Where should a B2B SaaS company start with AI adoption?
Start with a business problem, not the technology. Audit your processes to find repetitive, rule-based, high-volume work, then pick one well-defined problem where success is measurable within weeks. For most B2B SaaS companies, customer support automation is the best first move because the workload is predictable and even a basic ticket classifier or knowledge base can cut first-response time by 30-50%.Should we build custom AI or buy off-the-shelf tools?
Build what differentiates, buy what commoditizes. Build custom AI when it's core to your value proposition and you have proprietary data that creates a competitive moat. Buy off-the-shelf when the capability is commodity functionality, when you need speed to market, or when your team lacks AI expertise. The hybrid path, buying foundation models and building custom layers on your unique data, is often best for companies in the middle.Do you need a technical background to lead AI adoption?
No. You don't need to understand neural networks to make good AI decisions. You need a framework for evaluating opportunities, a way to assess vendors without getting sold, and the discipline to run pilots and measure results. The same skills that made you a successful CEO, identifying opportunities and executing with rigor, apply directly to AI. Some situations, like building AI into your product or working in a regulated industry, do warrant bringing in a fractional CTO or technical advisor.How do you measure whether AI adoption is actually working?
Attach a business metric to every AI initiative before development begins, never a purely technical one like accuracy. Measure business impact (revenue influenced, cost saved, hours returned), adoption (are people actually using it daily), and capability (team skills, data pipeline reliability, model performance over time). If you can't measure the business impact of an AI feature within 90 days of deployment, something is wrong with the feature or your measurement.What are the most common AI adoption mistakes founders make?
The biggest mistakes are technology-first thinking (starting with "let's use GPT-4" instead of a business problem), delegating AI strategy to the CTO instead of owning it as the CEO, expecting transformation in 90 days when real impact takes 6-18 months, neglecting change management, and rushing to build before fixing data quality. Trying to do too much at once is the most reliable way to fail; run one major AI initiative per quarter, not five at once.