Back in November last year, Forrester posted an article with a stark warning: "AI hard-fact — treat it like a plug-and-play panacea and fail." The hype surrounding Artificial Intelligence (AI) has only grown since then.

Today, a cursory Google search of the term "AI" results in 2.4 billion entries. That's a lot of AI chatter! While a Google search may be a blunt instrument for measuring the true impact of AI, it does illustrate just how "big" it has grown. If your inbox is anything like mine, I'm sure you're only too aware that AI-everything is being used to solve AI-anything. That is exactly why Forrester's prediction was so apt.

I'm sorry to be the one to break it to you, but AI does not work (quite) like that. There's no doubt that AI is an exciting set of technologies, with the ability to significantly transform many sectors. High-profile names who weigh in on the topic of AI (such as Elon Musk) further add to the hype. But when you distill it down to what matters, organizations looking at AI, Machine Learning (ML), and BOT technologies are looking for two things:

1. To solve an existing issue

2. Accelerate the organization so they are closer to achieving a future goal or vision

As more digitally advanced companies reap the extreme benefits of AI, the "AI advantage" is ever more clear, but only for those organizations able to harness it effectively.

The secret to success with AI? Organizations need to prepare for it.

We call it AI Readiness.

Gearing up to AI

By failing to prepare, you are preparing to fail. —  Benjamin Franklin

We are really proud of how far Infostretch's AI-powered testing suite, ASTUTE, goes in breaking down the complexity of AI implementation, to the extent that organizations don't need in-house AI experts to use it. However, just to be clear, these technologies are not simple, and they need to be configured and optimized. Even more importantly, they need the right environment and digital maturity for success. So, any organization looking to invest in AI for software testing should first ask itself, "What are we doing to become ready to implement AI solutions for the SDLC?"

AI Readiness is about making the test environment automated, agnostic, and optimized to save time and money while creating a uniform way of executing and analyzing test cases. An AI Readiness program puts in place the systems, processes, engineering, and automation needed to begin implementing AI solutions for testing.

• Here are some of the common testing challenges an AI-ready approach can overcome:

• Large numbers of test cases, usually in heterogeneous formats

• Need to automate test cases / low automation coverage

• Test cases that have accrued over a long period

• Low to no test case traceability

• Need to consolidate frameworks, like HP ALM, Selenium or Tricentis Tosca.

While a Quality Engineering program might seek to address similar challenges, if AI is the end goal, organizations need a specific plan to pave the way towards it. With an AI Readiness program, organizations who are serious about adopting AI in their testing can prime the test environment so AI has maximum impact. But even before they begin to implement AI, the benefits of it will be apparent — streamlined, automated testing that cuts time-to-market and resources.