Going Beyond AI In Customer Support

Co-Founder and CEO Ascendo.AI; Board Member, Investor; Speaker on Enterprise AI and Entrepreneurship.

There are many tools coming up in the market that are branded as artificial intelligence (AI) or expert tools. The customer support market is especially proliferated with robotic process automation (RPA), chatbots and automation technology. Many companies are at a loss on how to evaluate them and know when to use what. Their focus is on return on investment (ROI), and these tools don't specifically cater to the customer's needs.

Below, I discuss how to think beyond buzzwords and explore the differences in technology to help business leaders pick the right solution for their customer support needs. 

Why complex systems need new thinking in customer support

Complexity is increasing exponentially. Complex systems fail in complex ways, and complex failures need dynamic and adaptive responses.

• Complex systems contain a mixture of latent issues. A system’s complexity means that it is impossible for it not to contain multiple flaws.

• Most flaws are unlikely to cause significant issues. They are regarded as minor factors during operations.

• The flaws change constantly due to evolving technology and organizational factors, and even as a result of efforts to resolve existing flaws.

• Complex systems run while broken. Redundancies in the system and the ongoing expertise and effort of humans ensure that the system continues to function, sometimes in degraded mode.

• Issues have multiple causes, not a single root cause. Each individual flaw is unlikely to cause a major issue. It is the linking of multiple faults that creates the circumstances required for a significant failure.

Innovate customer support from the ground up

The level of complexity has increased not just because products are complex but also because the environment and usage are complex. In this ever-increasing complexity, support teams should focus on three main goals:

1. Solve today’s problem.

2. Make sure it doesn’t happen again. 

3. Predict problems before they happen.

Whether it is self-service, community, agent assist or auto-support, problem prevention and problem elimination are at the core. As companies look for ways to do the above, many of them believe incorporating artificial intelligence (AI) can help in the following ways:

1. Automating repeated tasks. This is called automation or robotic process automation (RPA). In this method, you program the repeated tasks and let the computer execute.

2. Rules-based and statistics-based tools that are an expansion of traditional business intelligence (BI) engines.

3. Machine learning algorithms that identify anomalies and patterns within data in the enterprise.

4. Identifying relevance using natural language processing (NLP).

Ideally, a tool can be called a true AI tool when it uses all the above along with:

1. Extending NLP to include content, intent and relevance.

2. Expanding machine learning to provide prescriptive actions.

3. Utilizing tribal knowledge from the users.

Tribal knowledge: Going beyond AI in customer support

As author Adam Grant discussed in his book Originals, intuition helps only when it is in the area of expertise .

The key to a successful AI journey involves using knowledge and learning from humans, creating a feedback loop and incorporating it into learning. AI is good for complex systems and dimensions of data. Feedback procured from humans on prescriptive actions provided by the tool adds another dimension.

At Ascendo, we believe one of the most important dimensions is the interactions a user has with the tool. These interactions become a critical learning opportunity. A learning engine knows what an agent is looking for, what actions the data suggests, what the agent decides and how the agent interacts with the tool. It has the ability to learn from data and feedback not just from its users, but also from user interactions.

When we include tribal knowledge from interactions, we've seen that the prescriptive actions are more optimal than just utilizing data. Essentially, we have expanded the dimensions of decision making as reflected in the images below.

Increase your chance of success

When you are evaluating a product, ask the following questions to see how in-depth the tool is. These questions will help you go beyond the AI discussion and discover what is behind the buzzwords:

• What role does artificial intelligence play in this tool to help customer support teams?

• How do relevance and feedback enhance the machine learning dimensions of the tool?

• Can the tool go beyond these dimensions to identify intent and learn from interactions?

Understanding the realities of AI and using tools that incorporate and thrive in feedback loops and interactions can increase your chances of success. 

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