When Is the Right Time to Implement AI Solutions for Customer Service?

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AI solutions are becoming increasingly sophisticated and accessible in customer service. Unsurprisingly, that has resulted in most businesses racing to implement AI as quickly as possible–without stopping to consider if they’re actually ready for it.  

There’s nothing more frustrating than sinking months of effort into training an AI solution that turns out to be the wrong one for your business.

We’ve helped hundreds of companies implement Siena, and a few characteristics set successful teams apart. 

Here’s everything you need to determine when your business is ready for AI adoption and how to ensure that effort is a great investment. 

Prerequisites for success in implementing AI solutions in customer service

AI is already transforming customer service.

A whopping 96% of businesses believe generative AI will enhance customer interactions. 

Businesses typically start considering AI solutions when:

• They have a rapidly growing customer base, making it hard to train new support team members at the same pace.
• They’re finding it difficult to maintain high-quality responses while handling a growing volume of cases.
• Response times are starting to lag or become pretty inconsistent.
• Their customers are starting to expect instant, personalized interactions, which is hard to meet at scale without automation.

Ultimately, the question isn't if you should implement AI, but when. Customer expectations are already evolving and competition is increasing, so businesses that don't leverage AI risk falling behind in both profitability and customer experience.That said, there are a few specific foundations that you need to have in place before you can have the results with AI you’re looking for. 

Clear goals and metrics

Measurable goals make a massive difference.

They’ll give your team direction, make it easy to identify if a solution delivers what it promises, and enable you to make a better case for investing in that AI solution.

Clear goals could be:

• Reduce cost per ticket from $2 to $1. 
• Reduce average response time from 24 hours to 1 hour.
• Handle 50% more tickets without increasing headcount. 

Even better is tying AI metrics directly to broader business KPIs like customer lifetime value or repeat purchase rates.

Setting up goals around cost, efficiency, or team capacity is easy (Siena has an ROI calculator just for that), and you can include guardrail metrics that control the quality of those interactions. For example, you might want to maintain a 90+% CSAT rate or increase your NPS score from 30 to 60.

Aiming for specific outcomes like this is much more motivating than implementing AI to increase automation or because all your competitors are doing it. Those goals rarely carry a team through the full implementation. 

They also make it hard to select the right provider. If all providers market themselves with the same buzzwords and you don’t have specific goals, it’s tempting to go for the cheapest option–without assessing if that’s the right one for you.

Robust Documentation

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The training process will be much faster and go much more smoothly if you have robust documentation.That can be in the form of:

• Detailed product catalogs providing a crystal-clear picture of the ideal customer and what the product offers.
• SOPs (standard operating procedures) documenting your typical customer service processes. 
• FAQs or troubleshooting guides helping your customers solve questions independently.

Many companies might think they can implement an AI agent and meet this documentation demand at the same time. 

That’s usually a bad idea. 

Out-of-date or incomplete documentation will actively slow your training down. And training an AI solution will require extra resources that you might not have if you’re trying to update that documentation simultaneously. Companies can struggle for months trying to simultaneously document processes and train AI, ultimately extending their implementation timeline by 3-4x.

It’s best to start a project to improve your documentation while you shop for the right AI provider.  

Dedicated resources

You also need the extra bandwidth in your customer support team to dedicate time to training an AI agent.

It’s usually a bad sign if you’re trying to implement an AI solution when you have a one-person CS team, and that person is expected to handle the ticket load while taking this on as an extra project. 

AI implementation takes time and you need the resources for it. 

It’s essential that you have one person who is properly dedicated to the project, excited about AI and its potential for transforming your customer experience, and ultimately accountable for the results of that training–an “AI manager,” for example.

Some teams can spread out the responsibility of ongoing maintenance and quality checking across the team, but they can rarely train an AI agent from scratch as a collective responsibility. 

Ample Training Time

It’s easy to see the value of working with AI when you’re about to have a big milestone event, knowing your volume will double or triple in the coming weeks. That might be in the weeks leading up to Black Friday or right before your peak season over the winter. 

While it is true that AI can massively help your business during those times, it also needs time for training. Some providers might have an easier and faster hurdle than others, but you often don’t want to cut it too close. 

It’s actually best to start the implementation during downtimes in the year, when you can spend time on it. 

How to choose the right AI provider

Say you know your goals, have the documentation ready, and have ample time and resources; the next step is finding the right provider for your business. 

This choice also influences the other factors since different models will take differing amounts of time and effort to train.

There are two key aspects to consider:

1. The technology they work with.
2. Their industry expertise. 

Generative AI vs. rule-based systems

Generative AI vs. rule-based systems

There are two main technological approaches to AI agents in customer service:

• Generative AI is a stochastic model. 
• Rule-based systems are deterministic models.

Rule-based systems follow preset patterns to deliver consistent, predictable responses. They essentially follow a sophisticated decision tree: if A happens, respond with B. These systems are great when you need strict control over responses but can feel rigid and might struggle with unique situations.

Generative AI adapts responses based on context. This flexibility means they can handle complex customer interactions more naturally. But that means there’s inherent variability: the same question might receive slightly different responses. You need some amount of risk tolerance if you use a generative AI model. 

When you have stringent expectations for which terminology is used when, you’ll find it extremely frustrating trying to force a generative AI model based on OpenAI’s GPT or Anthropic’s Claude to use that terminology consistently. Working with a more deterministic model will likely be the better choice for your business.

That said, if you have a complex product catalog or want a model that can handle nuance and provide recommendations, you will typically benefit more from generative AI's flexibility.

Industry Specialization

Another aspect to consider is whether that provider is specialized for your industry.

Providers specializing in your sector likely understand your tech stack, integration requirements, and customer expectations. For example, ecommerce-focused providers offer seamless integration with platforms like Shopify or Stripe and understand seasonal demands. 

This can drastically speed up your AI implementation. 

For example, Siena offers a ton of templates for automating subscription management, returns and refunds, and order tracking. That means ecommerce businesses can often adopt Siena very quickly. Working with another provider that isn’t tailored for that might require you to build out those common use cases manually.

The timeline for implementing AI solutions in customer service

How long it takes depends on the factors we’ve covered here. 

Some companies take many months, or even years to implement AI successfully. That suggests zero readiness. 

The typical range we’re using is a few weeks to two months. The key variable? Your team's preparation. With proper documentation and a dedicated AI manager, we can usually expect a 35-40% automation rate within the first 4 weeks.

The fastest implementations typically have:

• Complete, organized documentation ready.
• A dedicated (maybe even full-time) AI manager assigned.
• Clear success metrics established.
• Bandwidth across the team for testing.

Start with testing, end with success

2024 was the year of testing, with businesses gaining their first experience using an AI agent. 

2025 is trending toward informed adoption, as companies now understand their requirements and can tell what they need to be successful.

Ultimately, you need hands-on experience working with different technologies. This will help you identify the specific use cases you want to automate, the value automation brings to your customers (and your business!), and the effort involved in maintaining an AI solution over time. 

Whether you're dealing with complex product catalogs, subscription services, or specialized customer needs, having that experience and knowledge will help you find the right solution quickly.

Ready to explore AI that's built for your business? Siena specializes in helping ecommerce brands transform their customer experience. If you want to learn more how Siena would work for your organisation, let’s chat. Book a time with our team here.