Adult retail businesses can achieve greater success with artificial intelligence (AI) by focusing on management skills and integrating AI into daily operations, rather than prioritizing technical expertise. This approach emphasizes guiding AI as a new employee and leveraging existing retail management capabilities.

Operational Insights Drive AI Effectiveness

Zondre Watson, general manager of technology and analytics for adult retail chain Ero-Tech, highlighted that successful AI implementation in adult retail depends more on management skills than technical proficiency. Watson, who has a background in finance, chocolate, and controlled chaos, blends retail knowledge with AI tools to manage 17,000 products. Watson's experience indicates that the biggest misconception about AI is that success relies on technical skills.

Watson described a task involving payroll management across more than a dozen entities with varying schedules. This task required logging into one portal, downloading reports, renaming files, uploading them to another system, and ensuring accuracy in QuickBooks. Initially, Watson applied AI to this problem, which helped with some manual work but still took nearly an hour each week. By approaching the problem like a manager, contacting the payroll provider, identifying the bottleneck, and building an automated process, Watson reduced the time to process months of backlog to under 30 minutes. This outcome was achieved without programming skills.

Many businesses have experimented with AI, but the challenge now lies in achieving results beyond generic outcomes. When AI tools are given requests without context, such as writing a product description or marketing email, they often produce competent but generic content tailored to an average business. This is because the AI lacks information about specific customers, store positioning, the local adult retail environment, or content restrictions. This lack of context often leads retailers to feel underwhelmed, indicating that the issue is not the technology itself but insufficient information provided to the AI.

The distinction in AI success is not between businesses that use AI and those that do not, but between those who know how to guide and evaluate AI and those who accept the initial response. Watson suggests treating AI like a new employee rather than a vending machine. A new employee, despite being capable, requires training, explanations of business operations, examples of good work, and correction of mistakes. Similarly, providing guidance to AI dramatically improves the quality of its output. The skills required to manage AI, such as onboarding employees, training staff, evaluating products, and overseeing daily operations, are often already present in adult retailers.

Leveraging Existing Retail Skills for AI Integration

Watson identified four factors that contributed to the success of the payroll project. First, understanding the work, including how payroll flowed, where problems occurred, and what a successful outcome entailed, was crucial. This knowledge was brought by Watson, not the AI. Second, the ability to break the project into smaller steps, a management skill used for assigning employee responsibilities, was important. Third, knowing what "good" looked like was essential. The initial automation still required too much manual effort, and Watson recognized it was not finished because of a clear standard for completion. This judgment, similar to a buyer evaluating a product for their store, applies to AI outputs; a polished output does not necessarily mean it is correct. Finally, building on existing systems rather than starting from scratch made the process more efficient, with subsequent AI projects often being easier due to accumulated learning.

For retailers interested in using AI more effectively, Watson recommends starting with one frustrating task rather than a grand strategy. This could include writing product descriptions, managing inventory reports, or organizing purchase orders. Before beginning, defining what success looks like is important. The first AI response should be treated as a draft, refined through questions and continuous improvement until it meets established standards. Asking the AI to explain itself can also facilitate learning during problem-solving. The learning curve for AI is smaller than often assumed, requiring a willingness to manage a capable, fast, but initially clueless new worker to a high standard.

The greatest value from AI will likely come from operators who understand their businesses well enough to teach a machine how they operate, rather than from the most technical individuals. AI requires a manager, not a programmer. Retailers who have successfully trained employees, managed inventory, negotiated with vendors, or run a retail floor already possess many of the necessary skills.

Key Facts

  • Zondre Watson, general manager of technology and analytics for Ero-Tech, emphasizes management skills over technical skills for AI success in adult retail.
  • Watson's experience with payroll automation reduced a task from nearly an hour weekly to under 30 minutes for months of backlog, without programming.
  • A survey conducted in collaboration with ServiceNow and Retail Dive's Studio by Informa TechTarget found that over half (52%) of surveyed retailers reported associates spend two to three hours per shift on manual tasks.
  • AI often produces generic results when not provided with specific context about customers, store positioning, or the local retail environment.
  • Treating AI like a new employee, offering guidance and training, significantly improves the quality of its output.
  • Successful AI implementation leverages existing retail management skills such as understanding operations, breaking down projects, and evaluating outcomes.