Pace yourself: Balancing velocity and stability in IT using artificial intelligence
Applying AI and machine learning to Pace-layer application delivery
Optimal application delivery has picked up speed over the past decades, but that doesn’t mean that every application and every aspect of business values agility and velocity highly. Particularly in banking, there are many competing attributes to rank, including stability of systems and security of information.
Gartner’s Pace-Layered Application Strategy is a way to describe how a company organizes around delivering systems with the right balance of velocity and stability. At ATB, our Enterprise Data Science team is working on applying AI and machine learning to optimize application delivery at each level of the Pace-layer model. With our internal strengths and valuable external partnerships, we are leveraging AI and machine learning in every aspect of our business.
At the systems of record layer, we are implementing systematic automation to ensure workflow is handled seamlessly and in an integrated fashion. Here, we are applying AI and machine learning by identifying events and applying decision models to rapidly decide and act, with minimal human interaction, while notifying the appropriate stakeholders. This eliminates mundane tasks normally performed by team members allowing them to focus on higher value activities that will have a direct impact on customer happiness.
For example, using AI to automate some aspects of payment processing, fraud detection, and customer on-boarding, without changing the underlying structure of systems, means that ATB team members can spend less time behind the computer screen, and more time building relationships.
At the systems of differentiation layer, we are focused on effectiveness in key strength areas of ATB that differentiate our business from our competitors and disrupt industry. Applying AI and machine learning in areas like customer account management and online experience optimization means greater insights and recommendations to our team members and customers at the right time, in a language that they can understand. We are designing unique online and in-person experiences that are data-driven and real-time.
In contrast to bimodal models of application delivery, which combine the system of differentiation and innovation layers, the traditional Pace-layer model separates these layers, despite the similar use of agile development practices.
For ATB, the systems of innovation layer is very much separate from the system of differentiation. It is the change engine of our work. It requires research and experimentation to apply and test the application of disruptive technology in our business.
We are working internally and externally with the best minds in AI and machine learning to rapidly accelerate proof of concepts against use cases. We have built business units specifically dedicated to innovation with different development timelines and processes.
This is where pace-layering comes in. Our system of innovation teams still need access to core data, through connections to system of record layers, but the development cycle allows for faster testing and deployment of solutions.
We can see the world of banking transforming, so we are leveraging our agile size and expertise in AI and machine learning to drive the rapid creation and adoption of AI-based applications across all layers of the organization. Pace-layer application development allows us to apply different development models and priority attributes as required - valuing stability and security in our systems of record applications and valuing velocity, agility, and learning from failure on our innovation teams.
The future of banking is coming, and AI and machine learning will help ATB be the first to get there.