64% of business professionals believe in leveraging chat assistants to create a personalized user experience. Personalized AI assistants present a fresh opportunity for enterprises to expand their business processes and user experience.

Traditional chat assistants and manual resolutions have served fairly. However, equipping ML, NLP, and Conversational AI can be transformative for businesses. AI assistants can be tailored and personalized to perfection through effective model training and the right set of data. Let’s discuss how!

What is a personalized AI assistant?

Compared to general AI solutions, personalized.AI assistants are tailored and specific to any business function. They aim to be relevant to users' queries and prompts by understanding the context and providing precise outputs. Let’s understand why we need personalized AI assistants.

Here are some challenges that personalized AI assistants can overcome:

• While generic AI assistants take less time to build and deploy, they struggle with complex queries because of a lack of understanding.

• Generic solutions are meant for simpler functioning but lack contextual knowledge and uniqueness for specific business operations.

• Standardized answers can not be as accurate and may even misdirect users occasionally. Specifics are the only way to be accurate in critical business criteria.

Benefits of personalizing AI assistant with your data:

Specific and precise assistance can help users solve challenges and find information effectively. The data to train AI assistants is the key to bringing such relevance and accuracy. A personalized AI assistant built upon your own data comes with the following advantages:

• AI assistants with your own data can provide the most accurate and relevant results for user’s queries.

• Unique practices and knowledge of one company can only be learned with its peculiar data; that is where a personalized AI assistant helps.

• An assistant based on the user’s data can make decisions and automate actions based on a predefined course of action.

• With the relevant information and context, an AI assistant could create a more intuitive and effective user experience.

• By customizing your own AI assistant, you gain full control over the data it accesses and stores. This level of customization empowers you to shape your AI assistant's functionality to perfectly align with your business needs.

What are several ways to personalize AI assistants?

AI assistants can collect data and personalize themselves in two ways. One of them is through explicit data. The explicit data can be used to feed your AI model before deploying it. Such data can make sure that the AI assistant is up to speed with unique practices and preferences.

Another type of personalization is Implicit data, where users' interactions can help AI assistants learn and personalize themselves based on everything they learn through each exchange. 

Having a team of AI engineers to architect your AI assistant with your own data can create a game-changing user experience. 

What kind of data can help personalize your AI assistant?

Every business has several touch points where they can harvest a handful of information, and the same data can be fed back to the AI assistant to personalize for your business. Here are some examples of different data you can consider for effective personalization:

• Customer data:

Gathering all your prolonged customer information can turn into insightful data that can shape your AI assistant. Data from sources like the company’s CRM portal can provide information about purchase history, past interactions, and demographics. 

The information about customers can be the compass for companies to derive the most effective strategies using predictions and statistics.

• Company policies and practices:

For enterprises, AI assistants can also complement the professionals in their jobs through quick research that can help complement their work, provide better customer experience, and even quickly learn about company practices.

It can also help automate flows such as ticket creation, assigning, and status updates. AI assistants can take care of some of the logistics and clerical work so your employees can focus on areas where their attention is better utilized. 

• Website user behavior:

Another indicator of customer interest for any brand is how users use its website, including their usage flow, product page visits, search queries, abandoned carts, and bounce rate. All of these website interactions can translate into meaningful insights for AI assistants.

• Interactions with support teams:

Digging deeper into customer support tickets and queries can lead to learning about business operations at a grassroots level. A personal AI assistant can learn a lot through analyzing support tickets, support agent chats, and even the transcripts of their phone calls.

This leads to uncovering customer interests, key queries, and the context behind the majority of customer interactions, which enhances the overall accuracy of assistants.

• Sales stats:

AI assistants can also learn about a brand’s positioning through its sales performance. Data such as sales figures for different products and offerings can help AI assistants learn and forecast demand in different scenarios.

• Market research & industry trends:

AI assistants can help support business operations by taking a parameter check on your unique domain. Imagine an AI assistant that has a vast knowledge of drug discoveries and can even help researchers with their experiments through ideation, predictions, and probability. 

Integrate data on industry trends, competitor analysis, and customer sentiment. This allows your assistant to stay informed about the competitive landscape, anticipate market shifts, and suggest strategic adjustments.

A quick guide about empowering AI Assistants with custom data

Of course, AI assistants require a lot of strategizing, experimenting, training, and fine-tuning. However, here are some steps that can give a bird’s eye view of personalizing AI assistant:

• Defining the end goal to understand where an AI assistant can help.

• Evaluating the right data points and information to complement the set goals.

• Data acquisition to collect the right data for model training.

• Data filtration is used to get rid of any misleading errors in data that can hamper your AI assistant's machine learning model.

• Training the AI assistant with your unique data and industry knowledge so that it can be relevant and accurate while outperforming any pre-trained generic AI assistants.

• Optimizing the AI assistant continuously by interacting with it and helping it learn through feedback.

• Enforcing security considerations, keeping in mind data privacy and security measures while implementing AI assistant.

In conclusion,

AI assistants that are trained with your unique data and processes can be much more valuable and helpful than generic alternatives. With deep learning over a period of time, these AI assistants can gain a specialization for you. The sooner you implement an AI assistant with your own data, the more return on invested efforts you will reap.

Reach out to us now if this is something that can potentially transform your business operations. We would be happy to consult, chat, and nudge you in the right direction.