By: Alex Morgan
Every company evaluating HR software this year is being sold some version of the same pitch: AI included, often at little or no extra cost. Tushneem Dharmagadda has seen this pattern before, and he doesn’t think it ends the way most buyers assume.
A Pricing Model Built to Change Later
As founder of workforce platform HubEngage, Dharmagadda watches AI pricing evolve across the HR tech market from the inside. His read on it is blunt: vendors are bundling AI into their core products now because early customers wouldn’t pay extra for it as a standalone feature, not because the economics of unlimited AI usage are sustainable long-term. He points to Uber and Netflix as the closest precedent, both once cheaper than the alternatives they replaced, both eventually raising prices once the market had adjusted around them and, in Netflix’s case, once the market fragmented into a dozen competing subscriptions that add up to more than the cable bill they replaced.
“I’m not saying companies should avoid AI,” he says. “I’m saying they shouldn’t assume today’s bundled or subsidized pricing is what they’ll be paying three years from now.”
The risk, in his view, isn’t any single vendor’s price increase. It’s a company ending up with AI billed separately inside its HR system, its communications platform, and half a dozen other tools at once, each reasonable on its own, all of it adding up to something nobody at the company actually tracks.
The Cost Nobody Puts on a Spreadsheet
Dharmagadda has a name for what happens after a company buys the cheapest tool for every individual problem instead of one connected system: workforce experience debt. Every additional login, every separate interface, every tool with its own narrow chatbot compounds into a workforce that no longer knows where to go for a simple answer.
“Every new tool adds another login, another interface, another place employees have to search for information,” he says. “Over time, they no longer know where to go to get an answer or complete a task.”
The framing borrows its logic from technical debt, the accumulating cost of shortcuts an engineering team eventually has to pay down. Dharmagadda’s version applies the same idea to HR technology: a company can keep adding tools that each look affordable on their own, while the real cost quietly builds in integration work, administrator time, training, and a fragmented daily experience for the employee stuck working across all of it. None of that shows up on the invoice for any single tool, which is exactly why it goes unmeasured for so long.
Where AI Actually Earns Its Keep
Dharmagadda isn’t opposed to AI in HR tech; he’s opposed to treating every feature as equally transformative. The category where he sees the clearest value is bulk content analysis. A chatbot can answer policy and procedure questions instantly, provided the underlying content is organized well enough to make that possible, and he points to survey analysis as an even clearer case. HubEngage’s own AI tools sort open-ended employee responses into more than 40 recurring themes automatically. HR teams have traditionally handled that kind of sorting by reading through every submission by hand. Those are the high-volume tasks he thinks are worth automating.
What he’s skeptical of is the smaller stuff sold as major ROI, an AI feature that helps someone write a slightly better email, marketed with the same enthusiasm as a tool that takes on a full manual review cycle. Time saved on a small task, he notes, doesn’t automatically translate into payroll actually eliminated, a distinction he thinks gets lost in most vendor pitch decks.
Why This Becomes a Leadership Problem, Not a Departmental One
The deeper issue Dharmagadda describes isn’t really about any single tool. It’s that most organizations buy workforce technology one department at a time. Communications picks its own platform. Engagement sits in a different budget. Operations runs its own systems. Each purchase looks reasonable in isolation because nobody is responsible for the total cost across the company. That’s the structural reason tool sprawl persists even at companies that are otherwise disciplined about spending elsewhere.
HubEngage’s pitch is built around collapsing that fragmentation: one platform spanning communications, operations, engagement, and microlearning, with native functionality where a company needs it and the ability to integrate with systems already in place where it doesn’t. The goal isn’t replacing every existing tool; it’s reducing how many separate places an employee has to check to get through a workday, and giving one team, rather than four, visibility into what the whole stack actually costs.
The Reckoning Dharmagadda Sees Coming
Two years out, Dharmagadda expects companies to start noticing how many times they’re effectively paying for the same AI capability. His example: an organization running four workforce systems, each with its own embedded chatbot, buys a “super chatbot” layered on top so employees only have to ask one place. The experience improves, but the company is now paying for AI in all four underlying systems plus the new layer plus the integrations connecting everything. Once usage-based pricing replaces today’s bundled allowances, all of those costs can climb at once, and nobody budgeted for that compounding effect because each piece was purchased separately, at a different time, by a different department.
The platforms he expects to hold up aren’t the ones with the most AI features. They’re the ones built to use intelligence that already exists inside connected systems rather than rebuilding it again in a new layer stacked on top, an architecture question he thinks emerging standards like MCP may eventually help resolve, though he’s careful to note that outcome still depends on how vendors choose to price access rather than on the technology itself.
