A benchmark study measuring how technology shapes employee experience and business outcomes across South Africa's major banking and insurance institutions.
The Altron Digital Business Employee Technology Experience Index (TEXI) is a benchmark study in South Africa to measure how technology shapes employee experience and business outcomes across the country's major banking and insurance institutions.
Results from the study indicate that there is a strong correlation between technology and employee experience outcomes, namely eNPS, job satisfaction, feeling valued, and staff retention. Overall, FSI employees have a positive technology experience, and the overwhelming majority believe technology enables their best work.
There is a strong link between technology performance and customer experience, with nearly half (47%) of all respondents reporting that technology friction has caused them to deliver a slower or worse experience to a customer or colleague. The impact grows sharply with increased disruption frequency. And the data suggests tech-related friction and disruption is estimated to cost institutions between R3.2m and R30m per 1000 employees per year.
IT leaders may not have a true picture of their IT support. Over 28% of the full sample agreed that they have stopped raising technology issues because nothing changes. What's more, an employee's tech experience is closely linked to the likelihood of them using unsanctioned software, devices, or tools (shadow IT).
The research finds FSI technology and EX leaders have three distinct levers to pull to improve outcomes, depending on the institution's goal. Some technology categories function as hygiene factors, meaning poor performance degrades the employee experience while strong performance is largely taken for granted. Others function as motivators, where strong performance actively drives advocacy, pride, and retention.
Taken together, the findings give FSI leaders a clear set of choices: where to invest to prevent damage, where to invest to build advocacy, and where to act before employees solve the problem themselves.
South Africa's banking and insurance institutions are operating in one of the most competitive moments in their history. Customer expectations have been reshaped by digital-native challengers. Talent is mobile, scarce, and increasingly sought by employers outside the sector. New service models are putting downward pressure on legacy economics. In an environment where institutions are largely competing on the same products, the differentiators that remain are operational efficiency, customer experience, and the quality of the workforce that delivers both.
These three things are not independent. Harvard Business School's service-value chain links them tightly: employee experience shapes operational performance, which shapes customer experience, which shapes commercial results. An employee equipped to do their best work delivers better service to customers. An employee who feels supported by their employer is more likely to stay, recommend their organisation, and invest discretionary effort. Conversely, employees frustrated by their tools and systems will, sooner or later, transmit that frustration to customers or to the labour market.
It's no surprise that employee experience has moved from the margins of strategy to the centre of board-level conversations across the sector. The challenge is no longer awareness. It is twofold. First, quantifying the relationship between technology investment and business results clearly enough to justify investment and elevate it to a strategic priority. Second, even where the willingness to invest exists, the question is which specific levers within their own organisation will deliver the biggest return.
Underneath both challenges sits a single unanswered question: how does the quality of internal technology service delivery affect employee experience, and what does that mean for business outcomes? Most organisations measure technology in operational terms: device fleet age, infrastructure stability, ticket resolution rates, system uptime. These metrics describe what IT does. They do not describe what it feels like to work with the technology IT delivers, and they do not connect technology performance to the outcomes that boards care about.
The consequence is that technology friction exists as a hidden productivity tax. It does not present as a sharp, visible cost. It accumulates instead in small daily moments: a slow application, an authentication loop, a slow network response, a delayed support ticket. But together, across thousands of employees and millions of interactions, this friction can erode advocacy, slow customer service, and quietly reshape the employer-employee relationship in ways that operational dashboards do not capture.
The impact of technology experience on employee experience, including job satisfaction, advocacy, and talent retention.
The impact on productivity and operational efficiency, including the productive time lost to disruption and friction.
The impact on service delivery and customer experience, including the proportion of customer interactions affected by technology friction.
The impact on future-readiness and competitiveness, including how prepared employees feel for an AI-enabled future.
The report that follows sets out what the data shows, what it means, and where it points for leaders deciding where to act first.
1. Harvard Business School: https://online.hbs.edu/blog/post/service-profit-chain
The Altron Digital Business Employee Technology Experience Index benchmark report sets out the major findings from the TEXI fieldwork and discusses major insights from across the South African FSI market.
respondents who are qualified FSI employees across 8 institutions and a ninth 'other' classification.
Respondant locationsA note on scoring methodology. A number of findings are reported using x-scores, calculated using the standard NPS methodology developed by Bain & Co. The x-score (e.g., +41) is found by taking the percentage of respondents rating 9–10 ('promoters') and minusing the percent rating 0–6 ('detractors'). Respondents scoring 7–8 are considered 'passives'. The only exception is the composite technology experience score (across all categories): for the composite score, promoters are the percentage of those scoring 8.5–10.
Our research shows a strong positive correlation between technology and employee and customer experience. For businesses that use advocacy as a measure of employee sentiment, respondents with positive experience of technology had much higher eNPS scores than those who didn't.
The good news for South African FSI institutions is that employees generally have a positive technology experience.
The link between technology and employee experience can be seen when we segment respondents by their overall technology experience rating (average across categories used).
The key measure of employee advocacy (eNPS) moves by 105 points between Tech Promoters and Tech Detractors. Employees in the 'tech promoters' segment are more satisfied with their job, more likely to feel valued by their employer, and less likely to want to leave the business compared to technology detractors. For those who have poorer experiences of their institution's technology, they don't feel valued, they use much more shadow IT to get things done, and report much greater impact on customers or colleagues.
| Business outcome | Tech Promoter | Tech Passive | Tech Detractor |
|---|---|---|---|
| eNPS | +72.2 | +13.5 | −33.3 |
| Job satisfaction (mean) | 9.17 | 7.65 | 7.06 |
| Feel valued (% 8–10) | 82.5% | 50.8% | 25.0% |
| Shadow IT use (% yes) | 47.5% | 54.8% | 72.2% |
| Intent to leave (% 7–10 likely) | 35.0% | 35.7% | 41.7% |
Over a quarter (27.8%) put quality of technology and tools in the top 2 factors keeping them in their current role. This is significantly ahead of pride in their employer (10.4% top 2) and team culture and colleagues (19.2%).
While overall experience is an indicator of positive eNPS, technology friction and disruption frequency is a consistent indicator of lower eNPS scores.
Our preliminary interviews with SMEs and customers suggested that there is a degree of friction that acts as a 'silent productivity killer' among FSIs. Senior management often cannot see when individual employees are experiencing downtime, such as the hours wasted in the IT support room trying to get a laptop sorted out, or the struggles of dealing with old computers and devices that do not connect properly. We discuss the cost and duration of disruption and friction later in the paper. But there is an important employee experience point when it comes to 'silent friction.'
There is a very strong correlation between disruption frequency and apathy (or learned helplessness). Among employees who experience multiple disruptions per day, half have stopped reporting IT issues because nothing changes — versus just 17% among those disrupted rarely.
Apathetic acceptance is the clearest hygiene-factor failure pattern in the data. Frequent disruption has taught these employees that nothing will change, and they stop reporting issues altogether. For institutions that use support tickets as a proxy measure of performance, this lack of reporting could obscure the true picture of their technology performance.
Our research framework anticipated that hybrid and branch experience could diverge from HQ, and the data confirms it. It also shows that remote employees are more likely to consider leaving the institution.
The EXI composite x-score for home/remote workers is 37.6, compared with 52.2 for HQ campus and 48.9 for branch. Intent to look elsewhere is 61.9% for home/remote workers, compared with 34.5% at HQ. The sample for home workers is small (n=21) and carries caveats, but the direction is consistent across every technology category and outcome metric.
Branch workers fall in the middle. Infrastructure friction affects them more than HQ, but they have a better experience of IT support than employees who work from home. A likely reason is that IT support has more control over the branch or campus technology, whereas an employee's connectivity or power at home are outside their control.
The data suggests the impact on customer experience from technology disruption is significant.
Nearly half (47%) of all respondents reported that technology friction has caused them to deliver a slower or worse experience to a customer or colleague. Even among Tech Promoters, 39% report delivering a slower or worse customer/colleague experience because of tech friction.
| Business outcome | Tech Promoter | Tech Passive | Tech Detractor |
|---|---|---|---|
| Customer / colleague impact (% yes) | 39.0% | 57.9% | 58.3% |
The risk grows sharply with disruption frequency. Among employees disrupted daily or more, 86% report having delivered worse service to customers or colleagues. Among the "multiple times a day" group, over half say it happens frequently, not just occasionally.
One of the challenges of determining an ROI for technology investment is in evaluating the effect on productivity. Part of the challenge is that common measures of productivity are fairly blunt — many listed businesses report revenue or profit per employee as an aggregate measure.
In our approach, we use salary as a proxy for productivity. We use salary because:
We measured frequency and duration of disruptions and correlated them against respondents' salary ranges. While this method is not perfect and results in a wide range of potential costs, it does illustrate the potential impact of technology friction and disruption on businesses.
The data suggests the average FSI employee in this sample loses an estimated 76 minutes per week to technology disruptions, equivalent to roughly 7.6 working days per year. The median is 12 minutes lost per week (1.2 days per year), which reflects the average being skewed by a smaller group experiencing high frequency disruptions.
At estimated employer cost rates – including salary and other employer costs – that translates to approximately R29 800 per employee per year on average, or R3,200 at the median. Again, the average cost per employee is increased by high earners typically reporting more disruption.
The mean estimate implies roughly R30 million per year in disruption-related productivity loss per thousand employees. The median-based estimate is R3.2 million, reflecting that most of the cost is concentrated in a relatively small group of heavily disrupted employees.
The by-frequency breakdown is the most useful cut for telling the story. Employees disrupted multiple times daily cost an estimated R143 000 per year each. Those disrupted rarely cost R600. That 235x ratio makes the case for targeted intervention.
Even with conservative assumptions, and allowing for variation between the average and median, FSIs will be experiencing a significant productivity cost.
We examined how employees use alternative methods (shadow IT) to get their jobs done. Workaround behaviour is prevalent across the board.
However, the deeper view is using disruption frequency as the lens. Employees who face frequent technology disruption are five times more likely to be regular shadow IT users. Among employees disrupted once a week or more, 72% use shadow IT (37% regularly, 35% occasionally). This contrasts with employees who experience disruption less than once a week (only 35% use shadow IT — 7% regularly, 28% occasionally). The "regularly" figure is the sharpest contrast (37% vs 7%).
Such levels of shadow IT use can be a security, compliance, and data governance exposure. It's a board-level priority with complex causes that requires a holistic approach.
| Business outcome | Tech Promoter | Tech Passive | Tech Detractor |
|---|---|---|---|
| Shadow IT (% yes) | 47.5% | 54.8% | 72.2% |
One of the key insights from our background research was the idea that people don't notice things like networks, hardware, or security when they work well. And more intangible metrics such as employee sentiment are hard to tie to technology investments. We looked at which technologies cause damage when they fail versus which technologies create value for employees when they're excellent. They're not the same list, and they need different investment cases. Our approach is based on a two-factor model of hygiene factors and motivators.
A hygiene factor is one where low scores strongly predict negative outcomes, but high scores don't strongly predict positive outcomes. Things like a working laptop, a reliable network, or a desk that doesn't collapse. When they work, nobody notices. But when they break, people get frustrated, disengaged, and start looking elsewhere. They can only hurt you, never really lift you.
A motivator is one where high scores predict positive outcomes beyond just the absence of pain. Things like feeling proud of your employer, having access to cutting-edge tools, or feeling like the organisation is investing in your future. These create positive energy. They make people advocate for the company, go above and beyond, and stay longer.
These have high experience scores but the weakest correlation to employee sentiment. When they fail, people disengage.
These factors have higher correlation to employee sentiment (eNPS, felt valued, supported). When they're excellent, people feel proud, valued, and invested in.
A hygiene factor rather than an employee motivator
Classic infrastructure categories (network, communication, hardware, security) score highest on raw experience score but show the weakest correlation with employee sentiment. Hence, they function as hygiene factors: when they're broken, people are unhappy, but nobody gets excited when they work well. However, network, productivity tools, and core business apps are the strongest predictors of customer impact. The things that directly affect customer outcomes are the tools employees use in the moment: network speed, the apps they work in, their productivity and collaboration tools, their devices. When those are poor, the customer feels it immediately.
The friction profiles of the two sectors point to somewhat different operating environments. Banks tend to cite more friction at the physical and support layer of IT: old hardware (cited as a top friction source by 21% of bank employees vs 11% of insurer employees), inadequate IT support (25% vs 18%), and lack of integration between systems (31% vs 24%).
The sharpest employee experience investment case
IT support is the opposite to infrastructure. The data shows it sits in the motivator classification, meaning that improving it doesn't just reduce complaints. It can actively drive positive employee experience measures that are relevant to board agendas.
People who rated IT support higher tended to also show higher eNPS, and people who rated it lower tended to show lower eNPS. The same pattern holds for job satisfaction, feeling valued, and trust in quick resolution.
What makes IT support distinctive is not that these correlations exist. Most technology categories will show some positive correlation with those outcomes. It's that IT support's correlations to NPS, felt valued, supported/empowered, and job satisfaction are the strongest of any category.
Of the 13 positive outcomes measured, IT support is the strongest predictor of 10 of them — including every measure of engagement, advocacy, perceived organisational investment, and trust.
There is a modest but real pattern in the data that suggested employees with access to more support channels rate their IT support more highly. The pattern is strongest in relation to walk-in support and device swap-out services. The effect is small relative to the broader IT support findings, but the direction is consistent with the wider pattern in the report and anecdotal evidence that higher-touch support channels deliver better employee experience.
The data suggests that investing in IT support can help improve employee satisfaction and retention. The importance of support may come as a surprise to some. However, the correlations with emotive outcomes make sense when you consider IT support as a signal that employers value their staff. It is analogous to customer support in consumer industries such as retail, where a positive support experience can be the most powerful driver of customer loyalty.
An opportunity for FSIs to empower their employees
AI is the future-readiness case. It's a clear motivator in the data on forward-looking outcomes. Frequent AI users show significantly higher agreement that their organisation is preparing them for the future, that they feel proud of the technology they have access to, and that their employer is investing in the right tools. Daily users agree with these statements at rates 25–30 percentage points higher than rare users.
However, individual use is ahead of institutional enablement. There is a clear sense that employers could do more to train and equip staff to use this transformative technology.
A portrait of self-sourced AI users emerges from the data. These are people who have taken initiative to find productive tools because their employer hasn't met their needs. They're more disrupted, more likely to have given up on internal support channels, more likely to be using unofficial workarounds across the board, more likely to report customer impact from tech friction, and significantly more likely to be looking for another job. They haven't disengaged from their work. They've disengaged from their employer's ability to support them.
Banks score 9 points higher on AI experience (+38 vs +29) and slightly higher on hardware (+61 vs +57). The AI gap is the largest reversal in the tech category data. Notably, this is experience of AI tools, not access or usage. Employees in South African insurers reported more use of AI tools but also greater use of self-sourced tools: almost half (46%) of insurer AI users use AI daily, compared to 33% of bank AI users. But more of that adoption is happening outside the employer's environment — 36% of insurer AI users rely primarily on tools they have sourced themselves, vs 26% in banks.
The investment case is twofold: AI is a significant opportunity to improve employee experience, and closing the self-sourcing gap protects against the retention and governance risks that follow when employees solve their own AI problems outside the sanctioned environment.
Technology experience is no longer just an IT concern
The ADB Employee Technology Experience Index set out to test whether the way internal technology is delivered shapes employee experience and business outcomes in measurable ways. The data is clear: it does, and the magnitude of the effect is large enough to warrant board-level attention.
The headline picture is positive. South African banking and insurance employees are broadly satisfied with the technology they use, and the foundational layers of the technology stack are working reasonably well. But beneath the surface, friction is reaching customers, eroding employee advocacy, and increasingly being met with workarounds and silence rather than reports. The cost of disruption is real, unevenly distributed, and largely invisible to the people responsible for addressing it.
The research points to four steps leaders can take to translate the findings of this study into action inside their own organisations.
The strongest correlations in this dataset are between technology experience and outcomes that already sit on board agendas. That gives leaders a defensible basis for moving technology experience into the same review cadence as eNPS, customer NPS, and operational efficiency.
Two organisations with similar EXI composites can have very different underlying profiles, and the right lever depends on the diagnosis, not the headline score. Understanding the challenge, whether through this study, internal feedback channels, or continuous measurement, should be an institution's first action.
The data points to three distinct investment cases. IT support is the sharpest employee experience lever, with the strongest correlations to advocacy and retention. AI is the forward-looking lever, with the opportunity to formalise an adoption pattern already running ahead of institutional governance. Core infrastructure is the customer experience lever, where network, applications and productivity tools quietly determine whether technology friction reaches the customer.
More than one in four FSI employees have stopped reporting technology issues, and nearly 30% of AI users rely on tools they have sourced themselves. Both are signals that institutions can do more to meet expectations. Prioritising responsiveness in fault fixing, and the provision and governance of AI tools, will help connect employees back to the organisation.
The lesson from this first-of-a-kind study is that technology experience is no longer just an IT concern. The institutions that make this a central part of their technology, employee and operational strategies will create competitive distance on the factors that increasingly influence an institution's success.
