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Organizational and Financial Friction in the Aerospace Industry

In the previous article, “The Landscape of Data and AI Adoption and Technological Friction in the Aerospace Industry,” data gathered from the 2023 Frictionless AI Global Survey Report revealed that the industry is largely taking a cautious approach to data and artificial intelligence (AI) adoption. The data also revealed that technological friction was having a tangible impact on the industry’s ability to adopt and implement enterprise data and AI strategies. 

In this article, we’ll explore what the survey data revealed about the remaining two areas of friction – organizational and financial friction – as they occur in the aerospace industry.


Organizational Friction in the Aerospace Industry

A reminder, friction is any issue, including delays, mistakes, misalignment, and miscommunications that lead to project delay or failure in data and AI strategies. Organizational friction is friction that exists between departments, teams, and individuals. Organizational friction can affect organizations both “horizontally” (between different teams and domains) and “vertically” (between departments, teams, and individuals at different levels of seniority or job function). 

To begin on the organizational side, there’s one interesting aspect that has both positive and negative connotations. Aerospace was the least likely industry to say they struggle to find enough data science talent at 70%, compared to the survey average of 75%. While it’s positive that the industry was the least likely to have trouble finding data science talent, 70% is still a very high number. 

That statistic becomes more troublesome when we consider that aerospace respondents were the least likely to say their organization has a structured data science enablement program in place at 79%, compared to a survey average of 89%. Moreover, aerospace was the least likely industry to believe they can scale AI projects without training domain experts to embrace data science at 52%, compared to the survey average of 60%. This makes the relative shortcomings of dedicated data science enablement programs even more glaring. And, adding to the woes, the data from the aerospace industry revealed that one of its three biggest obstacles to AI deployment was “lack of skills specific to AI within my organization.” Data science enablement programs are a direct way to combat this lack of skills, especially when talent is scarce.

Moving on, non-management respondents from the aerospace industry were the most likely group to say their decision-makers “have not allocated enough resources focused on AI or machine learning improvements” at 44%, compared to the survey average of 37%. 

Additionally, when asked what challenges are negatively affecting AI initiatives at their organization, aerospace was the most likely industry to say “our data experts struggle to explain insights to non-technical domain experts” at 32% (survey average was 26%), and “technical experts have difficulty communicating their needs to other departments using layman’s terms” at 37% (survey average 31%). This demonstrates a combination of both organizational and technological friction. These communication issues largely stem from the sector’s narrowly defined departments. While these focused departments help spur high-end technology, they also create silos that can slow or stop the sharing of useful information. As such, departments are often not operating with similar knowledge bases or jargon, which causes organizational friction.

When asked what their common first step is when starting AI projects, aerospace was most likely to say “assign a small staff of internal data experts to the project” at 48%, compared to the survey average of 32%. Given the worldwide lack of data science talent as demonstrated earlier, this is probably a cause of friction, as organizations only have a limited amount of internal data expertise and need to improve their ability to democratize data analytics via better tools and training.

Lastly on the organizational front, executives/decision-makers from the aerospace industry were the most likely to say they’ve made a mistake by “not understanding how the results of using the tools can inform our business strategy” when trying to implement AI tools at 48%, compared to the survey average of 33%.

The data is clear – aerospace organizations struggle with a combination of both organizational and technological friction, and in some instances, these types of friction can overlap and compound.


Financial Friction in the Aerospace Industry

Financial friction presents itself when budgets are tight, resources are spread thin, and projects need to provide a return on investment. It can be most apparent when trying to invest in a new initiative or trying to scale efforts with expensive, legacy investments.

As mentioned in the previous article, aerospace was the least likely to say they’re looking to scale their data science approach at 41% (survey average was 52%). The industry was also the least likely industry to say they’re looking to make a “financial and/or resource investment” at 37%, seven points lower than the survey average of 44%. Moreover, when asked what challenges are negatively affecting AI initiatives at their organization, aerospace was the most likely industry to say “leadership is too fixated on the upfront costs to understand how investing in AI or machine learning would benefit our organization” at 37% (survey average was 28%).

Beyond these financial-specific statistics, many causes of organizational and technological friction are also heavily intertwined with financial decision-making. Thus, financial friction can extend beyond “financial” decisions and impact the entire organization and its data and AI approach.



Friction can derail data science and AI initiatives for a variety of reasons. Throughout these two aerospace-focused articles, we hope that the landscape of data and AI adoption and friction has become clearer. 

Learn more about friction in today’s world – and how Altair can remove friction via our unique Frictionless AI approach. For more information about Altair’s data analytics and AI solutions and Frictionless AI initiatives, check out the following resources:



In March 2023, Altair commissioned Atomik Research to conduct an international, online survey of 2,037 professionals employed throughout several target industries in 10 countries who work with data in some capacity to drive valuable insights for their organization. Target industries included aerospace, automotive, banking, financial services, and insurance (BFSI), consumer electronics, heavy/industrial equipment, and technology. The data in this article is an examination only of the data gathered from the 71 respondents who identified themselves as working within the aerospace industry. The margin of error for the survey is +/- 3% with a 95% confidence interval.

*Due to the sample group’s small population in comparison to the overall survey, Altair does not consider this to be a comprehensive representation of the aerospace industry, but does believe it constitutes a “representative cross-section” that gives readers valuable insight into industry trends.

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