The HR AI winners won’t be who you think

I’ve been in this industry for 20+ years, and right now feels like déjà vu all over again. Back in the late 2000s, when cloud computing was the hot new thing disrupting HR technology, we saw incumbent client/server vendors frantically acquiring cloud solutions to stay relevant. Vendors like Oracle, SAP, and others would snap up innovative cloud startups, promising customers seamless integration and the best of both worlds.

The reality? Those acquisitions rarely delivered on their promises quickly. The resulting “cloud” solutions often weren’t truly cloud-native architectures—they were retrofitted hosted legacy systems with web interfaces. Meanwhile, pure-play cloud vendors like Workday and SuccessFactors (ironically, now the incumbents themselves) brought innovation and captured market share.

Fast forward to today, and we’re seeing the exact same playbook, now with AI as the disruptive technology. SAP SuccessFactors acquired SmartRecruiters. Workday signed definitive agreements to acquire Paradox, Flowise, and Sana. Payscale snapped up Datapeople. The pattern is unmistakable: established cloud vendors are acquiring AI-first companies to add much-needed state-of-the-art functionality and fast-track their innovation credentials. To be fair, these are smart strategic decisions—the incumbents are rapidly adding a wealth of AI functionality in areas where they’re not market-leading or don’t have deep expertise. Building these capabilities organically would take them years to catch up to what specialized AI companies have already developed.

But here’s the important question if you’re evaluating HR technology right now: Should you wait for these acquisitions to integrate, or should you continue with your original plans?

The Integration Reality Check

It’s worth noting that some of these acquisitions are only just announced (e.g., Workday’s Sana deal was last week as was Payscale’s Datapeople), which means we’re at the very beginning of the integration journey. Early announcements rarely reflect the engineering reality. Because when incumbents acquire young companies, the acquired company inevitably shifts focus from pure innovation to integration—both organizational and technical. This isn’t necessarily bad, but it fundamentally changes their priorities and capabilities.

From a technical architecture standpoint, integrating an AI-first solution into an existing enterprise platform is incredibly complex. Native AI systems operate completely different from the cloud-native HR platforms we know today. While cloud-native systems were designed around storing and retrieving data efficiently, AI-native architectures are built around continuously learning from data to make smarter decisions. Think of it this way: traditional HR systems follow predetermined rules and workflows, while AI-native systems adapt and improve their recommendations based on patterns they discover in your data.

These AI-native systems are designed with intelligence baked into every process—they don’t just store candidate information, they learn which candidates are most likely to succeed in specific roles. They don’t just track employee performance, they predict which employees might be at risk of leaving and suggest interventions. This requires a completely different technological foundation, one that can continuously process data to train and refine AI models in real-time.

Beyond these AI-specific architectural differences, the acquired company’s engineering team suddenly must worry about:

  • Data model alignment: Their lean, purpose-built data structures need to mesh with the incumbent’s complex, legacy-laden schemas
  • Infrastructure consolidation: Moving from their optimized architecture to the parent’s enterprise infrastructure stack
  • Integration standardization: What were once flexible, developer-friendly APIs often become more rigid to fit the parent company’s integration patterns
  • Authentication and authorization: Single sign-on, role-based permissions, and security protocols must be harmonized
  • Compliance and governance: Adhering to the incumbent’s (and their clients’!) enterprise-grade security, privacy, and regulatory requirements

This integration work isn’t just a few months of effort—it’s often 12-18 months of substantial engineering resources diverted from innovation. During this period, the pace of new feature development inevitably slows.

The Innovation Slowdown

Eventually every scale-up slows down the pace of innovation. It’s a sign of maturity.  It’s just that this is coming much sooner than it normally would, driven by the incumbent’s more established organization as well as the more stringent requirements of their enterprise clients. I’ve seen this story play out repeatedly. The scrappy AI startup that was shipping new features monthly suddenly finds itself in six-month release cycles, constrained by the parent company’s development processes, compliance requirements, and integration timelines. The very agility that made them attractive acquisition targets gets absorbed into enterprise bureaucracy. Meanwhile, other pure-play AI vendors continue innovating at startup speed, unfettered by integration concerns or enterprise politics.

The Native-AI Revolution

I’m fortunate to see the solutions and architectures of young, native-AI companies on a weekly basis, and I can tell you that there’s an incredible amount of HR innovation coming to market in the next few years. When I say native-AI, I’m not just talking about GenAI—though that’s certainly part of it. I’m referring to companies that were architected from the ground up with AI at their core, whether that’s machine learning for predictive analytics, natural language processing for candidate matching, or reinforcement learning for personalized learning paths. It’s the reason why companies like SAP, Workday, and Payscale are buying AI-native players — their own platforms weren’t built this way.

Native-AI architectures are fundamentally different from HR systems that have AI bolted on. They’re designed with data pipelines optimized for model training, inference engines built into their core processing loops, and user experiences that assume intelligent automation rather than manual processes. The innovation I’m seeing spans everything from recruitment and talent management to benefits analysis and workforce planning. And much of it won’t be constrained by the integration timelines of recently acquired companies.

AI Tech is Different – Really!

Native Cloud HR Systems rely on traditional business logic executed across distributed services, where AI might be one component among many. Native AI HR Stacks use machine learning models as the fundamental processing units, where traditional business rules are either learned behaviors or complementary validation layers. The entire system architecture is optimized for AI workloads, including specialized hardware utilization, model lifecycle management, and AI-specific observability tools.

Here’s a short overview to show you how different the two stacks are. The list is not exhaustive; I’ve just picked the concepts that are most applicable. (NB: I’ll write more about native AI stacks in an upcoming article, so if the table is too technical, I’ve got you covered!)

Architecture Component

Native Cloud

Native AI Stack

Core Design Philosophy

Applications designed specifically for distributed, scalable cloud infrastructure

AI/ML models are the primary processing layer, with systems architected to handle continuous learning and inference

Data Processing

Microservices handle discrete business functions across distributed nodes, often working with structured data and batch processing

Neural networks and ML pipelines process both unstructured (e.g., text, images) and structured HR data in real-time, with dynamic feature extraction

Scalability Model

Horizontal scaling through containerization and orchestration (e.g., Kubernetes); elastic scaling of compute resources

Model scaling through distributed inference, GPU clusters (or specialized hardware like TPUs), and dynamic compute allocation for resource-intensive AI tasks

State Management

Stateless services with external data persistence (e.g., databases, object storage)

Model state is preserved through vector databases, embeddings, and continuous learning pipelines—models are inherently stateful and evolve over time

Integration Pattern

API-first architecture with service mesh communication for modular, loosely coupled services

AI-first data flows with unified feature stores, model registries, and data pipelines feeding raw and processed features into models for real-time predictions

Data Retrieval Method

Static database queries and cached responses for consistent data access

Retrieval Augmented Generation (RAG) with dynamic context injection, semantic search, and real-time adaptive retrieval, ensuring models have relevant, up-to-date information for decision-making

System Evolution

Periodic releases with feature updates and infrastructure improvements in predefined cycles

Continuous feedback loops with Reinforcement Learning from Human Feedback (RLHF), automated fine-tuning, and weekly model iterations—AI models are never “finished” and require ongoing optimization and retraining

The Single Throat to Choke Myth

I often hear this argument: “We want a single throat to choke.” And I get it — vendor consolidation feels simpler, especially when procurement and compliance teams are involved. But consider this: if the integrated solution doesn’t meet your needs, you’re still dealing with multiple vendors. You’re just paying premium pricing for the privilege.

Best-of-breed solutions with proper API integration can provide good user experiences, fast innovation cycles, and competitive pricing too. The “complexity” of managing multiple vendors is often overstated, especially when modern integration platforms make API management straightforward. However, keep in mind that API integrations aren’t free. They require ongoing maintenance, monitoring, and potential costs for API calls or data transfers. Factor these integration costs into your total cost of ownership calculations when comparing best-of-breed approaches against integrated suites.

Don’t Hit the Pause Button

If you’re currently evaluating solutions in a space where your incumbent vendor just made an acquisition, here’s my recommendation: Continue your evaluation process. Here’s why:

1. Integration Timelines Are Always Optimistic

When vendors announce acquisitions, they love to throw around phrases like “integrated solution available in six months.” In my experience, meaningful integration takes 6-12 months minimum. Even then, the first integrated release is often more about basic data sharing than true product unification.

2. You Need Market Intelligence

Even if you ultimately decide to wait for the integrated solution, conducting a thorough market evaluation provides invaluable intelligence about:

  • What capabilities exist in the market
  • How your incumbent’s acquisition compares to alternatives
  • Pricing benchmarks for standalone solutions
  • Technical architectures you should expect from modern solutions

3. Leverage for Negotiations

Having evaluated alternatives gives you leverage in negotiations with your incumbent vendor. You’ll understand market pricing, capabilities, and timelines. This knowledge is invaluable whether you’re negotiating for the new integrated solution or considering alternatives.

Your Future-Fit HR Tech Stack

Don’t let acquisition announcements derail your HR technology strategy. The market continues to evolve rapidly, and delaying decisions based on integration promises often leads to disappointment.

History has a fun way of repeating itself in our industry. When cloud computing disrupted HR technology, it wasn’t the incumbents who successfully acquired cloud solutions that ultimately won. It was companies like Workday and SuccessFactors who were built from scratch for the cloud era. They had the advantage of clean architectures, modern design principles, and no legacy constraints holding them back. Today’s incumbents (many of them those same cloud disruptors from 15 years ago) are now facing the same challenge with AI that Oracle and SAP faced with cloud.

We’re witnessing the early stages of the next HR Tech disruption, and somewhere out there are the AI-native companies that will become the Workdays and SuccessFactors of the AI era. We just don’t know who they are yet. What I do know is that they won’t be constrained by the integration challenges and legacy architectures that today’s acquisition-driven strategies inevitably create.

If you were already evaluating solutions in a space where your vendor just made an acquisition, expand your evaluation to include the acquired solution alongside your original shortlist. You’ll gain market intelligence, maintain negotiating leverage, and make a more informed decision about whether to wait for integration or move forward with a best-of-breed approach.

The AI revolution in HR Tech is just beginning, and the vendors moving fastest with genuine innovation often aren’t the ones constrained by integration roadmaps and enterprise processes. Choose based on capabilities and results, not on vendor consolidation strategies.

Based on my time in this industry, I can promise you one thing: the HR technology landscape will continue to evolve faster than integration timelines. Make decisions that position you for future flexibility and speed, not just current convenience. The winners of the AI era may not be who you expect — they’ll be the ones who built for AI from day one.