AI can hand a solopreneur back hours every week, but much of the advice out there for tiny businesses is still stuck on writing captions. In this episode, Karin walks through five real AI systems worth your setup time, including a monthly money review, a process audit that produces actual documentation, a client onboarding engine, a campaign build and post-campaign analysis, and a decision brief that cuts your research time to twenty minutes.

Then she gets into the guardrails you need with AI as a tiny business owner: why you need a working idea of what good output looks like before you ask AI to produce it, and how to build that standard for yourself in about twenty minutes using the same tool.

Let’s get into it!

Resources:

Wisprflow for easy and accurate voice note transcriptions: https://wisprflow.ai/r?KARIN347

The Growth Table Small Business Collective: https://growthtablesbc.com

References:

1. Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775 to 779. The foundational paper on why automating a task leaves humans responsible for catching failures in work they no longer practice.

2. Parasuraman, R., and Riley, V. (1997). Humans and automation, use, misuse, disuse, abuse. Human Factors, 39(2), 230 to 253. The core review of how people over-trust and under-trust automated systems.

3. Skitka, L. J., Mosier, K. L., and Burdick, M. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991 to 1006. Experimental evidence that people stop checking for errors when a system provides an answer.

4. Kruger, J., and Dunning, D. (1999). Unskilled and unaware of it, how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121 to 1134. Relevant to why low baseline knowledge makes it hard to spot weak output.

5. Brynjolfsson, E., Li, D., and Raymond, L. (2025). Generative AI at work. Quarterly Journal of Economics, 140(2). Field evidence on productivity effects across a large workforce, with the largest gains among less experienced workers.

6. National Institute of Standards and Technology (2023). Artificial Intelligence Risk Management Framework, AI RMF 1.0. NIST AI 100-1. A vetted practitioner standard covering validity, reliability, and the role of human review in AI-assisted work.