Airlines: How AI-First Thinking Is Reshaping Airline Innovation
Airlines are racing to add AI features, but the teams seeing real gains are rebuilding their foundations. An AI-first approach shifts you from reactive operations to proactive decisions, personalization at scale, and smoother day-to-day execution.
If you're in product development, the takeaway is simple: stop bolting AI onto legacy layers. Redesign the core so intelligence is native in data, workflow, and retailing systems across every channel.
AI-First Means Architecture, Not Add-Ons
Many airline systems still treat AI as a plugin. That approach stalls after a few demos because it can't access clean data, apply context, or scale decisions across products and channels.
IBS Software chose a different path. It rethought its core architecture, data models, and operator workflows so that data, automation, and contextual decision-making sit at the platform's center. As Chris Branagan, CTO at IBS Software, put it: "Think AI-first, not AI as something tacked onto the side."
Beyond Hosting: Co-Engineering With AWS
IBS's Strategic Collaboration Agreement with AWS is built around co-engineering, not just cloud hosting. The teams work side by side on architecture choices, AI frameworks, and long-term product design.
"AWS is almost an extension of our architecture arm," Branagan said. "We go to them with problems, and they'll deep dive with us to figure them out." That includes direct access to AWS product teams, workshops, go-to-market support, and shared blueprints to keep the platform truly cloud-native and AI-first.
If you're formalizing your own approach, the AWS Well-Architected Framework is a strong baseline for decisions around reliability, security, and cost.
Bringing AI to Life: Use Cases That Matter
IBS challenged product teams to redesign workflows from the user's perspective - crew schedulers, retail managers, ops controllers - and let AI handle the repetitive work while surfacing high-impact choices.
In iRetail, this translates into context-aware offers with emotional intelligence. Example: "If someone's flying from the UK to the Faroe Islands in October, there's a good chance they're going to play golf." The system can lower the price of golf club carriage or surface a relevant partner offer - the familiar e-commerce model, applied to travel.
On the operations side, iFlight uses agent-like models to recover from disruptions. When a delay or equipment issue hits, the system evaluates crew, aircraft swaps, and schedule impact, then returns a set of viable options. The human makes the final call. AI does the groundwork; operators decide with clarity.
Trust, Safety, and Clear Guardrails
As autonomy grows, trust becomes a feature. IBS prioritizes controllable tools, strong governance, and rigorous testing. Some public models are off-limits if they can't be wrapped with guardrails.
Everything ships only after extensive real-world testing and CREST-accredited penetration tests - "We try to break it before anyone else can." If it doesn't add value, it doesn't ship. That's the bar. Learn more about CREST standards here: CREST.
Agentic Systems Are Coming Fast
Agent-based AI is already part of the roadmap. Multiple agents can analyze different constraints, pass results, and converge on the best outcome before the operator even asks.
This model demands AI-first architecture: event-driven data, explainable decisions, and human-in-the-loop controls. Legacy stacks can't support that level of autonomy or agility at scale. With AWS as a long-term partner, IBS is building for that future across retailing and operations.
What Product Teams Can Do Now
- Map end-to-end workflows as decision flows. Identify where AI can pre-compute options and where humans must approve.
- Centralize data with clean contracts and event streams. Treat context (inventory, policies, customer state) as first-class.
- Build a feature store and strong observability. Log inputs, outputs, and explanations for every recommendation.
- Adopt "recommend, explain, decide" patterns. Default to human-in-the-loop with clear override and audit trails.
- Stand up governance early: model registries, risk tiers, red-teaming, and CREST-style pen testing before release.
- Set value hypotheses and kill criteria per capability. Track impact on recovery time, take rate, NPS, and cost-to-serve.
- Co-engineer with your cloud partner. Align on reference architectures, MLOps, and cost controls from day one.
- Design for frontline usability. Fewer clicks, clear options, rationale surfaced inline, and fast handoffs across roles.
The Bottom Line
AI-first isn't a feature. It's an architectural decision that redefines how airlines price, sell, operate, and recover. The shift is underway, and the gains show up in fewer disruptions, higher offer relevance, and teams who spend more time deciding and less time digging.
For more information about IBS Software, click here.
If you're upskilling product teams for AI-first delivery, explore role-based programs here: Complete AI Training - Courses by Job.
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