White Papers

Decision Making Framework to Identify Successful AI Investments for Airports – Part 2

Part 2 Capturing Value: AI Investment Strategy

Leveraging AI for Competitive Advantage: The CEO’s Forward

I highly recommend this paper to anyone interested in using AI to enhance the capabilities and competitiveness of their airport. – Rishi Mehta CEO, WAISL Ltd.

As the aviation industry continues to evolve and adapt to new technologies, the adoption of AI is becoming increasingly important for airports to stay competitive and meet the changing needs of their customers. This paper provides a valuable resource for airport operators looking to implement AI in their operations, with a comprehensive framework and business strategies to guide their decision-making process.

AI should not be viewed as a standalone technology or tool, but rather as a means to achieve specific business objectives. By incorporating AI into a broader business strategy, airports can more effectively leverage the technology to enhance passenger experience, improve operations, and gain a competitive advantage. This approach involves aligning AI initiatives with overall business goals. Additionally, it is important to consider the ethical implications of AI and ensure that any implementation adheres to relevant regulations and guidelines.

Whether you are looking to test out various AI solutions in a real-world environment, invest in the development and implementation of AI with the expectation of long-term returns, carefully assess the potential benefits and drawbacks of AI technology, or experiment with AI solutions in a limited capacity before scaling up, this paper offers valuable insights and guidance to help you make informed decisions about AI investments.

By following the recommendations outlined in this paper, airport operators can not only improve their operations and create value for their organisation, but also navigate the ethical considerations and potential risks associated with AI adoption. I highly recommend this paper to anyone interested in using AI to enhance the capabilities and competitiveness of their airport.

Rachna has a B.Tech in Computer Science and Engineering from Jawaharlal Nehru Technological University, Kakinada. She began her career in the Enterprise IT department of a large conglomerate in the aviation sector and in a short time got an opportunity to work for the chairman of the group. Currently, she is part of the CEO’s office at WAISL and has been covering artificial intelligence and its impact on the airport ecosystem. Rachna Bojanki

Kumar has a B.Tech from IIT Madras and MBA from IIM Calcutta. He heads the Beta / POC team at WAISL and has extensive experience in fortune 500 companies like Deloitte, Cognizant, and Tesco. He has built start-ups in analytics, ecommerce, and remittances space. He has advised start-ups in artificial intelligence and is among the top 100 individuals/teams selected for the 5G hackathon. As a presenter at India Mobile Congress 2022, he presented the vision for AI, AR and 6G.

Deepankar has a Bachelor’s in Design from National Institute of Fashion Technology, Mumbai. He has extensive experience as a visual designer and has managed pan India creative requirements for both digital and offline media. He is a senior UI/UX designer at WAISL and has delivered several products including an e-boarding solution for airports, a corporate website, and other emerging technological solutions.

Executive Summary

Artificial intelligence (AI) can provide numerous benefits to airports, including advanced services and capabilities that improve the value and competitiveness of the airport. To capture value, it is important for airport operators to make informed decisions about investing in AI solutions and ensuring that their AI strategy aligns with their corporate strategy. This paper presents a framework for implementing AI in airports, which includes considering the strategic fit between the corporate and AI strategies, the potential for generating new revenue streams, the probable impact on employees and customers and improving operational efficiency, and the need for effective communication and collaboration among stakeholders. The framework also suggests evaluating the costs and benefits of AI investments and considering their potential to create value for the organisation, as well as the potential risks and ethical implications of using AI. Four business strategies for implementing AI in airports are discussed. They are:

Try and buy: This involves testing out various AI solutions in real-world environments before making a commitment to purchase, allowing customers to see which solutions work best for their specific needs and requirements. This model is useful when adoption of enhanced AI is focused on aeronautical customers.
Invest and harvest: This involves investing in the development and implementation of AI solutions with the expectation of realising significant returns over time through valuation gains, cost savings, improved customer experience, and enhanced efficiency. This model is handy when adoption of emerging AI technologies is focused on non-aeronautical customers.
Evaluate and decide: This involves carefully assessing the potential benefits and drawbacks of implementing AI technology in an airport and making informed decisions about which AI products and services to invest in. This model is suitable for airports that have a clear understanding of their needs and goals and are looking to invest in AI solutions that align with their corporate strategy. This model is useful when adoption of enhanced AI is focused on non-aeronautical customers.
Experiment and confirm: This involves ardent testing of AI solutions in a limited capacity before scaling up the implementation based on the results of the experiment. This model is particularly relevant for airports that are unsure about the feasibility or potential benefits of implementing AI solutions. This model is effective for the adoption of emerging AI technologies that are focused on aeronautical customers.

By following this framework, airport operators can ensure that their AI investments are successful and contribute to sustainable competitive advantage.

Summary of Part 1: Creating value

The framework aims to guide airport operators in making decisions about investing in AI solutions. The airport industry is a complex and multi-stakeholder environment, and AI has played a crucial role in its digital transformation. However, with the growing number of AI products and solutions available, it is important for airport management to carefully evaluate which ones will create value and be economically viable to deploy.

The framework identifies critical success factors that will ensure value creation through AI-powered digital transformation. These factors include the alignment of AI investments with the airport’s strategic goals, the ability of the AI solution to improve operational efficiency, and the potential for the solution to generate new revenue streams. The framework also emphasises the importance of considering the impact of AI on the airport’s employees and customers, as well as the need for effective communication and collaboration among all stakeholders.

Overall, this framework provides a comprehensive approach for airport operators to evaluate potential AI investments and make informed decisions about which solutions to implement. By following this framework, airport operators can ensure that their AI investments are successful and contribute to the creation of value for their business.

Part 2: Capturing value

“The essence of strategy is choosing to perform activities differently than rivals do,”
– Michael Porter, Harvard Business Review.

If an airport wants to be better than other airports, it must do things that are different from other airports. Using AI can help the airport make more money, but it’s not enough by itself. The entities that use the airport, like airlines and retailers, will get better because of the things that AI helps the airport do, but the airport might not make more money. For an airport to do well, it needs to make changes that are good for the people who use it and make sure they get some of the benefits from those changes.

To be able to compete with other airports that use AI, an airport needs to make sure it gets its fair share of the benefits from using AI. Just doing things that are different from other airports isn’t enough to make sure the airport stays successful. Other airports can copy what the first airport does and offer the same things. To be successful, an airport must make the right choices about when and where to use AI.

To make the right choices about using AI, an airport needs to think carefully about what it wants the AI to do. If the airport uses too much AI, it might not be worth the cost. But if the airport uses too little AI, it might not be helpful. To make the right choices, the airport needs to think about how much money it’s willing to spend and how helpful the AI needs to be.

The concept of fit among functions is a fundamental idea in industrial economics. This is essential because isolated activities often result in a loss of value. To capture value, the most important type of fit is strategic fit, which enhances uniqueness and amplifies trade-offs. Strategic fit occurs at three levels: consistency among activities, reinforcing behaviour among activities, and optimisation of effort. In these three levels, the whole is more important than the individual parts. A competitive advantage is derived from the entire system of activities. The fit reduces costs and increases benefits. A fit between the corporate strategy and AI strategy is imperative for capturing value.

In this second part of this paper, we talk about how an airport can use a special set of activities to make sure it uses AI in a way that helps the airport’s overall plan. By using this plan, the airport can make a lot more money from using AI. This part of the whitepaper explains how to do this and why it’s important.

Corporate Strategy

An airport’s long-term plan is called its corporate strategy. The plan tells the airport what it wants to do and how it’s going to do it. The corporate strategy should be based on the airport’s vision and mission, as well as what’s happening in the world around the airport. It should also be based on the airport’s business model and what the airport offers to people who use it. Some important parts of a corporate strategy for an airport could include the airport’s vision and mission, the things happening outside the airport that could affect it, the airport’s strengths and weaknesses, and the airport’s business model.

The goal of a company is to make as much money as possible for the people who own it. This is called shareholder value. Shareholder value goes up when a company makes more money than it spends. This is called earning a return on invested capital. If a company can make more money than it spends, it will be worth more and the people who own it will be happy. Put more simply, value is created for shareholders when the business increases profits.

To maximise shareholder value, companies can use two main strategies: increasing their operating margin and increasing revenue. Operating margin is a measure of how much profit a company makes on each rupee of sales after paying for the things it needs to make its products, like wages and raw materials. It is calculated by dividing the company’s operating income by its net sales. A higher ratio is better because it shows the company is good at turning sales into profits.

EBIT is another way to measure a company’s profitability. It stands for earnings before interest and taxes, and it shows how much money a company makes from its operations. By ignoring taxes and interest, EBIT focuses only on how well a company can make money from what it does. It is useful because it helps us see if a company is making enough money to be profitable, pay its debts, and keep running.

EBIT is based on two things: revenue and gross margin. Revenue is the total amount of money a company makes from selling its products. Gross margin is the amount of money a company keeps after it pays for the things it needs to make its products.

Pricing is how a company decides how much to charge for its products. The company will consider a lot of things when it sets its prices, like how much it costs to make the products, what other companies are charging, and what people are willing to pay.

Market segmentation is a technique used in economics and marketing to divide a market into smaller groups of customers with similar needs or characteristics. Each of these smaller groups, called market segments, has its own distinct characteristics and may respond differently to different marketing efforts. By dividing the market into segments, businesses can tailor their products and marketing strategies to better meet the specific needs and preferences of each segment, resulting in more effective and efficient marketing efforts. Having the right segment mix is critical to the success of a business.

For airports, maximising shareholder value involves implementing effective business models that generate profit and return on invested capital. A business model is a framework that describes how a company operates and generates revenue. The business model of an airport operator, for example, outlines the key activities, resources, and partners involved in the airport’s operations, as well as the revenue streams and cost structure. By understanding and effectively implementing a business model, an airport operator can improve its profitability and return on invested capital. This can provide numerous benefits, including increased efficiency, cost savings, and enhanced customer experiences.

Airport Business Models

Business models are the ways in which companies capture value. Osterwalder and Pigneur (2011) created a canvas structure with nine blocks that characterise the company. The blocks are the following: value propositions, customer segments, key activities, key resources, cost structure, revenue streams, channels, customer relationships and key partners.

The central block – the value proposition – describes the products and services that create customer value. The value created by the company solves a problem or suppresses an existing need and can be qualitative or quantitative.

On the right side of the central block, one can observe the customer segments that define the different groups of people or organisations that a company reaches and serves, the channels that describe how the company connects customers to give them value, the customer relationships that describe the relations that a company establishes with the customer segments and the revenue streams that describe how the company generates revenue from the customer segments.

On the left side of the central block, the key resources held by the company can be seen, needed to create the value proposition, as well as the key activities that describe the essential tasks that must be performed to produce and offer the value proposition, the key partners that define the strategic partners that can a company a competitive advantage in the market and finally, the cost structure that describes the cost of operations involved in creating and delivering the value proposition (Osterwalder & Pigneur, 2011).

Struyf (2012) studied the theory of business models and their application at airports. In his work, he describes how to design a business model in airports and the relationship between the elements of a model.

The identification of variables that characterise the business model of the airports was based on the elements of the airport industry that characterise each of the blocks of the proposed business model and for the large airports, the business model is depicted here.

In order to incorporate the essence of corporate strategy into a deceptively simple and powerful framework, we must develop a profound understanding of the airport revenue structure, which is key to the business model.

Airport revenue structure

Airports typically operate as public or private entities and generate revenue through a variety of business models, including aeronautical and non-aeronautical sources. Aeronautical sources of revenue include landing fees, passenger fees, and cargo fees, while non-aeronautical sources of revenue include rent, concessions, and advertising.

One common business model for airports is the dual till model, which separates aeronautical and non-aeronautical revenue streams and uses them to fund different types of expenses. Under this model, aeronautical revenue is used to fund essential infrastructure and services, such as runways, terminals, and security, while non-aeronautical revenue is used to fund additional services and amenities, such as retail, dining, and entertainment.

Another business model for airports is the single till model, which combines aeronautical and non-aeronautical revenue streams and uses them to fund all types of expenses. Under this model, the airport uses a mix of aeronautical and non-aeronautical revenue to fund essential infrastructure and services, as well as additional services and amenities.

In addition to these traditional business models, airports are increasingly adopting new business models that are based on innovative technologies and services, such as AI, IoT, and data analytics.

These new business models enable airports to improve efficiency, reduce costs, and enhance the customer experience, and can generate additional revenue through new services and partnerships.

Aeronautical revenues are related to airline, passenger, and freight processes. We are aware that an airline is a business offering air transportation services for people or cargo, and forms one customer segment of the wider aviation industry. Every commercial flight begins and ends at an airport, and airlines pay to land there, park their planes at a gate, sort baggage, etc. Having a diversity of airlines and nonstop destinations served is a goal of every airport. To do this, there must be the ability to invite, and have access for, new airport entrants.

Airports thus spend marketing budgets to attract new airline services, and every airline gets pitched by airports as to why their facility should be the next added to the airline’s route map. If an airline can operate around an airport for less, they can likely charge lower fares to that airport which in turn creates more people traveling.

Non-aeronautical revenues comprise commercial revenues from sources such as land lease, duty free, retail, parking fees, and other commercial activities. To give a brief background, airports realised in the late 1970s the potential to be more than just travel centres. They became central hubs for entertainment, retail, and services. They decided to capitalise on the fact that their potential customer has nowhere to go pre-flight except their stores or restaurants. About two decades ago, retailers did not seem too enthused about the prospect of setting up shops on the airport premises and had to be heavily coaxed and incentivised. But things have changed drastically since. More and more brands are turning towards airports as an opportune location to engage their customers, because they have access to a highly segmented audience. The retail stores offer a wide variety of products ranging from jewellery to clothing, footwear to electronics, sportswear to cosmetics, and designer goods. Spas, lounges, massage services, immigration services, food and beverage services to airlines and restaurants also command a sizeable chunk of airport spending. As people settle in for a lengthy wait, they take advantage of these indulgent opportunities.

It must be emphasised that although airports segregate revenue as aeronautical and non-aeronautical, passengers are the king. Once a passenger is through the security gauntlet, managing passenger time is often an airport’s biggest challenge. How do you make your portion of their travel experience enjoyable, so that passengers continue to book flights from their airport? Maslow’s Hierarchy of Needs theory suggests that addressing basic physiological and safety needs is essential for influencing people’s behaviour. This is relevant for the airport passenger experience because creating a pleasant atmosphere and reducing time pressure can motivate passengers to make purchases and continue using the airport. The Pareto Principle, which states that roughly 80% of a company’s profits come from its top 20% of customers, can be applied to airports as well. While it may be tempting for airport operators to focus on their most profitable customers, it’s important to also consider the needs of all customer segments in order to provide the best possible experience for everyone.

AI strategy spectrum

Companies that derive value from AI view it as an integral part of their corporate strategy. An AI strategy is essential for organisations that want to take advantage of the many benefits that this technology offers, while also ensuring that it is used in a responsible and ethical manner. By carefully considering the potential uses and impacts of AI, organisations can develop a clear and effective plan for harnessing its power to drive growth and innovation. When the AI strategy resonates with that of the corporate / business strategy, value is delivered to the shareholders. To identify the most suitable AI investment strategy, one must have a thorough understanding of the AI spectrum.

Essential AI

Essential AI for airports could refer to several different applications of AI technology in the context of airports. Here are a few examples:

AI-powered security systems that use machine learning algorithms to detect potential threats and suspicious behaviour
AI-powered passenger check-in and baggage handling systems that can process large amounts of data quickly and efficiently
AI-powered flight scheduling and optimisation systems that can help airport managers and airlines coordinate flights and make real-time adjustments to schedules
AI-powered customer service and assistance systems that can help passengers navigate airports and find the information they need.

Essential AI combines the power of big data, cloud, and data science to automate tasks or processes. This requires constant human intervention and among AI techniques, it is least preferable for aiding decision making. However, given that airports have matured and for a maturity level of Airport 2.0 and above, it is imperative to deploy these AI techniques. It helps human beings in increasing the efficiency of existing processes and helps in increasing productivity. Erstwhile Analytics and data sciences that has become essential to the running of an organisation would be categorised here. Overall, the use of AI in airports can help improve efficiency, security, and the overall passenger experience.

Enhanced AI

Enhanced AI for airports refers to the use of advanced AI technologies that go beyond the capabilities of essential AI in the context of airports. Here are a few examples of enhanced AI applications in this context:

AI-powered predictive maintenance systems that can identify potential issues with airport equipment before they occur, preventing downtime and improving efficiency.
AI-powered traffic management systems that can predict and optimise the flow of aircraft and ground vehicles on airport grounds, reducing congestion and improving safety.
AI-powered weather prediction and management systems that can help airport managers make informed decisions about flight schedules and operations in the face of adverse weather conditions.
AI-powered natural language processing systems that can understand and respond to passenger inquiries in multiple languages, providing personalised assistance and improving the customer experience.

Enhanced AI aids human intelligence. It is a smart collaboration between human beings and machines which makes humans capable of doing things they otherwise couldn’t do. The primal aim of this type of intelligence is to aid human beings in faster and smarter decision making. These platforms have the ability to process huge and complex data sets. This intelligence executes in a five-step process which begins with understanding the input data, interpreting it based on extensive data analysis, outputting new data after reasoning, taking human feedback on the output data and readjusting according to obtained feedback and finally assuring security based on cryptographic algorithms. Overall, enhanced AI can help airports operate more smoothly, efficiently, and safely by providing advanced capabilities for managing and optimising a wide range of operations.

Emerging AI

Emerging AI for airports refers to the use of cutting-edge AI technologies that are still being developed and refined in the context of airports. These technologies have the potential to significantly improve the way airports operate and provide even greater benefits than current AI applications.

Here are a few examples of emerging AI applications in the airport context:

AI-powered predictive analytics systems that can analyse vast amounts of data from multiple sources, including weather, flight schedules, and passenger behaviour, to identify trends and patterns that can help airport managers make better decisions.
AI-powered autonomous vehicles, robots and drones that can handle tasks such as inspection, maintenance, cleaning, and cargo handling, reducing the need for human labour and improving safety.
AI-powered virtual digital assistants in augmented reality (AR) that can talk to passengers, guide them through the airport and help them in augmented or virtual reality.
AI-powered biometric authentication systems that can use advanced algorithms to verify passengers’ identities using facial recognition, fingerprints, or other biometric data, improving security, and reducing wait times.

The most advanced or rather futuristic form of AI is referred to as Emerging AI. In this, processes are automated to the extent where they make machines and bots act independently without any human intervention. This form of AI looks like it’s straight out of a science fiction film and perhaps that’s why it’s not commonplace across organisations. Human beings are not yet prepared to give complete control to machines, bots, or systems as of today. In these cases, humans will have to be given the necessary aid to remain accountable for the decision making. This kind of intelligence will become commonplace as the number of digitally connected devices increases. Soon every household is expected to have at least fifty connected devices through 5G, Wifi7 and other advanced communication technologies. And when IOT, AR, and Metaverse take over, emerging AI will be needed to deliver these technologies in the real world rather than the rectangular screens that we see today.

In summary, essential intelligence involves identifying patterns and applying predetermined solutions to problems. Enhanced intelligence, on the other hand, uses existing data and information to suggest new solutions. While essential intelligence relies on human decision-making, enhanced intelligence is designed to enhance, rather than replace, human intelligence. Many tasks can be reliably completed using enhanced AI, and the next goal is to extend this capability even further with emerging AI technologies.

Decision making framework to achieve strategic fit:

Aligning AI strategy with corporate strategy is essential for ensuring that the organisation can take full advantage of the opportunities that AI offers. By aligning the goals and objectives of the AI strategy with those of the broader organisation, and investing in the necessary resources and capabilities, organisations can position themselves to capitalise on the potential of AI to drive growth and innovation. The AI decision making framework is built with this vision.

Try and buy model

The try and buy model is a business strategy that is suitable for aeronautical customers considering the adoption of enhanced artificial intelligence (AI). This model involves testing out various AI solutions in real-world environments before committing to purchase, allowing customers to determine which solutions best meet their needs. Enhanced AI could be used to provide advanced services and capabilities that enhance the value and competitiveness of the airport, such as traffic management systems, predictive analytics systems, or weather prediction systems. These services and capabilities could benefit aeronautical customers by providing new and innovative experiences that enhance the attractiveness of the airport as a travel or leisure destination and could provide a longer-term return on investment.

By allowing airports to try out various solutions and see how they work in real-world environments, the try and buy model can help airports to make more informed decisions, reduce the risk of investing in AI solutions, and foster partnerships with AI vendors. Under this model, airports can try out AI-powered solutions and see how they work in their specific environments before making a commitment to purchase.

This model offers several benefits for airports. Firstly, it allows airports to test out various AI solutions and see which ones work best for their specific needs and requirements. This can help airports make more informed decisions about which solutions to invest in, rather than making purchases blindly.

Secondly, the try and buy model can help airports to reduce the risk of investing in AI solutions. By trying out a solution before purchasing it, airports can ensure that the solution works as intended and provides the desired benefits. This can help to prevent airports from investing in solutions that turn out to be ineffective or unsuitable for their needs.

Thirdly, the try and buy model can help foster collaboration and partnerships between airports and AI vendors. By allowing vendors to showcase their solutions in a real-world environment, airports can provide valuable feedback and insights that can help vendors to improve their products and services. This can help to build long-term partnerships and create a more vibrant ecosystem of AI solutions for airports.

Invest and harvest model

The invest and harvest model is an effective approach for non-aeronautical customers looking to adopt emerging artificial intelligence (AI) technologies. This model involves investing in the development and implementation of AI solutions, with the expectation of realising significant returns over time through cost savings, improved customer experience, and enhanced efficiency. Emerging AI has the potential to offer a range of innovative services and capabilities to non-aeronautical customers, such as personalised services, predictive modelling, and intelligent automation. These offerings can enhance the value and attractiveness of an airport and provide long-term returns on investment.

While the invest and harvest model may require a high level of upfront investment, the potential returns can be significant. For example, the integration of emerging technologies such as the Internet of Things (IoT), augmented reality (AR), and virtual reality (VR) can enhance the capabilities of AI systems and provide passengers with more informative and engaging experiences. This can lead to increased sales and revenue for airport retailers and service providers, and improved customer satisfaction.

Airports can also benefit from the invest and harvest model by partnering with startups and taking a stake in their companies. By investing in startups that are experimenting with AI in the retail or customer space, and supporting their go-to-market efforts, airports can position themselves to benefit from the returns on investment.

Overall, the invest and harvest model is a smart business strategy for airports when adopting emerging AI technologies offered to non-aeronautical customers. By investing in the development and implementation of AI solutions and carefully harvesting the returns, airports can position themselves for long-term success in a competitive industry.

Evaluate and decide model

The evaluate and decide model is a business strategy that involves carefully evaluating the potential benefits and drawbacks of using artificial intelligence (AI) technology in an airport. This process helps airports make informed decisions about which AI products and services to invest in by considering the potential returns on investment and risks associated with implementing these technologies. This approach is particularly relevant for adopting enhanced AI solutions focused on non-aeronautical customers. By analysing the potential benefits and costs of using AI, airports can determine which technologies will be most beneficial to invest in.

Another important factor to consider is the potential risks associated with implementing AI technology in the airport. These risks can include technical challenges, such as the need for specialised expertise and hardware to support AI systems, as well as broader concerns about the ethical implications of using AI, such as the potential for bias and discrimination. By carefully evaluating these risks, airports can make informed decisions about how to mitigate them and ensure that their implementation of AI technology is responsible and ethical.

Once the potential benefits and risks of implementing AI technology in the airport have been carefully evaluated, the next step is to decide which AI products and services to invest in. This decision should be based on a careful consideration of the potential returns on investment, the potential risks, and the specific needs and priorities of the airport. By making informed and strategic decisions about which AI technologies to invest in, airports can position themselves for success in an increasingly competitive industry.

Overall, the evaluate and decide model is for implementing enhanced AI solutions for non-aeronautical customers. By carefully evaluating the potential benefits and risks of implementing these technologies, and then making thoughtful decisions about which ones to invest in, airports can maximise the potential returns on their investment and position themselves for long-term success.

Experiment & confirm model

The experiment and confirm model is a useful approach for developing artificial intelligence (AI) products and services for airports. This model involves training an AI system on a large dataset, integrating emerging technologies such as the Internet of Things (IoT), augmented reality (AR), and virtual reality (VR), and then testing and refining the system through experimentation. This model is particularly relevant for emerging AI solutions focused on aeronautical customers.

One example of how the experiment and confirm model could be used in an airport is in the development of a system for predicting flight delays. The AI system would be trained on past flight data, including information about departure and arrival times, weather conditions, and other relevant factors. By integrating IoT technologies, the AI system can collect real-time data about the location and status of aircraft, improving the accuracy of delay predictions. In addition, the use of AR and VR can create a digital twin that provides passengers with information about the airport layout and the location of various services and amenities. The AI system can then use this training data to generate predictions about the likelihood of future flights being delayed. These predictions can be tested by comparing them to actual delay times and any discrepancies can be used to refine the model.

Another potential application of the experiment and confirm model in airports is in the development of systems for optimising the allocation of airport resources, such as gates, baggage handling equipment, and personnel. The AI system would be trained on data about past airport operations, including information about passenger numbers, aircraft types, and flight arrival and departure times. By integrating IoT technologies, the AI system can collect real-time data about the availability and utilisation of airport resources and generate recommendations for resource allocation that maximise efficiency and minimise delays. These recommendations can be tested and evaluated through experiments, and the results can be used to confirm or refine the model.

Overall, the experiment and confirm model is an effective approach for developing AI products and services for airports, particularly for emerging AI solutions focused on aeronautical customers. By using this model, airports can invest only when it is ensured that their AI systems are accurately predicting and optimising important processes, leading to improved efficiency, customer satisfaction, and the overall airport experience.

Conclusion

The decision-making framework discussed aims to help airport operators identify successful AI investments. The framework presents a two-dimensional matrix, with the customer spectrum on one axis and the AI spectrum on the other. The customer spectrum ranges from aeronautical customers to non-aeronautical customers, while the AI spectrum ranges from Enhanced AI to Emerging AI.

The try and buy model is recommended for AI solutions that enhance decision-making and are focused on aeronautical customers, while the invest and harvest model is best for emerging AI solutions that benefit non-aeronautical customers. The try and buy model allows airports to test AI solutions before committing to a purchase, while the invest and harvest model involves making significant investments in AI with the expectation of realising returns over time. These models can help airports make more informed decisions about AI investments, reduce the risk of investing in AI solutions, and foster partnerships with AI vendors. By carefully evaluating potential AI solutions and selecting the right model, airports can maximise the value of their investments in AI.

This framework will empower airport operators to make informed decisions about AI investments that will successfully capture value at the airport.

Decision-Making Framework to Identify Successful AI Investments for Airports – Part 1

Part 1 Creating Value: Critical Success Factors for AI Investment Decisions

Navigating the AI landscape: The CEO’s Forward

The adoption of AI is becoming increasingly important for airports to meet the evolving needs and expectations of their customers. –Rishi Mehta CEO, WAISL Ltd.

Artificial intelligence(AI) has the potential to significantly improve the operations and competitiveness of airports, offering numerous benefits to both customers and airport operators. However, with a rapidly growing number of AI products and solutions on the market, it can be challenging for airport operators to identify the most suitable and cost-effective options for their organisation.

This paper presents a comprehensive framework for evaluating AI solutions in the context of airport operations, with a focus on identifying the critical success factors that drive value creation. The airport domain is a complex and multi-stakeholder environment, and this framework aims to guide airport operators in making informed decisions about AI investments that align with their corporate strategy and goals.

By considering the strategic fit between the corporate and AI strategies, the potential for generating new revenue streams, the probable impact on employees and customers, and improving operational efficiency, airport operators can ensure that their AI investments are successful and contribute to sustainable competitive advantage. The framework also addresses the need for effective communication and collaboration among stakeholders, as well as the potential risks and ethical considerations associated with AI adoption.

As the aviation industry continues to embrace technology and digital transformation, the adoption of AI is becoming increasingly important for airports to meet the evolving needs and expectations of their customers. This paper offers valuable insights and guidance for airport operators looking to leverage AI to create value and gain a competitive edge.

Rachna has a B.Tech in Computer Science and Engineering from Jawaharlal Nehru Technological University, Kakinada. She began her career in the Enterprise IT department of a large conglomerate in the aviation sector and in a short time got an opportunity to work for the chairman of the group. Currently, she is part of the CEO’s office in WAISL and has been covering artificial intelligence and its impact on the airport ecosystem. – Rachna Bojanki

Kumar has a B.Tech from IIT Madras and MBA from IIM Calcutta. He heads the Beta / POC team at WAISL and has extensive experience in Fortune 500 companies like Deloitte, Cognizant, and Tesco. He has built start-ups in analytics, e-commerce, and remittances space. He has advised start-ups in artificial intelligence is one of the top 100 individuals/teams selected in the 5G hackathon. As a presenter at India Mobile Congress 2022, he presented the vision for AI, AR, and 6G. –Kumar Subraman

Deepankar has a Bachelor’s in Design from the National Institute of Fashion Technology, Mumbai. He has extensive experience as a visual designer and has managed pan-India creative requirements for both digital and offline media. He is a senior UI/UX designer at WAISL and has delivered several products including e-boarding solutions for airports, corporate websites, and other emerging technological solutions. –Deepankar Kumar

Executive Summary

This paper gives a comprehensive framework that aims to guide airport operators to place their bets on the right Artificial Intelligence (AI) solutions. The airport domain is a complex socio-technical and multi-stakeholder environment. Airport operators struggle to keep up with the escalating expectations of their customers and passengers, who are now accustomed to sophisticated, fast-changing technological environments at every walk of their lives. Passengers have grown to expect painless self-service and instant unfettered access to the latest technological advancements. A walk through the lanes of the digital transformation of airports indicates the presence of an underlying blanket of AI that has enabled this transformation. AI is undoubtedly the present and future of technology solutions in airports. While the number of AI products and solutions is growing exponentially, it might not always be economically viable to deploy all these solutions at the airport. There arises a need for a framework to evaluate these products and solutions in light of the critical success factors that would help the management decide on the right AI investment destinations.

An AI-powered digital greenfield airport is expected to generate an additional 10% EBIT margin. The key question that needs to be addressed to realise this margin is
Which critical success factors are extremely important for AI-powered value creation?

Introduction

Airports are mini cities in themselves, which in the most fascinatingly complex yet efficient way handle multiple stakeholders and a case study around them could be an excellent and exhaustive choice for developing a framework. The importance of the modern aviation industry is difficult to overstate, but one of the main reasons for this is the globalised nature of the industry, helping to connect different continents, countries, and cultures. By design, airports have access to an ecosystem of customers in various segments without having to invest in marketing efforts. The more that airport management can support its customers, the more these customers can contribute towards its revenue.

The digital transformation of the airport industry includes the use of emerging self-service, big data, and open data technologies. The evolution of the digitisation of the airport industry is discussed in terms of their levels: Airport 1.0, 2.0, 3.0, and 4.0.

Airport 1.0 (Basic airport operation) refers to the traditional airport that relies on manual operations and basic IT solutions.

Airport 2.0 (Agile airport) is the early adopter of digital technologies, mainly partial self-service facilities like Wi-Fi and check-in processes. In this phase, one could witness the implementation of self-service and automation of key processing tasks such as passport check, bag drop, etc.

Airport 3.0 (Smart airport) involves the adoption of self-service at all levels of passenger services, including automated operations and mobility. Several focused initiatives to leverage digitalisation and to optimise flow monitoring and processing takes place.

Airport 4.0 (Smart airport) uses open and big data technologies to create value from real-time passenger information flow. The United States, Europe, and leading Asian economies have all started to adopt Airport 4.0. Superior proactivity and reactivity that will enable the airports to adapt to real-time solicitation of the airport operational needs, customer requests, and other technological advancements will be commonplace.

Airports are currently mature to implement 3.0 digital solutions. Across the world, all major airports are looking forward to implementing a 4.0 model by the end of this decade. Digital transformation using the power of artificial intelligence is the key to addressing the current and future customers who have grown to expect seamless access to the latest technological advancements.

AI-powered digitalisation is the key to the success of the businesses of the future. Pertaining to the airports, the revenue increase that would be brought to the table by digitalisation of airports is extremely uncertain. Nevertheless, digital interaction is a must-have!

Analysis by Arthur D. Little shows that AI-powered digital greenfield airport is expected to generate an additional 10% EBIT margin. Key questions that are brought to the table are

Creating Value: What are the critical success factors to achieve AI-powered digital transformation?

Capturing Value: How to strategically preserve most gains for the airport operators while sharing part of the additional profits between the stakeholders?

AI-Powered Digital Transformation

With how pervasive artificial intelligence is these days, leaders of all organisations know that AI can fundamentally change their businesses. Business leaders across organisations are hiring experts in the AI domain who would assist the management in deciding on AI investments. Companies often treat AI as a “technology thing” and hence struggle to deliver value. In fact, an IT focus on AI tends to generate less value than a broad strategic focus.

These AI experts in themselves despite possessing the necessary skills ranging from being acquainted with different programming languages to possessing knowledge about neural network architectures, might still not possess a necessary and crucial skill. Business leaders often think that when AI ideas are given the right kind of push with investments at one end of the tunnel, success comes out from the other end. However, this doesn’t always happen.

The Analytics Maturity Model survey, conducted by Carnegie Mellon University, has shown that only 6% had AI initiatives scaled across the enterprise. And 76% of organisations surveyed barely broke even with their investments in AI capabilities.

Another survey conducted by BCG along with MIT Sloan management review has found that “Significant challenges remain, and many AI initiatives fail. Seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made significant investments in AI. Even so, this means 40% of organisations making significant investments in AI do not report business gains from AI.”

The Gartner Hype Cycle for Artificial Intelligence, 2021 report finds challenges for businesses seeking innovative AI technologies with proven business uses. This hype cycle report addresses how AI applies to static business applications, devices, and tools. This report also focuses on the hype surrounding new AI technology and techniques with various levels of commoditisation and operationalisation to develop systems that reach beyond everyday AI. Early adoption of these innovations can drive significant competitive advantage and business value and ease problems associated with the fragility of AI models. This report will also open the ears of even those organisations that had ignored AI investments and force them to jump into the race.

From this hype cycle, it is evident that AI capabilities needn’t be extremely advanced to deliver business value. However, they should be fit-for-purpose and geared toward the organisation’s specific strategic goals and must be grounded in an honest assessment of the organisational maturity regarding data, capability, governance, and other dimensions.

Genuine success with AI always depends on generating revenue (either in the short term or in the long term) reimagining organisational alignment and investing in the organisation’s ability to use AI across the enterprise. None of this is easy to achieve. By coupling strategic intent with technology, then creating clear plans to develop tangible capabilities, organisations can establish a strong foundation on which to deploy AI. Thus arises a desperate need for a framework such as ours that would guide the combined team of decision-makers. This framework will empower business leaders to be strategic about AI. By adopting this framework, traditional enterprises that are struggling to steer AI investments can now confidently drive them.

“Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the time, money, and effort necessary to achieve resounding success with their proof of concept and demonstrate how the use of artificial intelligence will improve their business. Then everything comes to a screeching halt — the company finds themselves stuck, at a dead end, with their outstanding proof of concept mothballed and their teams frustrated.”
– Harvard Business Review

Critical Success Factors to Achieve AI-Powered Digital Transformation

It is undeniable that artificial intelligence is changing the way we do everything in every industry, from the possibility of self-driving cars to social media algorithms showing you

customised content. From the above section, we understand how instrumental its role has been in the digital transformation of airports. The hope is that machines will eventually replicate human intelligence and function without the need for human input or interference. This space is growing rapidly, and it seems likely that every industry will be impacted by it. Many major companies have invested heavily in AI, and any company that still hasn’t, fears being left behind in this race.

Being successful requires resisting the urge to indulge in what fantastical things AI can do. Instead, organisations/experts need to stay grounded in what practical things AI can help them achieve. Even as AI opens up new avenues for realising economic value, the organisation ought to lead, and technology ought to follow—while still articulating new ideas for what’s possible and constraints on what’s not.

Although the excitement to quickly join this AI revolution is tremendous, it is imperative that the company should be strategic about AI. Artificial Intelligence projects that best fit with its own corporate strategy should be selected for investments. To achieve this, the airport operator must closely analyse all the critical success factors that would drive the AI implementation. The combined impact of these critical success factors will determine the potential success of the company’s AI investments.

The visual framework for AI investment decision-making below illustrates the necessity of critical success factors for creating value and the need for strategic fit to capture value.

This framework is built top-down. An airport operator, making an AI investment decision, ought to begin by analysing their own corporate strategy. There is no single AI strategy that would promise universal fruitfulness. AI projects/programs that would have a superior chance of success should be the ones that must be invested in. The airport operator may choose a Build-Operate-Transfer model for developing AI capability. However, if the airport

operator decides to become AI first, then the strategy, skills, and shared vision must be adopted across the organisation. Being strategic about AI will ensure an increase in the profitability of the company. The five drivers of success namely, customer risk, competitive risk, brand value, value generation capacity, and expected or existing government regulation must be in sync. Building AI capability that strategically fits with the corporate strategy will break the barriers and help in realising the value of AI investments.

Although the framework is built top down, the implementation of the same is bottom up. Addressing the critical success factors will enable value creation. When AI strategy and corporate strategy are resonating then the company will be able to capture value. Using a quick questionnaire demonstrated here, an airport operator could easily evaluate potential projects in the light of the critical success factors.

Consumer’s risk is a potential risk found in all consumer-oriented products, that a product not meeting quality standards will pass undetected through the manufacturer’s quality control system and enter the consumer marketplace. However, when a new technology enters the market, the developer must ascertain the learning curve that the end user might have.

What is the learning curve for the customer to use this technology?
Brand value is the monetary worth of your brand. If a company were to merge or be bought out by another business, and someone wanted to use the name, logo, and brand identity to sell products or services, the brand value would be the amount they would pay you for that right to use.

Will the airport operator be the first to bring this AI technology to the airports globally or locally?

Value generation capacity is one of the most important activities that B2B companies engage in. It’s the process of planning, marketing, and selling products, with the aim of generating income immediately or in the visible future. Alternatively, it could be bringing in goodwill and other kinds of benefits that would deliver a topline or bottom-line growth in the near future.

Does this AI technology have the ability to bring cash/ kind into the airport operator?
Government Regulation has been one of the critical factors that drive investment by the

airports. Assessing the chances of these regulations coming through within the next ten years would enable one to address the investment opportunities effectively.

Will the regulators make this AI product/technology mandatory in this decade?
Competitive risk is the risk associated with the fact that there are often competing companies on the market, each of which seeks to obtain the highest position and consumer ratings in order to gain maximum benefits for themselves. When competitive risk is successfully addressed, one would visibly witness the increase in market share, sales, the degree of penetration of the company into international markets, and so on.

Are our competitors bringing this technology into the market?
The answers to the above questions would be recorded for various projects and the cumulative scores calculated for further shortlisting. It could either be a direct addition or a weighted average based on the business needs.

This cumulative score alone might be sufficient in certain business scenarios. However, it is prudent to incorporate the opinion of the leadership team into this AI investment decision-making. Opinions of the leadership team could be easily captured using simple questions and a Multi Criteria Decision Making (MCDM) algorithm could be used to obtain the weights (as against the equal weightage illustrated above).

The MCDM method deals with the process of making decisions in the presence of multiple criteria or objectives. A decision maker is required to choose among quantifiable or non-quantifiable and multiple criteria. The evaluations on qualitative criteria are often subjective and imprecise. The objectives could also be conflicting and therefore the solution is highly dependent on the preferences of the decision maker. Besides, it is very difficult to develop a selection criterion that can precisely describe the preference for one alternative over another. The evaluation data of subject alternatives suitability for various subjective criteria, and the weights of the criteria are generally expressed in linguistic terms.

When we go through a rigorous analysis of sending each project through the lens of its critical success factors, the likelihood of success of AI-powered digital transformation increases. For companies that want to capture the benefits of AI what truly matters is not the AI technology itself but whether there is a strategic resonance of AI with that of its corporate strategy. And then value can be created and captured by the airport operators.

References

1. “The Dumb Reason Your Al Project Will Fail” by Terence Tse, Mark Esposito, Takaaki Mizuno, and Danny Goh
2. Gartner Hype Cycle for Al 2021
3. Winning With Al, Findings from the 2019 Artificial Intelligence Global Executive Study and Research Project
4. The Pareto Principle (80:20 Rule) for Customer Success – SmartKarrot
5. Airport Retail – How It Can Help Your Retail Operation Thrive – Medallion Retail
6. Airline Industry: All You Need to Know About The Airline Sector! (revfine.com)
7. How Indian Airports are turning into retail hubs – Indian Retailer
8. Brand Value: Definition & How to Increase It | Qualtrics AU
9. What Is The Meaning of Revenue Generation? | Cognism
10. Competitive risk – CEÕpedia | Management online
11. Innovation capacity: how to develop it in your organisation (oxford-review.com)
12. Multi Criteria Decision Making Approach for Selecting Effort Estimation Model (arxiv.org)

References

The weights of each of the criteria are nothing but the eigenvalues of the pairwise comparison matrix constructed from the answers provided by the leadership team. The real and practical use of eigenvalues in decision-making is not contrived but we are led to them in a very natural way. AHP ensures that the opinion of the leadership team is mathematically used in every decision without going back to them each and every time.

How data can augment the airport throughput

Introduction

An airport is a complicated system where disparate contributors (Airport Operators, Airlines, Government Authorities, Security Agencies, Staff, and Passengers) actively participate in moving/transporting people and goods across the globe.

Today, airports are addressing increasingly complex operational challenges with exciting business and technological innovations. Smart digital investments demonstrate an attractive business case for airports, offering the potential to increase revenue and lower costs, while at the same time, offering a better travel experience.

Airports are undergoing a pivotal transformation, and digital tools are enabling new thinking, be it personalised advertising, enhanced passenger loyalty, or mobility for passengers.

Digital transformation at airports leverages multiple technology solutions, such as indoor geolocation, identity management, passenger flow management, data mining, artificial intelligence (AI), and automation & real-time monitoring of machines via the Internet of things, to not just improve safety and security but also streamline their operations and enhance revenues.

Airports have achieved impressive efficiency gains by implementing digital technologies. Saving minutes of time per passenger processing time from terminal check-in to the security hold area by leveraging facial recognition technology, reducing minimum connecting times by accelerating baggage handling for selected priority bags, and reducing infrastructure costs by up to 10% through energy efficient systems are just a few of the more prominent examples.

To make more out of available data, airports need to consolidate data, break the data silos, and form an open, interoperable, and collaborative system across participants and procedures. Harnessing the power of data, it is in the best interest of the airports to deploy full-fledged Digital Twins, not just for Airport Operations, but to also address sustainability needs through solutions for Infrastructure, Energy, and Water Management.

Deploying the digital twin breaks down information silos and helps access the right information at the right time, enabling situational awareness. Inspection data, work history, and extremely large datasets can be used in the context of a digital twin. Using extensive graphics and dashboards and real-time and reliable information helps to reduce the total cost of assets; better manage, make changes, and obtain accurate returns; and ensure better asset performance and return on investment (ROI).

To accomplish this, all the shortlisted airport-related data streams – from all participants together are required to bring in a single central data lake that is simultaneously accessible to airport stakeholders. This will create a single source of truth to refer to for airport operations. Actionable, real-time information on everything going on at the airport will be provided in dashboard views to anyone who needs to know, so that well-informed and quick decisions can be made in a crisis.

The digital twin also carries the necessary tools for advanced prediction and simulation of future developments to which airport operators can then respond proactively. In other words, the twin delivers effective decision-support tools for everything from short-term scenarios to infrastructure expansion decisions.

Additionally, since data is already located in one virtual space, new use cases, and applications can be developed on the fly by all interested parties. Potentially, the digital twin of an airport could even be visualised in a 3D model that would be accessible via virtual or augmented reality.

The digital twin of an airport is a high-value use case that unlocks the full potential of data for airports – and aligns with the vision of smart airports of the future.

Data-driven technologies that airports use to improve performance and master current challenges

In line with Airport 4.0, there are several data-driven technologies that airports are using to improve performance and address current challenges. Some examples include:

Predictive maintenance:Airports leverage data analytics and machine learning to predict maintenance needs, enabling proactive addressing of potential issues before they arise. This reduces downtime and enhances overall operational efficiency.
Baggage tracking:Many airports employ RFID tags and other tracking technologies to enhance the accuracy and efficiency of baggage handling. This minimises the occurrence of lost bags and enhances the overall customer experience.
Real-time surveillance and monitoring:Airports utilise various sensors and cameras for real-time surveillance and monitoring of the airport environment. This enhances safety and security and facilitates efficient management of airport operations.
Predictive analytics:Airports utilise data analytics to forecast demand for various airport services, such as parking and food and beverage. This optimisation of resource allocation improves overall airport efficiency.
Customer experience optimisation:Airports employ data analytics to enhance the customer experience by offering personalised recommendations for shopping and dining, real-time flight information, and tailored loyalty programs. This fosters increased customer satisfaction and loyalty.
Environmental sustainability:Airports utilise data analytics to optimise energy usage, reduce emissions, and implement sustainable practices, aiding in meeting sustainability goals and minimising environmental impact.

Where are we at with the use of data to enhance airport ecosystems

The use of data to enhance airport ecosystems has made significant strides in recent years, with many airports adopting data-driven technologies to improve efficiency, safety, and customer experience. However, there is still room for growth and development in this area.

One challenge faced by airports is the integration and management of multiple data sources. Ensuring that data is accurate, up-to-date, and secure can be a complex and time-consuming task. Additionally, airports must consider privacy and regulatory issues when using data, aligning with region-specific data protection laws.
The need for skilled personnel continues to be a challenge to fully realise the benefits of a data-centric strategy. As the use of data expands within the airport ecosystem, there will be a growing demand for professionals with strong data analysis and management skills.
Another factor is the fear of losing control over data. Stakeholders may be hesitant to share data, fearing that it may lose value or be exploited for commercial purposes by other parties. Consequently, data sharing remains the exception rather than the norm.
Despite these challenges, the use of data to enhance airport ecosystems is expected to grow in the coming years. Advancements in data analytics and machine learning technologies will enable airports to leverage more powerful insights and predictions, enhancing operations and customer service. Moreover, with the emergence of low-code and no-code platforms, the scarcity of skills and talent may soon be addressed through the rise of citizen data scientists.

How data analytics can be used in the airport ecosystem

Decision-makers need to have clarity on the outcomes they seek. They must align their needs with an understanding of the power of data and how it can be harnessed in connected and complex environments. Data, especially in large sets, presents the opportunity to gain insights and take action in ways that would typically require extensive efforts to unlock. With the Internet of Things (IoT), this often involves gathering various types of data over time, such as temperature, humidity, vibration, and usage, and analysing patterns. Statistical tools can then be used to better describe the data, compare different datasets, and discover correlations or causes of specific observations. Based on these insights, decision processes can be defined and automated through digital applications (software).

To illustrate, consider the example of a baggage handling system. By combining data collected through applications and IoT devices, a digital twin of the baggage handling system can be created. This digital twin can help optimise the operating process and identify bottlenecks, leading to improved efficiency and performance.

Typical Data Analytics Solution Architecture

Data analytics architecture refers to the framework and components that enable organisations to collect, store, process, and analyse large amounts of data. It typically involves several layers, including data acquisition, storage, processing, analysis, visualisation, and reporting.

Here is an overview of the components that make up a typical data analytics architecture:

Data sources:This includes the various sources of data that an organisation collects, such as structured data from databases and applications, as well as unstructured data from social media, sensors, and other sources.
Data acquisition:This involves collecting and ingesting data from various sources and preparing it for analysis. This may include processes like data cleansing, transformation, and enrichment.
Data storage:This refers to the infrastructure used to store large amounts of data, including data warehouses, data lakes, and other storage systems.
Data processing:This layer involves processing data to extract insights and value from it. It may include data mining, machine learning, and other techniques to identify patterns, predict trends, and make recommendations. Solutions such as Hadoop and Spark are commonly used for distributed computing and big data processing, while AI and ML tools like TensorFlow, Keras, and PyTorch are used for building and training neural networks.

Data analysis: This involves analysing data to gain insights and inform decision-making. It may involve the use of dashboards, reports, and other tools for visualising and exploring data. Tools like Tableau and SAS are used for data visualisation and business intelligence.

Data visualisation:This involves presenting data in a way that is easy to understand and interpret, using charts, graphs, and other visualisations. Programming languages like Python and R, as well as visualisation libraries like D3.js, are commonly used for data visualisation.
Reporting:This involves generating reports and communicating findings to stakeholders. Solutions like Excel, Crystal Reports, SAP BI, and MicroStrategy are used for reporting and business intelligence purposes.

Overall, a well-designed data analytics architecture should empower organisations to effectively collect, store, process, and analyse large volumes of data. By leveraging this architecture, organisations can make informed, data-driven decisions and gain a competitive advantage in their respective industries.

Utilising data’s full potential across interfaces on a unified, IoT platform

The Internet of Things (IoT) is permeating every aspect of our daily lives, from the vehicles we drive to the cities we live in, transforming how we shop and take care of ourselves. While the full potential of this innovation is yet to be explored, businesses and public offices can already harness its benefits by gathering vast amounts of data about customers and community residents.

The ability to collect and process insights in real time holds immense power. However, this power also comes with a responsibility. Tech teams must approach data collection and management responsibly, prioritising the design of reliable and secure application architectures.

In the realm of the Internet of Things, this often involves gathering various types of data over time, such as temperature, humidity, and vibration, and analysing patterns. Statistical tools are then employed to better describe the data and compare different datasets using regression analysis to uncover correlations or causes of specific observations. With these insights in hand, decision processes can be defined and automated through digital applications (software).

To fully leverage the potential of data across interfaces within a unified Internet of Things (IoT) platform, several key considerations come into play:

Integration of data sources:An effective IoT platform should be capable of gathering and integrating data from diverse sources, including sensors, cameras, and other devices. This ensures a comprehensive and accurate understanding of the data being collected, facilitating informed decision-making.
Interoperability:Seamless communication and data exchange between the IoT platform and other systems and devices is essential. Interoperability enables integration with existing infrastructure and promotes a more cohesive and efficient ecosystem.
Security:Protecting data integrity and confidentiality is paramount in an IoT environment. Implementing robust security measures such as encryption, authentication, and access controls safeguards data from unauthorised access or manipulation, ensuring its trustworthiness and reliability.

By addressing these factors, organisations can effectively harness the full potential of data across interfaces within an integrated IoT platform. This empowers them to make better-informed decisions, optimise operations, and drive innovation in various domains.

POSSIBLE SYSTEMS TO BE CONNECTED TO AN OPEN IOT PLATFORM

Moving towards the vision of the airport’s digital twin

A digital twin serves as a virtual representation of a physical system or process, often constructed using data from sensors and other sources. In the context of airports, a digital twin can play a crucial role in simulating and optimising various operational aspects, including baggage handling, ground handling, aircraft turnaround, and maintenance. Utilising the IoT operating system as its foundation, the digital twin forms an open and innovative airport ecosystem, facilitating end-to-end integration of processes and stakeholders.

Unlike a static virtual copy, the airport’s digital twin continuously receives real-time data about systems, processes, vehicles, and personnel, operating in parallel with the real world. This dynamic nature enables coordinated and collaborative actions across all ecosystem stakeholders and processes, focusing on monitoring, prediction, and use case development. It establishes a unified version of the truth, enabling continuous monitoring of ongoing activities and system statuses. Additionally, it empowers stakeholders to access customised dashboard information regarding system and process performance, facilitating joint decision-making processes with shared insights.

Furthermore, the digital twin serves as a predictive analysis tool, anticipating events that could disrupt airport operations and acting as an early-warning system. Through predictive analysis of potential outcomes, it can provide decision-support information on short notice, aiding in tasks such as the allocation of parking positions or gates for each plane.

The influence of the airport’s digital twin extends beyond short-term predictions, as it can also simulate trends, scenarios, and expected gains from infrastructure updates, impacting other turnarounds. This tool empowers stakeholders to optimise system performance and realise synergies, eliminating the need for duplicative data collection. With a collaborative approach, new service offerings can be developed by all ecosystem partners.

To progress towards the vision of the airport’s digital twin, several key steps can be taken:

Gather and Integrate Data:The initial step involves gathering and integrating data from various sources, including sensors, cameras, and other devices, to create a virtual model of the airport’s systems and processes.
Analyse and Visualise Data:Data analytics and visualisation tools can then be employed to analyse and visualise data from the digital twin, providing insights and predictions to optimise operations.
Test and Optimise:The digital twin facilitates the simulation of different scenarios and testing of optimisation strategies, enabling the identification of the most effective approaches for enhancing efficiency, safety, and customer experience.
Implement and Monitor:Once the most effective strategies are identified, they can be implemented in the physical airport. The digital twin continues to monitor the impact of these changes, allowing for further adjustments as needed.

By following these steps, airports can advance towards the realisation of the airport’s digital twin, leveraging data and analytics to optimise operations and enhance customer service.

The most efficient approach to creating an airport’s digital twin, whether by integrating operational data or virtualising the physical infrastructure, remains to be determined. However, beginning with a digitalised physical infrastructure, such as a 3D map of the airport buildings, provides a solid foundation. From there, the digital twin can be constructed by progressively integrating additional data streams into the platform.

These data streams may include information from various sources such as smoke alarms and ventilation systems from building automation, energy management data, baggage handling system data, input from passenger flow management systems, and performance data from aircraft and ground handling operations. As these data streams are harmonised and integrated, the digital twin gradually becomes a reality, providing a comprehensive virtual representation of the airport’s systems and processes.

The way forward: Where airports need to go from here

In this white paper, our focus has been on driving digitalisation forward from a technological perspective. We emphasise the significance of an open Internet of Things (IoT) solution as the pivotal technology for enabling this transformation. Such a solution facilitates secure and selective data integration, creating a plug-and-play environment conducive to open innovation ecosystems. Airport operators need to seek out IoT solutions that are aligned with their technological needs and are prepared to collaborate with them on this journey. Additionally, they should cultivate a network of trusted partners within the airport ecosystem, including airlines, ground handlers, authorities, and other users, who are committed to investing in the data integration process.

While acknowledging the current status of digitalisation in airports, we underscore that simply implementing technology is not sufficient. Airports must also foster a digital culture that empowers employees to drive change, ensures that all employees reap the benefits of digitalisation, and bridges generational gaps that may exist within their workforce.

To propel this cultural shift, the appointment of a “Head of Digital” alone is insufficient. Instead, airports require a comprehensive digital change management process, dedicated digital teams to spearhead concrete projects, and user-friendly airport apps designed to streamline operations for employees.

Alongside this cultural evolution, there is a need to reassess and adapt project selection and investment decision processes. These processes should allow digital projects to undergo proof-of-concept and technology demonstrator phases within a stage-gate framework, postponing the business case discussion. This approach provides digital technologies with the opportunity to progress technologically while concurrently refining the business model.

Ultimately, an enabling solution, a digital mindset, and meticulous project selection will determine airports’ success in advancing their digital journey and harnessing the power of real data.

Using machine learning to classify passengers into regions

Introduction

Enterprises across industries are going digital. In essence, digital is the ability of a company to adopt new computing and data technologies to improve its operational efficiency and delight its customers. The winds of change for going digital are blowing in the aviation industry too. Governments, airport operators, airlines, security agencies, and technology providers across the world are imagining new ways powered by technology to improve customer experience and efficiency.

Today, every organisation desires to leverage its data and transform it into something useful both operationally and commercially. Airport Operators – organisations who run the airports – wish to do the same. However, in many instances these Airport Operators do not own or have access to the data. This is especially true when it comes to data related to passengers traveling through an airport. Typically, the passenger data is owned by airlines under strict regulatory laws, even though airports are an important enabler in entire passenger journey.

The Challenge of Region-based Targeting

Commercial airport operators with huge retail areas love to target passengers individually. This data is however not easily available. In fact, in most regulatory environments, airports are prohibited from storing individual passenger information or targeting them individually for marketing purposes. Even when the data is available, unlike normal retail, it does not always in the interests of airports to acquire, store and retain every individual passenger’s details because only a fraction of the daily travellers are frequent flyers or repeat customers of the airport.

In a diverse country like India, this challenge becomes multi-fold. India has more than 30 regions where people speak different languages and follow different cultures. Indian retail is a complex market with several local region-centric brands. Air travel in India has been growing at a phenomenal rate since the turn of the century, with one-time, small-town travellers forming an ever-increasing mix of the daily travel load.

Solving for Region-based Targeting

WAISL is delighted to announce a new offering for Airports- the ability to classify passengers into regions!

WAISL has leveraged state-of-the-art machine learning techniques to create useful models that classify travellers into regions with a minimal amount of data points needed from the Airport Operator. This ability to activate the models with minimum data points greatly removes the obstacles to adoption for airports while at the same time ensuring compliance with all Government and security regulations.

Going a step further, WAISL has packaged the offering as an easy-to-consume cloud-hosted API that provides straightforward onboarding and a simple-to-understand pay-as-you-use commercial model.

Benefits of Region-based Targeting

In practical terms, WAISL’s offering enables airports to see future load by regions and adjust their operations accordingly. Specifically, with this capability, Airports can:

Enable dynamic advertising – programmatic advertising that changes based on expected region and gender profile. This makes advertising more relevant and useful, thereby increasing advertising revenue.
Enable dynamic signage – Signage that changes based on the region profile.
Smart resource allocation – Plan optimal allocation of resources like Washrooms, Wheelchairs, Gender-specific SHA gates, and more.

Part of D2A – A Suite of New Age Services Delivered in a SaaS model

This offering is available to airports as part of D2A- WAISL’s Digital Airport-In-A-Box. D2A is a suite of offerings that enable next-generation airports to transform their data into value-added services. D2A is a SaaS native platform with the following industry-first features:

Multi-tenant, with each Airport’s data encrypted at rest and in motion.
Easy onboarding and role-based access control.
RESTful endpoints to key services enable easier integration into on-premise applications.
Pay-as-you-use model enables transparent procurement and billing.

How It Works

This offering is part of D2A’s Analytics Suite – a set of offerings that leverage AI/ML to make data useful. Specifically, these offering leverages multiple proprietary machine-learning models created on general travellers’ data. We use non-identifying information in model preparation to ensure compliance with the regulatory environment and also to ensure that minimal data points are needed to activate the model downstream. For classifying into regions, we only need the passenger’s Given and Surnames while activating the model. We prepare and use multiple models and return the classification responses based on the highest-scoring model. We use both classical machine learning and deep learning approaches in the underlying models. Specifically, we use Random Forest, Gradient Descent, LSTM, and Ensemble modelling approaches in our model generation.

What We Offer

Solution Clusters

Innovation and Efficiency Across Industries

White Papers

Decision Making Framework to Identify Successful AI Investments for Airports – Part 2

Decision Making Framework to Identify Successful AI Investments for Airports – Part 2

Part 2 Capturing Value: AI Investment Strategy

Leveraging AI for Competitive Advantage: The CEO’s Forward

Executive Summary

Summary of Part 1: Creating value

Part 2: Capturing value

Corporate Strategy

Airport Business Models

Airport revenue structure

AI strategy spectrum

Essential AI

Enhanced AI

Emerging AI

Decision making framework to achieve strategic fit:

Try and buy model

Invest and harvest model

Evaluate and decide model

Experiment & confirm model

Conclusion

Decision-Making Framework to Identify Successful AI Investments for Airports – Part 1

Decision-Making Framework to Identify Successful AI Investments for Airports – Part 1

Part 1 Creating Value: Critical Success Factors for AI Investment Decisions

Executive Summary

Introduction

References

References

How data can augment the airport throughput

How data can augment the airport throughput

Introduction

Data-driven technologies that airports use to improve performance and master current challenges

Where are we at with the use of data to enhance airport ecosystems

How data analytics can be used in the airport ecosystem

Typical Data Analytics Solution Architecture

Utilising data’s full potential across interfaces on a unified, IoT platform

Moving towards the vision of the airport’s digital twin

The way forward: Where airports need to go from here

Using machine learning to classify passengers into regions

Using machine learning to classify passengers into regions

Introduction

The Challenge of Region-based Targeting

Solving for Region-based Targeting

Benefits of Region-based Targeting

Part of D2A – A Suite of New Age Services Delivered in a SaaS model

How It Works

Solutions that speak for themselves

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