Eggplant Blog
This is Michael's third blog in his Shift Up series. You can read the first blog here and second blog here.
On May 21, 2018, Bank of America announced that it was rolling out its chatbot, Erica, to all its mobile customers. On the surface, the premise makes sense. It’s making the bank more relatable. It’s providing real-time customer support to people where artificial intelligence (AI) assistants like Siri and Alexa are becoming the norm. It doesn’t have the limitations that some phone-based IVRs have, and it aims to provide immediate assistance instead of making us wait for a human (we’ve all shouted “representative” or pressed zero dozens of times to get a real person). Erica is a great way for Bank of America to optimize the customer experience.
But let’s pull back the covers and ask some basic questions. How does Erica know the customer so well? How does Erica pull from different sources of information? How does Erica know what products and services to offer? What systems, both homegrown and third party, does Erica need to be effective?
Sometimes I feel as if I’m the Forrest Gump of quality assurance (QA). Since 1998, I’ve been through the beginning of automated integration testing and service virtualization through being a co-founder of Class I.Q. (now IBM Greenhat). I’ve been through the first phases of an automated testing center of excellence (ACOE). I’ve been there for the start of risk-based testing, and I’ve been a part of the transformation of QA from a somewhat necessary function to something that is now the core and chief concern of any company putting out quality software and apps.
Everything about software has changed—how it’s architected, developed and produced, what it does, what users want from it, and how often they expect new features. To keep up, organisations are turning to continuous delivery and DevOps. Yet product teams still do a lot of manual testing, which consumes a lot of time they don’t have, thanks to shrinking test windows. Incorporating automation into your testing approach is a great strategy, but figuring out where and how to start isn’t necessarily quick and easy.
We recently co-hosted a webinar with Bloor Research about the Future of Testing, and in it, we conducted an informal poll about artificial intelligence (AI) and testing. When we asked what everyone thought the biggest advantage was to incorporating AI into a test automation strategy, attendees overwhelmingly selected team productivity and efficiency.
The focus on artificial intelligence (AI) in general, in technology, and particularly in testing, is prompting organizations worldwide to take it seriously. It’s hard to ignore AI’s potential benefits, including improved productivity and efficiency, fewer defects, a better UX, and happy customers. And with DevOps and continuous delivery here to stay, staying relevant depends on keeping pace, which is why test automation is so critical.
For a while now (about 10 years), Dev and Ops have been trying to get along. After all, collaboration between the two creates fast feedback loops and gets high-quality software into users’ hands faster. But with a new space emerging, digital experience management, Dev and Ops need to make a new BFF—the business—to stay in sync.
A new outlook, optimism, and wonder. For me, the start of the new year is always exciting and prompts a lot of questions about how our space and our solutions will evolve over the next 12 months.
The annual Gartner Symposium/ITxpo in Barcelona, Spain, is a great pulse check for what's on the minds of CIOs in large companies (like banks, utilities, telcos, governments). It's not necessarily the place to see the absolute latest technology, but it is the place to see what organizational problems CIOs are trying to solve with technology, and what companies are rolling out next year.