Experience Strategy and Design

By: Scott Brown

How is the role of experience strategists and designers changing?

First, the trend of using design methodologies to help solve business problems is continuing to provide value. We’re swimming further and further upstream with our clients and, with that, the expectations change.

To be effective in these scenarios, we have to leverage our design thinking chops and become experts in our clients’ industries. There’s always been a need to understand the business context of the problems we’re solving, but that need has been amplified as we’re addressing challenges that have traditionally been the purview of consulting firms.

Whether we’re digging into the KPIs of a value-based healthcare arrangement or mapping out the product development journey of a variable annuity, we must immerse ourselves in these industries and quickly become fluent in order to be effective.

Second, the experiences we’re designing for are evolving. Over the last decade, we’ve gone from designing static screens to more dynamic ones, and, ultimately, to designing systems that are driven by context and artificial intelligence.

This shift doesn’t stop at simply gaining a basic understanding of how AI works. Designers need to also understand the UX implications associated with how AI works:

These are the questions that will be keeping designers busy.

What’s the biggest thing that prevents companies from delivering great omni-channel experiences?

The biggest issue is focusing too much time, focus, resources, or dollars into any single touchpoint. This is often the result of siloed organizations, where it’s challenging to align budgets and efforts across workstreams. This makes it difficult to take a holistic view and establish a unified vision that addresses the entire customer journey. But all of the great case studies out there (e.g., Disney, Warby Parker, Starbucks, etc.) have this approach in common.

What’s the current state of AI in experience design?

One of the biggest challenges keeping AI from reaching its full potential is data — good, clean, structured data that machines can learn from and take action on.

Most data that corporations have to act upon doesn’t meet these standards. This means that a huge effort is required to gather, structure, maintain, and govern data for AI at an enterprise level. (See example below)

How can we make personalization better?

The convergence of new platforms and devices, unprecedented access to data, and technologies like AI are unlocking all kinds of possibility, but the industry is falling short of the full potential of personalization because it’s asking the wrong questions.

Personalization will start to improve when we shift the conversation from “How can we use personalization?” to “What’s in it for the user?”

Personalization that delivers relevance, convenience, and utility is what drives engagement, preference, and value for both the business and the user.