With decades of experience navigating the complexities of business management and corporate strategy, Marco Gaietti offers a unique perspective on the volatile world of HR technology. As a seasoned management consultant, he has guided countless organizations through tectonic shifts in operations and technology. Today, we delve into the high-stakes collision course between venture capital, artificial intelligence, and a rapidly evolving legal landscape. Our conversation will explore the paradox of record-breaking investments in the face of mounting lawsuits, the practical steps leaders must take to dismantle the “black box” of AI, and the hidden liabilities lurking within the industry’s most celebrated platforms.
In 2025, venture capital investment in work tech surged to over $6 billion, with the average deal size jumping 31%. How do you reconcile this investor confidence with the mounting legal challenges against major AI platforms? What specific due diligence steps should VCs be taking?
It’s a fascinating and frankly, a dangerous paradox. On one hand, you have this staggering $6.24 billion figure, a clear signal that investors see an almost unstoppable wave of AI integration into the workplace. They are betting on the efficiency and the data-driven promise. But on the other, you have these landmark lawsuits against giants like Workday and Eightfold AI, which threaten to pull the rug out from under the entire industry. The investor confidence feels a bit like a gold rush mentality; the potential reward is so massive that the risk feels abstract, at least for now. For VCs, due diligence can no longer just be about the tech’s capabilities. They must now demand a “fairness-first” audit. This means bringing in legal and ethical AI experts to probe the algorithms, demanding transparent documentation on how the models were trained, and stress-testing the systems for discriminatory outputs before a single check is written.
You’ve described a shift away from using an “algorithm as an alibi.” For HR leaders, what does this mean practically when selecting a new vendor? Could you outline a step-by-step process for auditing an AI tool to ensure it can transparently prove its fairness?
The era of “the computer said so” is definitively over. For an HR leader, this shift is monumental. It means you are now the ultimate owner of the algorithm’s decision, and you have to be able to defend it in plain English. When auditing a new tool, the first step is to demand transparency from the vendor. Ask them to prove how their AI is fair, don’t just accept their claims. Second, you must conduct your own internal pilot program with historical data. Run your past hiring or promotion data through the tool and analyze the outcomes for any demographic skews. Third, establish a human-in-the-loop oversight committee. This team should regularly review the AI’s high-stakes recommendations—like final hiring or termination suggestions—before they are executed. Finally, document everything. This “decision-making diary” becomes your evidence that you are not blindly trusting a black box but are actively managing your technology responsibly.
All-in-one HCM platforms are attracting huge investments, such as Rippling’s $450 million round. Since these systems centralize algorithmic decisions for compensation, performance, and mobility, what are the biggest hidden liabilities? Please share an example of how one biased function could create multiple compliance failures.
The all-in-one platform is both a blessing for efficiency and a potential time bomb for compliance. The biggest hidden liability is the cascade effect. When you centralize decisions, you also centralize risk. Think about it this way: let’s say a platform’s automated performance scoring module has a subtle, unintentional bias that scores employees who use more collaborative language—often women—slightly lower than those who use more assertive language. That single biased function doesn’t just create a performance management issue. It directly triggers a compensation compliance failure because those scores feed into the salary review algorithm. Then, it creates an internal mobility and promotion problem, as the system flags “lower-performing” employees as less suitable for advancement. Suddenly, one flawed assumption hidden deep in the code has generated potential legal exposure across pay equity, promotion practices, and performance management.
Pending lawsuits allege issues like age discrimination in hiring algorithms and violations of the Fair Credit Reporting Act. Beyond these specific claims, what other types of algorithmic assumptions within talent management systems represent the most significant, yet overlooked, legal risks for enterprises today?
While age and credit reporting are grabbing headlines, the most significant overlooked risk, in my view, lies in the seemingly benign world of sentiment analysis and retention modeling. Many modern platforms analyze internal communications—like messages on team chats or email tone—to predict which employees might be a “flight risk.” The algorithmic assumptions here are a legal minefield. The system might incorrectly flag an employee who is simply more direct or introverted in their communication style. It could also misinterpret cultural nuances in language, leading to discriminatory patterns. Imagine a manager preemptively passing over a “high-risk” employee for a key project based on a flawed sentiment score. This isn’t just bad management; it’s an unvalidated, biased system making career-altering decisions on what amounts to algorithmic guesswork.
The market saw 17 mega-rounds alongside 72 seed-stage deals, with investors funding niche solutions in 44 different subcategories. What does this simultaneous consolidation and fragmentation tell us about the future of the work tech landscape? Please provide some metrics you’re watching.
This dual-track market tells us a fascinating story. The 17 mega-rounds show that big money is betting on consolidation—the idea that a few massive platforms will eventually rule the entire HR ecosystem. However, the explosion of niche solutions across 44 subcategories and the health of the seed stage, with 72 deals averaging over $5 million, shows that the market fundamentally disagrees. It’s a sign that innovation is happening at the edges, in specialized areas like agentic sourcing or compensation intelligence where the all-in-one giants are too slow or clumsy to compete. This tells me the future isn’t a single platform but a “best-of-breed” ecosystem where specialized, AI-native tools plug into a central system. I’m closely watching the customer churn rates of the big HCM platforms and, conversely, the integration partnerships that these smaller, niche players are forming. That’s where the next chapter of the market will be written.
What is your forecast for the work technology market?
My forecast is that the next 24 months will be defined by a “great reckoning.” The flow of capital won’t stop, but the legal and regulatory pressures will force a dramatic shift in product development. We will see a new category of “compliance-first” AI tools emerge, where the primary selling point isn’t just efficiency but auditable fairness and transparency. The term “black box” will become toxic for any vendor. Companies that can’t prove how their algorithms work will not only lose deals but will become uninsurable liabilities. In short, the market is about to be split in two: the transparent, defensible platforms of the future, and the legacy “black box” systems operating on borrowed time.
