With such a strong decision-making ability, artificial intelligence (AI) has the incredible potential to reshape the world economy we know today.
The global profit from the AI market is expected to reach almost $100 billion by 2023. Being a powerful tool supporting humanity in analyzing zettabytes of data, it then returns with effective solutions and may act independently in situations that —even for software engineers — are hard to foresee.
However, amid its continuously evolving nature and the need for ensuring ongoing management, organizations still confront challenges when implementing AI.
On top of that, being strongly influenced by the pandemic, companies have to deliver software at the highest pace ever. Some of them seek salvation in introducing innovations but are not always ready for that.
Let's talk more about AI value for business and its future, the criteria for the technology introduction and the role of QA supervision throughout the process.
Creating Cognitive Capabilities For Business Today
To assist companies in attaining their mission-critical objectives, I suggest focusing on three major scenarios of AI utilization we already witness being effective:
• Business processes automation, ranking first on the list, is often performed through robotic process automation (RPA). Relatively easy to be implemented, it acts in a human-being manner receiving data from diverse systems. For instance, companies apply RPA-enabled and AI-enabled smart automation to handle hundreds of emails, classify them based on content, process them, manage replies and more with no human action needed.
• Detailed big data analysis predicts things like customer behavior or can help detect financial fraud. Machine learning — evolving so briskly — is able to recognize images and speech. It advances analytics capabilities and helps find matches in databases such as information related to the same companies but presented in various formats.
• Interaction with clients or staff through chatbots or intelligent agents enables round-the-clock customer support, the emergence of product recommendation services that ensure high personalization for potential buyers and much more.
When combining human and machine work, companies may embrace such benefits as an accelerated working process, increased employee effectiveness and creativity as well as growing customer satisfaction.
What To Expect From AI's Ongoing Development
According to McKinsey, 44% of businesses said costs were reduced after introducing AI, and the list of obtained benefits is far from over. It's obvious that organizations will continue with the technology in the upcoming years. There are several valuable directions to highlight:
• Artificial intelligence of things (AIoT). Imagine your internet-connected devices learning from gathered data, like a smart office solution detecting present employees and handling heating to follow an energy-saving mode.
• Smart manufacturing. AI in processing analytics may provide manufacturers with intelligent decision-making, which will prevent downtimes once already caused by the pandemic.
• Computer vision. To help companies overcome workplace disruption, their representatives may use computer vision to spot any process discrepancies, breaches in safety regulations and more.
Thus, the spread of AI and introducing it wisely may assist C-levels in incorporating risks in the long run, boosting productivity and reducing the chances of human errors.
Considering Criteria For Implementing AI
With AI gaining momentum across multiple industries, its efficient use largely depends on businesses' necessity and opportunity to introduce innovation.
When trying to understand whether it's a fit-for-purpose improvement, two criteria matter ― the nature of tasks and the cost of an error.
First, automation is not always the best match for tasks that demand compromising, setting priorities or emotion-based decision-making. However, if your company collects, stores and processes big data, AI may become your first choice; handling huge data volumes at a high pace can streamline a business model.
Second, human intelligence is the only way out (at least today) when it comes to making strategic choices like planning further guides for business development, as even a single mistake may lead to decreasing revenues or brand image deterioration.
Utilizing QA Support For Smart AI Introduction
Let's say your organization is an ideal candidate for putting innovation in place. What should a QA team bear in mind to verify the robustness of such a solution?
I advise considering five vital steps:
1. Focus on requirements. Their high variability and evolving nature make it hard to identify them completely. Therefore, the engineers must closely interact with product owners, business consultants and data scientists to obtain the requirements with the needed level of actuality and detailing.
2. Give due attention to test cases. The QA specialists should continuously work on designing new test cases and updating the test model. Due to the ever-changing AI solution, it's hardly possible to capture all probabilities.
3. Carefully determine test datasets. Data inside of a neural network is divided into three sectors. A training set ― the kit of input images of, for instance, printed circuit boards in which your neural network should identify defects, thus learning. A development set ― a new set for tuning your network depending on how well it performs on this set. Ideally, the network should operate equally well on both of them. And a test set ― a never-applied data to check the final algorithm. This set must contain data as close to the production as possible, which the QA squad should monitor.
4. Don't bail on cybersecurity testing. Breaches in security may trigger cases when AI-based systems used, for instance, in the BFSI industry become vulnerable to cyberattacks and may reveal sensitive financial data.
5. Leverage test automation benefits. As the AI system is trained consistently, considerable test automation effort is required to include emerging test cases, thus boosting test coverage, curtailing testing time and scaling down QA costs.
Conclusion
In March 2018, a self-driving Uber car was in an automobile accident that took the life of a pedestrian in Arizona, and software failure was a major factor in the accident. Today, we see that implementing AI is still at the pilot stage. To propel to its confident day-to-day usage, one may pay due attention to ensuring solution reliability and security amid the context of continuously growing and changing data.
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