Over half of INSEAD faculty named AI and digital transformation the leading issue for businesses to address in 2026. No other area registers as simultaneously the most significant growth opportunity and the most significant threat.
61%
of INSEAD faculty cite AI as the top business priority for 2026
44%
also identify AI as a leading business threat this year
2x
faster productivity growth in advanced vs. lower-income economies from AI
3
countries hold the lion's share of global AI infrastructure and capability
INSEAD faculty survey responses on AI: share identifying it as an opportunity vs. a threat, broken down by sector of expected impact.
Source: INSEAD Global Trends Report 2026 faculty survey.
Why both readings are valid
AI's broad applicability across sectors means it concentrates both upside and downside risk simultaneously. A technology that rewrites productivity curves in finance also displaces roles at scale.
Regulation urgency
Faculty responses reflect growing consensus that monitoring and governance frameworks must advance faster than the technology itself, particularly as AI intersects with geopolitics and financial stability.
Applicability as a risk factor
Unlike sector-specific risks, AI spans healthcare, defense, financial services, and education simultaneously, making containment strategies harder to design and coordinate.
INSEAD assessment: No other issue in the 2026 survey attracted such a high dual reading, where the same technology is named by the highest share of respondents as both a growth priority and a threat. That duality reflects AI's reach across critical systems rather than concentrated impact in a single domain.
Projected AI-driven productivity growth rates by country income group (2025-2030), and the widening gap between high-income and low-income economies.
Source: IMF World Economic Outlook 2025; McKinsey Global Institute AI Impact Report 2025; INSEAD.
Advanced economies
High AI readiness, strong digital infrastructure, and existing capital bases position advanced economies to absorb productivity gains at roughly twice the rate of lower-income peers.
Middle-income economies
Countries in this tier face a distinct pressure: reducing reliance on AI capabilities concentrated in the US and China while building domestic capacity fast enough to remain competitive.
Lower-income economies
Limited infrastructure, lower capital intensity, and constrained access to frontier AI systems mean productivity gains are projected to arrive more slowly and with greater variance.
The compounding problem: AI widens the productivity gap not through exclusion, but through speed. Advanced economies can deploy, iterate, and reinvest faster, creating a self-reinforcing advantage. INSEAD researchers note that without deliberate policy intervention, this cycle will deepen existing income inequalities at the global level.
Estimated share of global frontier AI capacity (compute, model development, data infrastructure) concentrated by geography, alongside regulation posture ratings.
Source: Stanford HAI AI Index 2025; OECD AI Policy Observatory; Georgetown CSET 2025.
US
Leads in frontier model development and compute investment. Policy stance in 2026 has shifted toward voluntary standards over binding regulation, prioritizing speed to market.
China
Advances domestically developed models while tightening state control over training data. Internationally promotes inclusive cooperation frameworks while restricting data sharing.
EU
Leads on binding regulation via the AI Act, with risk-tiered requirements now entering enforcement. Lags on compute capacity relative to the US and China.
Rest of world
Middle-income nations are pursuing sovereign AI programs to reduce dependence on the two dominant powers, with India, the UAE, and Saudi Arabia investing most aggressively.
Bloc formation: The world is not merely seeing trade fragmentation but AI bloc formation, where nations align their data governance, infrastructure investment, and regulatory frameworks with one of the dominant AI powers. INSEAD faculty flag this as a dimension of geopolitical risk that current business strategy frameworks have not yet fully priced in.
AI's dual climate role: business leader views on AI's ability to accelerate sustainability, set against projected AI energy and water demand growth through 2030.
Source: IEA World Energy Outlook 2025; Goldman Sachs AI Power Demand Report 2025; INSEAD faculty survey 2026.
The optimist case
A majority of business leaders believe AI can accelerate sustainability efforts, from optimizing grid management to reducing industrial waste. Several major corporates have committed AI-assisted emissions tracking.
The resource cost
Training and running large AI models consumes substantial electricity and water for cooling. IEA projections show global AI-related power demand growing sharply through 2028, concentrated in data center infrastructure.
Net emissions risk
Experts caution that AI's energy demands could offset emissions reductions achieved in other sectors. The arithmetic depends heavily on whether AI data centers are powered by renewables or fossil fuels.
The offsetting concern: INSEAD researchers note that AI's climate impact is not inherently positive or negative, but contingent on the energy mix powering it. A model trained on a coal-heavy grid while delivering marginal productivity gains is a net climate liability. The sustainability case for AI rests on policy decisions being made now about data center energy sourcing.
Analysis based on: INSEAD Global Trends Report 2026 · IMF World Economic Outlook 2025 · IEA World Energy Outlook 2025 · Stanford HAI AI Index 2025 · McKinsey Global Institute · Georgetown CSET 2025 · OECD AI Policy Observatory · Goldman Sachs AI Power Demand Report 2025