9 best AI voice agents for customer support (2026 buyer’s guide)

Phone support is one of the hardest parts of customer service to scale — and one of the most expensive to get wrong. Customers call when issues are urgent, emotional, or complex. Agents need deep context. Wait times matter. And unlike chat, there’s no room for looping scripts or brittle automation.

AI voice agents promise a way forward. But in practice, not all solutions are built for real support environments. Some modernize IVRs without improving resolution. Others demo well but struggle with escalation, integrations, or cost predictability once deployed. And many platforms labeled “voice AI” are still optimized for chat-first automation, not live phone conversations.

This buyer’s guide compares the best AI voice agents used in real customer support operations in 2026. It’s written for support, CX, and operations leaders evaluating vendors — not demos — and focuses on what actually determines success in production: voice quality, resolution depth, integrations, pricing models, and how well AI works alongside human agents.

The 9 best AI voice agents for customer support

Not all AI voice agents are built for real support environments. Some focus on conversational realism but struggle with end-to-end resolution. Others automate simple calls but break down under real-world complexity — limited escalation paths, brittle integrations, or poor visibility once they’re live.

The vendors below were selected based on how well they perform in production customer support operations, not demos. We evaluated each platform on its ability to resolve real issues, handle voice conversations naturally, integrate with modern support stacks, support human–AI collaboration, and scale predictably as call volume grows.

What follows is a practical, experience-driven breakdown of the strongest AI voice agents available in 2026 — starting with platforms designed to work with your support operation, not around it.

Assembled

Assembled is the only AI voice agent built on top of a modern workforce management platform, giving it a fundamentally different orientation from typical voice AI vendors. Instead of pitching full automation from day one, Assembled treats AI agents as part of the workforce — planned, measured, and optimized alongside human agents. For support teams that want to scale automation responsibly without sacrificing the customer experience, this hybrid approach is a major differentiator.

Assembled’s voice agent ties into the platform’s existing scheduling, forecasting, and analytics systems, allowing organizations to gradually increase autonomy with guardrails. You can even adjust AI handoff sensitivity to account for your team’s real-time capacity. A single workflow can be deployed across voice, chat, email, and even agent copilots, giving teams an efficient way to manage automation across all channels. Paired with conversation-based pricing and context-aware handoffs, Assembled is especially strong for companies that prioritize experience quality and long-term operational maturity over deflection metrics.

Key features:

• AI voice agent built on a workforce management and operations foundation
• Copilot → autonomous pathway for safe, gradual automation
• Unified agentic workflows deployed across voice, chat, email, and agent assist — built and managed with a no-code workflow builder
• Conversation-based pricing that avoids per-minute overages
• Intelligent handoffs based on sentiment, urgency and complexity
• Unified analytics showing AI and human performance side-by-side
• Deep integrations with major CRMs, CCaaS platforms, and support tools

Pricing: Flexible pricing options: $0.99 per conversation (fixed) or $0.40 per conversation plus $2.00 per fully automated resolution (usage-based) with no per-minute or per-agent fees in either structure. Enterprise plans available; contact sales for specifics.

Pros:

• Best-in-class for hybrid human–AI collaboration and orchestration
• Single workflow logic across channels reduces operational overhead and lost context between channels
• Context-aware handoff logic preserves customer experience quality
• Transparent, predictable pricing for longer or complex conversations
• Fast speed-to-value with no-code setup and plug-and-play integrations.

Cons:

• Not a telephony-first platform, so some advanced call-center telephony features may require partner tools.
• Advanced reporting is powerful but may require onboarding time to fully understand all dimensions of human + AI analytics.
• Real-time capacity-aware routing is unique, but may require calibration for organizations with highly fluctuating staffing models.

G2 rating: 4.8 ⭐️(20 ratings)

Best for: Mid-market and enterprise support teams that want to scale AI responsibly — prioritizing customer experience, human-AI collaboration, and operational maturity. Ideal for organizations that want automation to work with human agents, not replace them, and for multi-channel support orgs looking for unified workflows and analytics across voice, chat, and email.

Cresta

Cresta positions itself as a premium platform built around deep human–AI collaboration — not just automation or deflection. Its roots in conversation intelligence show up everywhere: the product is designed to learn from real interactions, surface what drives outcomes, and improve both AI performance and human coaching over time. Rather than treating voice AI as a standalone “voicebot,” Cresta frames AI agents, agent assist, QA/coaching, and analytics as one operating system for the contact center.

Where Cresta stands out is in the breadth of lifecycle and governance tooling it brings to AI agent deployments. It emphasizes an “AI Agent lifecycle” (discover what to automate, build, test, deploy, optimize) backed by large-scale simulation and evaluation, plus real-time supervisory controls and an operations center for human-in-the-loop oversight. That orientation makes it a strong fit for complex environments — especially regulated, multilingual, high-volume contact centers — but it also introduces trade-offs: the platform is typically high-touch, implementation-heavy, and likely overkill for teams looking for lightweight voice automation or fast pilots.

Key features:

• Unified platform spanning AI agents, real-time agent assist, and conversation intelligence
• Automation discovery that analyzes historical conversations to identify what to automate first
• No-/low-code AI agent builder for persona, escalation rules, compliance boundaries, and “sub-agents”
• Automated testing and simulation to validate behavior and edge cases before go-live
• Agent operations center for real-time oversight, risk detection, and human intervention
• Omnichannel context retention across voice and digital channels
• Enterprise security and compliance posture (SOC 2, ISO 27001, HIPAA, ISO 42001, etc.)
• High-touch support model with strong services/engineering partnership for deployments

Pricing: Custom pricing only. Quote-based enterprise model; pricing varies by channels, volumes, modules, and implementation scope.

Pros:

• Deep conversational insights that strengthen coaching and inform automation strategy
• Strong human–AI collaboration model (agent assist + automation + oversight)
• Robust testing, evaluation, and optimization workflow for higher-assurance deployments
• Broad enterprise integration coverage across CCaaS, CRM, and knowledge systems
• Strong governance posture for regulated and risk-sensitive environments

Cons:

• Implementation and ongoing optimization can be complex and resource-intensive
• High-touch, quote-only pricing limits accessibility for smaller teams and quick trials
• Some users note a learning curve and occasional integration/transcription edge cases
• Fewer public reviews than mainstream CX platforms, which can make benchmarking harder
• Over-engineered for teams seeking simple, fast voice automation

G2 rating: 4.3 ⭐️(42 ratings)

Best for: Large contact centers (upper mid-market to enterprise) that need both AI automation and real-time human support — especially teams that care about conversation intelligence, governance, and a structured lifecycle for deploying and improving AI agents. Not ideal for companies prioritizing quick pilots, transparent pricing, or lightweight voice-only automation.

Sierra

Sierra positions itself as the premium, brand-safe alternative in the AI voice agent market — less about deflection, more about emotionally intelligent agents that can resolve issues end-to-end. It leans heavily into “enterprise-safe” messaging and founder credibility (Bret Taylor and Clay Bavor), framing Sierra as an Agent OS you can “build once and deploy everywhere,” rather than a chatbot bolted onto an IVR. In practice, Sierra’s strongest and most consistently validated channel is voice, where its conversational quality and interruption handling tend to outperform more generic, turn-based voice wrappers.

Where Sierra stands out is in two areas: (1) voice experience quality (natural pacing, interruption-friendly flow, empathetic tone) and (2) commercial alignment via outcome-based pricing — you pay when the agent successfully resolves a case, not per minute or interaction. That said, the product reality can be more mixed than the pitch: implementations often take meaningful time and partnership, omnichannel performance is not always even (voice typically leads), and public materials don’t clearly indicate copilot-style human–AI collaboration features, which many modern CX teams now expect as part of a “hybrid” operating model.

Key features:

• Outcome-based pricing (pay only for successfully resolved cases)
• “Agent OS” architecture designed to run the same agent across channels
• Enterprise guardrails and brand protection framework
• Lifelike, interruption-friendly voice tuned for natural conversational flow
• Strong parsing of spoken structured inputs (emails, order numbers, acronyms)
• Deep call center and compliance-oriented integrations
• Voice testing and simulation tooling designed for real-world conditions
• High-touch implementation model for complex deployments

Pricing: Outcome-based pricing — pay only for successfully resolved cases. No public rate card; costs vary by volume, complexity, and scoped outcomes.

Pros:

• Voice conversation quality is consistently described as natural and interruption-friendly
• Strong brand safety and compliance posture for risk-sensitive environments
• Handles messy inputs well (IDs, addresses, acronyms, long strings)
• Integrates into existing call center infrastructure without requiring rip-and-replace
• Leadership credibility and strong positioning with enterprise buyers

Cons:

• Implementations often take weeks to months despite “build fast” positioning
• Omnichannel performance can be uneven (voice is generally strongest)
• Public materials don’t clearly indicate copilot / agent-assist human–AI collaboration features
• Outcome-based pricing can complicate forecasting during spikes or unusual volume patterns
• Long or highly complex conversations may expose context limits and require careful scoping

G2 rating: 4.3 ⭐(13 ratings)

Best for: Brand-sensitive, high-volume organizations that want premium voice automation with strong brand controls and are comfortable with high-touch implementation and custom pricing. Not ideal for teams prioritizing rapid self-serve deployment, predictable unit economics, or a deeply integrated hybrid human–AI workflow model (copilot + agent + WFM-style orchestration) out of the box.

PolyAI

PolyAI is a premium, enterprise-focused voice AI vendor built for one thing: making phone automation feel genuinely human. Its core differentiator is call quality in real-world conditions — noisy telephony, interruptions, accents, and long, multi-turn flows — backed by a voice-first stack (including proprietary SLU and phoneme-level capabilities) and strong global language support. The product is anchored in PolyAI’s Agent Studio, which it positions as a “voice-first omnichannel” platform — meaning it can extend beyond voice, but the technology and experience are optimized for phone conversations first.

In practice, PolyAI stands out most when the phone channel is high-stakes: high volume, regulated environments, complex customer intent, and strict expectations around brand voice and CX. The trade-off buyers should expect is that PolyAI can operate more like a high-touch, managed partner than a fully self-serve “builder” platform — with reviewers praising outcomes and support, while noting occasional UI slowness and a desire for more autonomy in iteration and experimentation.

Key features:

• Voice AI agents designed for natural, interruption-friendly phone conversations (voice-first foundation)
• Speech analytics / “data and insights” style reporting built around call audio and transcripts
• Agent assist capabilities (real-time support for human agents) (positioned as part of the platform)
• Enterprise-grade security posture (SOC 2 Type II and ISO/IEC 27001 are explicitly listed)
• Strong enterprise reputation signals in reviews for voice naturalness, containment, and support partnership

Pricing: Quote-based, per-minute usage pricing for ongoing use; PolyAI states that per-minute pricing includes maintenance, proactive performance improvements, and 24/7 support. PolyAI does not publish a standard per-minute rate.

Pros:

• Best-in-class perceived voice quality (often described as warm / human-like) 
• Strong containment and robustness in complex, multi-turn phone interactions 
• Enterprise-ready trust signals (security/compliance disclosures; high-touch support model) 
• Clear fit for global and multilingual phone operations

Cons:

• Limited self-serve control for rapid iteration (reviewers note dependence on PolyAI for some changes/testing) 
• UI can feel laggy at times (noted by reviewers even with very high ratings) 
• Pricing transparency is structural (per-minute) but not numeric (harder to benchmark without a quote) 
• If your strategy is primarily omnichannel today (not voice-led), PolyAI may feel heavier than more chat-first, self-serve platforms

G2 rating: 5 ⭐(12 ratings)

Best for: Large enterprises with heavy phone volumes and high CX expectations (regulated industries, complex call drivers, multilingual operations) that want a premium, voice-first solution — and are comfortable trading some self-serve agility for a more guided, outcomes-focused deployment and ongoing optimization.

Decagon

Decagon is an enterprise-grade AI agent platform that treats voice as a first-class channel alongside chat, email, and SMS — all powered by a single agent “brain” with cross-channel memory. The core pitch isn’t IVR modernization. It’s concierge-style automation at scale: AI agents designed to resolve full workflows, reduce support headcount, and replace large portions of outsourced or in-house teams.

In practice, Decagon delivers impressive automation depth for high-volume environments, particularly where workflows are well-defined and repeatable. Buyers should be aware, however, that Decagon’s model increasingly resembles a high-touch, engineering-led deployment. Teams evaluating the platform often report dependence on Decagon’s Forward Deployed Engineers (FDEs) to design, maintain, and evolve complex Agent Operating Procedures. As Decagon moves further upmarket to compete with vendors like Sierra, some SMB and lower mid-market teams also report slower access to hands-on support, making the platform feel heavier — and harder to operate independently — outside of large enterprise contexts.

Key features:

• AI voice agents with low-latency, interruption-friendly conversations
• Unified agent “brain” with cross-channel memory across voice, chat, email, and SMS
• Agent Operating Procedures (AOPs) for automating complex, multi-step workflows
• Enterprise analytics, testing, and QA tooling (Watchtower, simulations, observability)
• Human escalation with automatic conversation summaries
• Deep integrations with Zendesk, Salesforce, and modern telephony stacks

Pricing: Quote-only. Decagon publicly describes per-conversation and per-resolution pricing models, but does not publish baseline rates. Pricing varies by volume, automation scope, and deployment complexity.

Pros:

• High reported deflection and resolution rates (often 70–80%+ in production)
• Strong G2 sentiment around automation depth, analytics, and implementation partnership
• Voice quality and conversational realism reinforced by ElevenLabs partnership
• Well-suited for large-scale workforce reduction and BPO replacement strategies

Cons:

• Many teams report reliance on Decagon’s engineers for ongoing changes, testing, and optimization, limiting self-serve agility
• Support experience can be uneven for SMB and lower mid-market teams as Decagon prioritizes enterprise accounts
• Premium, opaque pricing makes early-stage budgeting difficult
• No native workforce management or hybrid human–AI orchestration layer
• Platform complexity can be overwhelming without strong internal technical resources

G2 rating: 4.9 ⭐️(18 ratings)

Best for: Large, cost-focused enterprises with high volumes and repeatable workflows that are explicitly aiming to replace or materially reduce human support capacity — including BPO-heavy operations. Less suited for teams that need lightweight tooling, fast self-serve iteration, or consistent hands-on support without deep engineering involvement.

Ada

Ada’s AI voice agent is part of a broader enterprise automation platform — not a standalone voice-first product. Voice is routed through Ada’s proprietary Reasoning Engine™ and managed alongside chat, email, SMS, and social within a single CX automation layer. For large, high-volume organizations willing to standardize on one platform, Ada can unlock meaningful automation and strong outcomes across channels. But the tradeoffs are real: pricing is opaque, some deployments require meaningful platform consolidation, and the public “end-customer” sentiment doesn’t always match the polished marketing story.

Where Ada stands out is in its operational tooling: playbooks for SOP-style automation, granular coaching to improve responses over time, broad multilingual support, and deep integration coverage — especially for teams that want one place to manage automation across channels. The biggest risk isn’t capability on paper; it’s experience quality in the wild. Reviews suggest Ada can perform extremely well when well-designed and tightly governed, but poorly configured deployments can lead to looping behaviors and frustrating escalation paths — which is especially costly on voice, where customers have less patience for dead ends.

Key features:

• AI voice agent powered by the Ada Reasoning Engine™ (understand → isolate → retrieve → create → resolve)
• Voice as part of a true omnichannel platform (chat, voice, email, SMS, social) with broad language support
• “Playbooks” that automate SOPs and multi-turn workflows across channels, including voice
• Granular coaching tools that allow response-level tuning and continuous improvement
• Telephony support via Twilio (Ada-managed or bring-your-own), plus SIP/CCaaS integrations
• 100+ integrations across major CRMs, helpdesks, and CX stacks (with variable complexity)
• Trust and governance posture designed for high-stakes customer service environments

Pricing: Custom, enterprise quote-only pricing. Often resolution- or usage-based, with no published voice rate card. Typical contracts skew enterprise-scale.

Pros:

• Strong outcomes highlighted in public case studies when deployed well
• Deep omnichannel automation beyond voice alone
• Powerful SOP / playbook model for repeatable, high-volume interactions
• Granular coaching and feedback loops that can steadily improve performance
• Broad language support and a mature integration ecosystem

Cons:

• Mixed public sentiment on end-user experience, including reports of loops and frustration
• “Walled garden” approach can require replacing or consolidating parts of the CX stack
• Opaque pricing makes true costs harder to predict and compare
• Integration complexity can be meaningful in certain ecosystems
• No native workforce management or capacity planning for hybrid human–AI operations

G2 rating: 4.6 ⭐(167 ratings)

Best for: Large enterprises with high conversation volume that are prepared to centralize automation on a single platform, invest in strong conversation design and governance, and prioritize omnichannel scale over a lightweight voice-only layer. Not ideal for teams looking for transparent pricing, plug-and-play voice automation on top of an existing stack, or hybrid human–AI operations with integrated capacity planning.

Forethought

Forethought offers an AI voice agent, but its real strength (and complexity) comes from being a broader enterprise AI orchestration platform, not a voice-first product. Voice is powered by the same multi-agent framework (Discover, Solve, Triage, Assist) and Autoflows engine that runs across chat, email, web, and other channels. That architecture can unlock capabilities most voice-only vendors don’t have — like using historical ticket data to generate workflows, identify knowledge gaps, and continuously improve automation strategy over time.

The upside is a more strategic, end-to-end approach to automation for large support orgs with messy data, complex knowledge, and multiple channels. The downside is that deployments tend to be data-heavy and tuning-intensive, with longer timelines and higher total cost than teams looking for fast, tactical voice automation. And like other “platform” players, there’s a real risk of customer frustration if escalation paths aren’t designed well — especially on voice, where loops and blocked handoffs become reputational quickly.

Key features:

• AI voice agent built on a multi-agent ecosystem (Discover, Solve, Triage, Assist)
• Autoflows for dynamic, reasoning-based workflows and action-taking automation
• Knowledge and content gap detection to surface missing/weak articles
• Workflow generation informed by historical case and conversation data
• Multichannel automation across voice, chat, email, web, and more
• Enterprise-grade routing, analytics, and optimization tooling
• Integrations with major CRMs and support platforms (e.g., Zendesk, Salesforce, Intercom)
• Agent assist layer available as part of the broader platform (copilot-style support)

Pricing: Custom enterprise pricing (quote-only). No public voice usage rates. Contract size typically scales with channels automated and implementation scope.

Pros:

• Reported strong automation outcomes in larger deployments
• Strategic capabilities beyond basic voice automation (data-driven insights + workflow generation)
• Well-suited for organizations with deep or complex knowledge bases
• Multi-channel platform that can support end-to-end automation, not just call handling
• Strong integration story for enterprise CX stacks

Cons:

• Longer implementations that can take months, with real ongoing tuning required
• Pricing typically targets enterprise budgets; hard to evaluate without sales engagement
• Higher risk of over-deflection and poor CX if escalation isn’t carefully designed
• Not optimized for teams wanting quick, lightweight voice activation
• Mixed end-user sentiment in some public channels (looping / difficulty reaching a human)

G2 rating: 4.3 ⭐(163 ratings)

Best for: Large enterprises with complex support operations that want AI transformation across channels — and have the ops maturity to invest in data prep, tuning, governance, and escalation design. Not ideal for teams seeking plug-and-play voice automation, short pilots, or transparent pricing.

Fin Voice by Intercom

Intercom is best known as a customer service platform, and Fin Voice is positioned as an extension of its broader Fin AI Agent rather than a standalone, voice-first product. Instead of building a dedicated voice solution from the ground up, Intercom brings the same AI agent used across chat and email into the phone channel. The result is an omnichannel experience that prioritizes speed to value and platform consistency over deep voice specialization.

On paper, Fin Voice is marketed as enterprise-ready, with strong security credentials, performance guarantees, and broad channel coverage. In practice, however, Fin Voice appears best suited to SMB and lower mid-market support teams with relatively standardized workflows and well-defined use cases. While the conversational quality is strong and latency is well tuned for natural phone interactions, teams with highly complex enterprise environments — including bespoke processes, heavy customization, and nuanced escalation logic — may find Fin Voice less flexible than they expect.

Key features:

• AI voice agent powered by the same Fin engine used across chat and email
• Ultra-low-latency responses designed for natural, interruption-friendly calls
• Phone-specific response tuning with short, paced answers rather than long chat outputs
• No-code configuration for knowledge, policies, and Procedures
• Pre-deployment simulation and testing tools to preview call behavior
• Seamless handoffs to human agents with transcripts and summaries
• Omnichannel consistency across voice, chat, and email within Intercom’s platform
• Built-in helpdesk, inbox, and automation tooling

Pricing: Fin AI Agent is priced at $0.99 per successful AI resolution across channels, with Intercom platform plans starting at $29 per seat per month. Fin Voice itself is offered via custom, sales-led pricing, with no public per-minute or per-call rates published.

Pros:

• Fast time to value for teams already using Intercom
• Strong conversational quality that feels natural on the phone
• No-code setup that non-technical CX teams can manage
• Robust testing and preview tools before launch
• Unified AI behavior across chat, email, and voice

Cons:

• Voice capabilities are newer and less mature than chat and email
• Limited flexibility for highly complex or bespoke enterprise workflows
• Brand-voice customization can be constrained for teams with strict guidelines
• Pricing and packaging skew toward bundled platform adoption
• Less suited for large, highly customized enterprise contact centers

G2 rating: 4.5 ⭐(3,658 ratings)

Best for: SMB and lower mid-market support teams — particularly those already using Intercom — that want to add voice automation quickly using the same AI agent across chat, email, and phone, without investing in a voice-specialized or heavily customized solution.

Bland AI

Bland takes a fundamentally different approach to AI voice agents than most platforms in the category. Rather than positioning itself as a turnkey CX tool, Bland is best understood as voice AI infrastructure: a developer-first system designed for teams that want maximum control over voice models, call logic, hosting environment, and scale. Its most distinctive capability is rapid voice cloning from short audio samples, which allows brands to deploy highly customized or persona-driven voices without long training cycles.

In practice, Bland excels in environments where engineering control and call volume matter more than ease of use. Dedicated GPU infrastructure enables massive concurrency and large outbound programs, but that power comes with meaningful trade-offs. Buyers should expect a services-heavy implementation model, higher technical complexity, and more effort to model true total cost of ownership — particularly for outbound-heavy use cases where minimums and add-ons can accumulate. While Bland advertises visual tooling and no-code elements, real-world feedback suggests the platform remains far better suited to technical teams than to CX or operations-led buyers.

Key features:

• Rapid voice cloning from short audio samples for custom or branded voices
• Dedicated GPU infrastructure for high concurrency and large outbound programs
• Developer-first APIs, webhooks, and programmable call logic
• Conversational Pathways for deterministic, on-script call control
• Inbound and outbound calling at scale (including batch and campaign workflows)
• Memory across calls and channels (voice, SMS, chat)
• Deep integration support via APIs, SIP, and BYOT telephony

Pricing: Public, tiered plans are documented, with usage-based per-minute rates and outbound minimums. While pricing is more transparent than many enterprise voice vendors, real-world costs can become complex once concurrency limits, outbound minimums, transfers, and carrier fees are factored in — making TCO harder to predict for buyers without clear volume models. 

Pros:

• Exceptional scalability for high-volume inbound or outbound calling
• Industry-leading voice cloning and custom voice control
• Deep engineering flexibility and deterministic conversation design
• Strong fit for security- and compliance-sensitive environments
• Appeals to teams that want to “own” their AI rather than rely on frontier model providers

Cons:

• Requires significant technical expertise; not accessible to non-technical CX teams
• Professional-services-heavy onboarding and iteration
• Latency concerns reported in third-party comparisons may affect conversational flow, depending on deployment
• Pricing complexity (minimums, add-ons, telephony costs) can obscure true cost
• Support experience appears stratified, with white-glove assistance primarily reserved for enterprise tiers

G2 rating: Not yet rated on G2

Best for: Large, technically sophisticated organizations that need extreme call scale, custom voice identities, and deep infrastructure control, and that are comfortable with multi-week implementations and a services-heavy operating model. Not a fit for teams seeking fast deployment, predictable TCO, or no-code voice automation owned by CX or operations teams.

How to choose the ideal AI voice solution for your business

Choosing an AI voice agent isn’t just a technology decision — it’s a strategic investment that will shape your support operations for years. After working with hundreds of teams adopting voice AI, a clear pattern emerges: the most successful implementations start with aligned goals, rigorous evaluation, and a plan for long-term scalability. Here’s a practical framework to guide your decision.

Prioritize business goals and needs

Start with outcomes, not features. Before evaluating vendors, clarify what success looks like for your organization. This ensures your pilot, KPIs, and vendor selection remain aligned.

Ask yourself:

• What problem are we solving?
  (Cost reduction? Lower wait times? CSAT improvement? After-hours coverage?)
• What does success look like in 90 days, 6 months, 12 months?
• Which KPIs will prove ROI to your leadership team?

Most teams cluster around a few core goals:

The key is matching your pilot and vendor evaluation to the specific outcomes you want. For example:

• If you want higher resolution, look for platforms with strong CRM, billing, and backend integrations.
• If you want better routing, prioritize advanced NLU, sentiment detection, and pre-handoff verification.
• If you want better after-hours coverage, look for consistent performance, smart follow-up workflows, and contextual escalations.

Teams that define narrow, measurable pilot goals — like “resolve 25% of cancellation requests” — see time to value significantly faster.

Ensure interoperability with current systems

Weak integrations — not weak AI — cause most failed voice implementations. Early in your evaluation, test how well a platform fits into your existing infrastructure across four layers:

1. Telephony and contact center platform
Your AI must slot naturally into tools like Five9, Talkdesk, Zendesk Talk, Genesys, or Zoom Contact Center.

If SIP connections are required, plan for 4–6 weeks of setup, testing, and validation.

2. CRM and ticketing systems
Look for real-time, two-way sync for:

• accurate case creation
• full customer history
• automated wrap-up notes
• consistent categorization

Poor CRM integration creates downstream reporting and QA issues — and undermines trust in automation.

3. Knowledge bases
Your AI should be able to pull from and stay aligned with:

• Notion
• Confluence
• Guru
• Google Drive

…and it must respect permissions and version control cleanly.

4. Backend systems
This is where meaningful automation happens. Ensure the AI can interact with:

• order and fulfillment tools (Shopify, ERP systems)
• authentication APIs
• billing/payment systems
• custom internal applications

A unified performance view — like Assembled’s — ties AI activity, human activity, and WFM data together so teams can understand operational impact without juggling dashboards.

Watch out for vendors who:

• require manual data exports
• can’t adapt to future CRM changes
• split reporting across multiple interfaces
• don’t support real-time syncing

Ask every vendor: “Show me how your AI accesses our CRM data during a live call.”

The strongest partners will have a crisp, confident answer.

Pilot programs for testing performance

Never buy an AI voice solution without proving its value in your environment — with your data, your edge cases, and your workflows.

There are three common pilot structures:

1. Opt-out trials (30–90 days)
Good when you’re already strongly leaning toward a vendor.

2. Paid pilots
Best for validating a specific workflow before expanding.

3. Proof-of-concept (2–12 weeks)
Tight, time-bound tests focused on validating critical capabilities.

Best practices for voice AI pilots:

• Scope narrowly.
  Good: “Resolve 25% of cancellation requests.”
  Bad: “Resolve 25% of all cases.”
• Use preview tools first before exposing customers.
• Start with small volumes (20–100 calls) and scale only after quality is validated.
• Plan integration timelines for SIP, authentication APIs, and backend connections.
• Define success criteria up front — technical, operational, and value-based.

A successful pilot proves five things:

1. The AI works with your systems.
2. Responses are accurate and on-brand.
3. Agents can manage and refine workflows without engineering.
4. Core metrics move in the right direction.
5. The vendor is responsive, transparent, and collaborative.

Plan for evolving demands

Your needs today won’t match your needs in 12–36 months. A future-proof AI voice solution should be able to grow with your business across five dimensions.

1. Multi-channel expansion
Workflows built for chat, email, or voice should be reusable with minimal changes. This protects your investment as your channel mix shifts.

2. Geographic and market expansion
If you’re expanding into new regions or launching new brands, your AI should support:

• multiple languages
• time zone–aware handoffs
• regional compliance
• multi-brand routing and reporting

3. Automation maturity
Support operations typically evolve from:

• 5–10% automation (simple FAQs)
• to ~30% (AI-assisted workflows)
• to 40–50%+ (full autonomous resolution)

Choose a platform that supports this climb without requiring full rebuilds at each stage.

4. Workforce management integration
As automation grows, staffing strategy must evolve. Integrating voice AI with WFM data ensures:

• accurate forecasting
• SLA protection
• capacity-aware handoffs
• right-sized staffing plans

5. Flexibility across brands, segments, and products
If you operate multiple brands or business units — or if you’re a BPO — look for:

• per-brand workflow controls
• granular routing rules
• detailed segment-level reporting

Ask vendors directly:

• “What happens when we triple our automation?”
• “If we switch CRMs, how painful is migration?”
• “Can we take our workflows and data with us if we change platforms?”

A scalable platform gives clear, confident answers — not vague reassurance or lock-in.

Why Assembled

Assembled is designed for support teams that want to scale voice automation while maintaining a high-quality customer experience. Rather than treating AI voice agents as a standalone layer, Assembled brings automation into a broader support orchestration platform, with shared workflows across voice, chat, email, and agent assist.

This approach makes it easier to introduce automation gradually, refine behavior with no-code tools, and ensure handoffs, routing, and reporting stay aligned with how the support operation actually runs.

Book a demo to see how Assembled’s AI voice agent supports hybrid human–AI support with shared workflows, context-aware handoffs, and predictable pricing.

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