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Design Trends for Digital Employee Experiences


In 2017, we observe evolving design trends that originate in emerging technologies and efforts to address the needs of digital transformation.

The trends that we believe will contribute to the shape and definition of digital employee experiences within the enterprise range from “chunking experiences” into simple, meaningful “bites,” to enabling specific users through hyper-personalization and access to alternate paths to supporting experiences through artificial intelligence (AI). Beyond this, audience and behavior-based design, in addition to the broad use of metrics and analytics, can influence user engagement and participation.

These trends will significantly change our experiences and interactions with products and services. If successfully applied, they will leverage the depth of technology to augment peoples’ inherent capabilities and provide experiences that feel supportive and humane. While many companies are still in the early stages of their digital transformation journey, we believe the following trends are worth assessing to understand the value that they bring to the digital employee experience.

Conversation: a natural way to interact

In 2016 we witnessed the mainstreaming of applications that relied on chat bots and conversational UI to help people achieve goals using voice or text commands, instead of buttons or links. These types of applications eliminate the need to understand an interface, and the requirement to learn a new interface as people move from one device to another. Additionally, the use of conversation (voice in particular) feels comfortable because it’s like interacting with a person. Conversational UI is today providing efficient ways to get simple things done in the consumer space. Personal assistants like Alexa or Google Home help by turning on lights, playing music, or finding information online in response to voice commands. Other services like Domino’s Anywhere utilize more widely available formats such as tweets or text messages (in addition to personal assistants) to order pizza. For more complex interactions, hybrid interfaces in consumer apps like Operator or KLM’s messenger app combine the ease of conversational UI with rich graphical feedback such as photos, maps, select buttons or other formats.

This year, conversational UI will improve based on learnings from the explosion of applications in 2016. Better natural language processing will reduce task abandonment due to misunderstood language. Continued development of hybrid interfaces will support more complex interactions. Overall, conversational UI interactions will become more effective and efficient, and generally more satisfying.

For enterprise solutions, we see opportunities for conversational UI to provide efficient ways to complete tasks and access information. Getting people to the “right” information supports organizational alignment and reducing time spent completing simple tasks frees it up for more nuanced work.

Artificial Intelligence: machines that learn and teach

Trending together with conversational interfaces are advances in AI that are moving beyond expert systems and explicit algorithms. Companies and researchers on the forefront of technology are developing deep learning systems which can understand input without a specific algorithm. In 2016, Google Translate researchers developed a system which could use the concept of transfer learning to translate from one language to another based on its pre-existing ability to translate from each language into English.

In the consumer space, AI currently powers chat bots and other conversational interfaces, and is being used to give people input on how they can better accomplish a goal. Call centers are using AI systems to analyze speech and exchanges that reps have with customers. In some call centers, the AI system acts as a real-time coach, influencing a rep’s interactions by telling them when a customer is upset or suggesting changes to behavior, such as speaking more slowly.

Within the enterprise, we see tremendous value and potential for AI to supplement the experience through the provision of meaningful and relevant information and data. This input can support better decision-making, enhance process support, and positively influence employee behaviors.

Hyper-personalization: experiences that adapt to the person

Hyper-personalization is a natural progression in experience design. It creates the best experience to meet the unique needs of a specific person. To provide the best outcomes, hyper-personalized experiences are based on expressed preferences, needs, and habits combined with data collected by behind-the-scenes technology.

We are seeing these types of experiences in the consumer world for established business models and as engines of emergent models. Hotels are using AI to customize guest experiences before they arrive, setting room temperature or TV channels based on personal preferences. Stitch Fix, an online personal styling service, builds an individual profile based on a customer’s answers to questions combined with captured data from online interactions, recommendations, and purchases. This profile becomes the basis for product recommendations which change as the profile is refined over time.

Within the enterprise, hyper-personalization may provide the richest area in which design can support changes in behavior and work practices. Based on the availability of large sets of meta-data, employee solutions can evolve from tools that act as static aggregators and entry-ways into transactions, information libraries, and data sets into tools that fundamentally adjust to the situation of the person using them.

Our Approach

The methodology used by the Experience Design Studio at Logical Design Solutions combines a people-centric design approach with digital technologies to enable greater connectedness between an organization and its people. Emerging design trends present new ways to create connections. We must consider how these trends can contribute to employee experiences that exceed expectations.

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