AI chatbots have become a practical way for businesses to answer questions faster, extend support beyond business hours, and reduce routine workloads. But successful chatbot use depends on more than just choosing the right tool. Fisher Phillips highlights ten common deployment pitfalls—and the precautions that help reduce the legal, privacy, and operational risks.
Many are already guarding against key concerns. Organizations are deploying risk assessments, bot disclosures, human-in-the-loop controls for employment contexts, and baseline vendor due diligence as increasingly standard starting points in AI governance. But less obvious and faster-moving risks still remain:
1. State “wiretapping” exposure and the class action trend. Plaintiffs’ lawyers have argued that chatbot providers and the surrounding website tech stack can capture substantive visitor communications without adequate consent.
2. Privacy drift and confidential input risks. Chatbots tend to collect more personal and confidential information than businesses intend, and transcripts can become sensitive datasets.
-
Precaution: For external chatbots, build a data-flow map that documents what’s collected, where it goes, who can access it, and how long it’s retained. Audit this against your privacy notices. For internal chatbots, adopt a written acceptable-use policy that spells out what information employees may and may not include, especially confidential, personal, or regulated data.
3. Ignoring the “companion creep” problem. Without boundaries, bots can slide into “companion” behavior where bots trained for business unit chat start offering emotional support or personal guidance.
4. No human escalation protocols. Without escalation triggers, high-risk chats—threats, harassment, self-harm indicators, legal complaints—create serious legal concerns.
5. Treating launch day as the finish line. AI is constantly evolving and the bot may look much different than when it was initially deployed.
-
Precaution: Monitor logs, audit performance, update disclosures, retrain staff, and retest after model or regulatory changes.