Hugo Farinha, CTO and Co-Founder at Virtuoso QA.
Implementing a compelling use case for AI adoption takes up a lot of time and brain space for CTOs and CIOs. The pace, scale and speed of the cloud empowers users, but creates its own battle as technology moves faster than organizations can adapt their practices. This is where artificial intelligence comes into its own. Development is faster and easier, and AI helps it get there and stay there.
Knowing where to deploy people and what tools and platforms to implement to help them keep up with the pace of cloud transformations can feel like a blur of options with no solid foundations to make a decision possible.
Having worked in QA testing with AI at our core since 2017, we’ve seen every permutation of AI integration and know what works — and what can lead to an expensive mistake. Contrary to popular belief, artificial intelligence did not suddenly appear in the last couple of years. Machine learning and predictive analytics have evolved over many years, and we’ve been there since the beginning.
If you need a starting point, here are some tips and questions to ask your technology partners:
1. Create a joint mission.
When you outsource the assessment of your AI platform and partners, lay the groundwork. Be clear about the goals of the joint mission. Otherwise, you risk focusing the research on one element and ignoring the more difficult aspects. If everyone understands the full integration strategy, goals and parameters, you can be more confident about the options presented to you.
This also means having a strategic AI roadmap to align your AI initiatives with long-term business goals. Before choosing platforms or tools, determine how AI can help achieve specific goals such as reducing costs, improving team skills, or accelerating development cycles.
2. Ask your provider, “What doesn’t your product do?”
A good provider will be open and confident in telling you what their product will not do. Beware of the “all things to all men” approach. For example, if you have a legacy platform and it works for you, we recommend keeping it. Don’t talk yourself into a more radical overhaul than you need.
3. Stay sensible about the human element.
Have an open-minded approach to the culture of change that comes with AI in terms of job roles. People will feel a certain element of fear about the impact it will have on job roles, and while those roles will undoubtedly change, they won’t necessarily be replaced.
In testing, for example, manual controllers could be replaced for front-end testing, but engineers would still have to do the back-end and interpret the data revealed by the AI. No matter how far artificial intelligence goes, humans will always be involved. AI is not an oracle. It doesn’t know the workflow. It can help with lead times, efficiency and increased productivity, but this is a result of human planning and management. that’s where you get the overall performance.
4. Ask your provider for AI insights on product decisions.
When considering launching a new product or feature, it’s important to harness the power of artificial intelligence to gather valuable insights. Ask your provider, “We’re considering releasing X. What information could AI provide to help us make a well-informed decision?”
AI’s ability to generate detailed reports and in-depth analytics can be a game changer for decision makers. It will highlight potential gaps in your strategy, identify risks and provide actionable data that allows your team to make more informed and confident decisions.
5. Define clear metrics to evaluate AI success.
It’s important to know how to measure the success of your AI implementation to understand your ROI and forecast accuracy. Understanding what you’re measuring and why gives organizations concrete ways to evaluate AI efforts in terms of operational performance, cost reduction, or better customer insights. Continuously monitor and improve your AI initiatives based on these metrics.
6. Ask AI to ask AI.
This is no playground. If you lack this business understanding of your end goal in an AI project, then you are wasting your investment in time and business value.
Use productive AI platforms like ChatGPT as an assistant. Ask him to help you understand the process and clarify your thoughts. Treat your use of AI as an exploration, not an endpoint. Think about where it adds value and deploy it in the most efficient way possible. a legacy system can do very well with what AI can struggle with. No one tool will do everything your business needs, including AI, so don’t be afraid of exceptions.
You don’t buy a car.
This is not a purchase you make and then move on from. Don’t treat it as “Let’s go shopping today!” decision, but more “Let’s start looking at the market and doing research and evaluations.” By the end of the process, you may find yourself with several options: do nothing, improve your current solution, switch to a new one, or realize that the product you need doesn’t exist yet and wait for future technological developments. However, in most cases, “doing nothing” will not be a realistic option.
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