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HR technology is no longer peripheral to how organizations operate. A fast-growing assortment of platforms is touching the employee life cycle and capturing workforce data. At the October 2025 HR Technology Conference, more than 400 vendors showcased their wares, including new AI-enabled offerings. But despite this wealth of options, industry and consulting reports frequently attest that these proclaimed advancements in systems and decision-making remain largely unrealized; notably, SHRM found that fewer than half of both HR professionals and employees regard their company’s HR technology as effective.
Each platform promises an alluring array of benefits, the most compelling of which is the ability to improve evidence-based decisions by establishing a single source of truth: current, centrally accessible workforce data. On this foundation sits the additional promise of advanced visibility through dashboard and reporting capabilities that provide instant access to information about the workforce’s skills, capabilities, and outcomes. Yet another promise is the ability to embed and standardize best-practice frameworks across units, functions, and geographies.
The logic is straightforward: When talent decisions are supported by structured processes, shared data, and embedded analytics, leaders can shift from making fragmented judgments to intentional, data-informed decisions. These platforms serve as the lenses through which you are able to see and understand your own systems in order to pursue your own priorities.
But your interests differ from those of your vendors, who prioritize scalability and repeatability. Commercial platforms are fit for sale; your organization’s unique strategy requires systems that are fit for purpose. The criteria, workflows, and reporting abilities embedded in vendor platforms are designed to work for many clients — including your competitors.
The Accumulation Trap
The proliferation of HR technology in the market is too often mirrored within organizations. Unlike in the days of enterprise resource planning, businesses today maintain an ever-expanding portfolio of specialized tools across the talent life cycle. (Both of us have collaborated with organizations that have maintained anywhere from 80 to hundreds of different systems.) Decision makers are often surprised to find that the issue is not a lack of technology but an over-accumulation of platforms.
Few organizations’ HR technology stacks emerge from intentional architectural design. Instead, systems evolve through the steady addition of point solutions — individual tools acquired to solve localized or immediate problems and gaps. Each addition seems beneficial in isolation; it may, for example, fix a reporting limitation, add a missing hiring capability, enable employee surveys, or support a new learning initiative. But the assumption that more technology equals progress is fundamentally flawed.
Over time, these point solutions accumulate, as talent processes and decision-making authority are distributed across teams, functions, and geographies. Individual teams select different tools without having a comprehensive view of the broader technology ecosystem — undermining coherence, given that data often needs to be extracted, translated, reconciled, and transferred across systems. As AI reduces coding barriers, it is easier than ever for an organization to augment or build its own tools to replace limiting vendor solutions. Local vibe-coded point solutions are initially inexpensive but may lack stability, security, or be difficult to integrate.
Without intentional design tailored to the enterprisewide infrastructure, adding more platforms to your talent infrastructure can breed misalignment, with two significant organizational consequences: increased waste, and divergence from your strategic priorities.
How HR Tech Can Create Waste
The accumulation of HR technology systems often results in functional, financial, and decision waste for organizations. A fundamental and nearly universal business problem is data fragmentation. When data privacy and security are managed within each tool, as is often the case, competing models can create risks, such as inappropriately blocking data needed for essential functions in other tools — or inadvertently releasing data from one tool that is protected in another. Entire roles are now emerging for employees to bridge these gaps as organizations rely on “human glue” to manually extract, translate, reconcile, and transfer data across multiple systems.
Such fragmentation is a governance issue, not a technical one; when various systems collect, store, and interpret data in conflicting or incompatible formats, fragmented pipelines and algorithms that are using incomplete or inconsistent inputs can produce imprecise outputs. Lags in data availability delay decision-making as stakeholders wait for needed information, which could be out of date before it even arrives. Worse, if different systems provide different answers, competing interpretations and misaligned perspectives can proliferate. When different people have a different set of data about the same problem, it’s difficult to come to a shared understanding of what’s important or even what’s actually happening, which limits the organization’s ability to develop coherent plans.
Confusion and blurred accountability arise when there is no single party responsible for the quality of the entire tech stack. Duplication — when multiple tools perform the same or similar functions under separate contracts, renewals, and support models — can erode coherence and multiply costs. As multiple systems perform overlapping functions, different tools operate as the system of record for different processes. This competition is created and sustained by vendors and their internal champions, who attest to the value of that specific system above others and promote it as the authoritative platform.
Lastly, systems can grow outdated and diverge from current decision-making processes. Many vendor platforms prioritize static logic despite “talent” being inherently dynamic. The workflow still runs and the data is generated, but continuing to use logic or criteria that are outdated can actively sabotage progress. Imagine wearing shoes you’ve outgrown for the rest of your life just because they fit you at one time. Decision-making underpinned by “that’s the way we’ve always done things around here” is a red flag for your infrastructure. Strategy-as-experience appeals to precedent, but strategy is executed through evolving practices and ways of operating. Prioritizing organizational legacy contributes to decisions based on outdated assumptions.
Mapping Your Intent: Strategy First
When adopting vendor technology for talent management, organizations that don’t first consider whether a system supports desired outcomes can unintentionally cede command of their own strategy to vendors.1 A platform’s embedded decision logics, criteria, and processes reflect the vendor’s commercial interests, not the user’s. Vendor offerings are fit for sale, not fit for purpose, with initial and ongoing customizations requiring repeated investments to address recurring limitations. By accepting system defaults, you can lose ownership of the infrastructure that shapes your talent decisions — a primary mechanism of str
