Voice AI Agents

High-value use cases for voice AI automation:

Outbound prospecting:

(1) Cold calling at scale: Call 1,000+ prospects/day vs 50-100 for human SDRs

(2) Lead qualification: Ask discovery questions, assess fit, score leads

(3) Meeting booking: Schedule demos directly on calendar after qualifying

(4) Follow-up calls: Reach out to inbound leads, trial users, or stale opportunities

Inbound response:

(1) Lead response: Call inbound form fills within 60 seconds

(2) Appointment confirmation: Remind and confirm upcoming meetings

(3) Basic support: Answer common questions, troubleshoot simple issues

(4) Routing and triage: Qualify caller intent, route to appropriate human

Customer success:

(1) Onboarding calls: Guide new customers through setup

(2) Check-in calls: Proactive outreach to gauge satisfaction

(3) Renewal reminders: Contact customers approaching renewal

(4) Survey administration: Conduct NPS, satisfaction, or feedback surveys

Data collection:

(1) Information gathering: Collect missing data from leads

(2) Event registration: Confirm attendance, answer logistics questions

(3) Appointment rescheduling: Handle no-shows and reschedule

Where voice AI struggles:

(1) Complex technical discussions requiring deep expertise

(2) Highly emotional situations (angry customers, sensitive issues)

(3) Negotiation or pricing discussions

(4) Multi-party conference calls

Best ROI: High-volume, repeatable conversations with clear scripts and decision trees.

Top voice AI platforms by use case:

Outbound sales calling:

(1) Bland.ai: Developer-friendly API for building custom AI phone agents. Best for technical teams.

(2) Air.ai: Autonomous AI SDR that handles full conversation (10-40 minutes). Best for complex sales qualifying.

(3) Trellus: AI-powered dialing with real-time battlecards and conversation prompts for human reps (AI-assisted, not fully autonomous)

Inbound and routing:

(1) Dialpad AI: Contact center platform with AI voicemail transcription, routing, and assist

(2) Aircall: Cloud phone system with basic AI features for call routing

(3) JustCall: Sales dialer with AI-powered analytics and coaching

Conversational AI platforms:

(1) Gong: Conversation intelligence with AI analysis (records and analyzes, doesn't make calls)

(2) Revenue.io (formerly RingDNA): Real-time AI coaching during live calls

(3) Chorus.ai (ZoomInfo): Call recording and AI insights

Voice infrastructure (build your own):

(1) ElevenLabs: Premium text-to-speech API for realistic voices

(2) Deepgram: Speech-to-text API with real-time transcription

(3) OpenAI Realtime API: Build conversational voice agents with GPT-4

(4) Twilio: Programmable voice infrastructure

Full-service AI calling:

(1) Conversica: AI assistant for lead follow-up via email and voice

(2) Reply.io: Sales engagement with AI-powered calling features

Best practice:

(1) Non-technical teams: Start with Air.ai or managed service providers

(2) Developers: Build custom with Bland.ai or ElevenLabs + Deepgram

(3) Hybrid approach: Use Trellus or Revenue.io for AI-assisted (not fully autonomous) calling

Cost comparison: AI vs human calling teams:

Human SDR costs (annual):

(1) Base salary: $50-70k/year

(2) Commission/bonus: $10-20k/year

(3) Benefits and taxes: +30-40% ($18-28k/year)

(4) Overhead: Tools, training, management ($10-15k/year)

(5) Total per SDR: $88-133k/year ($44-66/hour)

(6) Call capacity: 50-100 calls/day, 20-30% connect rate = 10-30 conversations/day

Voice AI costs (per month):

(1) Bland.ai: $0.09-0.12 per minute of conversation

(2) Air.ai: $5,000-15,000/month for managed service + per-call fees

(3) ElevenLabs + Deepgram (DIY): $0.20-0.40 per minute (API costs)

(4) Call capacity: 1,000-10,000 calls/day, unlimited scale

Break-even analysis:

(1) Human SDR cost per conversation: $15-30 (assuming 10-30 convos/day)

(2) Voice AI cost per conversation: $0.50-2.00 (3-5 minute call)

(3) Cost reduction: 90-95% cheaper per conversation

ROI scenarios:

(1) Replace 1 SDR making 25 calls/day: - Human: $110k/year - AI (625 calls/month × $1.50): $11,250/year - Savings: $98,750/year (90% reduction)

(2) Scale outbound 10x: - Humans: Hire 10 SDRs = $1.1M/year - AI: $50-100k/year for AI platform - Savings: $1M+/year

Hidden costs and considerations:

(1) Setup and training: 1-3 months to configure AI agent workflows

(2) Compliance: Legal review for AI calling in your jurisdictions

(3) Quality monitoring: Human QA to review AI call quality

(4) Human backup: Still need reps to handle escalations

Best ROI: High-volume, simple qualification and booking tasks where cost per conversation matters more than conversion rate.

Legal and regulatory requirements for voice AI:

United States regulations:

(1) TCPA (Telephone Consumer Protection Act): - Requires prior express written consent for robocalls to cell phones - AI calls are considered "artificial or prerecorded voice" under TCPA - Violations: $500-1,500 per call in damages - Best practice: Only call leads who opted in or have existing business relationship

(2) FTC regulations: - Prohibition on deceptive practices - Recommendation: Disclose AI nature of caller early in conversation - Example: "This is an AI assistant calling on behalf of [Company]"

(3) State-specific laws: - California: CCPA requires data privacy compliance - Some states require explicit AI disclosure - Do Not Call registries must be respected

International regulations:

(1) GDPR (Europe): - Requires consent for automated decision-making - Data processing must be disclosed and consent obtained - Voice recordings are personal data requiring protection

(2) Canada (CASL): - Anti-spam law covers voice calls - Requires express or implied consent

Best practices for compliance:

(1) Disclosure: Clearly state AI nature of call within first 10-15 seconds

(2) Consent management: Maintain records of opt-ins and respect opt-outs

(3) Recording consent: Inform callers if conversation is recorded (varies by state - some require two-party consent)

(4) DNC list compliance: Scrub against Do Not Call registries

(5) Business relationship: Prioritize calling existing customers, trial users, inbound leads

Risk mitigation:

(1) Legal review: Have attorney review AI calling program before launch

(2) Insurance: Obtain TCPA compliance insurance

(3) Vendor selection: Use platforms with built-in compliance features

(4) Call limiting: Avoid aggressive call patterns that trigger complaints

Bottom line: AI calling is legal with proper consent and disclosure, but requires careful compliance management to avoid costly violations.