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Artificial intelligence (AI) tools are everywhere. You can barely go to the bathroom without an AI system there ready to assist. Every tool in your CX stack is leveraging some kind of AI for market segmentation or personalization. But the value of AI for CX doesn’t stop at tools.

In fact the prepackaged tools are not the most helpful thing about AI for your CX. The thinking that goes into designing and building AI systems is itself the most powerful tool for reexamining your customer experience. Let’s dig into three AI problem-solving concepts and see how you can use them to refine your CX stack and create exceptional customer experiences.

AI Inspiration #1: Problem Reduction

In the 1950's first wave of artificial intelligence, researchers created a software program that could mimic how humans solve problems. The design of the Logic Theorist program was based on breaking problems down into smaller, simpler problems. By solving each component problem, the intelligence system built up to solving the major problem. The solutions to the minor problems compounded into the ultimate solution.

CX challenges can present as big, overwhelming problems. Using Problem Reduction, you take a big problem like creating a 360° view of the customer across all of your enterprise platforms and reduce it to one preliminary question: “What needs to be done to do that?”

You’re saying, “Well … it’s not that easy.” But it actually is. Let’s test the logic with a non-technical problem. Imagine your goal is to get a book. Problem Reduction breaks it down into options for achieving the goal:

  • Buy the book.
  • Borrow the book.

Each requires additional steps. Do you have money to buy the book? If so, you’re all set. If not, a new problem has emerged: "Get Money." Decomposing problems allows you to view many different ways to solve the same problem, revealing the lowest cost (time, resources) option.

Let’s sketch out a Problem Reduction for our 360° view:

  • Goal: Obtain a 360° view of each customer.
    • Option: Use one system for all our data.
      • Problem: We currently have multiple systems.
      • Problem: All the systems are disconnected.
    • Option: Connect all the data across systems.
      • Problem: No uniform identifiers for customers across systems.
      • Problem: Different systems define a customer in different ways.
    • Option: Implement Business Intelligence (BI) system to create a single view.
      • Problem: No standard BI system across departments.
      • Problem: Each department has a different point where a person becomes a customer.

Using Problem Reduction, you can map out potential solutions. Your CX stack helps you solve problems, but AI thinking shows you how to solve them.

Related Article: Robotic Process Automation: Power to the People in 2021

AI Inspiration #2: Tasks and Processes, Not Jobs

You’ve probably heard: AI is destroying jobs! But this is the wrong way to think about AI. AI isn’t about jobs, it’s about tasks. In business, a collection of tasks make a process. A collection of processes make a job. AI thinking forces you to break down “work” to the task level, even further to the click level if you’re using Robotic Process Automation (RPA). When you think about your customer experience, are you thinking at the touchpoint level, task level, process level or job level?

No matter which CX technology you’re using, you’ve probably built a customer journey map. Different mapping methodologies have different representations. For our purposes, think of a journey map as a series of phases and the emotional context of the customer that accompanies each phase. Do your phases have the right level of granularity to adequately represent a customer? For example, in mapping a customer’s help desk call journey, do you include transfers and their attendant emotional transitions? A phase may be too high-level to give an accurate picture of what’s really going on.

Leverage your CX stack to get the right fidelity of data. You might need to tweak your system to capture more data on specific phases. The key is to identify the fidelity that works for your business and tune your systems (and diagrams) accordingly.

Related Article: Customer Journey Mapping: Navigating a Course to Better Customer Relations

AI Inspiration #3: Enhancements, Not Replacements

AI is not a replacement for humans. We at Mind Over Machines talk about this a lot, including in our Workforce Ascension & Enhancement (WAE) framework,  because workers are afraid of automation until they see its outcomes. AI can’t do everything. It can’t even do most things yet. AI can do very specialized things that it is directly trained to do. Beyond the limitations of AI, automating every aspect of business for cost savings reduces innovation and value generation. If everything is done the same way every time (how AI needs to do work), there is no creative spark toward organizational growth.

AI-powered CX systems enable humans to create higher value customer experiences with your brand. And the great news is your employees already know how to best use the time created by CX automations. Just ask them. But be prepared for criticisms of your current CX stack that indicate you aren’t getting the ROI you expected. If your team spends more time managing the technology than innovating personal touches along the CX journey, you need to examine whether your tools are actually enabling business or just getting in the way.

Related Article: Do CDPs Really Make Marketers Independent of IT?

AI: From Hammer to Mindset

“When all you have is a hammer, everything looks like a nail.” AI has flooded the business world so much that we see AI only as a tool, a hammer to be applied to everything. Approaching AI as a mindset will enable you to maximize its value for your business. Don’t get hung up on new CX tools and features. Use the thinking and problem-solving techniques of AI engineers to transform your CX from technology stack to impactful, value-generating capability.