Catching up, carefully | Straight Talk

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A CIO offers insights on how to systematically move from legacy-bound laggard to technology-current leader.

In order to stay competitive and provide the services our customers want, we have to be looking at the latest technologies.  But here’s the problem: We have a lot of legacy systems to maintain. Those systems require a lot of care and feeding, and the time my engineers spend doing that is time not spent building new products, features, and services for our customers.

If you look at the most valuable companies in the world right now, the Googles and the Amazons, all of them have been relentless in their pursuit of differentiation. They’ve worked on new technologies and they’ve been able to implement them very quickly, because they don’t have the weight of legacy systems holding them back.

Systematic modernization

Since 2010, we have been focused on systematically modernizing everything we do. That has involved moving to the cloud, exiting many of our legacy systems, hiring the right talent, and giving our teams the tools and systems they need and want to use.

For the cloud, we’re working very closely with Amazon in the US, and that means moving our platforms there.  This is not easy. It’s one thing if you’ve got a startup, but when you’re talking about a mature Fortune 100 company with millions of customers , you need to take a different approach. 

It’s been an amazing journey. It has entailed a mindset shift, in how we think about building, testing, deploying and supporting an application. Every single step of that process, you have to rethink if you’re going to actually modernize and adopt this technology. 

And so we have been systematically looking at each of those components.  How do we make sure that our engineers know how to build?  Are you building a micro-service, and what is it?  How do we make sure they have the right tools to test?  How do they have the right pipelines to actually deliver?  And then once it’s live in production, how do you think about supporting that application? 

Investing in innovation

This has meant investing in innovation labs, which are important in driving our transformation into a technology company. They’re also important for recruitment. Saying “Come work at a bank” is not that appetizing to a technologist.  It’s better when you can say, “Come work for us, and you’ll be able to print your own circuits; you’ll be on the first team to move a platform onto the cloud; you’ll be able to explore different ways of using data, and machine learning.”  

So far, it’s gone well. Not every piece of technology we’ve built has been successful.  But there are some examples of things that started in the lab that were either moved into a line of business, or the concept was adopted, and then picked up by other parts of the organization.  

Not all of these contributions have been technical either. For instance, there is now almost a religious debate about open floor plans versus cubes. And there are good reasons to do both. One of the things you’ll see at our lab in Richmond is that we have built a hybrid experience. You can be open, with everyone sharing a space. Although often noisy, such spaces make it easier to collaborate. But we also have places like the library, where people aren’t allowed to even talk. And there are phone booths and different kinds of nooks and crannies where you can work in your own zone. This gives people a choice, depending on how they work and what they’re working on.

Blockchain’s potential

One technology we’re exploring is blockchain. It has numerous potential potential applications in financial services from transactions to simplifying the back office.

We are investigating not only blockchain technology and how to leverage that within financial systems but how to accelerate our business in areas like rewards.

As with any emerging technology, there are a lot of potential opportunities and challenges. Smart contracts could have a transformative blockchain application, but security is still a big concern. One area we’re exploring is around alternative techniques for implementing secure and private smart contracts.We’re also exploring potential paths to help overcome some of the tech challenges that preclude industrial-scale application of Blockchain for key financial use cases.

Another question we’re asking, though, is what should not be solved via blockchain? The protocol is very powerful and it has a lot of potential. But at the same time, it won’t solve every single problem. And so we’re being really selective in making sure that we aren’t moving to blockchain just because everyone says that’s what you should do.

Staying skeptical

These skeptical questions about new technologies are important to ask. The Industrial Revolution completely transformed everything that we do.  It’s why we have our schooling system, why we have roads, air conditioning, why we have the world that we know today.  As it emerged, there were a lot of concerns about it completely eroding jobs, which proved true in the short term. At the same time, it has been an amazing thing for humanity. In similar ways, new technologies like such as artificial intelligence will provide amazing opportunities for us as. But we will need to watch it closely. 

We’re very attuned to the ethics of it all. Our center for machine learning team is focusing on research in several areas that advance our efforts in accountability, explainability, governance, security, and privacy across our machine learning work.

Maintaining trust is key

Maintaining our customers’ trust is of paramount importance to us.

Our focus as a company is on building technology that looks out for our customers and their money – with the goal togive people the ability to live a life they’ve imagined. We are a business. We have to make money. But we also recognize that we’re doing that on behalf of the customer.

The Takeaways 
Managing the tension between adopting the latest technology and maintaining legacy systems requires a systematic approach to modernization.
Blockchain will be transformative but it can’t solve every problem. You also need to focus on what it can’t do.
The best AI puts people first