Artificial intelligence (AI) is transforming the aviation industry in various ways, enhancing passenger experience and improving operational efficiency.
Computer power is growing, algorithms and AI models are becoming more sophisticated, and, who knows, experts think – most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI — data. Billions of gigabytes every day are collected by networked devices ranging from web browsers to turbine sensors. These are driving AI adoption.
Recently, the Economist stated that the true winner of the AI race may not be the country that invents the best models. It is more likely to be the country where governments, businesses and ordinary people use AI at scale every day. For everything from economic growth to military power, technological diffusion ultimately matters more than technological innovation.
Mckinsey, the global strategy firm, stated that organisations are starting to make organisational changes designed to generate future value from gen AI, and large companies are leading the way.
Organisations seeking to do well in AI must not only be very good at data acquisition, but must also be very good at spotting automation opportunities. So, for a company to become good at AI means, architecting for a company to do the things that AI makes it possible to do really well.
Now, for a company to become good at AI requires a process. It points to the fact that becoming good at AI is not a mysterious magical process. Instead, there is a systematic process through which many companies, almost any big company can become good at AI. This is also true for the stakeholders in aviation.
Furthermore, there is expert opinion on a five step process of engaging in AI and doing well at it. The first is to take and execute pilot projects with the aim of gaining momentum. The second is to build an in-house AI team. This team will be helpful in strategic data acquisition for AI. They should also have their roles well defined for them within the team. Another is to provide broad AI training. In the spirit of collaborative decision making, it is important that all in the airport community understand the value to be created in adopting AI for processes. AI should not just be engaged in without a roadmap. An AI strategy should be adopted with the understanding that AI is an enabler of business rather than a cost centre. And, communication is vital. All members of the airport community should be carried along.
Developing an AI strategy for aviation requires several steps. Determine which areas of aviation to focus on, such as safety, efficiency, or customer experience. Establish specific, measurable, achievable, relevant, and time-bound (SMART) goals for AI adoption.
An analysis of the current situation is also important. Assess existing technology infrastructure, including hardware, software, and data management systems. Determine areas where AI can improve processes, reduce costs, or enhance decision-making.
In developing an AI use case, it is important to explore various AI applications, such as predictive maintenance, flight route optimisation, or customer service chatbots. Select the most promising use cases based on feasibility, impact, and alignment with goals.
In building a data strategy, gather relevant data from various sources, including sensors, logs, and customer feedback. Implement data validation, cleaning, and normalisation processes to ensure high-quality data.
In selecting AI technology, choose suitable AI platforms, frameworks, and tools for developing and deploying AI models. Ensure selected technologies can scale with growing demands and meet security requirements.
In developing and deploying AI models, develop and train AI models using selected technologies and data. Evaluate model performance, accuracy, and reliability before deployment.
It is vital to track Key Performance Indicators (KPIs) by monitoring metrics, such as accuracy, efficiency, and customer satisfaction. Also continuously improve by refining AI models and strategies based on insights gained from performance data.
Regulatory and ethical considerations should not be overlooked. Adhere to relevant regulations, such as those related to data protection and AI safety. Consider potential biases, transparency, and accountability issues related to AI adoption.
AI adoption involves a change management process. So organisations seeking to do the same must foster a culture of AI adoption. Provide training and resources to help employees understand and work with AI technologies. Foster a culture of collaboration between AI developers, domain experts, and stakeholders.
AI strategy should not be static. Assess the effectiveness of AI strategies and identify areas for improvement. Adjust AI strategies to reflect changing business needs, technological advancements, and emerging trends.
While there are challenges to consider, such as data security and infrastructure compatibility, the future of AI in aviation is promising. As the industry continues to evolve, AI is expected to play a pivotal role in transforming aviation operations and maximizing efficiency.
business a.m. commits to publishing a diversity of views, opinions and comments. It, therefore, welcomes your reaction to this and any of our articles via email: comment@businessamlive.com
