Why firms are quietly rehiring staff AI was supposed to replace

Some companies have recently laid off employees because business leaders thought AI was ready to shoulder the entire workload for those functions are quietly reconsidering that decision. Their experience should be a wakeup call for other businesses.

AI is a transformational technology that is changing the way organizations function, but human oversight is essential to achieving an AI deployment that works as expected. A lesson early adopters in the HR software and payroll services have absorbed.

Moving from pilot programs to full execution on AI initiatives even a 1% error rate in AI tools can lead to risk in compliance issues, financial discrepancies, and security breaches, due to HIPPA and other confidential payroll data.

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AI is an experience that has broad implications, not just for HR and payroll software, but across the enterprise.

In many ways, HR and payroll are ideal test cases for AI applications, and those functions have been among the first to demonstrate real value using the technology. Here’s a look at some of the lessons learned that can apply to any area of business.

When 99% Accuracy Is Unacceptable

Why are HR and payroll ideally suited as an AI proving ground? Because there’s no room for error in a function that touches every single employee and that involves the most sensitive personal data most companies hold: information about their people’s compensation, job performance and health.

HR and payroll functions involve many data-intensive and high-volume processes, which also happen to be exactly the type of functions where AI is most suited to augment, not replace, human effort. One of the main selling points of AI is that it can handle repetitive work and manage discrete tasks and scenarios that exist across all departments.

That said, HR and payroll have unique challenges when it comes to deploying AI given the sensitive nature of the work. It’s regulated by laws like HIPAA, which defines protected health information and mandates compliance with stringent rules under penalty of law.

In addition to the added layer of external risk, the stakes are high internally too since even a 1% error rate, which is considered excellent in most contexts, is unacceptable in HR and payroll, given the impact on affected employees. So, at the outset, leaders in the sector had to focus on data security and carefully decide which functions AI can handle.

The focus on data security and informed decision-making on where to deploy AI creates a blueprint for leaders across the business. In practice, it requires expertise that is specific to the platform the company uses and an uncompromising commitment to good data governance. Without these elements, the AI investment as well as the company’s data are at risk.

The Enduring Importance of the Human Touch

Some early adopters are demonstrating that AI can move beyond the hype and generate real results, but so far, they are a distinct minority. MIT research indicates that only 5% of enterprise AI pilots pay off, but those that do succeed tend to use systems engineered for their specific business context and recognize the necessity of human oversight.

That’s because business context varies widely, so while a generic solution might seem impressive in a demo, it can quickly fail when it encounters the irregularities and nuances that inevitably arise in the real world. One reason human oversight is essential is because humans, not machines, are capable of fully understanding business nuances.

A large language model like ChatGPT that accesses publicly available data can be a great starting point for general research or an incredibly useful tool for tasks like suggesting travel itineraries. But even the chatbot’s makers admit the AI tool isn’t completely reliable and is subject to hallucinations.

If the chatbot experiences an ill-timed hallucination while advising about a critical business process, the results could be catastrophic. Since chatbots aren’t integrated into internal systems, they don’t know the nuances around configurations, business processes and other details needed to make informed decisions.

Tips for a Successful AI Rollout

The safest approach for a successful AI launch is to deploy AI for tasks or analyses that are subject to review by people with the expertise. It’s becoming increasingly critical that service partners work with AI agents in systems, like SuccessFactors, as they would with traditional, human contacts in their organization to validate results and make decisions.

Leaders should also embed AI from technology and service partners, rather than external layers, to ensure the most connected, beneficial AI solutions. That’s the strategy used by companies that access top performing enterprise AI HR and payroll solutions.

Another tried and tested tip: determine the organization’s top labor-intensive business processes and identify the ways AI can simplify those specific tasks. In some cases, it makes sense to embed an AI agent into a workflow to complete tasks. In other cases, a large language model chatbot can be helpful by quickly performing analysis on massive datasets that would take people days, weeks or months to do.

But in virtually all cases, a level of human oversight will still be required, along with a good governance mindset and a keen focus on data security and integrity. Without solid, secure data and a technology platform that is purpose-built for specific tasks, AI initiatives will fail to generate the expected returns.

Worse than that, mishandled data and business decisions made without the application of human expertise can harm people and organizations. When that happens, AI doesn’t get the blame — the people who deployed it without adequate guardrails do.

Ultimately, that’s how it’s supposed to work; leaders are accountable for their actions, not machines. Getting to AI that just works as expected requires accountability, which is a uniquely human trait. It’s time to embrace that quality and deploy AI that amplifies people’s capabilities instead of replacing them.

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