← Perspectives
Every HR software demo this year has an artificial intelligence moment. Very few of them survive a second question.
Key Takeaways
Gartner projects that 33% of enterprise software will include agentic AI by 2028, up from less than 1% in 2024.
At the same time, Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027 due to governance, cost, and ROI challenges.
Mid-market buyers must look past polished demo scripts and interrogate how AI features act on data, handle errors, and scale financially.
Evaluating technology decisions alongside a broader
hcm workforce management advisory
framework protects your organization from expensive shelfware.
What Is Happening With AI in HR Software Today?
If you have attended a human capital management vendor presentation lately, you know the script. Halfway through the walkthrough of payroll processing or performance reviews, the presenter pivots to a glowing chat interface. The system promises to draft job descriptions, answer employee benefit questions at midnight, and summarize performance reviews with a keystroke.
The market is flooded with new entrants. According to Gartner, there are more than 30 new 'AI-native' HCM vendors projected to enter the market by 2028, while traditional suites are retrofitting their existing databases with generative capabilities. This rush has created immense pressure on mid-market leadership teams to modernize their tech stacks before they fall behind competitors.
Yet enthusiasm often outpaces operational reality. Gartner projects that by 2028, 33% of enterprise software will include agentic AI—up from less than 1% in 2024. However, that same research warns that over 40% of agentic AI projects will be cancelled by the end of 2027 for governance, cost, and ROI reasons. The gap between a polished sales demonstration and a reliable enterprise tool is wide. To bridge that gap, buyers need a practical evaluation framework.
1. What Data Does the AI Actually Act On?
The first question to ask a software representative concerns the underlying data foundation. Sales presentations often imply a deep, contextual understanding of your specific workforce dynamics, employee handbooks, and historical compensation bands. In practice, the reality is frequently more compartmentalized.
Ask the vendor whether their models are trained on your proprietary tenant data alone, or if your queries feed into a broader, multi-tenant or public model. You need to know if the system accesses static documentation like uploaded PDF policies, or if it queries real-time database tables regarding payroll history, accrual balances, and organizational hierarchies.
If an assistant provides an answer about leave policies or wage-and-hour rules, it must pull from your verified internal documents rather than generalized internet training sets. When evaluating systems that touch sensitive workforce data, understanding your compliance posture and data boundaries is non-negotiable.
2. Who Actually Approves the AI's Actions?
A major evolution in workplace technology is the shift from generative text tools to agentic systems. Generative tools simply write things; agentic tools take actions. An agentic feature might draft a merit increase, initiate a title change in the core database, or trigger an offboarding workflow without direct human initiation.
You must determine where the human-in-the-loop checkpoint sits. Can the system execute changes directly, or does it park every recommendation in an approval queue for an HR generalist to review? If the software executes actions autonomously, what audit trails exist to trace why a specific decision was made?
In real-world HR operations, accountability cannot be outsourced to an algorithm. If an automated workflow misclassifies a worker or mishandles a sensitive personnel change, the employer retains full responsibility for the operational and legal fallout.
3. What Happens When the AI Is Wrong?
Every software model hallucinates or misinterprets context occasionally. In marketing automation or content generation, an incorrect output is an inconvenience. In human resources, an incorrect output regarding benefits eligibility, wage calculations, or leave entitlements creates immediate legal and cultural liabilities.
Ask the vendor for concrete examples of failure modes. How does the system signal uncertainty when it encounters an ambiguous employee query or conflicting company policies? Does it gracefully escalate the ticket to a human specialist, or does it present incorrect information with unwarranted confidence?
Furthermore, ask about liability. If a software recommendation leads to a compliance violation or an incorrect payroll adjustment, what contractual recourse does your organization have? Software service agreements typically disclaim liability for algorithmic errors, leaving your internal team to absorb the cleanup costs.
4. What Does the Technology Cost at Scale?
Software pricing models are shifting alongside technological capabilities. Traditional platforms charge per employee, per month (PEPM). However, advanced computational features introduce new cost structures that buyers often miss during initial negotiations.
Vendors may gate advanced automation behind premium tiers, charge consumption-based fees tied to token usage or query volume, or impose heavy implementation fees for custom agentic workflow configurations. A tool that looks economically viable for a 500-person organization can become prohibitively expensive as headcount scales or as usage spikes during annual open enrollment.
Before signing a multi-year agreement, model your projected headcount growth against the vendor's pricing tiers for automated features. Ensuring financial predictability is just one part of a disciplined approach to managing hcm cost across your organization's lifecycle.
5. What Happens to Your Data and Workflows If You Leave?
Software evaluations rarely begin with an exit strategy, but they should. When you build custom prompts, train internal assistants, and map automated workflows inside a specific vendor ecosystem, you create significant operational stickiness.
If the platform underperforms, or if your organization outgrows the vendor, what happens to those digital assets? Can you export your trained workflows, or are they proprietary code locked inside their database? Discovering that your custom logic evaporates upon contract termination is a painful lesson.
Evaluating vendor lock-in before you sign protects your operational continuity. Whether you are building an internal tech stack or reviewing broader outsourcing models, maintaining portability is essential for long-term flexibility.
Comparing Traditional HCM Features vs. Emerging AI Capabilities
To help frame your internal discussions, the comparison below outlines how traditional software functionality contrasts with emerging capabilities in modern evaluations.
Evaluation Dimension Traditional HCM Functionality Emerging AI-Native Features
Primary Interface
Structured menus, forms, and report builders. Conversational prompts and natural language queries.
Workflow Execution
Rule-based triggers requiring explicit manual inputs. Autonomous agentic actions across multiple modules.
Error Handling
Deterministic validation errors stopping data entry. Probabilistic interpretations that require careful auditing.
Cost Structure
Predictable PEPM subscription licensing. Layered feature gates and consumption-based usage fees.
Frequently Asked Questions
What is agentic AI in human resources software?
Agentic AI refers to software applications designed to autonomously execute multi-step workflows, make decisions, and take actions on behalf of users rather than just answering questions or summarizing text.
Why are so many AI projects expected to be cancelled?
According to Gartner, over 40% of agentic AI projects will be cancelled by the end of 2027 due to governance gaps, unpredictable scaling costs, and a lack of clear return on investment.
What data should HR buyers ask AI software vendors about?
Buyers need to know whether the AI acts on proprietary company data, public web data, or anonymized vendor pools, and whether company data is used to train public models.
How do you price AI-native HCM platforms at scale?
Vendors often price AI features through per-user licensing, metered usage fees for token consumption, or tiered feature gates that increase costs as headcount grows.
What happens to customized AI workflows if we leave the vendor?
Most proprietary AI configurations, trained models, and automated workflows built within a single platform do not export and are lost when migrating to a new vendor.
Get an Objective Second Opinion on Your Tech Stack
Navigating software demos and vendor promises requires an experienced eye. If you are evaluating a platform's AI claims and want a second opinion before signing the contract, that's what the live conversation is for.
This article is general operational guidance, not legal advice. Requirements change, so confirm current rules with qualified counsel before acting.
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