Scripted chatbots handled basic routing. Machine learning improved tagging and article suggestions. Even IBM Watson, once hyped as the future of support, underdelivered when applied to real tickets. These tools optimized around the edges. They didn’t solve the complexity at the core of enterprise service.

The generative AI boom has reignited interest. Enterprises want to scale support. Startups want to build category-defining tools. And investors are chasing a $700B market. But widespread deployment remains limited. Hallucination risk, siloed data, and integration friction stall adoption before pilots even begin.

At Maxitech, we see the gap clearly. Most startups are optimizing for autonomy. The real opportunity is systems that embed inside workflows, resolve issues end-to-end, and prove value fast.

To understand what enterprise-grade AI in support actually requires, we have to look at where early systems fell short and why that still matters.

Early AI Efforts in Customer Support (Pre-Generative Era)

Before generative AI, most applications of artificial intelligence in customer service were narrow, brittle, and rule-bound. Early tools like Eliza and interactive voice response (IVR) systems simulated interaction without actually understanding context. By the 1990s and 2000s, companies deployed decision-tree chatbots to handle repetitive inquiries, often with rigid scripts that broke down under unexpected input.

In the 2010s, machine learning improved the backend of support operations. AI models helped tag tickets, route issues, and surface relevant help center articles. These systems added efficiency, but they weren’t transformative. Even flagship efforts like IBM Watson overpromised and underdelivered when applied to real customer conversations.

Despite the hype, AI in this era remained assistive, not autonomous. It worked in the background, enhancing human workflows without replacing them. The promise of AI-powered customer support existed, but only in theory. Most systems lacked the flexibility, nuance, or language understanding to handle the complexity of real-world support at scale.

 

Limitations of Early Chatbots and Automation

While early AI systems helped streamline support operations, their front-end performance fell short. The promise of automation quickly ran into hard constraints, especially when bots faced unstructured language or unfamiliar queries.

These first-generation chatbots were rigid. Without real language comprehension, they could only process narrowly phrased questions. Any variation in wording often broke the interaction. The result was brittle logic wrapped in a friendly interface, but one that failed under real conditions.

They also couldn’t reason or adapt. Responses were pre-scripted, based on keyword matching, not true understanding. Empathy, nuance, and problem-solving were out of reach. Customers quickly recognized this and lost patience.

A 2022 survey by UJET reflected that gap. A large majority of users said chatbot experiences left them frustrated or unresolved. Many ended up escalating to human agents anyway, creating more work rather than less.

Instead of reducing volume, these early bots often passed half-solved problems downstream. Support teams absorbed the fallout, and customer trust suffered. What looked like automation was really deflection, and enterprise buyers noticed.

This set the stage for something better. The need for systems that could understand, act, and resolve—not just route—was clear.

From Chatbots to Agent Assist: The Rise of Intelligent Retrieval

As chatbot fatigue set in, a more pragmatic approach took hold. Instead of trying to automate entire conversations, a new wave of tools focused on assisting human agents in real time. Forethought was one of the first to define this model.

When it launched in 2018, Forethought introduced an AI assistant designed to help agents respond faster. By pulling in relevant help center articles, past tickets, and internal documentation, it surfaced answers directly within the agent’s workflow. These “candidate responses” improved first-touch accuracy without removing the agent from the loop.

This assistive approach solved a different kind of problem: not how to replace support teams, but how to reduce their cognitive load. Other tools, like Zendesk’s Answer Bot, followed similar logic. They embedded AI into helpdesk and CRM systems to suggest replies, classify tickets, or route inquiries more efficiently. 

While these systems didn’t achieve end-to-end resolution, they showed real gains in speed and consistency. Agents could respond more accurately, and customers got answers faster. The tools learned from past interactions and improved with use, creating the first signs of real knowledge compounding inside support teams.

The shift mattered. It reframed AI as a collaborative tool rather than a standalone product. By proving AI could deliver value inside existing workflows, this era laid the groundwork for the next generation of customer support automation.

The Generative AI Leap: What Changed in Customer Support

The release of large language models like GPT-3 and GPT-4 marked a turning point in AI’s role in customer service. Until then, AI lived mostly in the background, suggesting help docs, tagging tickets, or assisting agents behind the scenes. That changed when generative models moved AI to the front lines.

For the first time, machines could respond to customers in fluid, human-like language. AI was no longer just a routing layer. It became capable of understanding intent, maintaining multi-turn conversations, and generating personalized responses that felt coherent and context-aware.

This shift was more than stylistic because generative AI brought real functional gains.

Instead of relying on decision trees, modern AI agents interpret long, free-form messages and respond with tailored answers. They remember what was said earlier, clarify confusion, and adapt their tone based on the conversation.

They’re also built to act. 

Startups like Sierra showed how generative models can trigger backend actions such as checking account status, processing refunds, or updating records without human intervention. This redefined the chatbot paradigm because now, AI could complete entire workflows (not just respond to pre-scripted FAQs).

Other tools have focused on solving accuracy and context challenges by deeply integrating with enterprise knowledge bases. For example, Glean’s customer support solution uses retrieval-augmented generation to surface precise, verified answers from internal documentation, ensuring agents and automated systems deliver consistent, trustworthy responses.

Global scale became attainable. 

A single model could now handle support in dozens of languages, instantly translating and generating content without manually scripted flows. Companies no longer needed to build separate support infrastructure for each market.

Voice capabilities improved as well. Speech synthesis tools like ElevenLabs and Cartesia made AI voices indistinguishable from humans. Startups like Telli built entire AI call centers on this foundation, blending realistic voice output with task execution.

Together, these capabilities transformed AI in customer support from a supplemental tool into a frontline agent. Support teams began trusting AI to summarize conversations, handle resolutions, and manage volume autonomously.

This leap didn’t solve every challenge. Issues like accuracy, brand alignment, and oversight still matter. But the floor of what AI could do was raised, and enterprise teams took notice.

New Startups Flood In: YC’s Generative Customer Support Startups (2023–2024)

The launch of ChatGPT ignited a tsunami of new startups in AI-powered customer support. Nowhere was this more visible than in Y Combinator’s recent cohorts. Beginning in early 2023, dozens of YC-backed startups launched with a single mission: reimagine customer service using generative AI.

• Yuma AI focuses on e-commerce, embedding AI agents into platforms like Shopify to handle FAQs and order issues. The company claims to automate up to 60% of support tickets for early adopters, and raised a $5M seed to scale across retail helpdesks.
• Kapa AI serves technical B2B companies by turning dense product documentation into an LLM-powered assistant. Used by teams at OpenAI, Mixpanel, and Docker, Kapa provides in-app support that answers API and SDK questions in real time.
• Calltree targets voice-heavy environments like call centers. Its AI reps answer phone calls, connect to CRM dashboards, and resolve issues with speed and consistency.
• Telli, a voice AI startup, handles scheduling and intake calls for service businesses. With just six employees, the team has processed nearly one million phone calls, showing the scale possible with voice automation.
• Eloquent takes a novel approach by learning directly from screen recordings of human operators using internal tools. Instead of relying solely on API integrations, Eloquent’s system observes real workflows to automate complex, bespoke processes, making it well-suited to companies with deeply customized back-end systems.

These are just a few of the startups flooding the space. Some target specific verticals, while others build tools for broader enterprise use. Not all will survive. Competition is rising, and the ability to integrate deeply and prove ROI will determine who breaks through.

What’s clear is that generative AI reshaped the founder landscape. Support is no longer a back-office function. It’s a proving ground for what AI can really do.

VC Investment Surge in Customer Support AI (Late 2022–2025)

As generative AI moved from novelty to enterprise priority, venture capital followed. Between late 2022 and 2024, funding surged for AI-driven customer support platforms, even as the broader tech market pulled back.

According to Menlo Ventures, enterprise spending on generative AI apps grew nearly sixfold from 2023 to 2024, reaching $13.8 billion by Q4. An estimated 31% of that was directed toward customer support use cases, making it one of the most heavily funded areas in the stack.

Top deals reflected this shift. 

Sierra raised $175 million at a $4.5 billion valuation. Parloa secured $120 million to expand its voice AI platform into the U.S. Decagon announced a $131 million Series C to scale its generative customer support platform for large enterprises. 

Other major rounds went to Cognigy, DevRev, and Crescendo, while younger players like Telli and Yuma AI also drew early backing.

Strategics have followed suit. Salesforce, Zendesk, and Oracle have all integrated or acquired AI capabilities for service automation.

By 2025, the funding environment began to normalize, but conviction remains high. Customer support AI solves real pain points like cost pressure, scale limitations, and inconsistent response quality, which is why it continues to attract capital even in a more selective market.

This capital wave accelerated the race to build category-defining AI systems for enterprise support.

How the GenAI Era Differs from Previous Customer Service AI Waves

Clearly, generative AI has redefined customer support. Compared to earlier waves of chatbot automation, this era brings a step-change in performance, flexibility, and capability.

Naturalistic Language Boosts Performance

First, the quality of AI-generated responses has improved dramatically. Older bots were limited to scripted replies, often sounding robotic and breaking down with unstructured input. Today’s language models understand nuance, ambiguity, and tone. They generate full conversations that mirror human agents in clarity and empathy, significantly expanding the types of queries AI can handle without escalation.

Flexibility Extends Task Execution

Second, generative agents go beyond conversation. They complete tasks. With API integrations, AI now resolves issues from start to finish. A customer can request a refund, and the AI can trigger the workflow, issue the label, and send confirmation without involving a person. Startups like Sierra are already operating this way.

What’s more, support AI is no longer confined to a chat window. These systems operate across email, voice, chat, and even social channels and maintain context throughout. A conversation that starts via email can continue on a call without losing its history or relevance.

Enhanced Capabilities Through Adaptive Self-Learning

Finally, generative systems learn and evolve. Instead of being manually scripted, they adapt from historical support data and continuous feedback. The more they’re used, the more accurate and aligned they become, with reinforcement loops improving both performance and consistency.

What sets this generation of chatbots apart is not just better technology, but broader ambition. AI is no longer an add-on. It is being architected as a core layer of enterprise support operations.

The Evolution and Impact of Voice AI in Support

Voice support has traditionally lagged behind digital channels, stuck with outdated IVR menus and rigid phone scripts. But that’s changing fast. Voice AI is evolving to match the fluency and flexibility seen in generative chat interfaces.

Modern voice agents now combine speech recognition, large language models, and high-quality voice synthesis to deliver real-time conversations that sound human and adapt to complex input. Tools like ElevenLabs and Cartesia have made AI voices nearly indistinguishable from real people.

On the input side, transcription models like OpenAI’s Whisper can handle unstructured speech, enabling AI agents to understand multi-part questions and respond appropriately. This is a major leap from past systems that relied on scripted prompts or fixed commands.

Importantly, voice AI now supports hybrid deployments. Agents handle common queries, then escalate more complex issues with full context passed to human reps. This reduces repeat explanations and shortens resolution time.

Use cases are expanding quickly. Companies like Parloa and Telli are deploying voice agents across retail, healthcare, and logistics. Enterprise vendors like Five9 and Genesys are embedding AI into call flows for authentication, routing, and summaries.

As the technology improves, voice AI is shifting from novelty to necessity. It’s helping companies field high call volumes without compromising experience and freeing human agents to focus on high-stakes conversations.

Emerging Trends: Agent Co-Pilots, Vertical Specialists, and AI Quality Control

As AI adoption deepens, customer support teams are shifting from experimentation to optimization. Three trends are shaping how support AI is being built and deployed today.

Agent Co-Pilots

AI isn’t just for automation. It’s increasingly being used to assist agents in real time. Co-pilot tools live inside an agent’s inbox or console, suggesting replies, surfacing context, or drafting responses. Platforms like Intercom, Zendesk, and Cognigy now offer co-pilot functionality that reduces agent workload while improving response speed and accuracy. These tools effectively turn every agent into an AI-augmented rep.

Vertical AI Agents

One-size-fits-all support bots are giving way to domain-specific AI. Startups like Gradient Labs build financial services–focused agents that understand banking workflows and compliance requirements out of the box. In healthcare, fintech, and e-commerce, AI agents trained on industry-specific data are outperforming generalist tools. This vertical focus enables faster deployment, higher accuracy, and clearer ROI.

AI Evaluation Infrastructure

With AI agents handling more conversations, companies are investing in quality control systems. This includes dashboards that track accuracy and resolution rates, feedback loops from agents and customers, and even AI-powered self-evaluation. Platforms like Crescendo and Maven are building QA layers that help companies audit performance, flag uncertainty, and ensure brand alignment. As trust becomes a gating factor, robust evaluation systems are becoming a must-have for enterprise adoption.

Together, these trends signal a maturing market. Success in support AI will increasingly hinge on depth, observability, and domain fit, not just automation volume.

What Will Differentiate the Winners

As the field matures, differentiation is no longer about who has the most advanced model. It’s about who can scale adoption and stay embedded once deployed. Two traits will separate the winners from the rest: strong go-to-market execution and high switching costs.

GTM Strength

Enterprise adoption doesn’t happen through tech alone. Winning startups combine technical quality with credible sales strategies, partner ecosystems, and founder networks. Teams like Sierra’s, led by ex-Salesforce and Google executives, secure early traction through trust, access, and proven playbooks. The ability to navigate complex sales cycles, integrate with incumbents, and speak the language of support leaders is a GTM advantage most teams underestimate.

Switching Costs

Stickiness comes from embedding into workflows and learning from proprietary data. The deeper an AI platform integrates into systems like CRMs, order databases, or internal knowledge bases, the harder it is to rip out. Over time, these systems become tailored to the company’s voice, policies, and customer behavior. Replacing them means starting over.

Vendors that combine embedded automation with clear outcomes and continuously improve through usage build defensible moats. Hybrid models that pair AI with human agents add another layer of reliance.

Ultimately, the winners will be the companies that position themselves not as tools, but as infrastructure. Enterprise buyers are looking for systems they can trust to handle scale, compliance, and complexity. The teams that earn that trust and prove long-term value will define the next generation of customer support.

Build What Enterprises Will Trust

“AI customer support is no longer about chatbots. It’s about trust, integration, and time-to-resolution. We back the teams solving for that.” ~ Burak Arık, CEO of Maxitech.

GenAI raised the ceiling on what’s possible in customer support, but the hardest problems remain unsolved. Enterprises need systems that can act with context, pass compliance, and improve performance inside real workflows. That’s where the opportunity is.

If you’re building that kind of reliability, not just reach, we want to hear from you.

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