There's no shortage of ways you can integrate AI (artificial intelligence) into your support stack to save time, increase efficiency, and scale your support capacity. For SaaS startups — especially those with a small support team and relatively uncomplicated CX processes — one of the most effective places to start is by leveraging AI to power self-service support.

Before we look at ways to do that, let's define a few terms that will be used in this guide.

AI terminology

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence focused on the communication of humans and computers through language. It can perform tasks like translation, sentiment analysis, and topic segmentation.

Large Language Models (LLMs)

Large Language Models (LLMs) are advanced AI models that process and generate human-like text by learning from a vast amount of written material.

LLMs like OpenAI's GPT (Generative Pre-trained Transformer), Claude by Anthropic, and those developed by Mistral  use a technique known as transformers, which enables them to consider the context of words and phrases within a body of text to generate coherent and contextually relevant outputs. These models are trained on text from the internet, and can perform a variety of language-based tasks without task-specific training.

Generative AI

Generative AI refers to artificial intelligence technologies that can create human-like text, images, audio, and video. They use deep learning models — particularly neural networks — to analyze and learn from large datasets, and then generate new, original content based on that learned information.

Key generative AI techniques include Generative Adversarial Networks (GANs), transformers, and autoencoders. In addition to mimicking the styles and patterns they learn from, these models are also capable of understanding the underlying structures and dynamics — meaning they can create innovative content of their own.

Conversational AI

Conversational AI refers to technologies that enable computers to simulate real-time human conversation. This field combines elements of NLP and machine learning to develop systems that can engage in dialogue with human users, providing responses that are contextually appropriate and informative.

Conversational AI is used in chatbots, virtual assistants (like Siri and Alexa), and more sophisticated conversational agents. These systems are designed to interpret the user's intent, manage dialogue flow, and generate responses that mimic human conversational patterns.

Why might you use an AI self service solution?

‍

AI technology can have wide-ranging benefits for CX teams — from increasing operational efficiency, to reducing overheads, to scaling support capacity.

Faster resolutions

AI customer support agents can respond to multiple customers at once, with no wait time or delays. They can accommodate fluctuations in demand, and provide instant customer assistance even during peak periods. This can greatly reduce overall Time to Resolution (TTR) — and positively impact CX metrics like Customer Satisfaction Score (CSAT) and Customer Effort Score (CES).

24/7 availability and increased support capacity

AI self-service solutions can handle high volumes of customer interactions, in multiple languages, at any time of day. This is especially helpful for teams that want to meet customer expectations for support availability in timezones or languages other than their own.

User friendly support

In the majority of cases, AI can exchange a volley of messages with a user in less time than it would take for them to receive a first email response from a business.

A user that receives real-time responses from conversational AI will likely resolve their query much faster than one who has to wait for email or even live-chat contact. This speed is a huge way to enhance user experiences, and makes it easier for customers to clarify or ask follow-up questions to ensure they get a helpful answer in a timely manner.

Consistent brand experience

Specialist CX solutions make it easy to configure your AI's responses to match your brand tone of voice. This is a great way to provide a consistent customer experience across brand touchpoints, and also gives your business the opportunity to determine how your AI responds in different scenarios: e.g. how does it reply if a customer expresses irritation about talking to AI? Is it empathetic, neutral, or does it use humor to dispel their frustration?

Types of AI self-serve support tool

Chat bots and AI customer support agents

An AI support bot is embedded in your product and can have conversational dialogues with customers to provide information, address common questions and issues, or route inquiries to the right agents or resources. You can train them on your knowledge base and other data sources.

The Outverse AI support agent, for example, is powered by an LLM and can find verified solutions from across your documentation, changelogs, and other sources. It can be embedded within your product or app to provide instant answers to your customers at the moment they need them.

While they're likely what comes to mind first, AI support agents aren't the only type of self-serve support tool that uses AI. There's also AI search, which works similarly but is embedded within your help center instead.

Intelligent knowledge base search

A comprehensive, accurate knowledge base is the foundation of self-serve support. That said, customers don't always have the time or inclination to comb through pages of documentation to find answers — which is why AI search is such a fantastic multiplier for self-serve support. AI can retrieve answers on behalf of your customers, unlocking the value that's held in your docs and support materials.

With a paid Outverse plan, your customers can ask AI for answers rather than searching your knowledge base directly. They can engage conversationally, and ask follow-up questions for clarification.

The basic Outverse search is also powerful: rather than just keyword matching, the intelligent search understands the intent behind customer queries and returns the most relevant content. It uses powerful semantic relations to return fast, accurate results to queries, even when they're phrased differently. This is has a much greater success rate than basic keyword search, and helps customers find answers faster.  

AI tools for knowledge base maintenance

Some knowledge base software uses AI to automate elements of knowledge base maintenance. For example:

• Letting you know when an article hasn't been refreshed in a while
• Prompting an article review if performance is poor
• Suggesting new additions or amendments to existing content

Implementing an AI self-service strategy

Implementing an effective AI self-service strategy requires some planning and groundwork, but if you're using an out-the-box solution, implementation should be relatively simple. A custom build, on the other hand, is far more complex, and requires a proportionate amount more planning. Whichever route you go down, there are a few things to do first to help ensure success: 

Make sure your knowledge base is accurate and up-to-date

An AI support agent or search function is only as effective as the material it's trained on. If your docs are outdated and incomplete, your chatbot will share misleading information with customers. Likewise, a super powerful knowledge base search can only return results if they exist.

Update your knowledge base ahead of introducing any AI self-serve tooling, and make maintenance an ongoing priority. Outverse can make this easier if you use the native knowledge base too: insights from your AI agent's interactions can inform doc update suggestions, making it easy to continuously improve docs based on their performance in live support situations.

Outverse also integrates with other knowledge base software like Zendesk, so you can use Outverse AI without migrating your documentation from your current provider. Wherever your docs are hosted, make sure they're up to date.

Map potential impact

Start by identifying the specific problems you want to solve with AI self-service. Prioritize which customer journeys or use cases will provide the most value initially. Set measurable goals like reducing ticket volume by 30%, improving customer satisfaction scores, or lowering operational costs. This will help you track progress and ROI.

Choose an Artificial Intelligence platform

While you can build your own custom solution using something like OpenAI's API for customer support, using a specialist solution like Outverse is a better option for most SaaS businesses:

• You don't need to invest resources in building and maintaining a solution yourself
• Instead of using one model, independent vendors typically use a variety of best-in-class models for specific use cases
• You'll always have access to the latest developments

Evaluate different AI vendor options on factors like accuracy, scalability, ease of use, integration capabilities, ongoing support, and costs. For the majority of startups and scaleups, an independent vendor is likely the best option; large enterprises may also want to explore the option of building their own, and weigh up the benefits of each route.

Plan integration with existing systems

Plan how your AI assistant will integrate into existing tools like your CRM, ticketing systems, and product itself. If you're using separate products for different self-serve support channels, make sure they can work together — or use a platform like Outverse where your knowledge base, AI tooling, and support forums co-exist.

Design the AI-to-human hand-off process

With API integration, you can design seamless hand-offs between AI and live agents. It's worth discussing as a team what you'd like your hand-off process to be like in situations where an escalation is necessary.

Determine what information you'll need from the customer ahead of time to make the hand-off seamless, and decide how best to manage expectations when this occurs.

Training and optimization

Training your AI with relevant datasets is key to ensuring high accuracy. You can continuously test your AI assistant and analyze its conversation logs to identify gaps and areas of improvement.

When you use a specific CX vendor like Outverse, you also benefit from the continuous finetuning of AI models based on best-in-class knowledge bases and other sources.

Measuring success

A few commonly used measures of success:

Customer Satisfaction (CSAT) Scores

CSAT measures how satisfied customers are with their overall experience. Send surveys after self-service interactions to gauge satisfaction levels. Look for increases in CSAT after launching AI self-service.

Time to Resolution (TTR)

TTR measures the average time it takes for a customer's request to be successfully resolved. Faster resolution improves customer experience.

Customer Effort Score (CES)

CES measures perceived effort on the customer's part. Was it easy to find the solution they needed, or was the support process arduous and confusing? Lower effort improves customer satisfaction.

Self service success rate

The percentage of queries resolved successfully without needing human agents is a key metric. Aim for AI resolution rates of 80% or higher for simple queries. Analyze unresolved queries to improve the knowledge base.

Deflection rate

Deflection rate measures the percentage of total support traffic handled entirely by self-service. Higher deflection reduces call volume and support costs. Businesses with call center agents may also look to measure call deflection rate.

Usage volumes

Track the number of users and queries handled by each channel over time. Growing usage indicates adoption and engagement.

Feature usage

Analyze usage for different self-service features — e.g. knowledge base, search, AI assistant, customer forums — to identify most valued capabilities to expand on.

Net Promoter Score (NPS)

Gauge customer loyalty by asking how likely they are to recommend the service. Increasing NPS indicates improved customer relationships.

AI self-service for startups

‍

In periods of growth, startups and small companies can find their support needs increasing overnight. They often face challenges in maintaining the same high-standard customer experiences in these scenarios, due to limited resources and finance. AI self-service solutions can be a game-changer, providing startups with cost-effective and scalable ways to enhance customer experiences.

With AI self-service, small businesses can automate repetitive tasks, freeing up human agents to focus on complex issues. The AI handles common FAQs, simple account management, taking orders and more — improving efficiency without sacrificing quality.

Outverse is available through an affordable subscription model, with no upfront investment in infrastructure required. This predictable, pay-as-you-go pricing is ideal for cost-conscious startups, and means you always have access to the latest AI capabilities and benefit from the ongoing development of the product.

AI self-service for large enterprises

Enterprises need scalable and efficient self-service channels that can handle huge amounts of queries and requests. AI-powered virtual assistants and chatbots allow enterprises to automate a significant portion of repetitive and routine customer inquiries, freeing up human agents to focus on complex issues.

Some key considerations for large enterprises deciding whether to build their own or use an off-the-shelf solution:

Cost

Build: The initial investment for custom development can be high, considering the costs of skilled talent, development time, and ongoing maintenance and updates.

Buy: Off-the-shelf solutions may have a lower upfront cost and predictable ongoing expenses like subscription fees or fixed costs per resolution.

Customization and flexibility

Build: Building a solution allows for complete customization to fit precise enterprise needs and integration requirements with existing systems.

Buy: While off-the-shelf solutions offer less customization, they are usually designed to meet the needs of a wide range of users and scenarios, with some level of configurability.

Time to deployment

Build: Developing a custom solution can take a significant amount of time, delaying the time to benefit.

Buy: An off-the-shelf solution is generally quicker to implement, enabling faster deployment and quicker realization of benefits.‍

Scalability

Build: Custom-built solutions can be tailored to scale according to specific business growth plans and changing requirements.

Buy: Off-the-shelf software is typically built to scale, but it might not handle unique or unexpected scaling scenarios as effectively unless specifically designed to do so.

Expertise

Build: Requires high internal or outsourced technical expertise, not just to build but also to maintain and update the solution. This is unlikely something an existing team can do on the side, and will require dedicted resource.

Buy: Using an off-the-shelf solution often means relying on the vendor’s expertise for maintenance, support, and upgrades, reducing the need for in-house technical skills.‍

Support and maintenance

Build: The enterprise is responsible for all aspects of support, maintenance, and troubleshooting, which can be resource-intensive.

Buy: Support and maintenance are typically provided by the vendor, often as part of the subscription fee, which can reduce the burden on internal IT staff.

Security and compliance

Build: Custom tools can be designed to meet specific security and compliance requirements, but maintaining them is also the enterprise's responsibility.

Buy: Vendors usually ensure their products comply with industry standards and regulations, but it's crucial to verify that these meet the enterprise's specific requirements.

Integration with existing systems

Build: Custom solutions can be tailored from the start to integrate seamlessly with existing business systems and workflows.

Buy: Integration capabilities can vary; some off-the-shelf solutions may require additional work to integrate effectively with existing systems, but most can integrate smoothly with CRM systems, ERP platforms, and other complex enterprise IT systems and workflows.

Innovation and upgrades

Build: The company controls the roadmap for new features and upgrades, but this can also be a limitation if the company lacks resources or falls behind on the latest AI developments.

Buy: Vendors often continuously update their products with the latest technologies and features, providing regular innovation without additional investment from the buyer.

‍

By leveraging AI's capabilities for handling scale, personalization, and automation, large enterprises can transform self-service into an efficient, brand-aligned experience for their customer base. The result is happier customers, reduced costs, and the ability to focus team members on higher-value activities.

The future of AI self-service

AI self-service solutions are evolving fast. This is another great reason to use an out-the-box option: a custom build will require a greater investment to stay on the cutting edge, and most businesses simply don't have the facilities to do this. A few innovations on the horizon that will impact CX applicaitons:

Advancements in NLP

As NLP algorithms continue to improve, AI agents will become better at understanding nuanced customer queries and handling complex dialogues. Key areas of advancement include contextual understanding, intent recognition, and sentiment analysis. With more human-like NLP capabilities, AI self-service solutions will be able to hold even more personalized conversations.

Multi-modal interactions

While most current AI self-service platforms rely on text-based conversations, future solutions will likely combine multiple modes of interaction — for example, enabling customers to speak their questions and have the AI agent respond via chatbot. This will be huge for accessibility, and will make AI interactions feel more natural and engaging.

Continuous learning and self-improvement

A defining feature of AI is its ability to continuously learn from new data. Over time, AI self-service agents can analyze real customer conversations to identify gaps in their knowledge. This allows the AI to expand its capabilities and handle new topics, without needing explicit retraining by developers. With continuous learning models, AI self-service solutions will become increasingly autonomous and effective at resolving customer needs.

‍