If you’re one of the lucky few who can honestly say, “I just never seem to have enough work to do. I really wish I had more,” I envy you because you’re probably the type of person who doesn’t need to implement new technologies, such as AI and machine learning, to be more productive.
But if you’re one of those people who says, “I'm swamped. I can’t ever get through my to-do list,” you’d likely benefit from implementing a virtual assistant that uses AI and machine learning to completes tasks for you. A virtual assistant who’ll stick with you, get smarter, and learn how you do what you do in an intelligent way.
AI helps you work smarter
Your virtual assistant can complete a lot of the routine tasks you don’t like to do, including verifying information, sending text messages, making phone calls – tasks that get boring for us. AI can sift through large data sets to uncover insights that we can’t. It brings back information so that we can analyze it and use it to make intelligent decisions.
For example, maybe part of your job includes verifying contract data or information inside a long document. Or maybe you have to compare revisions of a certain document to the original to check for errors. The problem is that sometimes we can look at a document or an email a dozen times and not spot any errors. But the first time another person looks at it, he or she will spot a glaring spelling mistake.
AI or your virtual research assistant will spot spelling errors, punctuation mistakes – all these types of things because it isn’t susceptible to the sort of mental fatigue that plagues us.
AI technology is beneficial because it helps us work smarter. But even though we understand the benefits of new technology, we’re often afraid of it and because of that we don’t embrace it. So, rather than even dipping our toes into the new tech landscape, we just back off and ignore it – at least until it becomes too big to ignore.
Using AI for performance testing
Another really good area where you can use AI is conducting performance testing quickly. Performance testing gets missed a lot until the very end of a technology project because it takes so much time.
Part of the focus of performance testing is checking the speed of a software program. With AI you can automate UI tests in the beginning, instead of the end, of the development process. AI is cheaper, faster, and more reliable. It can go in and do a performance timing analysis and, for example, note how long it takes for the user to log in.
Despite AI’s benefits, the biggest leap is starting to leverage it. Once your company begins using it, you’ll probably want to implement it everywhere. At PinkLion AI, we offer AI-powered software tools that help testers, developers, DevOps engineers, and product managers release remarkable apps faster than ever.
Once you have AI in place, you can scale your software testing like never before. As time goes on, the value of AI increases, enabling your team to spend more time focusing on overall quality rather than redundant tasks that are better suited for machines.
When you have robots running on your system, they’re training and learning day over day, run over run. They’re getting smarter and smarter each time. The sooner you take that first step and put the bots in, and then start figuring out where to use them, the farther ahead of your competition you’ll be. If you put your bots in three years before your competitors, you’ll be leaps and bounds ahead of them because they’ll never be able to catch up.
Gaining a competitive advantage with AI
AI is a great technology to create a competitive advantage. That’s because it generates a lot of information, and research, and data. What we’re seeing in the marketplace right now is that the companies that leverage the data and understand how to mine that data are the winners.
The issue, though, is how do companies wade through the hype and determine which AI tool is best for them because it’s very confusing. One of the reasons for that is that there aren’t any standards. Vendors entering the market are just trying to gain market share and it’s not clear whether their technologies use real artificial intelligence and machine learning. And potential customers are getting turned off or they’re afraid because they’re buying and using technologies that don’t really fit their needs.
Companies that want to implement AI should first figure where to start, which entails understanding where your pain and your problems are and then determining if AI is a good fit. Don’t just buy it and put everywhere.
Right now, anyone in a leadership position in a technology organization should have a strategy around this and they should be working with people on the business side to develop a strategy.
The fact is that business owners are looking at AI to help them on the business side. And if you don’t have a strategy on the technical side, the business people will likely impose their strategy on you, which may not be the right fit. That’s why the technology teams need to be driving an AI strategy that verifies, validates, tests, and creates the velocity they need to produce quality software.
You should work together to develop a comprehensive strategy that meets the business needs and the technology needs. What we’re seeing is that some of the tech that just became available on the technology side is netting out some key learnings and quantifiable information that can help drive the business to make better product decisions.
PinkLion AI can help you find the AI and machine learning tools that are right for your organization. At PinkLion AI, we look at all the tools in the marketplace and evaluate them against each other in an agnostic way to determine what works and what doesn’t to help companies make the best buying decisions.