Insights for Better Customer Experience
Insights for Better Customer Experience
Enabling AI/ML across the SDLC.
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Thanks to Ashok Reddy, Group GM, DevOps, Ali Siddiqui, GM, Agile Operations at CA Technologies, Karl Kleinert, VP of Sales at Intellinet (Managed Service and Cloud Solution Provider) and Animesh Mukherjee, VP IT Service Management at FIS (Banking Software Services) for sharing their perspectives during their showcase at Built to Change Summit.
According to the Sloan Review from MIT, 97% of executives are investing in or building AI initiatives.
Here's the evolution of self-deterministic software:
- First generation = Mainframe monolithic
- Second generation = Web client-server
- Third generation = Mobile, cloud, and microservices
- Fourth generation = AI Blockchain
The modern software factory will support each evolution. In fact, there are similarities between the self-driving car and the self-driving software factory:
- Level 0 = no automation — descriptive analytics
- Level 1 — driver assistance— human monitoring
- Level 2 — partial automation — automatic root-cause analysis
- Level 3 — conditional automation — predictive hybrid multiple
- Level 4 — high automation — self-healing operations
- Level 5 — full automation — self-deterministic applications
Analytics-driven applications are powered by CA Jarvis and fueled by advanced analytics. Jarvis is a group of algorithms with an automatic baseline looking at data in order to make predictions. The results are reduced mean time to resolution (MTTR) and fewer false alarms.
A message is automatically generated by CA Operational Intelligence following the launch of a campaign from marketing affecting the CRM and payment processes. Internal customers are flooded with alarms. Root cause and symptoms are both reported as alarms as a result of multiple siloed tools. There is a need to dedupe tools and to separate the root cause from the symptom. There is a single pane of glass so everyone involved can collaborate. The appropriate team member can run an automated action for remediation.
Such automation is necessary for IntelliNet, a fully managed cloud service provider to provide a consistent level of service to its customers.
FIS is managing global payments for financial services. They generate a lot of data and need insights. With the AI ops journey shifting to the cloud, the challenge is even greater. Heterogeneity is imperative as FIS continues to add more mainframes while they use the cloud. They need to choose tools that are open and allow for universal connectivity. The tools must connect in and out.
Another challenge is FIS is moving to multiple clouds. Most customers are not making a discrete decision on moving to additional clouds; however, they are being pulled into more clouds by SaaS providers.
This leads to another challenge of managing disparate things and applying standards and SLAs. Leveraging AI/ML to re-contain is a key challenge for IntelliNet clients. As they go on their AI Ops journey, they need to be able to see how will it help the business. Intellinet is able to provide consistency, contain costs, and provide a positive CX with fast MTTR
For FIS, cost is important, but keeping payment systems up is more important. Predictively, catch metrics is something that is going to happen and get in front of it and prevent it. When there is an impact, how fast can can FIS find the root cause and fix it? How does FIS bring insights to its customers?
Organizations need to automate automation with an understanding of how people work. CA is examining how to help organizations and people lead with machine learning? They are looking at people managing IT operations and developers and bringing the two together by providing insights that promote collaboration. Shift left and bring insights based on the persona of what impact can we provide.
Many customers don’t know how to begin tacking these issues. There is a lot of inaction. AI/ML can accelerate customer adoption by using the right tool for the job.
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