The competition for the one digital assistant to rule them all is on.

Microsoft announced that Cortana will connect into household appliances with its latest Windows 10 Creator Update. Amazon won CES 2017 without being there when third-party vendors showcased their products – with Alexa embedded. Soon, nearly two billion Facebook users will also access M, typing questions via text in a chat box.

Companies are recognizing that users are experiencing what psychologists term “cognitive overload.” As more and more devices become “smart” and connect to the Internet of Things, consumers are overwhelmed with the amount of data they can access – and need to manage. After all, what is the point of having a smart thermostat if you aren’t actually using the ‘smart’ insights it provides? Consumer and enterprise tech companies have the solution: the digital assistant.

Much like their human counterparts, the assistant’s success is determined by how quickly it gets to know you, including your preferences, context, background and motivations, and how efficiently it can apply that information to deliver the right content or recommendation in real-time. If digital assistants can do both of those well, they’re here to stay – and well beyond 2017.

The Digital Assistant Opportunity

In order to “own” the future of consumer tech, it’s not just a matter of multiplying the number of new product launches with exciting features. Winning in this space means becoming so helpful and so ingrained in consumers’ lives that switching to a different brand and suite of products will just be, quite simply, too painful. Digital assistants do this by promising to solve the cognitive overload problem, and with machine learning, are becoming more personalized by offering greater relevance.

IT leaders need to prepare for when this consumer technology inevitably infiltrates enterprise software, with some adaptation. Employees face the same cognitive overload issues that consumers do, but compounded by the approach to custom enterprise software. Enterprise IT departments traditionally build systems for functionality, integration and security, and think about user adoption later. A 2014 Deloitte study found that 72 percent of employees cannot find the information they need.

Digital assistants in the enterprise environment will deal with some major complexities, especially around security, privacy and mission-critical user experience. It’s important to note that digital assistants will need to “earn” the trust of the employee, just like any other colleague. The employee needs to trust not only that the information is correct, but also that private information will remain private.

The digital assistant also needs to understand the security privileges in that system, as well as the context of the user. For example, a field salesperson asks the assistant, “What do I need to know about this prospect?” Context plays a role in the answer. When driving to a meeting, the salesperson needs few quick bullet points read aloud as they drive in response to that question; but a few hours before, as the salesperson sits at a desk to create the presentation, they want the assistant to review and search all previous communication to understand what the prospects wants to see in the meeting.

Why Search and Machine Learning are Crucial to the Solution

The two most crucial elements for a digital assistant to achieve critical mass: great search and machine learning.

Search is the key driver in digital assistants. When a user asks a question of a digital assistant, it is essentially a search query they are asking the assistant to input. The digital assistant, however, needs to do more than that by understanding their context and conversational style to decipher their true intent. It is also important to note here that in voice-driven products without the ability to enter or view text, being able to search well becomes even more paramount. Having a voice-based assistant read through an entire page of search results for the best Italian restaurant in the area, for example, is poor user experience. The user will quickly lose interest. At most, the digital assistant can read three options for results, but with a deep understanding of their preferences in selecting those three. A powerful view of the user’s online behavior patterns needs to support the search function.

A basic example of this is asking the assistant “What’s the weather?” A ‘dumb’ digital assistant may pull up definitions for weather, explanations for weather vs. climate, etc. An intelligent digital assistant, however, would not only know that you are in Dallas, Texas and need to know the weather there, but will also take into account that, for example, you are about to fly to Austin, Texas and are very interested in the temperature of Lady Bird Lake due to your interest in stand-up paddleboarding.

This is where machine learning comes in. In order to build that brand stickiness that delights the customer, the assistant, much like a regular assistant, needs to constantly become more and more personalized and attuned to complex preferences and needs.

The bottom line is that digital assistants’ future depends on their ability to search and leverage machine learning to become more personalized with results. The recent investments and product launches indicate a lot of promise, but staying power depends on more in today’s evolving consumer and enterprise technology space.

Here at Coveo, we know how powerful search and machine learning can be. We’ve worked with companies to deliver a high level of personalization in the workplace and consumer markets, in particular using our patent pending, machine learning-based analytics service. It automatically studies behavior patterns to understand what content leads to the best outcomes for the user. It’s a “concierge” to website or intranet visitors that understands the intent of the visit, has the most up-to-date information, and uses the knowledge of what worked well for similar users to answer questions and make relevant recommendations

The business outcomes speak for themselves, from increasing mobile conversions by 125 percent for a media company and upskilling employees to proficient 12 times faster at a major healthcare company.

No matter how you are planning to “own” your market with a more personalized experience, it is important to understand how critical search and machine learning are in your solution – and how these capabilities have an impact way beyond the search box. Download our guide to the Intelligent Workplace to find out more.

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About Rachel Schultz

Rachel Schultz is the Content Marketing Manager at Coveo. She blends her background in journalism seamlessly with her B2B marketing expertise and obsession with data to create compelling content for the Coveo community. When she’s not working, you’ll find her reading (strictly non-fiction), hanging out with her puppy or taking in all NYC has to offer with her husband.

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