Empowered by AI to Get Close to Customers

Last Published: Aug 05, 2021 |
Dr. Henna Karna
Dr. Henna Karna

Get close to customers using AI

AXA XL is the property and casualty (P&C) and specialty risk division of multinational insurance giant AXA. It is known for resolving even the most complex risks for its customers, which range from mid-sized firms to the world’s largest multinationals.

I run the digital transformation initiative at AXA XL. I’m going to tell you about our particular digital transformation journey, its highs and lows, and how we dealt with them. And the first points I want to make is that, first, this journey is all about the customer; and that secondly, that it follows approximately the same trajectory as the well-known five-stage cycle of grief: from denial, to anger, to bargaining, depression, and, finally, to acceptance.  

Introduced in 1969 by Dr. Elizabeth Kübler-Ross, this model was dedicated to helping us understand human behavior in the face of traumatic loss. And when we think about radically changing the way we serve our customers – because that’s what we’re doing with digital transformation, after all – it can help to think about it this way. Because we are losing something – the status quo. The way we do things now. What we’re comfortable with. And those things go double for our customers. We’re asking them to change their ways, often quite dramatically.

That’s why I recommend not focusing on the technology, nor on your organizational structure. Those considerations can limit us. Instead, think of the emotional journey you’re embarking on – and on which you are taking your customers.

The truly transformational digital transformation

Here’s how the cycle plays out when you disrupt the status quo, based on the classic Kübler-Ross model.





First, your customers are likely to be in denial that things need to change. They’re stable. Passive. And in a way immobilized. Because stable is a very comfortable place. The job of the digital transformer at this point is to build credibility – establish that he or she has something of value to offer to the customer – keeping in mind that people are loath to give up their existing ways of doing things. Problem? What problem? Nothing to see here. Once you establish credibility with your customer, you must define the need for change, very clearly and concretely, also while the customer is in the denial stage. You need to paint a vision of a new way of doing things. How do you know if you’ve succeeded during the denial stage? You’ll understand who the customer’s key stakeholders are, as well as their priorities, and you’ll start to hear things like, I never thought of it that way before.

Be prepared: next comes anger. Why do we have to do this? the customer will demand. You have to be exceedingly rational at this point. Do this by showing them the opportunities that exist by doing things differently. Quantify it. Give them the business cases. Even make them squirm a bit. Wow, I had no idea we were wasting this kind of money!

Customers enter into the bargaining stage now. What if we do something else instead? Depression is to follow, and is potentially the hardest part of the cycle to get through. You face the inevitable resistance. Do we really have to do this? It’s important to emotionally connect with customers at this point, move beyond the rational argument to an emotional one. The goal is to help them understand that the pain they’re in is real – but also that you can help.

Next – and this my addition, nothing to do with Kübler-Ross – your customer will wake up enough from its depression to want to know, what is the value proposition? You must have a convincing argument ready. You’ll know you’ve succeeded when the customer finally acknowledges that the status quo was not tenable.

And, finally, you get to acceptance: Yes, I see, this will be good for us. This is when you emphasize and evangelize the advantages of all you’ve done for the customer.

Where data and AI come in

Keep in mind throughout all this, we’re not just looking to get customers to agree with us. We’re looking to get them to evolve. But often, the delta between where we are, and where we want to be, is too severe. That's where we need data and advanced technologies like artificial intelligence (AI) and machine learning.

That’s because we’re after something grander than just data volumes. We need the kinds of insight data can provide to be in the DNA of our work. That’s where Informatica’s intelligent CLAIRE engine has been such a great enabler for us.

Today, when we think about transformation and about data-driven companies, we consider a matrix with two axes.

There's the advancement paradigm axis, which goes from existing/legacy state, to incremental change state, to the transformational state.Then there’s the impact on the organization axis, which moves from establishing a data-driven model, to a mid-state of going digital, to an end state of disrupting with digital data. The goal is to evolve to a place in the upper right quadrant of the matrix, where the disrupting with digital data has led to a truly transformation state. But the key word is evolve. We don't want to create a revolution. And AI is how we evolve.


advancement paradigm axis


AI has the ability to take higher-order complexity and distill it down to a much more simplified pattern recognition. By leveraging that, we finally get to our target state. We previously thought about data in a siloed manner. But by using AI to connect the dots, to look for the differentiation factors, and to create new IP, we are evolving to our desired transformational state.

Conclusion: the race with ourselves

With all this in mind, let me talk about the race we’re competing in. It's a race with ourselves. I used to run track and field. My school didn’t have a women's team. So I ran on the men's team, and being the only woman, I lost every race at the beginning. But the great thing was that my racing time mattered. Because I knew my own times from previous races, and also the times published for women runners from schools that did have female teams. And I could measure myself against those benchmarks.

Likewise, this is a race within our industry, but with ourselves. First and foremost, it's a race that helps us understand how obsessed are we with our customers. How much we understand the precision of our data. And how our creativity and competitiveness are empowered by AI. For these reasons, we've been able to leverage CLAIRE at AXA XL very, very well.

Watch the on-demand replay of Dr. Karna’s presentation during the Intelligent Data Summit for AI-Powered Innovation series premiere on Informatica Live.

First Published: Jul 22, 2020