For the second year in a row, researchers affiliated with MIT Sloan Management Review, working in collaboration with Boston Consulting Group, convened an international panel of artificial intelligence experts to examine how organizations are approaching responsible AI. This work is part of the broader Responsible AI initiative, which draws on global executive surveys and expert panels across industries and regions to assess how companies define, govern, and invest in responsible AI practices.
The conclusion from this latest research cycle is blunt. Most experts believe organizations are not investing enough in responsible AI, even as AI related risks become harder to ignore.
What Responsible AI Means
Responsible AI refers to the policies, practices, and systems that ensure artificial intelligence is used in ways that are ethical, transparent, safe, and accountable. These efforts are meant to reduce bias, protect privacy, manage risk, and build trust in AI systems. In practice, responsible AI often includes governance frameworks, risk monitoring, internal standards, and oversight mechanisms that guide how AI tools are developed and deployed.
The initiative focuses not only on technical safeguards but also on leadership awareness, organizational culture, and long term accountability.
Why Investment Is Falling Short
The data shows a clear gap between awareness and action. Eleven out of thirteen expert panelists were reluctant to agree that companies are making adequate investments in responsible AI. This aligns with the 2023 global executive survey, where fewer than half of respondents said their organization was prepared to invest meaningfully in responsible AI, even if it meant higher costs or reduced revenue.
Several reasons explain this shortfall. Profit pressure is a major factor. Some experts argue that many companies prioritize speed to market and revenue growth over risk management, especially during the current surge of interest in generative AI. The fear of falling behind competitors has shortened development timelines and, in some cases, reduced attention to safety and oversight.
Another challenge is scale. As AI spreads across more business functions, the scope of responsible AI programs expands. Generative AI tools, in particular, increase the number of users and use cases that responsible AI teams must support, driving up costs and complexity.
There is also a risk perception problem. Growing awareness of AI risks does not always translate into accurate assessments. Some organizations underestimate how serious or far reaching those risks can be, leading them to underinvest in safeguards.
What the Data Shows
Survey results reinforce these concerns. In the global executive survey of more than 1,200 respondents from companies with at least 100 million dollars in annual revenue, only 48 percent said their organization was prepared to invest to a moderate or great extent in responsible AI initiatives. Nearly one third said they were prepared only to some extent, while others reported minimal or no readiness.
Experts also pointed out that there is no shared standard for what counts as an adequate investment. Without common benchmarks or verification mechanisms, companies struggle to judge whether their spending is sufficient or effective.
What Experts Are Saying
Panelists broadly agree that responsible AI investments lag behind overall AI spending. Many note that organizations are racing to adopt AI capabilities while playing catch up on safety, governance, and risk management tools. Others emphasize that responsible AI programs vary widely in design and maturity, making it difficult to compare efforts across companies.
Several experts stress that uncertainty should not become an excuse for inaction. Even if standards are still evolving, AI risks already pose real threats to reputation, compliance, and long term value creation.
The researchers offer three core recommendations for organizations that want to close the investment gap.
First, leaders must build stronger awareness of AI risks. AI can drive growth, but without proper guardrails, it can also undermine value. Education, cross functional discussion, and inclusive responsible AI programs can help leadership understand both sides of the equation.
Second, companies should accept that responsible AI investment is ongoing. As AI technologies evolve and regulations change, responsible AI programs must adapt alongside them. Delaying investment only makes future compliance and risk management more disruptive and expensive.
Third, organizations need clearer metrics for responsible AI investment. Leaders should agree on what counts as an investment, how success is measured, and how responsible AI funding connects to broader AI projects. Like cybersecurity, the payoff often comes from preventing problems before they occur, even if that success is not always visible.
A Growing Leadership Challenge
The message from the data is consistent. As AI becomes more central to business strategy, responsible AI can no longer be treated as a secondary concern. Experts warn that companies that fail to invest adequately now risk falling behind not only on compliance and trust, but also on innovation itself.
