Answering the ROI Question
43% of executives are willing to invest 20% of annual revenue in AI, but how will they measure ROI?
Engineers and executives have different views on how to measure the value AI delivers. Engineers are more likely to look for ROI from longer-term strategic improvements (61% vs. 53% of executives), such as enhanced brand reputation, innovation capacity, and competitive advantage. By contrast, executives tend to be more focused on achieving ROI through operational improvements (71% vs. 60% of engineers), such as efficiency gains and productivity improvements.
Strikingly, only 24% of respondents report full clarity on the ultimate ROI of their AI efforts. Further muddying the waters, engineers and executives tend to over- or underestimate the clarity their counterparts have on ROI. As companies work to integrate AI in their operations, this lack of clarity could create confusion about how leadership guidance aligns with their stated strategic goals.
Who understands AI ROI?
Executives
19%
believe they have full clarity of AI ROI
Engineers
31%
think their leadership have full clarity of AI ROI
Executives
17%
think engineers have full clarity of AI ROI
Engineers
28%
believe they have full clarity of AI ROI
TE Takeaway
When rolling out AI tools, it is important to define success. At TE, achieving engineering productivity is our priority. We want to see improved project output over the same duration of time and have set a goal of 30% more engineering hours in five years. Secondly, we expect improved quality in our projects and products. If done wisely, a bigger data source can provide better directed analysis of critical simulations. The Jevons paradox tells us that, as technology advances, efficiency gains will be lost by an increase in work. With this possibility in mind, it is crucial to ensure that the implementation of AI improves productivity for both internal processes and external outputs.
Ruediger Ostermann
Chief Technology Officer
Transportation Solutions