an insurer, or a professional body asks.

One page covers it. Here are some suggestions as to what you might include:

• The list of approved tools, and the tier or settings each must be used on
• The prohibited data list from earlier in this guide
• Who can approve a new tool, and what evidence they need first
• How often the list is reviewed, and by whom
• What you tell clients, and where that appears
• What happens if data goes somewhere it should not, and who is told within what timeframe

Keep it short enough that people read it. A one-page policy that is followed protects you considerably better than a fifteen-page policy sitting unread in a shared drive.

For clients, a short paragraph in your engagement letter and a page on your website will usually be enough.

Say what you use AI for, say what you do not use it for, say what happens to their data, and say that a qualified person reviews the work. Clients rarely want more detail than that. They want to know somebody is in charge.

Final thoughts

Data security is the most cited barrier to AI in the profession, and it has earned that position. It is also the barrier most amenable to being solved by an afternoon of deliberate work.

The practices that get this right do not have better technology than everyone else. They have a written list of what may go where, a habit of stripping identifiers before pasting, and one person whose job it is to approve new tools. None of that requires budget or specialist expertise.

Here is the takeaway: Decide your prohibited data list this week and circulate it. Move anything touching client records onto embedded tools inside software you have already assessed. Get training exclusions in the contract rather than the settings menu. Write one page describing your approach, and put a short version in front of clients before they think to ask.

Do that, and the 62% barrier turns into a competitive position.

Very few practices can currently answer a client who asks what happens to their data. You can be one of them.

Read below—State of the nation: AI in accountancy and bookkeeping, produced by AccountingWEB in association with Sage