Artificial intelligence (AI) is rapidly transforming industries, but a new study by NTT DATA reveals a critical challenge: The development of responsible AI governance is being outpaced by the speed of AI innovation.
This “AI responsibility gap” not only raises red flags for the future of AI adoption but also threatens to undermine the very progress AI promises. For this report, “The AI Responsibility Gap: Why Leadership is the Missing Link,” NTT DATA surveyed over 2,300 C-suite leaders and decision-makers across 34 countries, exposing a disconnect between AI enthusiasm and the preparedness to manage its implications.
“The enthusiasm for AI is undeniable, but our findings show that innovation without responsibility is a risk multiplier,” said Abhijit Dubey, Chief Executive Officer, NTT DATA, Inc. “Organizations need leadership-driven AI governance strategies to close this gap—before progress stalls and trust erodes.”
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Areas of Concern
While excitement about AI’s potential is palpable, leadership teams are struggling to balance innovation with accountability and ethical considerations with 80% of executives acknowledging that leadership, governance, and workforce readiness are lagging behind AI advancements. According to NTT DATA, this deficiency puts investments at risk, compromises security, and erodes public trust.
The report highlights several key areas of concern:
- Executives are divided on whether responsibility or innovation should take precedence, creating ambiguity and hindering the development of cohesive AI strategies.
- The lack of clear government regulations is stifling growth, with over 80% of leaders citing regulatory uncertainty as a barrier to AI investment and implementation.
- 89% of C-suite leaders worry about AI security risks, yet only 24% of CISOs believe their organizations have the framework to balance risk and value creation.
- A majority of respondents (67%) report their employees lack the skills to work effectively with AI, and 72% admit they lack an AI policy to guide responsible use.
- 75% of leaders acknowledge a conflict between AI ambitions and corporate sustainability goals, forcing them to reconsider energy-intensive AI solutions.
Key Recommendations
NTT DATA’s report emphasizes the urgent need for leadership-driven AI governance strategies to close this widening gap. The report calls for a shift towards “responsible by design” principles, where AI is built with security, compliance, and transparency integrated from the outset.
Finally, the report advocates for global collaboration on AI policy, bringing together businesses, regulators, and industry leaders to create clearer, actionable governance frameworks and establish global AI standards.
What This Means for ERP Insiders
Demand transparency in how AI algorithms are used. Users must also ensure they can understand the reasoning behind AI-driven insights and decisions to build trust and accountability. Users should be able to understand why AI has suggested something, not just accept it blindly. This is especially important in areas like financial forecasting or supply chain optimization, where AI decisions can have significant business impact. NTT DATA’s report shows that 89% of leaders are worried about AI security risks and demanding explainability can help mitigate some risks by identifying potential biases or errors in AI algorithms.
Advocate for responsible AI governance within your organization. This includes fostering clear policies on data privacy, security, and ethical AI usage within the ERP environment. For instance, consider the use of AI in HR modules within an ERP system. Users should advocate for policies that prevent bias in AI-driven recruitment or performance evaluations. Given that 72% of respondents in the NTT DATA report admit they do not have an AI policy in place, this advocacy is crucial.
The integration of AI into ERP systems will require new skills and competencies. ERP users should proactively seek training and development opportunities to ensure they can effectively work alongside AI-powered tools and leverage their potential. For example, users might need to learn how to interpret AI-generated reports, understand the limitations of AI models, or even collaborate with AI systems to make better decisions. Upskilling could involve training on data analysis, machine learning basics, or even specific AI functionalities within the ERP system. This will empower users to not only use AI tools effectively but also contribute to the ongoing improvement and refinement of these systems.
