AI Governance — The Unavoidable Imperative of Responsibility
Artificial Intelligence (AI) is no longer a niche technology; it’s becoming integral to product development. But with this rapid adoption comes the crucial need for governance—specifically, governance rooted in responsibility. Responsibility here means being personally accountable for AI outcomes, both positive and negative, while always acting with that accountability in mind.
AI governance is more challenging than with past technologies for three main reasons:
- Many AI users in product development lack formal training and the engineering discipline that typically accompanies new tech adoption.
- Users often access data without sufficient oversight, risking inaccuracies and irrelevant inputs that lead to issues like AI “hallucinations.”
- AI carries many poorly understood risks that newcomers may not recognize.
Unlike Product Lifecycle Management (PLM) systems—where guardrails keep processes aligned with strategic goals—AI often operates without these safety measures. This lack of guardrails makes governance essential.
The Scope of the AI Challenge
AI itself isn’t new, but its sudden widespread use and the rise of generative AI tools like ChatGPT have escalated concerns. Poor data quality feeding Large Language Models (LLMs) is a key issue. Many executives underestimate both the value of data and the importance of governance. This gap needs closing.
Effective AI governance goes beyond error tracking and user accountability. It requires alignment across the organization, based on four core elements:
- Ethical AI: Principles of fairness, transparency, and accountability.
- AI Accountability: Clear assignment of responsibility and ensuring human oversight.
- Human-in-the-Loop (HITL): Integrating human judgment to verify and override AI where necessary.
- AI Compliance: Meeting legal requirements such as GDPR, CCPA, and the AI Act.
Augmented intelligence, where AI supports human decision-making, always involves a human element. Despite appearances, AI is human-created and should be managed accordingly.
Key Pillars of AI Governance
- Transparency: AI models must be explainable with clear decision pathways and auditable results.
- Fairness: Bias detection and mitigation must be proactive.
- Privacy and Security: Protect personal data and ensure model integrity.
- Risk Management: Continuous monitoring throughout the AI lifecycle.
A Solution Provider’s Viewpoint
From the perspective of solution providers like Hexagon Manufacturing Intelligence, AI governance is about providing necessary guardrails for production-ready AI. It’s not only regulatory compliance; it’s about demonstrating to customers that AI systems are safe and reliable.
One major challenge is the lack of clear legal definitions for AI. Whether it’s a simple regression model or complex generative AI, traceability, explainability, and structured monitoring are essential. Explainability allows users to understand and verify AI decisions, building trust and improving workflows.
Industry Trends Backing AI Governance
Industry research, such as McKinsey’s Global Survey on AI, confirms that organizations are creating structures to extract value from generative AI. However, governance practices have not kept pace with this adoption, increasing risks related to bias, security, and compliance.
McKinsey also highlights the emergence of Agentic AI—AI systems that act autonomously in decision-making processes. These require new management models treating AI as “corporate citizens,” with clear governance on what decisions AI can make and how humans and AI interact.
For product development, AI’s integration is a tipping point. AI should augment human creativity, with humans curating AI-generated outputs and decisions.
Why Governance Has Lagged
- Validating AI outputs is difficult, especially as systems evolve from advisory to autonomous roles.
- There’s a lack of rigorous model validation and unclear ownership of AI-generated intellectual property.
- Regulatory guidance is shifting globally, creating uncertainty around compliance.
- Bias remains a significant problem, with AI systems sometimes amplifying existing inequities.
- Many AI models operate as black boxes, lacking transparency and explainability.
- Cybersecurity risks and adversarial attacks threaten AI system integrity.
- Public trust is weak, hindered by fears of misinformation, job displacement, and regulatory conflicts.
As one industry expert put it, policing the rapid proliferation of AI models is futile. Instead, governance must focus on how AI is used, setting guardrails like explainability, monitoring, and accountability anchored in safety and fairness. When managed properly, AI can protect both ROI and reputation.
Compliance Challenges in Product Development
Compliance challenges include:
- Data Privacy: Safeguarding personal information processed by AI.
- Intellectual Property: Managing ownership of inventions, algorithms, and data.
- Data Security: Maintaining confidentiality, integrity, and availability of AI data throughout its lifecycle.
- Discrimination and Bias: Preventing unfair or discriminatory AI outcomes.
Additionally, the environmental cost of AI is rising. Data centers supporting AI require massive capital investment and energy consumption, demanding a balanced approach to governance that also considers sustainability.
Building an Effective AI Governance Framework
Governance starts with policies aligned to organizational goals and the establishment of an AI ethics committee or oversight board. Risk assessment methodologies should be implemented to monitor AI processes for transparency and fairness.
Continuous auditing and feedback loops are essential to maintain accountability throughout AI decision-making.
Case studies from leading organizations show that lifecycle governance is cost-effective and critical. Measuring ROI should focus on:
- Cost savings, risk reduction, and reputation management.
- Metrics for compliance, bias reduction, and transparency.
- Business case alignment with stakeholder priorities.
- Continuous improvement driving innovation and efficiency.
Assigning ownership and accountability, paired with ethical design that prioritizes societal and environmental benefits, is key to successful governance.
The Core of Responsibility
Responsibility in AI is complex. Questions like “Who is responsible?” and “For what?” are no longer straightforward. Without comprehensive governance, accountability is unclear.
Creating a culture of responsible AI use requires collaboration across the organization and with external partners. Diverse expertise and cross-functional teams reduce blind spots and build trust.
The goal is clear: AI governance becomes everyone’s responsibility.
Final Thoughts
Govern smart, govern early, and govern always. Human oversight is non-negotiable in AI. The moment to act on AI governance is now. Organizations that take proactive steps to embed responsibility into AI development and use will be best positioned to succeed.
Your membership also unlocks:
