Is HR Tech Undermining Your Strategy?
MIT Sloan Management Review ·
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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 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.
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.
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 strategy execution that shapes your talent decisions.
In balancing many competing priorities — expense, speed, capacity, strategy — organizations need to make trade-offs. Satisficing is a trade-off that occurs when someone accepts “close enough” default categories, thresholds, workflows, and definitions. All organizations face the practical realities of maintaining systems, minimizing customization costs, and integrating platforms. If decision makers aren’t clear themselves on what strategy execution requires, a solution that is low-cost or easy to integrate seems like a sensible choice — and the trade-offs aren’t immediately obvious.
Satisficing occurs when organizations restructure their workflows to fit with the systems, adopt standardized talent definitions, and use available capabilities and data fields as proxies rather than designing more nuanced, context-specific criteria and processes. Settling for “close enough” happens too often, especially when organizations accept the embedded aspects of vendor-designed systems. Enacting talent decisions based on externally designed assumptions will limit outcomes. Perhaps the platform assumes that talent is an attribute inherent in all individuals, whereas your organization believes that talent can be acquired and expanded through further development opportunities and that talent is transferable across contexts when leaders value a team-based approach.
A “close enough” tool may meet expense and speed requirements but fundamentally hamper the pursuit of strategic objectives. This is particularly salient because talent management and data-driven decision-making must take into account an organization’s unique strategy and its specific workforce needs and preferences in order to make effective talent decisions. Simplifying a hiring profile to fit platform fields, amending a performance rating scale to ensure data sharing across systems, or selecting only one critical role for succession planning because the system won’t allow users to select more: Each decision seems reasonable in isolation because it allows work to continue. But what began as a small and isolated compromise compounds as each satisficed area becomes embedded in everyday practice. A patchwork of suboptimized and unscalable processes can skew what is prioritized in talent decisions.
Articulating the outcome you want to achieve — whether it’s greater agility, more innovation, or better retention — is essential for effective business impact, as is the decision-making infrastructure embedded in your HR technology stack. When the links among criteria, processes, and outcomes are not articulated or customized for your particular organization and objectives, the parallel link between talent decisions and strategy execution becomes weak or nonexistent. The system may produce operational output in the form of data and reports without facilitating meaningful strategic impact.
With a strategy-first approach, talent systems, criteria, and processes are intentionally designed to reflect your organization’s strategic priorities. Being specific about the reasoning — the “why” — underpinning talent decisions begins with clarity: What is the issue you seek to address or rectify? What is the opportunity you seek to capture? Unstated assumptions and beliefs are often embedded in this reasoning — many times implicitly, such that you may not even realize you’re making them. Assumptions seem as natural as the sun rising in the morning but may not be fit for your purpose and should be explicitly surfaced and tested.
Criteria are the “what” — what you look for, measure, and prioritize. Processes are the “how” — how you assess, identify, develop, and structure your workforce. These design choices should be anchored by your organization’s unique strategy. In this context, hiring profiles match strategic priorities, performance reviews evaluate what matters, and development plans target capabilities required for strategy execution.
Based on our extensive work with organizations, we’ve learned that examining an organization’s talent management decision infrastructure is essential to ensuring that it advances the company’s strategy. To assist in this investigation, we offer the Strategy-First Check-In, a practical diagnostic you can use to help determine whether your HR tech stack supports strategic precision. These four steps aim to surface precisely where your systems may diverge from your priorities.
Defining what an ideal system would look like in the absence of “close enough” constraints helps clarify what are you tolerating that you would never actively choose. Take a holistic view of your existing HR technology stack to identify which elements add value and which have merely become accepted ways of doing things.
Consider what you would include when designing a fit-for-purpose talent system from scratch. Reassess the links between talent criteria, processes, and outcomes to map out an ideal technology ecosystem that actually serves your strategy. What strategic outcomes and business impact do you seek to achieve? Which processes are essential or add value? Which salient strategic criteria and definitions of talent should be used in the newly designed system?
The next step is to inventory the active systems and tools running in your HR technology ecosystem. In large or complex organizations, that alone can be a significant amount of work. Building a list of the user-facing and back-end tools that are currently in place to run all of the various operations across HR, never mind determining the owner accountable for each system, can be daunting. Often, forming partnerships with IT and finance is required to uncover all of the separate elements at play within just the HR space.
The biggest benefit comes from understanding how each tool’s embedded criteria, processes, and decision logic shape talent decisions. Consider the following aspects of your HR technology stack.
System purpose and history: Establish why each system exists and how it became part of the ecosystem.
Ownership and users: Identify who is responsible for and uses each system.
Data and decision logic: Understand how each system collects, stores, and deploys data and shapes talent decisions directly and indirectly.
Ecosystem fit and value: Assess the fit and value each system contributes to your unique strategy.
Gathering answers to the questions outlined above will enable you to answer deeper questions about how your talent tech stack enables or limits your goals and priorities and whether the system fits your current strategic needs. Of course, doing this effectively requires clarity on your organization’s existing talent strategy; for many organizations, getting a clear, coherent and consistent talent strategy may be the first, most critical step in this process.
Strategic alignment: Determine how each system reflects your strategy.
Embedded criteria, processes, and decision logics: Specify assumptions embedded within each system.
System interdependencies and decision logic: Examine the coherence of decision logic across systems.
System value and human work: Assess the strategic value of each system and identify human work.
This investigation will help you determine whether your systems are shaped by business priorities or by software limitations, and where you have compromised ideals for convenience.
The Strategy-First Check-In provides a structured way to assess the extent to which your talent technology stack and associated decision-making infrastructure are advancing or constraining your unique strategy. This comprehensive analysis lays the foundation for designing fit-for-purpose systems.
The purpose of the exercise is not simply to cancel your vendor contracts. Organizations benefit from designing and driving their talent stack intentionally, and a thoughtful transition requires an examination of interconnected chains of systems and workflows to identify risks to data and operational health. That involves evaluating the power and agency vendors exercise over your talent criteria, processes, and decisions to understand who — your organization, a vendor, or a group of vendors — is currently driving your talent decisions. Next, you should examine the implications of using vendor-designed definitions, workflows, and default settings, and understand whether their influence is intentional or has emerged through gradual slippage over time. From there, you can determine where leaders and functions have continued to be the authors of talent systems.
When you design your talent systems, it enables your organizational strategy to serve as the anchor for talent conversations. And when your talent decisions are treated as investments rather than expenses that must be controlled, it strengthens your ability to deliver strategic outcomes. Rather than restructuring your talent decisions to accommodate vendor-designed tools, you are customizing your HR systems to enable better organizational decision-making. When doing so, keep the following in mind:
As vendors continually add capabilities, new features might obviate the need for specialty tools. Eliminating such tools and letting the updated platform perform their functions reduces strain on the IT function: Having fewer data connections to manage is more cost-efficient and frequently creates higher-quality output as multiple functions or processes work together coherently.
Examining new functionalities will also help you decide whether to standardize on a single HR platform. Many organizations find that they’re using only portions of multiple platforms, often for legacy reasons, which adds operational complexity and data confusion to their HR IT system. HR leaders should identify where tasks or workflows could be simplified, streamlined, or automated.
Another common design strategy is to identify “uni-tasker” platforms that perform single functions. They are often the best candidates for replacement, if few or no other processes depend on them. Where a function can’t be absorbed by other tools already in your ecosystem, in some cases it may be a reasonable candidate for a homegrown or vibe-coded addition, where an AI agent may contribute. Using AI to automate routine tasks or to remove steps altogether can return both human and IT capacity back to the organization. The caution here is that you must pay careful attention to data privacy and stability.
Designing and driving your talent systems means that your organizational strategy serves as the anchor for talent conversations. Ultimately, the promises of HR technology stem from intentional design, not technology accumulation. When you invest the time reviewing how your technology stack advances or diverges from your strategic priorities, you’re better able to reclaim ownership as the designer and the driver of talent systems and ensure that your organization focuses on the wins that matter.