The Next Generation of AI Assistants Will Do More Than Answer Questions

Digital assistants are rapidly evolving. The first generation of Apple’s Siri and Google Assistant applications were simple to use question-and-answer tools. However, recent progress in AI for digital assistants enables more complex interpretation of input and interaction. As a result, new assistants are moving beyond a simple prompt-and-response interaction. They can deal with far more complex information, execute longer chains of actions, and even understand a user’s context. 

Future AI assistants will focus more on understanding context, recognizing patterns, and even helping a user decide on a course of action in complex situations. They will be crucial in many areas of life, business, finance, health, and other fields of human endeavor that generate vast amounts of data that must be processed, analyzed, and understood quickly. 

From Answers to Context 

As digital assistants move beyond basic Q&A and provide more context around their answers, they can better account for information that changes over time and take into account a user’s specific circumstances. 

Consider someone who wants to better understand their spending, savings, and progress toward financial goals. A basic financial tool might show account balances and recent transactions, but it may not explain what those numbers mean in a broader context. With AI financial planning, users can go beyond reviewing raw data and get help connecting spending patterns, savings priorities, and financial trade-offs to the goals they are trying to reach. 

This also applies to other areas where users want deeper insights to make informed decisions. For instance, in business, a user who asks whether sales are down could also benefit from learning about changes that occurred before the sales decline and which factors to monitor to respond to it. 

AI Assistants Will Become More Aware of Goals 

Function-oriented software normally consists of many separate functions. Open a dashboard. Choose a report. Enter a request. Then you work through the resulting information. You struggle with it. With an AI assistant, it will focus on your goals. How can it help you reach your goals? 

At the user level, this is the difference between looking up the data you need and being served the information you want to see to achieve your goals. This means new AI assistants can account for the nuances of human behavior and be influenced by both past behavior and context. 

Even more, the person asking the question matters greatly. For example, a marketing manager may care about website traffic, but only if that traffic converts into qualified leads, which in turn can generate revenue. An AI assistant that can read and understand the larger objective behind a question will return far more relevant answers. 

Personalization Will Make Assistants More Useful 

Personalization is another trend we’ll see more of in AI assistants, since they can already provide adequate responses to most inquiries. Two people may ask the same question but need very different answers based on factors like their current priorities, past behavior, and more. 

The first example is a sales manager at a mid-sized software company. One executive would like a summary of pipeline movement over the last quarter, while another would like to review revenue risk and overall business performance. 

By personalizing information for a user’s specific needs, an AI assistant can also reduce information overload. Instead of displaying a deluge of metrics, the system can highlight the most salient data points, allowing the user to stay focused on the task at hand. 

AI Will Help Users Ask Better Questions 

Today’s user-interfacing software mostly targets users with very specific queries. These systems have been optimized to focus on a narrow range of preconceived questions, delivering answers before users have fully formed the question. 

AI assistants of the future can also help users identify trends, unusual behavior, and other factors that the user has not even considered yet. In the example above, the AI assistant would identify the sudden change in customer engagement and suggest related issues the user might be interested in. 

More critically, users without technical data knowledge can interact with it in a highly natural way. Simple questions yield basic answers, but as users learn more about their data, they can ask more complex questions. 

Human Judgment Will Remain Essential 

Data insights help people make better choices. However, data alone cannot account for human priorities, constraints, and idiosyncrasies. 

Systems can organize information, identify relevant patterns, and explain the options available. However, a human is best placed to make the final decision they can, based on personal goals, constraints, and even unexpected events. For now, such AI assistants are more than just fast answers to queries. As they evolve, they will be judged by how well they understand their interlocutors, their objectives, and the complex, multi-dimensional information they process. 

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