Executives see room for revenue growth, but AI opinions split
Despite economic and geopolitical headwinds, most senior leaders still see room to grow. In a global survey of more than 1,500 board members and C-suite executives conducted by consulting firm Protiviti, 69% "somewhat" or "completely" agree there are significant opportunities to increase revenues over the next two to three years. The clear message: pursue growth, but weigh risk-adjusted returns in capital allocation.
Ecosystems over expansion
Leaders are betting on partnerships to move faster and do more with less. Sixty-two percent say ecosystem development is a priority, with strategic alliances viewed as a key enabler of co-innovation, broader market access, and operational efficiency.
Geographic expansion, however, is no free lunch. Only 52% put it on the front burner, reflecting uncertainty tied to trade policy, geopolitics, and regulatory complexity in cross-border operations.
- Define where partnerships can accelerate roadmaps, reduce CAC, or improve unit economics within 12 months.
- Set a partner scorecard: complementary capabilities, data-sharing posture, integration effort, time-to-value, and governance fit.
- Pilot with clear exit criteria and shared KPIs to avoid "zombie" collaborations.
AI: priority and risk in the same breath
AI is both a growth driver and a headache. Thirty-one percent of leaders are focused on integrating AI into current technologies and processes, yet AI sits sixth among near-term global risks. Cybersecurity is the top risk and investment priority, and IT infrastructure and performance jumped to fourth from 13th last year.
The top AI hurdles over the next two to three years: data and cybersecurity exposure (31%), integration across existing tech and processes (31%), and preparing the workforce to capture value (29%). Most leaders aren't worried about sci-fi threats - they're focused on organisational and operational risk.
- Stand up an AI governance rhythm with the CIO, CISO, Legal, Risk, and HR. Tie it into enterprise risk management and product lifecycle reviews.
- Prioritise AI use cases by financial impact, feasibility, and risk. Run time-boxed pilots with defined guardrails and a kill-or-scale decision.
- Get the data foundation right: classify sensitive data, enforce access controls, log usage, and formalise data contracts with partners and vendors.
- Refresh cyber controls for AI-specific threats (prompt injection, data leakage, model supply chain). Update incident response playbooks and tabletop them.
- Right-size infrastructure: capacity planning for compute and storage, cost controls, and reliability SLOs across MLOps and core IT.
- Upskill by role. Product, engineering, finance, and operations need different tracks and certifications; tie learning goals to performance plans.
- Contract smarter with AI vendors: spell out data usage, provenance, evaluation rights, model updates, SLAs, and exit terms.
If workforce enablement is a bottleneck, accelerate role-based upskilling with curated learning paths: AI courses by job function.
Capital discipline in an uncertain environment
Growth ambitions don't replace discipline. Boards and CFOs should test every dollar against risk-adjusted returns, especially as cyber risk rises and infrastructure demands grow. The bias should be toward scalable partnerships, clear AI use cases, and foundational IT upgrades that reduce risk while opening new revenue options.
- Approve partner selection criteria and a governance framework for data sharing and platform integration.
- Review the AI risk register, control baseline, and investment roadmap alongside cyber and IT priorities.
- Allocate a multi-year envelope for cybersecurity and core infrastructure to support AI and analytics workloads.
- Greenlight 3-5 alliance pilots with shared KPIs and quarterly readouts to the board.
For survey details and methodology, see Protiviti's research hub: protiviti.com.
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