Dive Brief:
- Cost savings, return on investment and workforce efficiency are now the top metrics for measuring artificial intelligence success, compared with last year when innovation-led measures such as product design and data analysis dominated. That’s according to TE Connectivity’s 2026 Industrial Technology Index, which surveyed 1,000 C-suite executives and tech workers globally.
- Executives are more likely to define AI value through operational gains, with 71% of leaders prioritizing efficiency and productivity improvements compared with 60% of engineers. By contrast, engineers favor strategic improvements such as improved brand reputation and competitive advantage.
- The shift from innovation to more concrete ROI is partially a response to financial pressure on organizations, causing business leaders to place renewed focus on measurable returns.
Dive Insight:
With AI adoption now exceeding 80% across organizations, industry emphasis is shifting from proving the technology’s viability to demonstrating its business impact.
Yet, while industry members are increasingly aligned on the value of AI, there is debate over how to best define its ROI.
At the leadership level, TE Connectivity’s report found financial accountability is taking precedence. The rapid shift from last year’s focus on innovation-led metrics reflects broader economic pressures, pushing executives to align AI investments more closely with near-term returns.
The report warned that in doing so, businesses risk narrowing their focus too greatly on AI initiatives with immediate returns, missing out on potentially transformative innovations.
In addition, inconsistent definitions of AI ROI runs the risk of company-wide confusion as to its role. Almost one-third of engineers think their leadership has complete understanding of AI ROI, yet only 19% of executives said they have “full clarity,” according to the report.
Despite this lack of alignment, there is still a general thirst to deploy the technologies at scale, with 45% of engineers and 49% of executives reporting that they want to experiment with AI tools as soon as possible.
Eagerness to experiment comes with a warning from engineers, however, with 40% admitting they worry AI could limit their ability to apply judgment or creativity, compared with 27% of executives. This caution makes engineers more likely to question AI is delivering meaningful innovation, according to the TE report.
The tensions point to a deeper challenge: While organizations broadly recognize AI’s value, they are struggling to align on how that value should be measured. The disparity between executive and engineer opinions also creates a divide on whether AI should be considered for its technical capabilities or its financial worthiness.
The result is a delicate balancing act. As organizations scale AI, leaders must reconcile the need for immediate returns with the longer-term innovation potential that made the technology compelling in the first place.