By Ilona Węgłowska-Hajnus, director, PwC Poland
The paradox of modern HR
People operations sit at the core of organisations, being responsible for operations such as payroll, employee data, compliance and the day‑to‑day employee experience. When the area doesn’t work, the whole business feels the effect of it.
In recent years, rapid advances in HR technology, including AI, have allowed people operations to benefit from efficiency gains. These tools can allow for a genuine and significant improvement in service quality, reliability and cost efficiency.
Yet many organisations see a very real issue: despite investments in HR systems, payroll solutions, human capital management (HCM) platforms, workflows, chatbots and AI, the expected benefits just aren’t there. Payroll errors still occur, SLAs are missed, teams are overloaded, and trust in HR and payroll data remains low. Research shows that most HR leaders do not see significant business value from AI yet, highlighting the gap between technological potential and real operational outcomes.
The HR Tech myth: “Invest in technology and the problem will disappear”
The assumption that implementing an HR/HCM platform, automating processes, launching self‑service, and adding AI will be enough to deliver real‑time data and ready-to-use compliance often fails. HR Tech is frequently treated as a plug‑and‑play solution for ‘fixing’ HR, payroll, and administration, especially while under pressure to cut costs and improve employee experience.
Problems arise when technology is treated as a universal solution rather than as a tool embedded in a coherent operating model. In practice, many organisations find that despite extensive implementation efforts, technology does not deliver the full value promised. Instead of simplifying work, employees and managers face more operational tasks, while rigidly designed processes fail to handle real‑life scenarios, generating numerous exceptions and manual workarounds.
For example, an organisation with several thousand employees implemented a centralised system for workforce planning, as well as time and attendance to simplify processes and automate compliance. After go‑live, misalignment between standard system flows and regulatory and organisational complexity led to many exceptions, manual interventions, and off‑system workarounds, reducing the expected benefits.
Another organisation replaced local, email‑based hiring and contract‑change processes with a single workflow meant to speed-up decisions and embed compliance rules. In practice, the rigid workflow did not reflect daily realities, triggering off‑system communication, escalations, and longer completion times.
Five assumptions and the reality of HR Tech value:
“Once a system is implemented, automation will organise the processes.”
Automation works only where processes are stable, rule‑based, and supported by complete data. When data is incomplete or rules depend on local interpretation, automation doesn’t remove work—it shifts it into a manual handling of non-standard issues. Standard cases speed up, but complex ones still require human judgment.
“Self‑service will eliminate a large portion of HR work.”
Self‑service reduces HR workload only when it is part of a deliberately designed service model, with clear rules, communication, data quality controls, and governance. Otherwise, it simply shifts work onto employees and managers, increases error rates, and generates additional corrections in payroll, reporting, and controls.
“AI will eliminate errors and improve decision‑making.”
AI is effective in pattern recognition, case classification, and anomaly detection, but its effectiveness depends on data quality and clear decision‑making mechanisms. It does not replace operational foundations.
“Real‑time data guarantees control.”
Real‑time data does not build trust without shared definitions, structured-source systems, and data-quality controls. Without these, the organisation sees inconsistencies better but does not necessarily resolve them faster.
“The system provides ready-to-use compliance.”
Compliance is a daily way of working: decisions, approvals, data access, documentation, auditability, and handling of local exceptions. These responsibilities are always a responsibility of the organisation and cannot be handled in full by a tool. A system can support compliance, but it does not maintain it.
From HR technology to business value:
HR Tech as a catalyst, managed services as the foundation
Modern HR platforms, automation, and AI are powerful catalysts for change: they enable standardisation, faster service delivery, and better access to data. On their own, however, they do not guarantee sustainable results. To translate technological potential into tangible benefits, organisations need managed services as an operational foundation -a model that closes accountability gaps, stabilises service delivery, and ensures predictable outcomes. This requires a shift in perspective: from “we have implemented a tool” to “we manage an end‑to‑end service” in a measurable, repeatable way that is resilient to organisational, regulatory, and scale‑related changes.
What are managed services in people operations?
Managed services are a service delivery model, not just a task‐transfer mechanism. The key distinction lies in accountability for outcomes: quality, timeliness, compliance, employee experience, and cost predictability. In People Operations, Managed Services bridge business needs and technology by absorbing volatility (reorganisations, policy changes and regulatory updates), managing exceptions, maintaining service stability, and continuously improving processes and system configurations.
Key components of a mature managed services model:
End‑to‑end ownership:
a single, coherent accountability model for the entire service, so that someone owns the outcome, not just the process.
SLAs, KPIs, and governance:
clearly defined scope, standards, exceptions, and closure criteria that make the service measurable and scalable without sacrificing quality.
Expert teams and escalation paths:
stable expert capabilities and transparent escalation models to handle exceptions and local issues consistently.
Quality control and root‑cause analysis:
built‑in validation, error‑trend analysis, and elimination of root causes particularly critical in payroll.
Continuous optimisation and change management:
mechanisms to adapt processes and system setup as regulations, structures, and policies evolve.
Data and decision governance:
agreed definitions, authoritative data sources and clear decision rules as the basis for trust, compliance and reliable reporting.
Synergy instead of conflict: the division of roles between technology and managed services (including AI)
An effective operating model is not built on competition between technology and managed services but on their complementary roles and tight alignment.
Technology (HR Tech / HCM / workflows / AI) automates standard processes, structures data, and provides a single source of truth. It enables integration and reporting and supports self‑service and communication. AI enhances this value by handling case classification, user guidance, anomaly detection and automated data completion.
Managed services take over where automation ends. They manage exceptions, make operational decisions, ensure quality, compliance, coordinate changes, and drive continuous improvement. In doing so, they provide service stability and continuity in the face of business volatility.
Modern people operations are not created by technology alone, nor by processes in isolation. They emerge from the deliberate integration of both into a coherent service delivery model. HR Tech and AI are powerful accelerators of efficiency, but only when embedded within a managed-services model with strong governance, quality control, and continuous improvement, they translate into stable, predictable and tangible business value.
