High-ticket SaaS onboarding is expensive. CS teams. Training calls. Manual walkthroughs. Has anyone here implemented AI to: • Reduce onboarding time? • Personalize training? • Answer account-specific questions? I’m exploring ways to use LLMs as contextual onboarding assistants tied to product data.

Anyone else dealing with AI scope creep? Project starts as:“Build us a chatbot.” 3 weeks later: • Can it summarize sales calls? • Can it analyze churn? • Can it automate onboarding?• Can it predict LTV? I’ve started implementing a strict: IN-SCOPE / OUT-OF-SCOPE document before kickoff. It’s reduced chaos massively. Curious: How are you protecting your scope while still being flexible?

One thing that’s helped me close AI consulting deals faster: I build a mini prototype in 48 hours. Not polished. Not production-ready. Just enough to show the direction. It: • Builds trust • Exposes real data issues • Shifts conversation from theory → execution Are you guys doing something similar?Or selling fully scoped plans upfront?

Curious how SaaS teams are thinking about AI and expansion revenue. Instead of: “AI as a feature” Has anyone successfully implemented: “AI as a premium upsell layer”? Example: • Smart reporting • Predictive insights • Automated ops modules Are customers paying extra for intelligence — or expecting it bundled?

For SaaS founders doing $20k–$100k+ ACV: Are you adding AI features because: A) Customers asked for them B) Competitors added them C) You see real workflow leverage D) Marketing pressure I’ve noticed a lot of AI features get added but never deeply integrated into the product experience. The real value seems to come when AI reduces: • Time-to-value • Setup friction • Manual ops How are you deciding what AI to actually build?