On your journey toward AI-driven experience management, stay in sync with all your fellow travelers

“If you define the problem correctly, you almost have the solution.”


-Steve Jobs

In last week’s blog, we focused on how AI can move beyond the realm of personalizing and automating digital experiences by focusing on optimizing human-to-human interactions, and outlined the merits of leveraging emotional analytics.

This week, we’ll take a look at ChatGPT’s answer to the next question in our series: What’s the right balance between leveraging AI to drive insights and keeping it simple and transparent so that people trust the recommendations and incentives tied to CX analytics? We’ll outline our own thoughts on striking this balance, after quickly sharing some thoughts on how well ChatGPT did answering this question.

Chat GPT’s Answer (repeated here, verbatim from ChatGPT): 

Finding the right balance between leveraging AI to drive insights and keeping it simple and transparent is critical to building trust in CX analytics and ensuring that people trust the recommendations and incentives tied to them. Here are some strategies to achieve this balance:

By following these strategies, businesses can leverage AI to drive insights while keeping the solution simple and transparent, building trust in the recommendations and incentives tied to CX analytics.

How Did ChatGPT Do?

Chat GPT does a good job emphasizing that adopting AI to build insights requires buy-in from your organizations’ stakeholders at all levels: leaders, managers and frontline employees. We touched on the importance of change management ourselves in this blog about integrating AI into your organization’s strategy and operations, and this week gives us an opportunity to explore this further. Seeing ChatGPT focus on this in synthesizing the wisdom of crowds reinforces that insufficient attention is paid to change management.

The End-Goal of Investments in AI-driven Insights

Ultimately, your investments in AI-driven insights should power a continuous improvement cycle and value creation. However, two areas of friction must be addressed before this can be fully achieved: 1. a lack of alignment among your organization’s stakeholders on the right use cases to focus on for AI and CX; and 2. your organization’s willingness to trust the AI and engage with these insights as you analyze an exponentially growing sea of unstructured data. Both of these require attention to change management and culture, not just turning on the technology or building a world class data-science capability.

Forging Alignment on the Right Use Cases for AI in Your CX

Even the most capable organizations won’t see their AI investments flourish without ensuring organizational buy-in from the top-down. A key first step is making sure there is a shared set of priorities for where to focus resources and what problems you are aiming to solve (e.g., reducing call-center volumes, website and mobile app optimization, boosting the ROI from product launches etc). Building this alignment helps ensure you can sustain the organizational commitment of resources and leadership support to get over the finish line for your prioritized use cases. Skip this step, and you are likely to get stuck in “fits and starts”, “siloed experimentation” or “a long road to a small house” to use a few of my favorite phrases for describing why initiatives don’t reach their potential.

Overcoming the Complexity Gap

Beyond getting upfront alignment on the right use cases to focus on, another common change management trap is to get enamored with the power of the analytics and lose sight of the need to keep things simple and ensure broad-based adoption of your use cases, which likely means getting people to trust the insights and apply them in the work they are doing to improve the CX. The opacity of AI’s inner workings means taking time and effort to acclimate stakeholders, as well as taking an iterative and pragmatic approach to how you build insights using unstructured data, which is the fuel in the engine for an AI-optimized CX. One example of unstructured data is social media, which you can mine to build insights about your products and service experiences.  This is a huge treasure trove of data, particularly for B2C brands.  The volume of live chat has already surpassed social media, and in-app messaging is close behind.  Qualtrics shared a fun video of this recently as a race among different sources of unstructured data, where the pace of change is growing exponentially.  Qualtrics recently acquired Clarabridge (since rebranded Qualtrics XM Discover), which pioneered the use of AI to generate insights from unstructured data, and has continued to invest in innovation and developing easier to adopt workflow solutions across a growing set of use cases.

Applying AI-driven insights to different use cases is the best path to creating value, but without leveraging unstructured data your investments won’t reach maturity. Yet research from MIT in 2021 found that 82% of companies are not mining unstructured data yet! The change management challenge, however, isn’t just starting to analyze unstructured data. It’s doing it in a way that manages the complexity involved, not letting the perfect be the enemy of the good. In many cases, this may mean that you should focus first on using text analytics of the unstructured data to showcase supplemental insights before replacing more traditional approaches based on survey data or other structured data sets. It’s often better to build towards a more complete solution in stages, ensuring you have buy-in along the way. And be careful not to change incentives before you’ve built momentum and have the necessary buy-in.

Creating Intuitive Connections with Emotion

One very powerful place to focus initially is to develop insights about the emotions your customers are feeling along the customer journey. For example, an “emotional motif” can be developed to make your CX vision more relevant and guide ongoing improvement efforts to focus on a specific set of emotions you are looking to evoke along the customer journey. See this blog co-authored with Lou Carbone for more on this topic. 

JourneySpark Consulting is excited to work with a great set of strategic partners to help clients address these issues. Click here to see our recent videos with Experience Engineering, Farlinium, and GK5A. 

Up Next Week

In next week’s blog, we’ll be writing about the best practices when using AI to design seamless customer interactions. I’ll continue posting weekly for this AI series every Wednesday covering each of the ten questions I posed to ChatGPT, collaborating with my business partners for JourneySpark Consulting. I’ll also continue to post one of my book reviews for my favorite ten business books every Friday. If you’d like to jump ahead to see all ten reviews now, click here to check them out.

We’re looking forward to continuing the conversation!