Responsible Corporate AI Deployment: What Executives Need to Do Now
New findings released December 9, 2025 show clear momentum and equally clear gaps in how companies plan to deploy AI responsibly. The research blends views from corporate leaders, investors, and the American public. Quarterly tracking will continue through 2026, giving leaders a consistent signal on expectations and best practices as they shift.
Where the three groups agree
- AI's upside: Majorities expect net societal benefits within five years. Corporate leaders are most bullish (93%), followed by investors (80%) and the public (58%).
- Safety matters: AI safety and security rank near the top for each group (public: 53%, investors: 62%, corporate leaders: 46%).
- Back the workforce: Training is seen as essential by the public (90%) and investors (97%). About three-quarters of leaders plan to implement AI training for employees.
Where views split
- Safety spend: Investors and the public expect companies to invest more than 5% of total AI budgets in safety. Many executives plan 1-5%.
- Who benefits from AI gains: Executives currently lean to shareholders (28%) and R&D (30%), with less earmarked for worker training (17%).
- Environmental planning: Roughly a third of both the public and corporate leaders expect negative environmental effects from increased AI use. Only 17% of leaders include environmental impact planning in their AI roadmap, and 42% say it is not part of their strategy.
Executive playbook: 90-day actions
- Set a safety floor: Commit at least 5-10% of your AI budget to safety and security. Adopt a recognized framework like the NIST AI Risk Management Framework. Fund model evaluations, red-teaming, monitoring, and an incident response plan.
- Assign ownership and cadence: Name a senior AI risk owner (with budget). Establish a cross-functional council spanning product, risk, legal, security, HR, and audit. Review incidents, metrics, and model changes on a monthly and quarterly rhythm.
- Back your people with real training: Stand up companywide AI literacy plus role-based tracks for priority jobs. Tie learning to workflows and measurable productivity goals. If you need ready-made pathways, explore AI courses by job role to accelerate implementation.
- Pre-commit value distribution: Ring-fence a fixed share of AI-driven gains to workforce training and safety (e.g., 20-30%), then define your split across R&D and shareholders. Make the policy public to build trust.
- Measure the environmental footprint: Track energy use and emissions per workload, prefer efficient models, and select vendors with clear energy data. For context on expected grid impacts, see the IEA's guidance on data centers and AI. Add "sustainability by default" to procurement and architecture reviews.
- Be transparent with customers and employees: Publish AI principles, known use cases, human-in-the-loop criteria, and escalation paths. Disclose material AI-driven decisions that affect users or workers. Report safety incidents and fixes.
- Build the metrics that matter: Track model performance drift, safety incidents, time-to-detection, false-positive/negative rates, training completion, task-level productivity, and environmental intensity. Tie incentives to these metrics.
- Pulse sentiment and adjust: Use quarterly surveys and feedback channels to understand shifting expectations across customers, employees, and investors. Close the loop by sharing what you changed.
Why this matters for strategy
The data is clear: people support AI's promise and expect disciplined safety, real employee support, and environmental accountability. Companies that set these guardrails early will move faster with fewer surprises. Those that delay will spend more time in cleanup and catch-up mode.
The surveys were fielded with The Harris Poll, Robinhood Foundation, and Gerson Lehrman Group. The full report and methodology are available from the organization and will be updated quarterly through 2026.
Next steps
- Confirm a safety budget floor and publish your AI principles.
- Launch role-based training with measurable outcomes; if you need a jumpstart, review the latest AI courses.
- Set environmental KPIs for AI workloads and hold vendors to them.
- Pre-announce how you will allocate AI-driven gains across workers, R&D, and shareholders.
The companies that act on this now will earn trust, attract talent, and ship better products with fewer risks. That is the edge.
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