From AI Pilots to Enterprise Outcomes in Aviation

There’s a difference, however, between AI activity and enterprise value. It’s relatively quick and easy to launch an AI pilot, demonstrate a use case, and generate momentum inside the organization. Getting that pilot into production, at scale, across the enterprise, is a completely different challenge.

And it isn’t happening in a vacuum. An estimated 91% of travelers have already used AI in their travel planning, raising the bar for what passengers expect from AI-enabled service. Competition is moving faster, while executives and investors are simultaneously demanding a cohesive AI strategy. Airline retailing initiatives, focused on delivering better digital experiences and generating stronger ancillary revenue, are adding even more modernization pressure.

For AI to improve performance across the enterprise, aviation organizations need a scalable foundation that prepares both the technology and the business to move beyond isolated wins.

Why Aviation AI Pilots Stall Before Production

Pilot Performance Often Hides Human Intervention

It isn’t unusual for teams to overlook or forget to document when a human had to intervene in the AI process, such as when a team member needs to correct, direct, or override an AI system. This creates a false sense of security, providing an inaccurate picture of the AI’s autonomous performance.

Without insights into where humans had to step in, enterprises can develop unrealistic expectations about what systems can handle and unclear boundaries for when humans need to review and escalate potential AI issues. The result is weak evidence of the AI’s true business value, since the pilot’s reported performance reflects human correction as much as it reflects the AI system itself.

Production Requirements Arrive Too Late

Pilot projects often rely on exported data for their proof of concept rather than being integrated into the enterprise’s live systems. And that gap creates problems down the line. Siloed data from legacy systems means the pilot isn’t connected to the organization’s central data platforms.

Without a realistic view of how the integration will proceed, enterprises often face costs that the pilot didn’t surface. Security reviews are frequently delayed until the production planning stage, and ROI calculations don’t begin until after the enterprise has already committed resources.

Production decisions require organizations to weigh technical feasibility, security, operating costs, and expected results from the start, not after the pilot has already proven the concept. Organizations that wait too long may find a promising pilot turning into a project that no one will sign off on, with a price tag too high to justify after the fact.

Large Ambitions Delay Early Value

AI is exciting, and as enterprises explore its possibilities, they tend to think big. But starting the first project with advanced agentic systems and aggressive KPIs can push value further out of reach, not closer.

Aviation organizations that successfully move AI pilots into enterprise deployments start with focused use cases. These use cases solve a defined operational or passenger problem, have clear human handoffs, use accessible data, and produce evidence that supports further investment. That evidence becomes the business case for the next use case, and the one after that.

Questions to answer before approving an AI pilot

The Foundation Enterprise AI Needs to Scale

Moving beyond pilot purgatory requires more than just picking the right first use case. It requires a solid foundation, modernized systems, trusted data, and reliable operations that AI can actually run on.

01 Modernize and Connect Legacy Systems

Aviation enterprises rely on decades-old systems, siloed data, and mainframe applications to run their daily operations. These systems were often built decades ago, and the people who best understood them have since retired or moved on, leaving little documentation behind. As a result, data stays siloed, systems can’t talk to each other, and any AI use case that depends on real-time integration hits a wall before it starts. Modernization projects are often abandoned once the team realizes the multi-year backlog that must be cleared first.

Sapient Slingshot, Publicis Sapient’s AI-powered modernization framework deployed on AWS and available in AWS Marketplace, accelerates migration by 50–70% and reduces costs by up to 40%, according to Publicis Sapient internal case studies. Faster development enables shorter feedback cycles with actual users, letting teams course-correct based on real usage instead of guessing during a long planning phase.

Implementing Slingshot allows organizations to use their existing codebase and deployed platform as the source of truth, meaning the modernization reflects what the system actually does today, not outdated or missing documentation. This process also reconstructs requirements that were never previously documented, giving aviation enterprises a more complete picture of system capabilities and reducing the risk of costly gaps or rework later.

By taking a minimally invasive approach to modernization, updating suitable ancillary systems without replacing every core platform, Slingshot helps organizations avoid the scope creep that turns a six-month project into a four-year one. Moving relevant data and applications to cloud-based infrastructure adds another layer of value, giving aviation organizations consistent, fast access to data wherever users are located.

02 Activate Intelligent Automation With Trusted Data

Once the enterprise modernizes systems and connects data, AI agents have something tangible to work with. Bodhi, Publicis Sapient’s AI orchestration platform and an AWS-validated solution, activates that connection. It gives enterprises a way to deploy agents across their approved systems and data sources, rather than relying on isolated tools that only handle one narrow task.

With Bodhi, aviation enterprises can build agents that plug into modern APIs and support real transactions, not just answer questions. Agents draw on trusted data whenever and wherever the application needs it, rather than a limited data set assembled for a single pilot. Additionally, Bodhi lets organizations set clear limits on what agents can complete independently, flagging exceptions or higher-risk decisions for human review.

03 Sustain Digital Experiences and Operations

AI has to prove its value in the pilot phase and keep proving it once it enters production. That’s especially true in aviation, where the tolerance for downtime is close to zero. An airplane isn’t just a vehicle; it’s a moving warehouse, carrying everything from passengers to equipment that may need to be shifted to another aircraft on short notice. Every person and asset connected to that plane, from the flight crew to the catering cart, is accounted for on a tight schedule with little room for error. In aviation’s tightly connected 24/7 environment, even brief system interruptions can cascade into significant operational delays — making continuous AI operations monitoring essential.

Ongoing AI operations can help streamline workflows. Additionally, ongoing AI operations can detect issues before they affect other systems, reducing cascading failures and improving 24/7/365 reliability across the organization.

Four Business Outcomes Aviation AI Can Improve

Operational Resilience

Disruption management is one of the clearest examples of AI’s impact on operational resilience. When a flight is canceled, AI can process a high volume of rebooking requests at once, resolving what used to take hours of manual work. AI can also detect technical or operational issues before they spread, supporting faster incident response and reducing the downstream effects of even a short outage. The result is a more reliable schedule, more consistent service, and faster recovery when disruptions do occur.

Workforce Productivity

Contact centers see some of the strongest productivity gains from AI. Self-service support handles routine questions, automated systems quickly process booking changes, and IT requests reach the right team faster. Employees spend less time on repetitive requests and more time on work that requires their judgment, such as complex customer issues.

Passenger Experience

Airline passengers expect their travel experience to match the responsive, personalized service they get from AI in other parts of their lives. Personalized offers and ancillary recommendations, delivered across global touchpoints, shape how passengers perceive an airline just as much as the flight itself. When a proprietary AI experience doesn’t match what passengers expect elsewhere, they quickly get frustrated.

Asset Utilization

Predictive maintenance allows aviation enterprises to identify aircraft servicing needs before they become problems. Earlier identification helps aviation organizations make better aircraft servicing decisions, reduces unplanned downtime, and keeps operational assets available when they’re needed most.

What an AI Pilot Must Prove Before Production

How Publicis Sapient and AWS Help Aviation Organizations Scale AI

Taking full advantage of AI across the aviation enterprise requires more than just cloud infrastructure or industry knowledge. Organizations need partners with proven expertise in connecting modernization, AI deployment, and security with ongoing operations.

Match the Model to the Business Need

Pilot teams often default to larger, more expensive models than a use case calls for, simply because they’re the most familiar option or seemed the safest at the time. But those larger models come with higher costs that can quickly add up, minimizing or completely negating any anticipated cost savings.

Publicis Sapient helps aviation organizations right-size their AI models using the AWS model infrastructure. With Amazon Bedrock, organizations can access a broad selection of foundation models from leading AI companies through a single API, select the right model for each task, and reduce inference costs by up to 75% through intelligent routing and distillation.

For domain-specific needs like predictive maintenance and demand forecasting, Amazon SageMaker AI helps teams accelerate the path from experimentation to production deployment, reducing the time and complexity typically associated with operationalizing ML models.

Using the organization’s unique production requirements, we build a financial case that reflects the true cost of running AI at enterprise scale, not the artificially low cost of a pilot.

Build Security Into Production Planning

Starting security reviews after a pilot is being deployed can lead to costly rework, delayed launches, or a project that stalls right before it reaches production. Instead, aviation enterprises should build security into their production planning, including:

An enterprise is only as secure as its weakest point. Amazon Bedrock AgentCore gives aviation organizations a way to deploy and govern AI agents at scale, with identity controls, secure enterprise connectivity, memory, and real-time observability, all without requiring additional infrastructure. By working with AWS, aviation organizations can use proven tools and safeguards to securely operate AI applications in production.

Combine Industry Expertise With Global Infrastructure

Leading aviation enterprises are benefiting from the collaboration between Publicis Sapient and AWS. By combining Publicis Sapient’s aviation industry expertise, legacy modernization capabilities, and AI tools with AWS’s global cloud infrastructure and services for building and deploying AI applications, aviation organizations can scale AI initiatives across every region they operate in, without being limited by where their data or systems currently sit.

Move From AI Activity to Enterprise Results

In the aviation industry, a piecemeal operational approach is not an option. Organizations need a connected, integrated approach that spans systems, data, digital experiences, and ongoing operations.

Publicis Sapient and AWS provide the expertise and infrastructure aviation organizations need to move AI initiatives from the pilot phase to successful system-wide deployment. The result is that AI doesn’t just demonstrate potential; it delivers measurable outcomes across the board.

Publicis Sapient is a technology company that provides enterprise AI platforms and services. With over 30 years of digital business transformation experience, we enable enterprise clients to transform how they operate and serve their customers, unlocking new value and enabling them to thrive in an AI-driven world. Our platforms Sapient Slingshot, Sapient Bodhi and Sapient Sustain use AI built off this deep enterprise context to help organizations modernize their legacy tech systems, build agentic solutions, and automate their IT operations. The combination of our AI platforms and the expertise of our people enables us to deliver faster and more effective outcomes through solutions that are specific to the unique needs of our clients’ businesses, their industries and their customers. Publicis Sapient is the technology hub of Publicis Groupe, uniting 20,000 people worldwide across 28 countries.