On October 23, the EMBRAS 2026 international HR awards ceremony took place in Tashkent. Three banks received awards in the Innovation category: KMF Bank from Kazakhstan, and ANORBANK and Orient Finans Bank from Uzbekistan.
At first glance, the results are hardly surprising: artificial intelligence, digital platforms, and automation. Technology has long been an essential part of conversations about the future of HR.
But a closer look at these projects reveals something more interesting. The three companies were solving entirely different problems. One wanted to accelerate employee development, another sought to eliminate fragmented HR services, and the third set out to transform recruitment.
Their results raise a question that often gets lost in discussions about innovation: How can we tell whether a new HR technology has actually improved the way an organization works?
KMF Bank: When Learning Stops Being a Separate Process
Gold went to Kazakhstan's KMF Bank for its AI Skill-to-Performance project.
The problem it addresses will be familiar to many organizations. Employees complete training programs, pass assessments, and receive certificates. Yet there is often a gap between completing a course and actually performing better at work.
KMF Bank sought to connect these processes into a single system that integrates learning, practice, skills assessment, and performance indicators. The system includes an AI coach, automated onboarding, and an AI-powered training simulator.
According to the EMBRAS organizers, 1,437 people have used the system. Requests to the learning and development team decreased by 69%, while the estimated time savings reached 1,890 hours per year.
New employees started reaching full productivity 30% faster. In just one week, the AI simulator conducted 501 training sessions for 167 employees across 55 branches.
That last figure is particularly revealing. This is not simply about enabling remote learning. The bank has developed a tool that can repeatedly simulate practical workplace situations without a proportional increase in the workload of internal trainers.
But the most interesting metric is not the number of training sessions delivered.
When employees genuinely become productive faster, learning stops being merely a cost center and becomes a tool for improving organizational performance.
Of course, the published figures alone do not establish causation. To evaluate the impact with confidence, we would need to know how new-hire productivity was measured, what the results were compared against, and what other changes were taking place at the same time.
Nevertheless, KMF Bank demonstrates an important shift: evaluating learning not only through participation and completion rates, but through changes in actual job performance.
ANORBANK: 40 HR Processes in One App
Silver went to ANORBANK for MYANOR, its unified employee application.
Imagine an ordinary workday. To read company news, an employee opens one channel. To access an HR service, they use another. To resolve an administrative issue, they message the person responsible. And when they need a document, they have to find the right system or contact.
To an organization, each of these interactions may seem insignificant. But when thousands of employees are involved, small inconveniences turn into a continuous loss of time.
ANORBANK brought corporate communications, HR services, process automation, and engagement analytics together in a single application.
According to EMBRAS, 86% of the bank's employees now use the app daily, and more than 40 HR processes have been automated.
Remarkably, the product was developed by a team of just four people.
MYANOR also includes a module called Pulse of the Company, which allows the bank to monitor employee sentiment in near real time.
What matters here is not so much the number of features as the thinking behind the solution.
Traditionally, HR systems are designed around organizational processes: personnel administration, learning, performance management, and communications. Employees are then expected to navigate the boundaries between these systems.
MYANOR takes the opposite approach: one entry point for everyday employee needs.
However, 86% daily usage is primarily an adoption metric. It does not yet tell us how satisfied employees are or how much time the organization saves.
A more complete evaluation would examine, for example, whether HR request resolution times have decreased, whether administrative workloads have been reduced, and whether the quality of employee services has improved.
Employee sentiment analytics raise another important question. How comfortable are people expressing dissatisfaction when their employer can regularly analyze their feedback? The value of this technology depends not only on the accuracy of the data, but also on employees' trust in how that data is used.
Orient Finans Bank: When AI Starts Evaluating Candidates
Bronze went to Orient Finans Bank for AI TAP — Talent Acquisition Platform.
The bank developed a digital recruitment ecosystem combining the OFB HR Yordamchi Telegram bot, a web platform, and AI tools for analyzing resumes.
The system automatically creates candidate profiles, analyzes resumes, and ranks applicants. Recruitment data is consolidated to provide end-to-end analytics.
According to EMBRAS, the solution has simplified applications, accelerated initial screening, and automated parts of the candidate management process. However, the published project description does not provide specific quantitative results showing reductions in time-to-hire or improvements in recruitment quality.
And this is where an important dilemma emerges.
Automating resume screening is relatively straightforward. Demonstrating that an algorithm actually helps identify better candidates is far more difficult.
For example, a system might reduce initial screening from several days to a few minutes. But if it systematically ranks strong applicants lower because their career paths are unconventional, those time savings may come at the cost of losing valuable talent.
In a study published in November 2025, the International Labour Organization highlighted this problem. Algorithmic HR systems can reproduce errors and biases embedded in historical data, while opaque assessment methods make those errors harder to detect.
This does not mean that Orient Finans Bank's project suffers from such shortcomings. The public description does not provide enough information to assess the quality of its algorithms.
It does, however, illustrate why speed alone is an insufficient measure of success for such systems. Quality of hire, the ability to scrutinize algorithmic recommendations, and human accountability for final decisions are equally important.
Three Projects, Three Different Levels of Evidence
If we set aside the technology labels and focus only on the published results, an interesting distinction emerges.
KMF Bank reports a reduced workload for its learning team, time savings, and faster productivity among new hires.
ANORBANK demonstrates high daily adoption and the scale of its internal process automation.
Orient Finans Bank describes its platform's capabilities and improvements to recruitment processes, but without comparable quantitative performance indicators.
This is not a ranking of the quality of the three solutions. Different challenges require different metrics, and public project descriptions do not necessarily include all the evidence reviewed by the judges.
Nevertheless, the distinction illustrates a fundamental problem in HR digital transformation: implementing a system, getting people to use it, and demonstrating its actual impact are three different things.
And the problem extends far beyond Central Asia.
According to SHRM's The State of AI in HR 2026, a study involving 1,908 HR professionals, 56% of HR functions do not formally evaluate the results of their investments in artificial intelligence. Only 16% use return on investment (ROI) metrics.
This creates a paradox. Organizations are increasingly adopting technologies designed to support data-driven decisions, yet the outcomes of those implementations are often assessed without a sufficiently rigorous measurement framework.
What Does This Mean for HR Leaders?
The experience of the EMBRAS 2026 winners is particularly interesting because they did not all start with the same technology.
KMF Bank focused on skills development and onboarding speed. ANORBANK concentrated on accessibility of HR services. Orient Finans Bank worked on recruitment efficiency.
In each case, technology was integrated into a specific business process.
And this is probably a more productive starting point for HR digital transformation.
Before choosing a platform, it is worth answering three questions: What problem are employees or the business experiencing? What is that problem costing the organization today? And what changes would demonstrate, several months later, that the problem has actually been solved?
Not every project needs to deliver an immediate, measurable financial return. Better access to services, reduced administrative workloads, and improved candidate experiences also create value. But organizations should at least attempt to measure that value.
There is another important consideration: making a process faster does not necessarily make it better. This is especially true when technology begins influencing decisions about people's hiring, development, and careers.
The EMBRAS 2026 winners deserve recognition for their achievements. But for the wider HR community, the real value of these awards lies in the opportunity to examine real projects and ask meaningful questions about them.
The next stage of HR technology may be defined not by how many AI tools organizations implement, but by how convincingly they can demonstrate what has changed as a result.
And that is a much higher standard than simply launching another platform.
Sources:
EMBRAS 2026 — Innovation Category Winners — Official announcement by the organizers, including project descriptions and reported results. (https://www.linkedin.com/posts/introducing-the-embras-2026-award-winners-ugcPost-7520794888074846208-Drwv/)
SHRM — The State of AI in HR 2026 — Research on AI adoption in HR. (https://www.shrm.org/topics-tools/research/state-of-ai-hr-2026)
International Labour Organization — AI in Human Resource Management: The Limits of Empiricism — Research on the limitations and risks of algorithmic HR management. (https://www.ilo.org/publications/ai-human-resource-management-limits-empiricism)