AI automation for customer experience is a rapidly growing, complicated market. With AI models being more widely available, new marketing terminology, and an incredibly wide variety of use cases that can be addressed, it can be overwhelming. A Forbes survey found that 73% use or plan to use AI-powered chatbots for instant messaging and 55% want to deploy AI for personalized services. If your business is ready to adopt AI tools for CX, it is important to understand the nuances and considerations clearly. Let’s explore ten considerations for implementing AI-powered customer support solutions.
Integration with Existing Systems
AI customer service software is only as valuable as the data it has access to. The promise of AI is that it can help generate meaningful and accurate responses, provide better context, and automate complex, repeatable tasks. If your current infrastructure does not allow for the tool to connect or read and write data to your systems, you will lose a lot of benefits. Achieving seamless integration should be the goal of any AI tool implementation.
First and foremost, your Customer Relationship Management (CRM) tool or your Help Desk tool, which are typically the sources of truth for customer experience teams, need to be compatible with the solution you chose. This integration is pivotal for leveraging your existing customer data and relevant customer metadata. However, this is just the start. To truly capture the power of AI tools, especially in the workflow automation or insights space, you need to integrate as much of your tech stack as possible. For example, logging or product usage data, order management details, enterprise resource planning (ERP) information, knowledge center articles, etc.
Unlocking the full potential of AI requires the power of historical data stored in your existing systems. By leveraging this valuable resource, AI tools for CX will deeply understand customer behaviors and preferences, enabling the delivery of personalized experiences and insights. To achieve this, it is essential to ensure seamless integration and expansion of your AI system with existing data repositories, thereby elevating customer service to new heights and driving business success.
Full Cost of AI
AI tools for CX often have more pricing considerations than tools in other categories. Tools in this space often handle multiple use cases and have the ability to reduce costs in many areas, which makes their true cost and return-on-investment harder to calculate.
The potential costs arise for two main reasons:
- Implementation. Be wary of companies that claim their AI models work out of the box. The very nature of AI is that it needs to be trained, tuned, and constantly monitored for quality and accuracy. Someone is doing that implementation. When examining cost, it’s prudent to ask what work will be owned by you, and what will be owned by the vendor. In addition to the implementation are the ongoing costs, either in hiring or training an expert, even fractionally, to monitor and maintain the model.
- AI-powered customer support solutions are changing the pricing models we are accustomed to. In many cases, usage-based or credit-based pricing has replaced seat-based licensing. Usage pricing models make more sense for the types of use cases that the AI market addresses. Especially tools who are likely to reduce your seat count by making your team more efficient. In the end, these newer models could save you money over other types of pricing. It is also harder to budget for and costs tend to increase over time, since the tool will likely take on more use cases and increase its success rate as it learns
For both of the reasons above, ROI is harder to calculate. For example, usage can be hard to predict and also depends a lot on the adoption from your customers, which may take time to achieve. As well, many of these technologies will improve efficiency of a team or potentially reduce the need for certain roles, which means more productivity and higher cost savings, both of which can increase ROI.
As a benchmark, KODIF customer Byte has reduced their customer support expenditure by 45%. An Intercom study also showed on average 11-30% of support volume can be handled by artificial intelligence. And Freshworks says “Efficiency features like AI-based ticket classification and automatic routing of incoming customer contacts to the right agent can save agents up to 1.2 hours a day.” By putting benchmarks like these together in a spreadsheet you can gain a good prediction of ROI and then adjust as you learn more information and adoption grows.
Customer Experience
When you implement AI customer experience automation, perhaps the most important question to ask yourself is: How will this impact my customer experience? Customer reactions to AI can range from delight to outright anger, depending on when and how it is used. Your goal should be to create a seamless and intuitive experience using technology that delight customers. For example, 59% of customers expect that they will get personalized experiences via AI Chat bots. If you fail to provide that, it can cause frustration. Repeated, robotic answers will not meet that expectation.
Other considerations include:
- Demographics: Do your customers want to use automation, or do they prefer the human touch?
- Empathy: Are you in an industry, such as healthcare, where automation might be taken as cold or uncaring about the situation?
- Path to an Agent: If a customer wants a human to talk to, how do they do it?
- Agent vs Customer facing: Automation can be used to make your agents incredibly efficient and the use of AI to the customer could be transparent. Do you want to expose the automation to the customer or just make your agents as productive and prepared as possible?
- Feedback: What are your customers telling you?
If your AI-powered customer support solutions are not improving your CX, then you need to rethink your strategy. The potential cost savings are not worth the loss of trust or reputation from a bad implementation of AI.
Functionality and Features
The features sets of AI tools for CX are vast. Depending on your business needs the tool you select could be completely different. Your AI customer service software has to align with your team’s needs. Key features to consider include:
- Natural language processing (NLP) for understanding customer queries and intent
- Conversational AI for chatbots or virtual assistants
- Sentiment analysis to gauge customer emotions
- Omnichannel support across various communication channels
- Contextual generative AI
- Full AI customer experience automation
Some tools may cover some or all of these types of use cases. KODIF is an example of a tool that uses its AI model to cover the end-to-end ticket lifecycle. From self-help and self-service, through to making a human-in-the-loop more effective, and finally fully automating a response and set of actions even in other tools.
The most important thing when choosing an AI customer service software is that your use cases are well defined. Map the features of the solution to the use cases and also ask the vendor to demo how they are resolved or ask for a trial period as part of your contract.
Customer Privacy and Data Security
Security and customer privacy have to be the highest priority when working with AI-powered customer support solutions. These tools operate on data and depending on your business, personally identifiable information (PII), intellectual property (IP), and proprietary information about you and your customers could all be part of the training of these models. According to a Forbes study, almost one-third of respondents said they have concerns about data security and privacy.
In addition to the data being secured, encrypted, and compliant, AI tools have the added burden of potentially using private data as part of any generated text or responses. It is not just a matter of being GDPR or other privacy acts such as the California Consumer Privacy Act (CCPA), it is about exposing secrets that you may not consider the AI knows.
These are real risks. The New York Times is taking legal action about their data being used in generated responses. “The complaint cites several examples when a chatbot provided users with near-verbatim excerpts from Times articles that would otherwise require a paid subscription to view.” It is not hard to imagine an AI tool leaking important business details or addresses or phone numbers as part of generated data it uses.
If you operate an AI tool that is customer-facing, it is also important to know who your customers are. For example, the EU AI Act stipulates that you must notify “a person of their interaction with an AI system and flagging artificially generated or manipulated content.” Laws like this may require you to consider how and where you implement this technology.
Continuous Learning, Monitoring, and Improvement
AI customer service software is constantly evolving. AI technologies and the solutions that implement them are rapidly growing and becoming better at what they do. To keep up with this change, and ensure you are getting the maximum value from the solution you have chosen, constant review, assessment, and change will all be a part of operating an AI solution.
By their nature, AI models are always learning as you send more data points to them. New workflows, generated responses, and intention training will all naturally occur and need to be reviewed or configured so that customer experience is not impacted by incorrect categorization or generated responses. Ensure you have the right monitoring resources and cadence to prevent negative impacts on your users or customers.
Two things to consider as you work with these tools:
- Ensure that the software you choose has features that allow you to review its work and teach the model what is and is not acceptable responses.
- Designate one or more people on your team to be fully trained on how the model works and what types of data, formatting, or other tools are the best to use to reduce the chances of hallucinations and keep responses up to date and accurate.
The important differentiation with AI tooling is that this is a continuous task. Unlike many process tools, which can be implemented once and work fairly well with only minor changes periodically, AI tools require much more oversight, especially in the initial months of implementation. OpenAI says that tuning “tailors a model to perform well on a specific task, often resulting in better accuracy and efficiency for that task … This is especially true in specialized domains where nuances matter.” They continue saying that models should “be continuously fine-tuned with new data to adapt to evolving conditions or to improve based on operational feedback”
Employee Training
The hype around AI tools has been real. Every viewpoint from dismissal as a fad to serious impact to employment and all the way to the apocalypse itself have been shared. A TalentLMS survey found that while 35% of US employees say that their work responsibilities have already changed due to AI tools, only 14% have received any level of training on the subject. Given the confusion in the market and media there are three aspects of employee training you need to consider.
What is (and isn’t) AI
Your employees know what a phone system is, and help desks, CRMs, HRIS software etc. are well understood, but AI is not. It is new, covers more breadth of use cases, and has a lot of misinformation around it. A quick, simple training course on the capabilities of AI customer service software will go a long way in removing the confusion and sorting out the fallacies that exist.
The company’s vision for AI
With over 77% of people expressing apprehension about job loss due to AI, anxiety is sure to rise in your organization if the goals aren’t clear. When you are implementing AI, help build trust that you have thought through the longer term implications of the tools. What will they be used for, what new roles will evolve, what upskilling will your employees receive, etc. This transparency will boost morale and engagement as you move forward with the implementation.
The AI Tool itself
Make sure your team understands the specific AI-powered customer support solutions that you have chosen and why. What benefits will they receive, how will the customer interactions change, what expectations and new workflows will it necessitate. Implementation of most AI tools require effective collaboration between the tool and your employees. The more prepared they are, the better your rollout will go.
Expanding your Solution
It’s best to implement AI tools for CX slowly and deliberately. Start with a handful of smaller use cases, and slowly add to them as you see success. Expansion also means how the tools are used. Many tools offer agent-facing and customer-facing features. As a best practice and a way to show value without impacting the customer, you can start by automating agent workflows through agent assist tools, copilots, or contextual generative AI responses that are visible internally only. Once you have validated accuracy in the details, the tone, and intention or sentiment detection, you can then expand the tool to respond directly to customers, or expose workflows to the customers via a chat widget for example.
Consider this expansion when choosing an AI-powered customer support solution. If you have use cases that span multiple types of interactions, channels, customer segments, etc. then making sure your AI-powered customer support solutions can grow with your business is important to avoid migration or multiple point tools that can increase maintenance or waste time and energy on multiple implementations.
Other considerations are how your AI customer service software expands its knowledge. For example, do you need to manually train for every new feature, product, or service, or will it automatically update by re-ingesting your knowledge base or newer ticket information?
Vendor Reputation and Support
As with any hype-cycle, there will be AI tools for CX that will either not succeed in the market or ones that are capitalizing on it with minimal effort. It is critical that you research the vendor’s track record, industry expertise, and customer support offerings as part of your decision making process.
Factors to consider include:
- Customer references and case studies
- Vendor’s financial stability
- Roadmap alignment to your needs
- Availability of training and documentation
- Ongoing support offerings
- Do they offer a trial?
Each of these factors will help establish longevity, alignment, and confidence in your success with AI customer experience automation. This market is overwhelming, and the marketing and hype surrounding many of these tools has to be examined before you commit to any type of contract. By carefully evaluating these considerations, you can select AI customer service software that aligns with your CX team’s needs, enhances customer experiences, and drives operational efficiency.
Success with AI customer experience automation
It is clear that AI-powered customer support solutions are a requirement as the support industry and customer expectations evolve. The biggest difference in these new technologies is that they are changing the way software should be evaluated. Many solutions are not point tools anymore. Features that could be categorized under the same type of solution behave differently or are tuned for a specific industry. Pricing models are adapting to value-based over user-based. Each of these changes the way you need to evaluate your selection, calculate its ROI, train your team, and administer your tech stack. AI customer service software is powerful and will help strengthen your support service offerings. To gain the full value be sure to watch out for these things while considering your options.
