HR Technology iPaaS Platforms: Key Considerations for Solution Providers & Customers

iPaaS: Key Context

Getting straight to the definition for starters, an iPaaS, or Integration Platform as a Service, is typically a cloud-based, modern software application for optimally connecting different enterprise systems and tools often having disparate tech stacks, data models and/or user experiences – all of which might be getting frequently updated. When the iPaaS is a good fit, or even ideally designed for a type of enterprise systems environment (such as the HR / HCM Technology realm), the proper integration of such highly valuable assets can result in quantifiable, often impressive ROI.

These iPaaS tools differ in their strengths and challenges, which will be discussed later. The latter scenario might be the result of a particular iPaaS not being adequately designed to be readily adapted in a way that accounts for each integrated system or tool’s purpose, scope, ongoing plans and even its likely shelf-life. When this occurs due to such iPaaS gaps or limitations, end-customer organizations usually find themselves scrambling to replace it using a third party iPaaS or try to build their own “system interoperability enabler” -– or perhaps determine which systems or tools may be causing the problem and make necessary adjustments there, such as with a tool-specific application programming interface (API).

Core to this iPaaS-related context is the fact that the number and complexity of HR / HCM enterprise systems and tools has exponentially grown over the years. And just as it relates to the HR Tech solution landscape, whole new categories of solutions are actually being created every few years. As an example, Wellness Solutions, while existing for some time on HR leadership’s radar, reached a point where increasing market demand and traction eventually spawned enough new vendor offerings in this area to require sub-categories to be created, such as Financial Wellness for the workforce. The same dynamic relates to Career Management and Employee Engagement, or the more widely cited examples that fall under Recruiting, Learning and Development, Workforce Management (including time and attendance, scheduling, and forecasting), Workforce Planning, and People Analytics or Talent Intelligence. 

More saliently, the findings of multiple studies have noted that medium-to-large sized organizations can easily have — and presumably want to integrate — a dozen or more HR systems of record. These are the much broader, foundational platforms for all workforce related data (current and historical), transactional workflows and planning/decision activities to be managed … but once we bring more specialized, best of breed solutions into the picture, the total number of HR Tech solutions might reach as high as eighty, as covered in this Bersin-published study.  

Moreover, even more complexity — and associated ongoing development costs (aka “technical debt”) come into play with HR systems environments involving larger data volumes, some amount of system fragmentation and/or the need for deeper technical expertise. While mitigating such integration challenges can be made possible by tools like APIs and middleware, these can also require comprehensive, ongoing testing for data accuracy, security, and performance. I’ll also assert it is near-impossible to predict the next specialized HR Tech tool or point solution that will be viewed as needed for some new specialized purpose related to leveraging and maximizing human capital for competitive advantage (my personal ‘HCM’ definition).

The main goal being covered here is to create a unified, automated HR Technology ecosystem that also eliminates manual data entry, reduces errors, and improves efficiency and end user productivity. The task at hand, simply put: Enable seamless data flows (autonomous or person-initiated) so that data across the entire HR Tech ecosystem – and adjacent, relevant tech ecosystems – is always current and accurate. It is only then that the wide range of workforce-related decisions, from day-to-day to the ‘very significant’ variety, can be made with confidence. 

For example, elevating organizational agility, something I’ve long viewed as the most reliable path to ascending the ranks within an industry sector, and even more critical during fluid and unpredictable times, is another highly sought after outcome from an iPaaS. This notion is reinforced in studies from Accenture among others, which have found that truly agile firms are more than twice as likely to achieve top-quartile financial performance. I’ll add that highly agile organizations probably don’t spend much if any time remediating integration problems with their most highly used systems.

This also needs to be achieved across an increasing number of HCM and (related) non-HCM systems where the ultimate reasons for the connection are not always known or anticipated. Furthermore, “maximum interoperability across people systems” now includes accounting for skills ontologies, a hot topic in HR Technology and Talent Intelligence. These ontologies are built around obvious and not-so-obvious data linkages related to skills. Today, with some HCM solution vendors having expanded this impressive skills graph capability to essentially form a neural network of connected nodes, both the complexity and effort ‘dials’ on HR Technology integrations has been turned up even more. All this leads to the topic of whether a chosen iPaaS can scale and readily adapt to an ever-changing HR Tech operating environment without a group of developers needed for every refinement or modification needed. This is why the best iPaaS solutions now routinely offer no-or-low code options and end-user-controlled configuration tools designed to augment what might be a large number of pre-built integrations.

Finally for this upfront key context, I’ll highlight that the lack of high-quality data access is particularly problematic in the ‘AI era’, which depends on current, consistent, and connected data flows to fuel everything from predictive analytics to bespoke AI copilots. To unleash the full potential of AI, enterprises must first preempt potential bottlenecks that would interfere with information flowing freely across relevant systems. Therefore, they must also ensure all ‘data pipelines’ are reliable and well-governed. The reason: when AI models are trained based on inconsistent or outdated data, the insights they generate can be misleading or incomplete without users even knowing this! This naturally undermines the raft of expected benefits from AI-powered tools, from employee engagement and productivity, to customer satisfaction and loyalty, to financial forecasting and modeling.

Integrations in the AI Era – HIGHLY Relevant Research Findings

The following, perhaps surprising findings are excerpted from what I believe is the most relevant and recent industry study on this blog’s general topic, and kudos to HR.com for making it widely available as well. HR.com’s “State of Today’s HR Technology and Integrations” survey ran between November 2024 and February 2025. They gathered responses from 153 HR professionals across virtually every industry, included respondents from around the world (although majority were from North America, especially the United States).

• Only one in ten respondents rated their HR technology stack at the ‘expert stage’ of maturity; and one can certainly infer integration issues as in the mix here. Most say their HR technology is at the advanced (26%) or moderately developed (38%) stage.
• Top capabilities included in the HR technology stack are payroll (79%) and recruitment/ talent acquisition (74%); although other industry studies (e.g., from Sapient Insights) have “L&D” steadily gaining ground.
• Only 39% say their various HR / HCM solutions are usefully integrated with one another; with 20% finding the integration to be poor (17%) or very poor (4%).
• Of those who indicate their solutions integrate poorly, 81% believe this lack of integration keeps the organization from attaining important HR goals.
• HR technology groups seem often struggle with integrations, funding, and actionable analytics; although the biggest pain point cited was “lack of integrations within/between systems.”
• 80% of organizations also plan to implement new AI initiatives in HR / HCM; and
• Lastly, from this small set of findings I chose to highlight, HR technology investment is poised for growth, with a focus on AI integration, process optimization, and improving user experiences.
  

The iPaaS Competitive Landscape

Once again, whichever iPaaS platform and solution provider an organization elects to build themselves or partner with, the goals seem quite universal: Move away from manual deployments and engineering-led processes, to fully-automated, end to end self-service experiences. And best in class iPaaS’s more specifically feature: built-in observability (around data health and transaction execution logs), transparent cost tracking enabled by each customer, the most diverse integration capabilities across APIs, files, electronic data interchanges (EDI), production schedules, and secure file transfer protocols (SFTP). Additionally, customer-specific iPaaS configurability can effectively be managed without the need for regular IT support. All these elements and attributes are demonstrably present in leading iPaaS offerings, which by way, should ideally be delivered in both ‘white label’ and ‘embedded’ options.

The sponsor of this blog, a leading iPaaS provider called Tavio, confidently offers the HR Tech industry’s first NO CODE deployment hub, which means ‘build once, deploy to many.’ This has been accomplished by separating integration code from data mapping and configuration, thereby enabling users to quickly create and deploy new implementations without generating new code. The company (the result of merging two popular integration specialist firms in the last year), augmented their iPaaS portal and configurability toolset with 350+ pre-loaded connectors. This also allows customers (vendors and end customers) to more fully leverage all the API’s their solution vendors have provided as well.

By way of contrast, some of the comments related to other iPaaS offerings, evaluative comments from users that populate ‘industry intelligence and product review sites’ like G2.com, are included below. Note that all of these appear on the G2 site as of the date of this blog’s publishing:

• “The ‘XYZ’ platform requires you to use that vendor’s proprietary development approach and connector framework, which does not support code-native development, which can substantially limit how developers solve more complicated integration scenarios.”
  
• “Users find that iPaaS vendor’s learning curve challenging, requiring more time than expected to fully explore its features and maximize its potential.”
  
• “Users also find that vendor’s costs, especially for connectors and master data hub records, to be notably high compared to competitors.”

Parting Comments

I’ll end by reiterating the major caution raised above, that the lack of high-quality data access can, and likely will, be even more problematic in the AI in HR Tech era. This is because current, consistent, and connected data flows fuel everything from predictive analytics to bespoke AI copilots. It is therefore a business imperative that enterprises prevent data flow bottlenecks, especially if they potentially interfere with needed ‘people data’ (or any adjacent, relevant data) from reliably flowing across and available to their enterprise users. The fact that employee-related costs are usually the largest cost item in an operating budget – often by far, makes this a non-debatable issue.

About the Author

Steve Goldberg has operated in senior roles on all sides of HR, Payroll, Talent Management and HR Tech for over three decades and on three continents. After 15 years as a practitioner exec in Fortune 500’s, Steve led HCM product strategy at PeopleSoft, co-founded Recruiting Tech, and Change Management firms, and directed HCM research practices at Bersin and Ventana Research. Currently serving as an independent industry analyst and advisor, Steve has been recognized as a Top 100 HR Tech Influencer multiple years, partly due to his focus on elevating organizational agility and other strategic HCM outcomes in talks and articles throughout the year. He holds an MBA in HR.