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Key takeaways
Not all AI customer support platforms are built the same. This checklist will help you separate truly unified solutions from disconnected point tools.
Use this checklist during vendor evaluations to ensure you're choosing a platform that delivers real proactive intelligence—not just faster reactive support.
Prioritize integration depth over breadth
Why it matters: AI is only as smart as the context it can access. Fragmented integrations create fragmented intelligence.
DO
Look for native integrations that create a unified data foundation across CRM, ticketing, chat, knowledge bases, and internal tools like Slack
Verify the platform can access complete customer context—not just surface-level data
Confirm integrations are bidirectional, so updates flow in both directions
Ask reference customers how long integration actually took and what data gets synchronized
DON'T
Accept platforms claiming '100+ integrations' without verifying they can actually unify your customer data
Accept platforms that require migration to proprietary systems or force a rip-and-replace
Settle for read-only integrations that can't write updates back to your source systems
Choose platforms purpose-built for AI from the ground up
Why it matters: AI-native platforms adapt to your business and improve over time. Rule-based systems break the moment customers phrase questions differently.
DO
Ask: Was this built for AI from the ground up, or did they bolt AI features onto an existing product?
Verify the platform uses training data from real B2B support conversations, not generic internet scraping
Confirm continuous learning that improves with your specific use cases
Look for intent understanding, not keyword matching
Ensure context is maintained across multi-turn conversations and weeks-long customer journeys
DON'T
Trust rule-based automation disguised as "AI"
Accept systems that require constant manual retraining or can't adapt to new scenarios
Demand fast time-to-value
Why it matters: Long implementation times kill momentum, burn budgets, and give teams time to revert to old habits.
DO
Look for out-of-the-box templates designed for B2B support operations
Confirm pre-trained models that work day one, not after 6 months of setup
Verify clear onboarding with measurable milestones in the first 30-60 days
Ask for reference customers who went live in weeks (not quarters)
DON'T
Accept vendor quotes of 6-12 months from signature to value
Accept vague timelines where "success" is defined as deployment rather than business outcomes
Accept implementations dependent on extensive custom development
Ensure you have control and deployment flexibility
Why it matters: Your support needs will evolve. You need a platform you can adapt without waiting for vendor roadmaps or engineering resources.
DO
Ensure enterprise-grade data security and governance built in
Confirm the vendor offers expert onboarding and implementation support
Look for centralized analytics across all interactions and workflows
Verify you can deploy new workflows without engineering support (no-code platform)
DON'T
Accept platforms that require technical resources for every workflow change
Choose vendors without clear security certifications or compliance documentation
Key questions to ask during vendor evaluations
The difference between point solutions and unified platforms is the difference between faster reactive support and truly proactive intelligence that prevents issues before they escalate.
Use these questions to see past the flashy demos and find a solution that will deliver sustainable value:
- How fast did your reference customers see measurable results? Ask for specific timelines and metrics.
- How deeply do your integrations actually work? Can you access complete customer context or just surface data?
- Does your AI get smarter with my data? Or does it require constant manual retraining?
- Can I deploy new workflows without engineering support? Show me how your no-code platform works.
- What happens if we need to customize? What are the limitations of your platform?
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