Building a Movement: Key insights from Assaia's inaugural User Conference
From September 17-19, 2024, Assaia hosted its inaugural User Conference alongside the Greater Toronto Airport Authority (GTAA). What emerged from this gathering was more than just a showcase of technology; it was a testament to the vibrant community of users striving to make airport operations safer, more sustainable, and exceedingly efficient.
From September 17-19, 2024 Assaia hosted its inaugural User Conference alongside our hosts, the Greater Toronto Airport Authority (GTAA).
From the moment customers began to arrive and share information it became increasingly evident that the most important and powerful thing that Assaia has ever built is the community of users from around the world that are making operations more safe, sustainable and efficient at airports. The ApronAI suite of products are tools that enable much of the change we’ve seen, but it's the leaders and users of the systems deployed at airports globally that truly create the lasting improvements that only human fabric and connections can achieve.
To quote one attendee working for a global airline: “Assaia has not just built a product, Assaia is leading a movement - a movement that works towards the ‘perfect turnaround’! And more and more airports and airlines are joining it.”
The insights shared amongst the user base and relationships established for ongoing conversations were truly invaluable. While we intend to make additional content available from the conference at later dates, we'd like to share the top 10 insights that repeatedly resonated across panels ranging from best practices, sustainability, safety, adoption, and collaboration across airport ecosystem stakeholders.
- Operating from the same data improves ecosystem performance. So many of the challenges experienced in the turnaround come back to the lack of standardized data available for airport, airline, ground handler, and air navigation service providers (ANSPs) to assess the operation. ApronAI delivers a standardized and comprehensive dataset delivered in a manner where undisputable images allow users to quickly agree on what’s actually happening and move faster towards conversations on mitigations and improvements.
- Shifting from intuition based decision making to data driven decision making. One of the large hub airport users stated, “Finally, you actually know what’s going on in your business.” Once the data is available, reporting processes can be refined in unique ways similar to how each organization is already used to consuming data, and a data driven culture can begin to thrive. Tough conversations with teammates or challenging decisions on how best to operate will always be much easier when backed up with robust data.
- Building trust between organizations. Historically there’s been a significant lack of trust between ground handlers, airlines, airports, and ANSPs. Undisputable data sets to evaluate issues, reduces the friction between stakeholders and rebuilds the critical trust which is necessary for the ecosystem to drive forward common goals (performance improvements, safety, sustainability).
- Focusing staff time to become more effective. Operations staff are often faced with what seems like impossible challenges to manage (peak bank periods, minimal buffer times between flights, never ending irregular operations, construction impacts, etc). ApronAI becomes a super power for frontline operations to better manage across extremely busy operations. Instead of one individual’s eyes looking at the operation, an entire system of CCTV cameras across the airport are deriving insights and alerting users at just the right moment when action is necessary.
- Incentivizing positive performance as opposed to penalizing negative performance. Service level agreements and performance based contracts are always better received by staff and more effective when painted in the light of opportunities to improve. Staff involved in servicing aircraft on the ground continue to see high rates of turnover. Not only do incentives improve performance, but they also contribute to increased engagement and retention. And with the sole source of truth being computer vision images, teammates can hold each other accountable in a fair way.
- AI should remain focused on the act and not the individual. In order to continue building trust with airport staff and following best practices for the responsible integration of AI, the systems cannot capture personally identifiable information (PII). AI brings attention to the human in the loop who is best positioned to address the individual.
- All stakeholders need to be involved in the projects early on and with intentional check-ins throughout. Many ApronAI projects start out with safety, and then operations and sustainability groups join later. Bring all stakeholders together at the onset to define scope of the project. Capturing measurable gains along the way and sharing those with the broader project team will help manage a positive change culture. As new teammates come on board, deliberate inclusion methods need to be employed.
- Technology is proven. There’s no longer any need to run costly proof of concept evaluations of computer vision technology at airports. Identify the scope of the project and get started on making broad improvements. It will be much less effective if just a subset of gates on a concourse or at a terminal is being covered. There are many use cases now available demonstrating the effectiveness of ApronAI and the industry can collectively learn from existing deployments. Pilots, proof of concepts, or trials will just slow you down in achieving the improved results airports, airlines, and ground handlers are looking for.
- Nimble team at Assaia, product development occurs in collaboration with their customers collective needs. Airport and airline operations are very dynamic and the tools being built to support them also need to be dynamic. The rapid cycle that Assaia has developed to learn fast, capture customer feedback, evaluate recommendations across the diverse group of users, and then incorporate changes in the product, is one of the key differences that sets ApronAI apart from the competition. This change management methodology based deeply in customer feedback is what feeds into Assaia’s product roadmap and ensures the tools and features being built, are what matter most to users.
- Is ApronAI an airline tool or an airport tool? Not surprisingly, it's both! Airport and terminal owners that oversee infrastructure have a responsibility to enable their tenants / users to achieve increased levels of efficiency, safety, and sustainability. The whole industry moves forward in our ability to move the next 4 billion annual passengers successfully when this mindset is adopted. Specifically, safety and sustainability is everyone’s responsibility. Airports may benefit more from the rollup of ApronAI data which is the predictive off block time (POBT), while airlines benefit more from the subtask data points of each turnaround. Some of our users shared that there are more than 30 stakeholders of ApronAI at their airports, and it's the entire ecosystem that benefits in the end.
The customers that partook in these conversations represented airlines, airports, and ground handling organizations from 12 different countries. And while some are just getting started with ApronAI, others have already seen their turn times shrink by 5 minutes, average taxi times reduced by 44%, and average reduction of ground delays by 3.4 minutes. Whichever point of the journey our customers are on, we learned that we all share a common goal in search of the perfect turnaround.
It's incredibly rewarding to be on this journey with such a diverse and professional group of aviation professionals and we look forward to expanding the community with even more opportunities to collaborate. This “movement to perfect the turnaround” is just getting started!
We’re already looking forward to our September 2025 event in Rome, Italy with our strategic partner Aeroporti di Roma! Look forward to seeing many more users there next year!
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