When a customer contacts your CX team, here's an example of what typically happens:

• Context gathering: 45 seconds
• Knowledge search & verification: 73 seconds
• Response composition: 48 seconds
• Resolution processing: 62 seconds
• Documentation & closure: 52 seconds

Total: 4 minutes 40 seconds, with customers waiting 2+ minutes for their first response while agents jump between systems, unable to communicate progress.

Across every interaction, agent, and shift, these inefficiencies cost millions annually, not from lack of effort, but from the fundamental friction of human-powered information retrieval.

Some of the fastest-growing companies are finding and fixing these costs with AI agents. ClassPass saw a 95% drop in its support conversation expenses while NG.CASH watched its autonomous resolution rate climb from 13% to 70%.

You've also likely reviewed dozens of AI vendors promising transformation. But instead of comparing features you'll never use and integrations you'll never need, what if you analyze two core metrics: deflecting tickets entirely through autonomous resolution, and making your human agents faster through intelligent assistance.

When you strip away the marketing promises and focus on time arbitrage, converting scattered inefficiencies into measurable capacity gains, the path forward becomes remarkably clear. Here’s how to evaluate your options and find the right fit for your team.

What is AI agent assist?

AI agent assist is a technology that analyzes conversations in real-time to help your customer service team. It provides agents with suggestions, automates documentation, and pulls up relevant information, which can reduce average handle time through these efficiency gains. The key is that it keeps the human agent in control.

Unlike autonomous AI that handles entire conversations on its own, agent assist acts as a copilot that supports human capabilities. It automates administrative tasks, allowing agents to focus on creative problem-solving and connecting with customers. 

Decagon is beginning to blur this line with Agent Operating Procedures (AOPs) that combine natural language instructions with code to handle complex workflows, achieving both deflection and assistance within a single system.

What tasks can AI platforms automate?

AI platforms can take a wide range of tasks off an agent's plate. The specific capabilities depend on the platform, but they generally fall into two categories:

Core assistance tasks

These are the foundational jobs that most agent-assist platforms can do. This includes real-time transcription of calls, figuring out what a ticket is about and categorizing it correctly, writing up post-interaction summaries for the agent to review, and checking to make sure compliance rules are being followed.

Complete workflow execution

More advanced systems can run entire multi-step processes from start to finish. This could involve complex actions, like verifying a customer's identity, processing a payment, or escalating a case to a higher-tier specialist, all done either independently or with an agent's approval.

How does autonomous AI differ from agent assist?

Autonomous AI manages the entire customer interaction without any human involvement. Its success is measured by its deflection rate, or the percentage of issues it resolves independently. 

For example, Rippling increased its deflection rate from 38% to 50%, and NG.CASH jumped from 13% to 70% using this technology.

Agent assist works with a human agent, who remains in the loop for the entire conversation. Its success is measured in productivity gains. Studies show that agent assist can make human agents about 13.8% more productive by taking repetitive tasks off their plate.

Will AI replace your human agents?

This is a frequent concern, but the goal of agent assist is not replacement. A helpful way to think about it is the "Speed-and-Safety" approach.

• AI handles speed. It takes care of the high-volume, repetitive tasks that are predictable and time-consuming.
• Humans provide safety. They manage the complex, nuanced, or emotionally charged issues that require empathy and critical thinking.

This model enables AI to free people from monotonous work, allowing them to focus on more valuable and strategic tasks, which is one of our core goals at Decagon.

Where does support inefficiency hide?

Support inefficiency is rarely caused by one big issue, but is rather hidden in the small, repeated steps your agents take dozens of times a day. During every customer interaction, agents have to search knowledge bases, toggle between different applications, and write up extensive documentation.

Think about a common request like a password reset. The query follows an identical pattern every single time, yet it consumes a significant amount of an agent's attention. They still have to navigate the same screens and fill out the same fields for a problem that has already been solved countless times.

While the specific time lost on these tasks varies by organization, the pattern is universal: agents spend more of their time working through processes than actually problem-solving for your customers.

How much does an AI platform cost?

The cost of an AI customer service platform depends on several factors. Pricing is usually calculated per conversation or per resolution, and the final amount varies based on the communication channel and your volume. 

For instance, voice conversations are often more expensive to handle than chat, and a higher volume of interactions typically leads to a lower cost for each one.

Pricing models also differ between vendors:

• Per-seat pricing gives you a predictable monthly or annual cost, which is helpful for budgeting.
• Usage-based pricing (per conversation or resolution) directly connects your costs to the value you receive.
• Enterprise contracts often include implementation and support, making the true costs more transparent from the start.

When an AI platform is implemented properly, companies can see a return on their investment within a few months, with top performers achieving significant cost reductions.

What's the real implementation timeline?

The time it takes to get started varies widely based on a platform's complexity. Decagon users can be up and running in a couple of weeks, depending on the size and needs of the business. This is because we use natural language to train our AI agents, meaning your own team can set up procedures without needing to rely on developers.

In contrast, many other platforms can take months to implement. They often require developers to write code for every procedure, which can create delays and make the system harder to update. While Decagon is developer-friendly, this ability to use natural language for training removes a major bottleneck for most businesses.

Which implementation approach fits your needs?

AI platforms can be introduced into your company in a few different ways. The right choice depends on your team’s technical resources, your existing systems, and how much control you want over the final product. Here are the three most common methods.

Method 1: High-touch managed service

This approach is like hiring a team of specialists to do a custom renovation. The vendor’s AI experts and engineers work directly alongside your staff to build and integrate the platform. This model is designed to connect with complex workflows and older, legacy systems.

Best for: Large companies that have complicated API requirements and need a solution tailored specifically to their environment.

Method 2: Self-serve platform builder

This model gives your team the tools to build the solution yourselves. It uses no-code or low-code interfaces, allowing non-technical staff to create and adjust workflows. Because your team can do the work, you can get started very quickly.

Best for: Teams that want to maintain full control, experiment quickly, and make changes on the fly without waiting for outside help.

Method 3: Legacy system add-on

This strategy involves adding AI features directly into the helpdesk or CRM platforms you already use. It’s less about bringing in a new system and more about improving an existing one. Since it works within your current software, it doesn’t require your agents to change their daily habits.

Best for: Organizations that are deeply invested in their current infrastructure, but this approach is often limited to simpler automation and may not produce the same return on investment.

Decagon combines the high-touch and self-serve approaches. This means a company can manage everything on its own, using simple natural language to build procedures (or code, if preferred). At the same time, full support from Decagon’s Agent Product Managers and Engineers is available, so you get the benefit of a powerful, easy-to-use platform with expert guidance whenever you need it.

What separates the leaders from the rest of the pack?

Not all AI platforms are built the same. While many vendors offer similar-sounding features, a few key differences in technology and approach separate the most effective platforms from the others. When evaluating your options, these are the areas that truly matter.

Core platform differences

Important risk mitigation features

Beyond performance, leading platforms also prioritize safety and security, including:

• Security certifications. For any company handling customer data, strong security is essential. SOC 2 compliance is the standard for enterprise-level platforms.
• Error prevention. An AI agent needs guardrails to prevent it from making mistakes. Look for platforms that have validation logic built in, such as Decagon’s Watchtower feature.
• Bias detection. It's important that AI systems treat all customers fairly. The best platforms include systems to identify and correct any discriminatory patterns that may emerge in the AI's behavior.

Where does AI deliver maximum impact?

While AI can improve efficiency in almost any industry, its effects are especially noticeable in sectors that handle high volumes of sensitive or complex customer inquiries. Here are a few areas where AI-powered customer service is making the biggest difference.

Financial services

In finance, security and accuracy are everything. AI platforms can securely handle tasks like payment disputes, fraud investigations, and requests for account access. For example, by utilizing sophisticated automation, the financial technology company Bilt Rewards achieved a $1.75 million reduction in support costs.

Technology and SaaS

Software companies often deal with complex product questions that require deep technical knowledge and integration with other systems. AI is well-suited to navigate these challenges. The productivity platform Notion, for instance, used Decagon to improve its resolution rate by 34% with improved efficiency.

Healthcare

For healthcare companies, protecting patient information is a top priority. HIPAA-compliant AI platforms can manage appointment scheduling, answer prescription inquiries, and handle insurance verification while meeting strict security requirements. By implementing this technology, the skincare company Curology successfully reduced its customer service costs by 65%.

E-commerce

Online retail lives on speed and accuracy. AI excels at managing common e-commerce issues, such as checking order status, processing returns, and resolving payment problems. The right AI partner can also improve how quickly a company responds to customer needs.

As Adele Hedden, Senior Director and Head of CX and Central Operations at Faire, explains:

“Working with the Decagon team is a fantastic partnership. They are always willing to listen, implement our feedback, and move at an impressive pace. Decagon has turned our customer enablement strategy upside down, allowing us to act faster than ever on behalf of our customers while eliminating the drag on engineering and product resources.”

Your efficiency opportunity starts now

The difference between a good and a great customer service operation is measured in seconds. Every moment your agents spend searching for an answer or toggling between apps is a hidden cost that adds up, day after day.

So, if you are ready to see how the most effective teams are solving this problem, get a demo to explore Decagon today.