AI for Business: AI and cognitive engagement

Blog and Live Stream series

This is post #5 in our AI for Business series. We have an accompanying Live Stream episode of this blog here.

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As we discussed in previous blogs in this series, to harness the power of AI you first need to clarify the problem you want to solve and your existing business processes. Only then should you consider where to introduce AI.

In the previous post we looked at using AI to provide cognitive insights into complex datasets. Now we will look at another application for AI; cognitive engagement (CE).

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What is cognitive engagement?

Imagine you’re running a business, and your customers and employees are interacting with your organisation across multiple channels; chatbots, emails, social media, and phone calls. They have questions, concerns, and expectations, but keeping up with every interaction in a meaningful way is overwhelming. You want to engage with them effectively, but at scale? That’s the challenge, and it is where cognitive engagement (CE) can help.

Cognitive engagement is the use of artificial intelligence to create meaningful, dynamic interactions between humans and machines. Unlike traditional automation, which follows rigid rules, cognitive engagement systems leverage AI technologies such as natural language processing (NLP), machine learning, and sentiment analysis to understand user intent, maintain context, and respond in a personalised way. They can handle complex queries, anticipate user needs, and even detect frustration or satisfaction, allowing businesses to enhance customer (and employee) interaction experiences at scale.

By continuously learning from interactions, cognitive engagement systems improve over time, making conversations more intuitive and effective.

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Use cases

The two primary use cases for cognitive engagement that might apply to your business are:

Customer engagement

The most obvious use case for cognitive engagement is AI-powered customer support. In most cases where this is prevalent today, chatbots and virtual assistants use natural language processing (NLP) and sentiment analysis to provide instant, human-like responses to customer inquiries. They can handle routine questions, escalate complex issues to human agents, and improve service efficiency. And, like virtually all AI systems, they can learn and improve over time through machine learning (ML).

These forms of interaction can be taken to the next level by layering on other AI driven technologies like Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) Synthesis. These additional technologies would allow a customer to engage with your brand by speaking to an AI customer support agent in a conversational manner, rather than by written word.

Employee engagement

As with customers, large enterprises might also benefit from having an internal employee assistance or HR support tool. These AI-powered virtual assistants could help employees with HR-related queries, onboarding processes, and internal knowledge management, improving workplace efficiency and employee experience. Here at thoughtbot, we use machine learning to answer questions from internal documentation.

In the previous blog post in this series, we looked at how cognitive insights can identify links in data that would be impossible to spot with the human eye. Cognitive engagement can take this one step further by allowing anyone to easily interact with these complex data sets. Rather than requiring data scientists to extract insights, business development employees could simply ask an AI assistant ‘Why has foot traffic dropped in one branch?’ and receive an immediate, conversational response.

As employee engagement is less obvious than customer-facing AI support, let’s look at an example where this might be useful.

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thoughtburgers employees understand decisions 🍔

As has been common throughout this series, let’s revisit our delectable AI-powered side-business, thoughtburgers (home to the tastiest and most thought-provoking burgers!).

As we discussed in the last post in the series, thoughtburgers has been a big hit and multiple branches have been opened in London. 🥳 We have used cognitive insights to streamline our deliveries in rush hour by tackling both driver scheduling and order triaging between branches. Happy days!

Orange boxes denote where cognitive insights were used to improve thoughtburgers’ delivery processes.

But, wait! Not so fast! We are facing a new problem; some of our restaurant managers and drivers are not happy. Some managers are confused as to why their branch is more or less busy than usual. Some drivers are curious as to why they have been scheduled on fewer shifts. Did they do something wrong? Are they being punished?

The answer to these questions is no. These decisions are actually based on sound data from our cognitive insights project but, without transparency, thoughtburgers will face confusion and resistance from our team. Transparency and engaging with the team will be critical to the successful adoption of this new technology.

And this is where cognitive engagement comes in. We could leverage CE to help the team better understand the logic behind the decisions being taken. Interacting with a Natural Language Processing chatbot would provide our team with an easy-to-understand way to query decisions, understand and interact with complex data, and to identify areas they need to improve if they want more shifts or orders for their branch.

Red boxes denote where cognitive engagement interactions could be useful for thoughtburgers’ employees.

For example, the manager of Branch A might now be able to understand that they are getting fewer orders in rush hour because their kitchen was so busy and stressful that it was leading to employee turnover, hurting their bottom line in the process. The manager of Branch B might realise they would get more orders if their food was prepared a little faster, and they can now take action to make this a reality. This gives our branch managers a sense of ownership, inclusion and control in the decision making process.

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Klarna - Benefits and challenges 💵

A good case study for the benefits and challenges of cognitive engagement is Klarna; a Swedish fintech company that provides online financial services.

Klarna made quite a splash when they stopped hiring in 2023. They were championing AI chatbots which they claimed could handle the majority of their customer service volume. On the surface, it’s easy to see why.

Benefits:

Imagine a world where you could pick up the phone, at any time of the day or night, to call a support agent. They answer immediately. No more will we hear “your call is important to us” followed by a tinny rendition of Symphony No 40 that would have Mozart turning in his grave (and not to the beat). You will also not have to listen to a spiel about “for accounts, press 1” and patiently wait until the last option (as you know you are going to end up in the “for all other queries category”).

Instead of this, you could simply start talking, in your own language, and the customer support agent would respond back and ask you further questions until the issue is resolved.

This is the dream scenario that Klarna and others are pursuing.

But:

While these agents are very good, they are not yet infallible. While customers appreciate the convenience of AI chatbots for simple inquiries, they quickly become frustrated when these systems can’t resolve complex issues. This is particularly true for financial services, where issues often involve nuanced situations and high emotional stakes.

Having one such negative interaction can be cognitively taxing and can quickly lead to customers abandoning a service and moving to a competitor with better customer support.

Recalibration:

Despite being initially bullish about AI customer support, in 2025 Klarna announced a recalibration. They aren’t abandoning AI, but they are reemphasising the importance of human interactions with their customers, stating that they want Klarna “to become the best at offering a human to speak to.”

A more balanced approach, where AI handles routine inquiries while human agents tackle complex issues and high-value customer interactions, seems like the right path forward and it’s a good example of finding the right fit for AI within a service.

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Bottom line?

Cognitive engagement is another incredibly powerful and beneficial application of AI. It can be used as a support agent or co-pilot to engage with both customers and employees.

While it can play an important role in your AI strategy, businesses must use AI as an enabler rather than a replacement for human connection. The key is balance; leveraging AI’s efficiency while maintaining the empathy and adaptability of human interaction.

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💡 If you’re ready to start using AI to transform your business, thoughtbot would love to work with you. Let’s talk about making your AI initiative a success!

This blog post is part of a series based on the course Artificial Intelligence (AI) for Business run by University College Dublin (UCD). I took this course and found it so helpful that I’m recapping my top insights. thoughbot has no affiliation with UCD.