Semiconductor Companies Get Tough on AI Investment, but Measuring Returns Remains a Challenge

HTEC research finds semiconductor companies are ahead in scaling AI, yet fragmented measurement is making it harder to connect engineering gains to business outcomes

PALO ALTO, Calif.--(BUSINESS WIRE)--HTEC, the global AI technology and engineering leader, today released its research on the semiconductor sector, which reveals that the sector is relatively advanced in adopting AI but faces a more difficult challenge: proving that technical improvements translate into measurable business value.

Nearly half (48%) of semiconductor respondents describe their organizations as scaling AI or already AI-native, compared with 44% across the overall sample. Yet AI investment growth is broadly in line with the wider market, with 78% reporting increased investment over the past year compared with 79% overall.

The sharper divide appears in how AI investments are selected and measured.

Semiconductor respondents lead the sectors surveyed in selecting strategic alignment as a criterion for prioritizing AI initiatives and in requiring a formal ROI model before approval. But once projects are underway, measurement becomes less consistent, making it harder to determine whether improvements in areas such as design, verification, or manufacturing ultimately reduce costs, accelerate delivery, or improve product outcomes.

Craig Melrose, Global Managing Partner at HTEC, commented, “Semiconductor companies know how to measure engineering performance. The next challenge is applying the same discipline to AI value. Saving hours in verification matters when those hours translate into lower costs, faster delivery, greater engineering capacity, or a better product. As AI scales, the companies that pull ahead will be those that can make that connection consistently and use it to decide where the next investment should go.”

Strong investment discipline, weaker value measurement

45% of semiconductor respondents select alignment with growth or differentiation as a criterion for prioritizing AI initiatives, while 36% require a formal ROI model before an initiative is approved.

After deployment, however, the picture becomes more fragmented. 38% say AI value is mainly reported on a case-by-case basis, compared with 30% across the wider sample, while only 28% mainly report value through an enterprise dashboard, compared with 36% overall. Missing baseline metrics and inconsistent KPIs across functions are also among the hurdles respondents identify when measuring the value of AI.

Only 22% say AI's purpose, value and expected outcomes are clearly articulated across their organization, the lowest proportion of any sector surveyed. The findings point to a growing gap between demonstrating that an AI application works technically and consistently showing what it contributes to the business.

Scaling AI is becoming an organizational challenge

The research also suggests that the next barrier to AI value may lie beyond the technology itself.

83% of semiconductor respondents agree that people and processes constrain the value of AI more than technology does. Yet only 22% actively monitor AI adoption and outcomes across functions and 21% provide comprehensive AI enablement for non-technical employees.

Resistance to change and adoption challenges are the most frequently selected barriers to realizing value from AI in non-technical functions, followed by unclear use cases, priorities, or success measures, and lack of organizational alignment.

This suggests that as AI moves beyond specialist engineering teams, semiconductor companies will need to bring the same discipline they apply to technical performance to adoption, accountability and measurement across the wider business.

AI risk concentrates around the highest-risk applications

The research also highlights how semiconductor companies are approaching governance as AI assumes greater responsibility within engineering and production environments.

More than half (57.5%) measure AI risk alongside value for high-risk use cases only, while 16% measure risk consistently across their AI portfolio. Among technical respondents, 33% identify model unpredictability and edge cases as the primary complication for human-in-the-loop oversight, the highest proportion across the sectors surveyed.

As AI becomes more embedded in critical workflows, research points to the importance of determining where AI can operate autonomously, where human judgment remains necessary, and how these controls evolve as applications move closer to decisions that affect product quality or production continuity.

From silicon performance to enterprise value

Semiconductor companies occupy a unique position in the AI economy: building the technology that AI runs on while simultaneously applying AI to chip design, verification and manufacturing. This makes the sector an important test of whether AI can move from technical capability to repeatable business value.

AI can help teams explore more design options, prototype faster, identify problems earlier, and shorten development cycles. But the research argues that technical acceleration creates value when it ultimately results in a better product or stronger business outcome.

The challenge now is therefore less about proving that AI can work and more about building a repeatable way to determine where it creates value, measure that value consistently, and scale what works across the organization.

About the Research

The findings are based on 200 semiconductor executives and technology leaders across the United States, United Kingdom, Germany, Saudi Arabia and the United Arab Emirates, drawn from a global survey of 1,500 senior executives and technology leaders.

The full HTEC AI Value Realization Report 2026: Semiconductor Industry is available at https://htec.com/insights/reports/the-htec-ai-value-realization-report-2026-semiconductor-industry/.

About HTEC

HTEC is a global technology and engineering company that helps organizations build AI capabilities from silicon to outcomes. HTEC helps its customers build an adaptive, vendor-agnostic AI stack designed to evolve as technologies, models and platforms change.

Through its forward-deployed model, HTEC works alongside customers’ teams to connect AI to business strategy and create a repeatable capability consistently and faster each time. OneLoopAi is at the heart of this approach, bringing greater transparency to measuring progress and continuously improving how AI is developed and deployed. The result is a stronger path from AI investment to measurable business value and AI at scale.

With more than 15 years of experience working with AI and machine learning, HTEC combines deep engineering expertise with strategy, product, design and data capabilities to turn AI ambition into production.