What can I use mainframe-based AI for?

It’s a funny old world. On the one hand you have people talking about AI replacing just about everyone and doing their jobs faster and more accurately. And, on the other hand, you have people talking about how just about everyone over 50 is leaving full-time employment and taking up more fulfilling occupations. Or else they are talking about just how boring their job is. It seems to me that the initial focus of AI should be on being able to do the work that no-one really wants to do – don’t you think?

The good news, in that respect, is that IBM’s much publicized watsonx enterprise AI platform is doing just that. Now available is the watsonx Code Assistant for Z. It’s built on a 20 billion parameter foundation AI model, and was trained on 1.5 trillion tokens of data, and is aware of 115 coding languages.

The problem that is being addressed is the fact that the majority of business applications running on a mainframe were written in COBOL, whereas the majority of programs running on cloud-based platforms are written in some other, more modern language, eg Java. For those mainframe sites seduced by the term modernization (who said mainframes aren’t modern?) and wanting to move their business applications into the cloud, they want to rewrite those existing COBOL applications. The problem is that very very few programmers view rewriting large pieces of COBOL applications in Java as anything more than a poisoned chalice. It’s not something they would view as an interesting or pleasurable way to spend their time. Things are made even worse by the fact the original program code – with any original documentation – has probably long ago disappeared.

That’s where Code Assistant for Z comes in. It’s a generative AI product built on the watsonx enterprise AI platform, which can help developers translate mainframe COBOL applications into Java. IBM's selling point is that the AI tool offers improved testing, faster re-writing of functionality into Java, and lower costs associated with updating the old COBOL code. Code Assistant provides automated testing processes as well, and can be used in each step of the ‘modernization’ process, converting the existing COBOL code to Java.

In addition to watsonx Code Assistant for Z, IBM previously announced Code Assistant for Red Hat Ansible Lightspeed, and plans to launch new product-focused versions to address other languages and improve time-to-value for modernization. IBM is also saying that the products will address the shortage of skilled developers that currently exists.

I talked about the latest Cost of a Data Breach Report from IBM Security back in August. I thought I’d just highlight what that report said about AI. Before we look at that, just a reminder that the survey found that the length of time it takes to identify a breach is 204 days, and once a breach has been identified it takes an organization, on average, 73 days to recover. The worldwide average cost of a data breach is US$4.45 million.

The report makes a strong case for the use of security AI, saying that organizations with extensive use of security AI and automation identified and contained a data breach 108 days faster than organizations that didn’t use AI or automation. What falls into that category includes the use of AI, machine learning, automation, and orchestration to augment or replace human intervention in detection and investigation of threats as well as the response and containment process. On the opposite end of the spectrum are processes driven by manual inputs, often across dozens of tools and complex, non-integrated systems, without data shared between them.

In addition, there were cost savings with AI and automation. The report found a US$1.76 million lower data breach costs compared to organizations that didn’t use security AI and automation capabilities.

There’s a lot of talk about the downside of AI, in fact, I’ve even written about it, but like all technologies that revolutionize the way people work and live, it has a positive side and a negative side. All organizations need to have an AI policy in place to ensure that employees are not using AI in any way that could harm the company. And, there is the possibility that AI could take away people’s jobs. I like to think, like the introduction of PCs into organizations which took away the typing pool and many office secretary jobs, it will also create new types of job, and the overall number of jobs available will actually increase.

Also looking on the positive side, things like Code Assistant are able to do jobs where there is a shortage of people who would otherwise be available to do it (ie developers), and it will do work that most programmers would rather not have to do (ie rewrite COBOL programs in Java).

The whole Code Assistant for Z approach by IBM seems a great step forward in the use of safe AI.