Sidework: Turning Insights Into Action With AI
AI Product Design · Experimentation · Prototyping
Role:
Independent Product Designer / AI-assisted Prototyping
Focus:
AI Product Design
Tools:
Lovable, AI-assisted Development, Generative AI
Overview
Less time analyzing, more time growing.
Sidework is a self-initiated AI-native product concept designed to help independent restaurant owners turn performance data into meaningful action. Rather than presenting another dashboard of numbers to interpret, Sidework identifies potential growth opportunities, explains the evidence behind them, and helps owners move from insight to execution. I designed and prototyped the experience to explore how AI, behavioral data, and experimentation could work together to make business growth more approachable, actionable, and human.
Problem
Restaurant owners are expected to make decisions about revenue, marketing, customer retention, and profitability, often while managing every other aspect of their business. Traditional analytics can reveal what happened, but understanding why it matters and deciding what to do next still takes time and expertise. The design challenge was to explore how an AI-powered product could close that gap without becoming another complicated system to manage. More importantly, how could it provide meaningful recommendations while remaining transparent about uncertainty and leaving the final decision in human hands?
Solution
I designed Sidework around a simple progression: understand what’s happening, identify an opportunity, recommend an action, and make it easy to evaluate the result. Using fictional restaurant data, the experience explores three growth opportunities: bringing back inactive customers, increasing average order value, and strengthening customer retention. The primary workflow takes a potential revenue opportunity from supporting evidence through campaign configuration, real AI-generated messaging, editing, and simulated approval. Rather than asking users to trust an unexplained recommendation, Sidework distinguishes observed data from interpretation, makes its reasoning visible, and gives owners control over what happens next. The experience also introduces experimentation and measurement, encouraging owners to evaluate whether an action creates incremental value rather than treating engagement or generated content as proof of success.
