Should I build an internal AI assistant for my team?
Yes, if your team repeatedly hunts for the same answers across scattered docs, an internal AI assistant pays off fast. But 'build' usually means configure, not code: ChatGPT Team, Claude Projects, or Glean can index your knowledge with no engineering. Start narrow (one team, one painful use case), measure time saved, and only expand once it's accurate and trusted. The hard part isn't the AI, it's keeping the underlying knowledge clean and current.
Decide buy vs build. For most SMBs, a managed option wins: ChatGPT Team or Claude with Projects for document Q&A, or Glean if you need to search across many connected apps (Slack, Drive, Notion, Jira) with permissions. Custom RAG builds give more control but add maintenance and cost, only worth it for unusual data or strict requirements. Whichever you pick, governance matters: control what's ingested, respect access permissions, and set rules on sensitive data.
Roll out in phases. Start with one high-friction use case, say, support reps answering product questions, prove it saves time, then expand to HR, sales, and ops. A 90-day plan should cover data prep, a pilot team, light training on prompting, and clear usage policies. Adoption fails more from poor change management than poor technology.
Measure to justify it. Track time saved per query, answer accuracy (spot-check against sources), and user satisfaction, and watch for hallucinations, which is why retrieval grounded in your real docs matters. Budget for upkeep: stale or contradictory documents are the top cause of wrong answers, so assign an owner to keep the knowledge base current.
Prompts to try
Copy these into ChatGPT or Claude to go deeper.
Design an internal AI assistant trained on company docs that answers HR, sales, and product questions.
Compare ChatGPT Team, Claude Enterprise, Glean, and custom-built internal assistants.
Generate a 90-day rollout plan for internal AI including governance, training, and ROI tracking.
Build a measurement framework (time saved, accuracy, satisfaction) for our internal AI.