Altair Newsroom

Featured Articles

Frictionless AI Organizational Role Breakdown: Examining Data and AI Friction Part Five

In this series’ previous four parts covering the three types of friction – organizational, technological, and financial – we outlined how and why today’s organizational data and artificial intelligence (AI) strategies run into difficulties. We also explored the landscape of these strategies’ adoption, including how prevalent they are, how organizations are using them, and more. In this article, we’ll examine the data from the Altair 2023 Frictionless AI Global Survey Report to dive deeper into how employees of different role levels (e.g. executives and non-executives) responded to the survey’s various questions.


Frictionless AI Organizational Role Breakdown

Within an organization, it’s important for teams to be in alignment so work can get done smoothly, quickly, and ahead of schedule, so everyone gets the most out of the organization’s investments. When departments, teams, and individuals aren’t in alignment, it causes organizational friction. Let’s analyze the survey data as segmented by organizational role to see what, if any, differences lie between executive-level and user-level employees when it comes to friction within organizational data and AI strategies.

To begin, the data suggests that executive-level and user-level employees don’t see eye to eye on data science enablement programs’ efficacy. Recall that most organizations create dedicated data science enablement programs to upskill current employees. Upskilling is important because both executive-level and user-level employees agree on the difficulties of finding data science talent — the groups said their organizations struggle to find enough data science talent at nearly identical rates (74% and 75%, respectively). However, while 96% of executive-level respondents said there was a structured data science enablement program in place at their organization, just 83% of user-level employees said the same. That means just 4% of executive-level employees said there was no data science program in place, compared to 17% of user-level employees. This disconnect may be a cause of organizational friction.

That wasn’t the only response that revealed a disconnect between executives and users. When asked if they believe their organization can scale AI projects without training domain experts in data science, 69% of executives said “yes,” while just 51% of users said the same.

Additionally, though by a smaller margin, more executives (33%) believe teams are working in silos and not communicating effectively across teams compared to user-level employees (25%). Below, you can see how executives’ and users’ views differ slightly on what they believe hinders their organization’s ability to deploy effective organizational AI strategies.

Here, you can see how executives’ and users’ views differ on what challenges they believe can be solved with AI within their organization.

There are also, however, many areas of alignment between these two groups. In general, executives and users are aligned on the challenges that teams and organizations face and understand a lack of talent leads to a great deal of organizational friction. For example, they showed nearly identical responses when asked what percent of data science projects hadn’t made it to production within the past two years — both groups said that more than half of such projects failed (33% and 32%, respectively). They also believed their organization makes working with AI tools more complicated than it needs to be (65% and 60%, respectively).



Overall, the data shows that there are a few significant areas of disconnect between executive-level employees and user-level employees surrounding organizational data and AI initiatives. These areas can be a significant source of friction throughout organizations. There’s an especially concerning disconnect between these groups concerning the availability of dedicated data science enablement programs. In a global landscape where basically every organization struggles to hire the data science talent they want, it's crucial that organizations’ existing employees have the ability to upskill their toolkit so they can start providing data insight to teams, departments, and beyond.

Click here to read the final part in this series, "Frictionless AI Geographical Location Breakdown: Examining Data and AI Friction Part Six."

For more information, be sure to check out more of Altair’s Frictionless AI resources, including: