How AI and Workforce Intelligence Improve Talent Retention in Aerospace

By Matt Finlayson, CTO of ActivTrak

Aerospace is no stranger to disruption. From supply chain breakdowns to rising inflation and shifting global demand, leaders in this sector are used to turbulence. But a study from the Aerospace Industries Association (AIA) and McKinsey finds the most pressing challenge right now is people.

The report confirms what most executives in aerospace already know: workforce shortages are dragging on productivity, particularly in engineering, skilled manufacturing and software development –– and the talent pipeline isn’t filling fast enough. To meet surging demand, the industry will need to find 30–40% more productivity from its existing workforce.

That’s a heavy payload. But there is a path forward — and it’s rooted in reimagining work with the help of AI and workforce analytics. At ActivTrak, our Productivity Lab analyzed workforce utilization across several industries and found aerospace has the most untapped capacity of all — a staggering 19%. That equates to roughly $1 million in annual productivity left on the table.

Multiply that across the sector, and the opportunity is enormous. While much of the industry is re-engineering its fleets for cost efficiencies, workforce optimization is equally critical if aerospace wants to scale sustainably.

Fueling Growth with Workforce Insights

What’s encouraging is that aerospace leaders are leaning into workforce analytics to diagnose where productivity stalls and why. These tools track real-time signals — such as shifts in focus time, sudden spikes in work hours or increased absenteeism, which can indicate early burnout or workload imbalance. The goal isn’t to surveil employees, but to give managers the visibility to rebalance teams, improve engagement and prevent small problems from turning into resignations.

When done right, the payoff is tangible. Companies that actively share productivity insights with their teams see a 20–30% boost in output, because transparency builds trust. Open conversations about workload and focus time shift the narrative from “working harder” to “working smarter.”

Now, with AI, workforce analytics are more powerful than ever. AI can sift through millions of data points to deliver personalized coaching insights — from nudges that optimize focus time to recommendations that align engineering hours with project outcomes. And as companies adopt new AI tools, workforce analytics can also measure the real impact of adoption — showing which tools boost productivity and which fall short.

Accelerating Automation with AI Tools

An Accenture study found that 87% of aerospace respondents plan to increase GenAI investment in 2025. The use cases range from design engineering to procurement — and the upside is real.

Some of the AI tools showing the most promise in aerospace include:

• GE Aerospace & Microsoft’s Wingmate: Generative AI-powered assistants that synthesize documentation, resolve quality issues and handle routine queries
• Monolith AI: A no-code AI platform that accelerates aerospace engineering through predictive modeling, design optimization and advanced analytics
• pSeven Desktop: Design-space exploration tool that integrates optimization, simulation and uncertainty quantification into AI-augmented workflows
• AnyLogic: Simulation software that embeds ML models for aerospace system modeling and synthetic data generation
• ePlaneAI: AI-native procurement and inventory optimization to reduce costs and avoid bottlenecks
• Ramco, QOCO, and Infizo: AI-driven solutions for MRO, predictive maintenance and parts tracking that boost operational efficiency

But adopting AI tools isn’t just about the technology itself. It’s about understanding their impact on productivity and performance. Workforce analytics can A/B test before and after scenarios: Are engineers more engaged? Is workload distribution improving? Are new tools reducing burnout or creating new friction points? The companies that thrive won’t be the ones that just buy AI — they’ll be the ones that measure its effects and iterate accordingly.

The promise of AI doesn’t stop at organizational insights. For example, in aerospace software engineering, workforce analytics can be combined with output data from tools like Jira and GitHub to paint a bigger picture: how time is being spent, how efficiently code is shipped and where bottlenecks occur. Managers get actionable insights — like which teams need more focus time — while developers receive personalized recommendations to improve throughput without burning out.

As aerospace pushes forward with AI adoption, workforce intelligence is proving indispensable, enabling organizations to build AI models that are both more accurate and more reflective of how teams work.

Elevating Productivity, Performance and Retention

The AIA and McKinsey study makes it clear: the industry doesn’t just need more people — it needs better ways of working. AI-powered analytics give leaders the insight to unlock hidden capacity, engage talent and build teams that can adapt on the fly.

Aerospace has always thrived on bold engineering. It’s time to bring that same ingenuity to the workforce. Leaders who invest in understanding how their people actually work won’t just survive the talent crunch — they’ll turn it into their next big competitive advantage.