Boosting space talent: Mapping gen AI’s impact on space jobs
Evaluating 70,000 space sector job tasks to pinpoint opportunities for gen AI adoption
Brett Loubert
Adam Routh, PhD
Thirumalai Kannan
Alexis Metzler
William D. Eggers
The space industry is growing at around 7% annually, which translates into demand for new and diverse talent.1 More than 38,000 jobs have been added to the industry in the last seven years.2 These include roles traditionally associated with the industry, such as aerospace engineers, and expertise familiar to other sectors, like legal staff, accountants, and operations personnel. Despite this growth, or partly because of it, the space industry has struggled to attract and retain talent, due to shortages, an aging workforce, and other issues.3 As the industry grows further, human capital challenges will likely continue to shape its trajectory.
Enter generative AI—which has been shown to help companies create process efficiencies, improve productivity, and facilitate reskilling and upskilling of existing talent.4 These and many other benefits potentially make gen AI an invaluable tool for addressing space industry talent challenges. Yet, getting started with gen AI can seem more complex than the rocket science many of these companies are familiar with.
To better understand how the space industry can adopt gen AI to offset existing talent challenges and provide space companies with important tools, the Deloitte Center for Government Insights analyzed more than 70,000 tasks performed by 5,180 space industry occupations, collected from five years of space company job postings. Of these tasks, 38% (26,817) were found to be suitable for gen AI augmentation, opening up entire areas of the industry’s work to automation for the first time (figure 1). (Read “Generative AI for government tasks” for more information on the methodology; also see the sidebar, “Analysis terminology”).
This analysis can provide a blueprint for how space companies can approach adopting gen AI based on their current and future talent needs. More specifically, it examines how gen AI could impact the work done by space companies, but not necessarily how those companies can offer gen AI as a product or service. While the data used reflects private sector space industry job postings, government space organizations may still find the analysis informative but should consider nuances of adopting and scaling AI in government. Of course, adopting gen AI tools is rarely a one-size-fits-all approach. In addition to identifying what job tasks can be augmented by gen AI, space companies should assess their talent strategy, develop an adoption plan, set measurable performance metrics for gen AI tools, and develop organizational governance and related policies to mitigate risks associated with gen AI use.
Space industry tasks ripe for gen AI augmentation
Thoughtful adoption of gen AI starts with identifying areas where it is needed and which tasks it can help with.
Our analysis found more than 26,000 space industry tasks spread across occupations (figure 2) ripe for gen AI adoption. While the analysis illustrates where there are opportunities for gen AI to bring value, it does not evaluate how gen AI is being used by the space industry due to a lack of available data on the subject.
Analysis terminology
The data presented and discussed in this article is derived from space industry job postings and mapped to Occupation Information Network (O*Net) categories.5 “Job family” and “job tasks” are terms used by O*Net to classify occupations and work activities.
Job family: Groups of similar occupations (for example, legal, management, business, and financial).
Job tasks: Activities found across occupations (for example, developing objectives and strategies, documenting and recording information, or performing administrative activities).
Our analysis does not suggest gen AI can augment entire O*Net job task categories; it rather suggests gen AI can augment specific tasks found within those categories. For example, a job posting for a contracts supervisor listed “develop and maintain customer relationships” as a job task. That task was aligned to the “establishing and maintaining interpersonal relationships” O*Net category. Our analysis suggests gen AI can augment the job posting task found within that category.
Gen AI for space industry talent challenges
An aging workforce
The space industry is struggling with an aging workforce and retaining management-level talent.6 As much as a quarter of the workforce has more than 20 years of experience and is eligible for retirement.7 These challenges can leave talent gaps while retirements can place institutional knowledge at risk. Each can make meeting organizational goals difficult. Gen AI can help augment management responsibilities to offset retirement or gaps in middle management roles.8
The management job families for the space industry include roles associated with senior leaders around the director level who are responsible for aspects of a company’s strategy and execution. According to our analysis, a total of 9,564 space industry tasks within the management job families are ready for gen AI augmentation. Notably, over half of management tasks suitable for gen AI fall into a few categories such as “developing objectives and strategies” or “organizing, planning, and prioritizing work” (figure 2). This suggests that companies may not need a wide variety of gen AI tools to support management roles because job tasks are concentrated. For example, a gen AI tool developed to support project management could aid tasks related to developing objectives and strategies; organizing, planning, and prioritizing work; performing administrative activities; and documenting and recording information. Even if a company opted to develop specific gen AI tools for specific task groups, according to our analysis, 66% of all management tasks suitable for gen AI could be addressed with relatively few tools.
Possible management gen AI use cases:9
- Advanced scenario modeling: Enable predictive challenger models to test various scenarios, market assumptions, and assess risk across short- and long-range time horizons.
- AI-generated negotiation strategies: Generate negotiation strategies and recommendations based on supplier data, business position, and market conditions.
- Automated data visuals and presentations: Automate the generation of visuals, graphics, and presentations to “tell the story” behind financial outcomes.
- Company valuation: Determine company valuation by leveraging predictive analytics and analyzing financial data and other relevant factors, helping to increase informed decisions about investments and acquisitions.
- Intelligent contract review: Automatically analyze and interpret contract terms, identify potential risks, ensure compliance, and streamline the review process, thereby enhancing accuracy, reducing legal costs, and accelerating contract management.
Filling experience gaps
At the pace the space industry is advancing, it can be hard for space companies to find the skills and experience they need in entry-level talent.10 From upskilling and reskilling to helpful resources that bridge experience gaps, gen AI can help. Indeed, gen AI has been shown to increase worker performance, particularly in middle-skill roles, by nearly 40% by augmenting and growing their skills and competencies.11 More specifically, gen AI can help with learning by curating knowledge and creating a more immersive and personalized learning environment with real-time feedback.12 Our analysis revealed nearly 5000 unique tasks that can be augmented by gen AI to enable learning and allow less experienced employees to take on more responsibility safely (figure 3).
Possible gen AI use cases for experience gap:13
- AI-powered virtual training: AI-powered simulated virtual training environments for service technicians.
- Answer specific business content–related questions for different lines of business: Decentralize business knowledge to allow quick access to Q&A based on multiple business documents.
- Augmented field worker: Use generative AI and augmented reality to provide frontline workers with an intuitive experience for guided work instructions, on-the-job upskilling or training, and intelligent assistance at the point of need.
- Hyper-personalized tutoring: Professional education “bots” that integrate with systems to provide hyper-personalization through a one-on-one interface, empowering practitioners to absorb learning material.
- Knowledge management: Automate the creation and publishing of knowledge content to optimize and increase accessibility; synthesize and summarize issues and resolutions from data available in various forum threads to add to the knowledge management corpus to minimize future resolution timelines and improve customer experience.
Less familiar space industry roles
The growth of the space industry has thrust it into less charted territory, which in turn can impact talent needs. For example, navigating uncharted regulatory waters can impose new knowledge and experience requirements on existing teams.14 Or maybe a small startup of engineers needs more marketing and legal support to meet growth targets. Yet, companies may not be in a position to hire new people right away. Gen AI has been shown to improve employee performance in areas like writing, programming, ideation, and other creative work.15 Gen AI can be well-suited (and getting better) at advising, coaching, and altering decision-makers, who then apply their judgment to codify decisions and execute tasks.16 This is why these tools cannot currently replace people and entire positions, but they can help by augmenting certain job tasks until the company is ready to hire full-time support.
Gen AI use cases for less familiar roles in the space industry:17
- Analysis of public legislation: Help support the reading, analysis, and consolidation of public legislation.
- Assist in creating interview questions for candidate assessment: For example, targeted to function, company philosophy, and industry.
- Automated financial planning and analysis: Automated budgeting, forecasting, and return on investment analysis for each period.
- Employee onboarding: Internal knowledge management chatbot could help employees to quickly search or query internal databases for information and policies pertaining to their role in order to increase information transparency and speed up employee onboarding process.
- Human resources process guide creation: Process documentation could be accelerated by leveraging gen AI to shadow key activities (for example, logging hours and requesting paid time off) and produce step-by-step guides.
Industry growth opportunities through gen AI
It shouldn’t take talent challenges for a space company to see the potential upside of gen AI on their business. The space industry could augment a significant portion of in-demand talent needs with gen AI by focusing on just a few in-demand job families and tasks. According to our analysis of space industry job postings, the top six in-demand job families share nearly half (14,019) of all in-demand job tasks suitable for gen AI augmentation (figure 5). Put simply, the skills and tasks that space companies are eager to recruit for are well-suited for gen AI augmentation.
Getting started
Across industries, many senior executives and company boards are interested in gen AI, in part because they believe it will change their business and industry.18 According to a Deloitte survey, two-thirds of surveyed organizations report increasing investments in gen AI based on the value it has created for them so far.19
With many in-demand space industry tasks ripe for gen AI, there is plenty of upside to consider adoption. However, companies should do so thoughtfully to capture the value while mitigating the potential risks that may come with gen AI tools. They can do just that by considering:
The job families and/or tasks they want to augment with gen AI: The analysis above provides insights into which job families and tasks can be augmented by gen AI, but it doesn’t speak to specific positions, which can vary across companies. Space companies should consider evaluating their talent needs against the task analysis to identify opportunities for gen AI augmentation. Ideally, this analysis could identify high-impact adoption opportunities. For instance, by identifying job families and tasks applicable to multiple roles, a few gen AI tools could empower several employees or teams despite them having different functions in the company. Or a company may choose to look for where gen AI can augment workflows across occupations and roles to focus on entire work processes.20
Redesigning talent strategies: Adopting gen AI tools can require time, resources, and even new talent, so it’s important to have clear goals and understand how they can impact talent strategies.21 Indeed, when leaders clearly communicate their goals for AI, their organization is more likely to achieve their desired outcomes for AI.22 Common goals for adopting gen AI include improved efficiency, productivity, and cost reduction.23 Improved efficiency and productivity are often directly related to talent. Three-quarters of surveyed organizations expect gen AI to affect their talent strategies over the next two years.24
Companies should assess how their broader talent strategy aligns with developing and adopting gen AI augmentation tools. More than exploring near-term talent needs, assessing a talent strategy should also consider how gen AI may impact talent in the medium to long term and how processes and career paths may need to change as a result. As gen AI matures, it’s likely company processes and talent needs will change too. Keeping an eye on each can help space companies remain at the forefront of gen AI adoption.
Setting measurable performance metrics for gen AI tools: As a company investment, it’s important to be able to measure the impact of gen AI tools. However, measuring outcomes of gen AI adoption can be challenging. Success can look different to different organizations, and measuring progress can take longer than the time companies have had to develop and scale these relatively new tools.25
The specific performance metrics measured may vary by company, but they should be clear from the outset of adoption. Additionally, given the novel nature of gen AI and the rapid pace at which it is maturing, measuring performance should include assessing unexpected outcomes, like alternative use cases or opportunities to redefine roles.
Setting clear responsibility and accountability measures for mitigating risks: Developing organizational governance and related policies to mitigate risks is important for technologies like gen AI that are new and evolve quickly. Good governance can take many forms, but it should include clear responsibility and accountability. Knowing which organizational leader is responsible for executing adoption, monitoring progress, and responding to potential risks and opportunities should be clear from the start. Importantly, without clear responsibility and accountability, it may be hard to develop trust in gen AI tools, and without trust, realizing the benefits of it may prove more difficult.26
Gen AI can help space professionals do their jobs better. At a time when the space industry is growing rapidly and experiencing talent challenges, gen AI can be an important tool for preserving industry growth. Exploring how gen AI can aid the space industry might not be as thrilling as exploring the moon or Mars, but it may help the industry get to those places sooner.
