Although a thriving technology with huge potential, AI (artificial intelligence) is also seen as a threat due to the challenges it poses in many top industries. Sophisticated companies in healthcare, aerospace, FinTech, and autonomous vehicles are struggling with AI adoption. To tackle concerns related to customer safety, data security, and data privacy, regulatory structures are trying to understand that new rules can easily make or break the AI strategy of a company with massive transformative potential.
Powered by systems that are response-based, the value of artificial intelligence will most likely come from human-to-machine augmentation. For the technology to become and remain relevant, industry consortiums, governments, and other official representatives must unite to settle on a unified rulebook. In the meantime, let’s explore the level of credibility AI has in healthcare, aerospace, automotive, and FinTech.
Credibility of AI in aerospace – machines and humans must align
Driven by two main directions – explainability and safety – the trustworthiness of artificial intelligence in aerospace is hindered by the EU’s Aviation Safety Agency. Numerous obstacles linked to safety and mitigation, learning assurance, and the development of a framework to sustain AI adoption have compelled businesses to pull away.
To increase credibility, AI must find strategies to improve technical robustness and accountability, but also environmental wellbeing and data governance. From a business perspective, processes done by machines versus processes done by humans must align in areas like technology stacks, implementation road maps, and security. Last but not least, artificial intelligence must find ways to become more affordable. Standardized and predictable elements are the key to worldwide adoption.
Credibility of AI in FinTech – collaboration, the main driver of AI adoption
FinTechs were among the first to adopt AI. From the very beginning, the companies in the sector embraced the digital by implementing AI in various parts of the value chain, including in data processing and additional rule-based processes. Since privacy is critical nowadays, FinTech companies are adapting their policies and encryption layers using AI technology.
AI-based platforms play a key part in preventing fraud related to loyalty programs and credit data. With commercial, banking, and trading units all dealing with customer attributes, the transition to AI has to be smooth; it must include a detailed roadmap with different systems, from cognitive all the way to self-resilient and self-managed systems.
Balancing ethical awareness, governance, and honest decision-making processes must be the backbone of all initiatives in FinTech. The more companies collaborate, the better chances they will have to drive AI adoption.
Credibility of AI in automotive – acceleration is driven by data analytics
Commonly used in safety testing, compliance, platform services, and communication, it’s safe to say that AI in the automotive industry has matured. As vision and control systems are increasing in popularity, adoption is constantly being accelerated by data analytics and encrypted batch streaming.
Furthermore, intelligent analytics and cloud-based API automation strengthens AI’s viability in automotive.
However, for the technology to go mainstream, the key is to make sure that the AI-powered systems can navigate different environments and scenarios without failure. To make it happen, the data must flow without errors, which can only be done by constantly verifying and testing the data at hand.
Credibility of AI in healthcare – universal regulations, the key to speeding adoption
AI in the healthcare industry happens at a platform and product level. In general, the technology is widely used to authenticate data, analyze historical records, and accelerate decision systems in real time. As far as adoption is concerned, prescriptive, descriptive, and predictive AI is currently going full speed ahead across the entire value chain.
For the general public to accept what AI has to offer, the technology will have to accept that privacy, data safety & integrity, consent, and algorithmic accountability are all fundamental to speeding adoption. In the absence of national regulations of AI in healthcare, practitioners, researchers, task forces, and councils must work together to draft proper guidelines.
In conclusion, there’s no doubt that artificial intelligence is a prominent technology. Its biggest benefit is that it has the tools and the systems to revolutionize Industry 4.0. To succeed, AI companies and other partners involved must collaborate with professionals and regulators to develop best practices and universal standards to speed its adoption and ensure sustainable growth.
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