AI in Aerospace and Defense - Thematic Research

AI in Aerospace and Defense - Thematic Research

Summary

The defense and security establishment is frequently debating the future impact of AI on the conduct of warfare, but it generally agrees on the fact that it has become far more than a fad. AI in defense must be understood within the framework of the traditional security dilemma, which refers to a situation where improvements in a state’s warfare capabilities necessarily increase the threat perception of competing states. In other words, the potential of AI to be a game-changer, significantly increasing the speed and accuracy of data analysis and decision-making, pushes leading nations to pour resources into R&D in order to keep up. The US and China are understandably hogging the headlines, but other powers such as France, Germany, Israel, Japan, Russia and the UK are also throwing themselves into the competition, creating complex patterns of international cooperation.

Potential applications of AI in the military world are numerous and, from a tactical point of view, appealing. Perhaps the most publicized and controversial application concerns vehicles and weapon systems that AI could make more autonomous during their mission, possibly up to target engagement decisions (so-called killer robots). It would also help to develop drone swarm technologies, coordinating in real time a fleet of (potentially) hundreds of drones. Intelligence and reconnaissance could also benefit from AI image recognition and analytics tools, multilingual speech recognition and geo-localization, all based on machine learning algorithms.

Training and simulations could gain a lot from AI, coupled with virtual and augmented reality simulated environments allowing trainees to confront bots, mimicking combat scenarios safely and realistically. AI tools could help logistics services monitor and automatically flag technical issues on pieces of hardware, again with the help of machine learning, and remove error-prone human’s appreciation in the process. The same principle applies in cyber defense, where AI algorithms could detect and resolve software vulnerabilities faster than the sharpest human technician.

On top of these mouth-watering prospects for military leaders, AI also offers long-term cost-cutting potential. Using AI for logistics management and maintenance could both increase reliability and efficiency, incorporating human response and soliciting spare parts and repairs only where and when they are required. Unmanned autonomous vehicles can save the lives of soldiers normally exposed to enemy fire, and also reduce vehicles’ operational costs.

Early data based on the US Navy’s experimental Orca unmanned submarine and Sea Hunter unmanned surface ship (both autonomous) shows a clear cost differential between manned and unmanned vehicles in favor of the latter. Both in terms of production costs and exploitation costs, an unmanned AI-powered vehicle is far cheaper to operate than its manned equivalent.

Despite AI’s great potential, a 2018 McKinsey study on AI use cases shows that the aerospace and defense industry has so far only been moderately impacted by the technology. Civilian industries such as insurance, retail or healthcare have done far more to integrate AI technologies into their supply chain. This suggests that substantial opportunities still exist in the aerospace and defense market.

Further growth will be reliant upon better data collection and curation capabilities. The explosion of interest in AI from the defense sector does not resolve the fundamental challenge of accumulating substantial quantities of data on which machine learning tools can be trained. Commercial industries have benefited from AI thanks to their ability to collect and store huge amounts of data from a wide variety of sources. In order to attain a similar level of efficiency in the use of AI, military forces will need to upscale and upgrade their data collection capabilities in domains such as ISR, maintenance, and simulations.

Scope

This report is part of our ecosystem of thematic investment research reports, supported by our “thematic engine”. About our Thematic Research Ecosystem -

• The author has developed a unique thematic methodology for valuing technology, media and telecom companies based on their relative strength in the big investment themes that are impacting their industry. Whilst most investment research is underpinned by backwards looking company valuation models, the author's thematic methodology identifies which companies are best placed to succeed in a future filled with multiple disruptive threats. To do this, the author tracks the performance of the top 600 technology, media and telecom stocks against the 50 most important themes driving their earnings, generating 30,000 thematic scores. The algorithms in the author's “thematic engine” help to clearly identify the winners and losers within the TMT sector. Our 600 TMT stocks are categorised into 18 sectors. Each sector scorecard has a thematic screen, a risk screen and a valuation screen. Our thematic research ecosystem has a three-tiered reporting structure: single theme, multi-theme and sector scorecard. This report is a Multi-Theme report, covering all stocks, all sectors and all themes, giving readers a strong sense of how everything fits together and how conflicting themes might interact with one another.

Reasons to Buy

• Our thematic investment research product, supported by our thematic engine, is aimed at senior (C-Suite) executives in the corporate world as well as institutional investors.


• Corporations: Helps CEOs in all industries understand the disruptive threats to their competitive landscape


• Investors: Helps fund managers focus their time on the most interesting investment opportunities in global Aerospace & Defense.


• Our unique differentiator, compared to all our rival thematic research houses, is that our thematic engine has a proven track record of predicting winners and losers.