Why AskWhai — A Manifesto
Small businesses are the engine of American economy and they create most new jobs. Food and retail establishments combined form the largest small business industry in America. As commerce shifts online or looks similar to online commerce, small businesses are struggling to adjust.
According to one estimate, 77% of online businesses shut within a year of launch. Because of the business models of the gatekeepers of the new economy, online businesses are incentivized to engage with their customers in ways that harm their long-term viability. Businesses pay a variable commission to acquire new customers through online ad platforms, and those user-friendly platforms are designed to encourage businesses to direct more and more of their revenue toward the platform as time goes on. Small businesses cannot escape this cycle due to poor understanding of important data that lies beyond the silos of dominant ad platforms.
This is quite different from 20 years ago, when a new business could pay a fixed rent in a mall or on a main street with good traffic, and start selling their product. Now, they have to work with a complex web of systems (such as web hosts, point-of-sale systems, credit card processing and fraud detection, social media, online ads, online ordering, delivery, loyalty and rewards programs, payroll, labor planning, vendor and inventory management) that are all distinct.
In essence, small business owners are overwhelmed with systems and data, but struggle to separate important and actionable information from the ever-growing noise. Low attention span is an automatic response to more noise, and hinders a holistic view of those challenges. Remember, data is not knowledge, and more information does not imply any actionable insight.
AskWhai is a Chicago-based startup with the goal of democratizing AI and data science for small businesses in order to make those businesses sustainable and profitable. As our first product, we are developing an AI-powered business assistant that automatically surfaces the most important and actionable insights from the distinct systems commonly used by small business owners. By unlocking the combined potential of data gathered from multiple sources, we can make suggestions based on a more complete picture of anyone’s business.
Each insight is unique to a business, and is designed to cut through noise and focus the business owner’s attention on the most important area of their business at any moment. As the business moves through different stages and its needs evolve, the assistant automatically adjusts to reflect this evolution.
Our approach to AI prioritizes explainability, rather than creating black-box systems that harvest any and all private data. Our approach is designed to develop trust between the operator and the automated assistant. For a new business, we envision the AskWhai assistant will guide the business owner through important steps of business formation and continued growth, up to its maximum potential.
Our AI platform is based on decades of research in math, statistics and computer science, including our team’s personal experiences in online commerce (at Zero Percent and Farmer’s Fridge), ethical business technology (at Microsoft), and data science research (at Argonne National Laboratory). Other AskWhai professionals have run small businesses of their own.
Finally, we believe that democratizing access to AI has 3 prerequisites. Our tools must:
- Work with small (and big) data
- Be explainable in human terms
- Cost 1/100th as much as comparable current AI technologies
We already see a future in which entrepreneurship is highly valued, and people feel empowered to reach the full potential of their business dreams. But entrepreneurial success cannot and should not be a high-stakes path like Hollywood or professional sports. We believe every small business, with a good product or service, deserves a fair shot at being viable and sustainable.
To learn more or join our journey, visit our website.
I’d like to thank Soren Spicknall and Howard Tullman for their contributions to this article.
