The Human Side of AI: Employment Law Expert Stephanie Goutos on Helping Female Founders Scale the Right Way

Artificial intelligence is rewriting workplace rules faster than most legal teams can keep up. Stephanie Goutos occupies a unique position at the intersection of legal innovation and employment law, helping translate rapid technology changes into practical frameworks companies can actually use.

As Head of Employment Law Practice Innovation at Gunderson Dettmer, the leading law firm serving high-growth companies and venture capital and growth equity funds, Ms. Goutos helps founders and executives navigate the evolving relationship between people, technology, and regulation.

What makes her work notable is its focus on what AI means for people. “AI integration isn’t just a technology problem,” she says. “It’s a people problem with legal consequences.

Recognized as one of the legal industry’s leading voices on AI strategy, implementation, and governance, Ms. Goutos bridges deep employment law expertise with hands-on understanding of how emerging technologies reshape work. She advises startups and investors on building AI-driven operations that are compliant, scalable, and defensible.

Her approach is grounded in practical foresight: establish strong employment law foundations early, disclose clearly how and where AI tools are used, and ensure human judgment remains meaningfully in the loop.

Her perspective is particularly relevant for female founders building companies in this fast-moving environment. She’s seen the common pitfalls that can undermine even the strongest ventures: worker misclassification, inadequate documentation, wage-and-hour gaps, and the costly fallout that comes when compliance trails growth.

With extensive experience in class action defense and employment counseling, combined with her expertise in AI governance and legal technology, Ms. Goutos offers a rare vantage point on how entrepreneurs can scale responsibly without slowing innovation. Her philosophy is simple but strategic: proactive compliance isn’t a constraint on growth - it’s what enables sustainable scaling.

In this discussion, Ms. Goutos shares her insights on the critical employment law considerations for AI integration, the most frequent mistakes she sees female-founded startups make as they scale, and the legal frameworks founders should have in place long before they’re sitting across from investors or acquisition teams.

For female entrepreneurs integrating AI into their businesses or workforces, what are the critical employment law considerations they need to address? How should they structure AI policies, manage employee privacy concerns around AI monitoring, and handle potential displacement issues while staying compliant with evolving employment regulations?

For female entrepreneurs integrating AI into their businesses, no one understands better that new technology is only as effective as the team using it. This is especially true for AI.

The legal implications of AI in the workplace are vast and intertwined, but the heart of responsible AI adoption lies in leveraging these tools to genuinely improve how people work, while staying alert to the complex, often nuanced risks. That balance is far easier said than done.

Many companies stumble in their AI integration because they treat it purely as a technology problem when, in truth, it’s a people problem with legal consequences. You can have perfect bias audits and compliant monitoring policies, but if your team doesn’t trust the tools, understand how they’re being used, or see how AI supports their own work, you’ll likely get quiet resistance - and greater risk, not less.

For entrepreneurs building the next generation of great companies, here are a few anchors I recommend:

Start with radical clarity and transparency

Tell your people what you’re actually doing. It builds trust, and in some jurisdictions, the law requires it. (For example, New York’s Local Law 144 mandates bias audits for AI used in hiring). Even where disclosure isn’t yet required, transparency is good business.

Be specific. Don’t say: “AI assists with hiring.” 

Say: “We use an AI tool to screen initial resumes and rank candidates based on the core skills outlined in the job description.” 

Companies should be able to clearly articulate what their AI does and why they are using it.

Keep humans meaningfully in the loop

“No fully automated material employment decisions” should be a baseline principle. But simply having a “human in the loop” is meaningless if that person is undertrained, afraid to override the system, or unsure how to raise concerns.

Effective oversight means reviewers are properly trained on what the AI considers and omits, have real authority and cultural reinforcement to override it, and know how to document their decisions. They also need clear escalation paths when they spot patterns of bias, inaccuracy, or data misuse. That structure doesn’t just reduce risk, it builds trust.

Build adaptive and forward-looking policies

The regulatory landscape is evolving faster than most compliance programs can keep up. Build policies that can accommodate new requirements without complete overhauls. Schedule regular audits of your tools for bias, accuracy, data handling, and legal compliance. Be prepared to pivot as often as the technology and regulations demand. Regular training sessions, updates, and reminders can help keep these policies top of mind and reinforce the organization’s commitment to protecting company data. 

Create a culture that values experimentation 

AI integration requires organizational learning and learning requires room for experimentation and, at times, failure.  It’s one of the most effective ways employees will learn. Create a culture that celebrates employees who push transformation, identify an AI's limitations, or catch bias before it scales. Those closest to the work often detect critical issues leadership might miss. Listening to and rewarding them creates psychological safety and accelerates responsible adoption.

With your extensive experience in class action defense and employment counseling at a firm that serves emerging companies and venture capital clients, what are the most common employment law mistakes you see female-founded startups make as they scale? How should founders structure their hiring practices, employee handbooks, and workplace policies from the beginning to avoid costly litigation? What employment law considerations should they address before fundraising rounds or potential acquisitions?

One of the most common challenges founders face is simply a byproduct of their success: moving so fast that legal compliance becomes a "tomorrow" problem. Founders are wired to build and sell, which is exactly what makes them successful. 

For startups, broader employment law challenges often surface during growth. Our philosophy is that proactive compliance isn't a barrier to growth; it's what enables sustainable scaling. Addressing these areas early is always far more efficient (and less expensive) than course-correcting later.

Here are the most common missteps I see - and how to avoid them: 

Misclassifying your team

In the early stages, relying on contractors or consultants offers flexibility, but misclassification is one of the most expensive errors a startup can make. The same scrutiny applies to determining which employees are exempt from overtime laws. Misclassification can result in back pay, penalties, and red flags during diligence. Getting it right early is far easier than retrofitting an entire workforce later.

Inadequate documentation and outdated policies

Relying on handshake deals or vague offer letters for early hires can lead to major disputes as a company scales. Every offer letter should clearly outline the at-will relationship and any equity terms. Careless language in an email or letter can accidentally imply a "for-cause" relationship (e.g., they can only be fired for a good reason). And handbooks are not set-and-forget documents - review and update them consistently as headcount grows and laws shift.

Wage and hour compliance gaps

The most common wage and hour violations include misclassifying employees, improperly paying with equity or below minimum wage, failing to pay overtime, lacking accurate timekeeping, and neglecting wage documentation. These are frequent sources of litigation and can erode investor confidence. Regular audits and clear pay policies are key safeguards.

Letting compliance lag behind headcount

Legal obligations shift at critical employee thresholds (typically around 15, 25, or 50 employees) triggering new reporting duties, benefits requirements, and mandatory training. Each inflection point should prompt a legal checkup to update policies, procedures, and compliance frameworks.