Introduction to digital assistants

Digital assistants are in all kinds of devices and services that we use every day—smartphones, tablets, TVs, cars, home appliances, speakers as well as weather, navigation, banking, and other mobile apps. They combine technologies like artificial intelligence (AI) and natural language processing to understand spoken or written language, discern user intent, access relevant information, and work seamlessly in different devices and services.

In this article, we’ll explore:

• Examples of digital assistants
• Differences between digital assistants, chatbots, and AI copilots
• Key features of digital assistants, how they work, and their benefits
• How different industries use digital assistants
• The future of collaborative AI

What are examples of digital assistants?

While digital assistants are AI-enabled tools primarily designed to simplify daily tasks, the breadth and scope of their uses and applications is virtually without limit. In cars, digital assistants provide hands-free, voice-controlled navigation, traffic updates, calling, and messaging to make driving safer. In smart TVs, digital assistants let you quickly find shows, control playback, or adjust volume by voice without having to scroll through long menus.

Smart speakers use voice-enabled digital assistants to let you play music, check the weather, and more without operating any physical controls. And when linked with smart appliances in your home, smart speakers let you control things like washing machines, refrigerators, ovens, lighting, thermostats, and security systems.

Some examples of popular digital assistants include:

• Siri from Apple in iPhone devices and Google Assistant in Android devices let users make calls, send messages, search the web, and more with interactive voice commands
• Amazon’s Alexa, as well as Siri and Google Assistant, also integrate into smart speakers and appliances so users can control and automate different aspects of their home
• IBM Watson Assistant is a virtual assistant platform that lets business users engage in intelligent, context-aware conversations across voice, chat, and other communication channels

What’s the difference between a digital assistant and a chatbot?

While both chatbots and digital assistants use the same technologies such as natural language processing to interact with users, both also vary in complexity and functionality.

Here are some key differences between digital assistants and chatbots:

• Digital assistants handle a wider range of tasks across different services and devices. Chatbots are focused on a specific, dedicated service such as booking hotels or flights, navigating a company’s phone menu, or ordering a product or service.

• Digital assistants tend to support continuous, more contextual conversations and transactions. Most chatbots have a limited ability to understand and retain information from previous interactions, treating each user transaction as a first-time engagement.

• Digital assistants are more integrated with different devices, appliances, and third-party services to complete different types of tasks, while chatbots typically reside on websites, messaging apps, and specialized web portals. Many chatbots are also limited to a single data source or system.

It’s also important to recognize that these differences are not universal, and that the scope and capabilities between digital assistants and chatbots often overlap.

What’s the difference between a digital assistant and an AI copilot?

AI copilots are the evolution of digital assistants. An evolution made possible by the integration of generative AI and large language learning models (LLMs) that help AI copilots do a much better job of understanding user intent than digital assistants.

Here are some of the key differences between digital assistants and AI copilots:

• AI copilots provide a deeper level of interaction than digital assistants. Copilots act more as your co-worker or partner to provide expert advice, assist in highly complex tasks, and even generate creative content.

• AI copilots differ in focus and purpose. While digital assistants are primarily designed to complete a wide range of specific tasks at your request, AI copilots have much broader capabilities to help you resolve issues and engage with you in proactive, collaborative problem-solving.

• AI copilots also differ in their role as a virtual expert that provides solutions, finds efficiencies, and improves outcomes across lines of business. Joule, SAP’s AI copilot, is a good example of how this role actually works in different business settings.

• AI copilots use generative AI—along with LLMs—to help ensure that conversations maintain contextual relevancy, including the ability to have long conversations with a user without losing track of the main subject matter or overall thread. Generative AI also powers content creation requests, everything from poems, stories, and emails to summarizing research, writing code, and advanced problem solving.

While AI copilots use generative AI technology to support the enhanced capabilities above, it’s also important to note that the line between AI copilots and digital assistants is also blurring, as many digital assistants are incorporating AI copilot-like capabilities.

What are key features of digital assistants?

Digital assistants have extensive features to support the growing range of needs and interactions required of them.

Here are some key features that make digital assistants highly effective for users:

• Understanding and responding to spoken or written language in meaningful and contextual ways to facilitate more intuitive, fluid interactions

• Personalizing responses and recommendations based on the user’s past requests, behaviors, and preferences for more relevant responses

• Integrating with third-party extensions to expand on existing capabilities and provide users with a wider array of services and applications

• Executing multiple actions with a single command so the lights and coffee maker turns on and the alarm turns off just by saying “good morning”

• Monitoring user activity and user-impacted events to proactively make suggestions and recommendations in interactions initiated by digital assistants

How do digital assistants work?

Digital assistants bring together different technologies, systems, and data to understand and respond to user commands and queries. Multiple AI technologies come into play to make digital assistants highly effective resources for users including:

• Speech recognition when you speak, the digital assistant’s speech recognition technology converts that speech to text using sophisticated algorithms and cloud-based services to accurately transcribe the voice input.
• Natural language processing—once the speech is converted to text, natural language processing is used to understand its meaning and context. This involves breaking the text into smaller units or tokens, and then identifying or tagging the function of each word in a sentence. It also involves detecting names, dates, and locations as well as user intent.
• Machine learning—digital assistants use machine learning to help improve all aspects of the user interaction from accurate speech recognition to understanding more diverse and complex queries. These models are trained on large volumes of data, including user interaction data, to make interactions more personal, contextual, and proactive over time.

What are the benefits of today’s digital assistants?

Digital assistants are not only convenient and often delightful to use, they’re also quickly becoming an indispensable part our lives. Whether used at home, on the road, or at work, digital assistants offer significant benefits for both users and the companies that provide them:

• Saving time by quickly setting reminders, composing emails, and other routine tasks with a single voice prompt, freeing up employee time for more value-added tasks
• Enhancing productivity across business processes by monitoring and streamlining workflows such as automating supply chain events or providing HR self-services
• Improving customer service by making digital assistants available across devices and platforms for round-the-clock customer service to take orders, resolve issues, or answer questions
• Reducing costs by fulfilling routine tasks for both customers and employees to make support team and training utilization more cost-effective
• Personalizing user experiences by learning user preferences and routines over time, so task execution and responses to questions get more tailored to each user

How are digital assistants used in companies?

Digital assistants are being leveraged across industries to improve operational efficiency, worker effectiveness, and customer service. Leading companies are investing in AI and using digital assistants in unique ways, establishing best practices that industry peers are adopting for themselves.

Below are just a few examples across different industries:

Digital assistants in healthcare

• Checking symptoms and providing initial diagnosis to help triage patients before they see a healthcare provider
• Supporting telemedicine by helping setup up appointments, managing patient records, and sending follow-up reminders
• Continuously monitoring glucose levels based on real-time data to adjust and administer insulin doses as needed
• Delivering personalized medicines based on DNA, scaling cell and gene therapies, and predicting clinical trial enrollment

Digital assistants in retail and e-commerce

• Using RFID technology to improve inventory accuracy, automate shelf stocking, and strengthen loss prevention
• Tracking inventory levels, automating stock reorders, and advance alerting on stock shortages
• Handling order inquiries, processing returns and refunds, and making product recommendations based on customer input and behavior

Digital assistants in manufacturing

• Monitoring production metrics and equipment status and alerting operators of potential issues before they cause downtime
• Analyzing production line data to identify defects and make sure all quality thresholds are met to maintain quality control
• Reviewing orders and deliveries, supplier relationships, and demand forecasts to optimize supply chains and inventory levels

Digital assistants in energy and utilities

• Integrating with smart home devices to monitor energy usage, provided energy saving recommendations, and manage utility services remotely
• Answering customers billing questions, providing usage information as well as real-time updates on service outages and restoration times
• Managing field service workers by optimizing their service schedules and dispatch priorities to ensure timely maintenance and repairs

The future of collaborative AI

As innovations in generative AI, machine learning, and nature language processing continue, digital assistants will become more like AI copilots and collaborative AI agents in general will improve their interactions with humans in new and dynamic ways.

As these agents evolve, they will also provide more direct and granular control over your entire digital footprint. With expanded system and data source integration worldwide, you’ll be able to use these agents to better manage how your information is collected, how it’s used, and when it’s deleted.

Deeper conversations and connections

Advances in AI and machine learning will also improve predictive capabilities of collaborative AI agents like AI copilots. They’ll act more proactively with suggestions and recommendations and adapt more quickly to changing user behaviors and environments. Advances in natural language processing, for example, will help future agents engage in deeper, more human conversations, switching ever more seamlessly from one topic to another or one language to another.

In addition, advances in image recognition will enhance AI capabilities around identifying objects, analyzing scenes, and augmenting reality. This will give users a more fluid and intuitive experience with collaborative AI agents, whether by test, voice, gesture, or even video. These advances will be particularly impactful for users with physical disabilities.

Lastly, as collaborative AI agents become more intelligent, versatile, and integrated into our daily lives, they’ll also become more adept at understanding who we are and the complex environments where we work, live, and play.

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