Classification
AI Ecosystem and Enabling Technologies
Overview
Emerging tech drivers are foundational or adjacent technologies that significantly accelerate the adoption, deployment, and impact of artificial intelligence (AI) systems. These include mobile computing (enabling ubiquitous access to AI-powered applications), augmented/virtual reality (AR/VR, enhancing human-computer interaction and data visualization), blockchain (providing data integrity and decentralized trust), the Internet of Things (IoT, generating vast real-time data streams for AI), and Privacy-Enhancing Technologies (PETs, safeguarding sensitive data during AI processing). Each of these technologies brings unique capabilities but also introduces new challenges, such as interoperability, security vulnerabilities, and scalability limitations. For example, integrating IoT data with AI requires robust data governance, while PETs may limit model performance due to privacy constraints. The interplay of these drivers with AI is dynamic and context-dependent, making their governance and risk management complex and evolving. As these technologies mature and converge, their combined influence is expected to reshape industries, create new business models, and transform how AI solutions are developed, deployed, and regulated.
Governance Context
Governance of emerging tech drivers is addressed in frameworks such as the EU AI Act and NIST AI Risk Management Framework (AI RMF). For example, the EU AI Act imposes obligations on high-risk AI systems leveraging IoT data, including requirements for data quality, traceability, and cybersecurity. The NIST AI RMF calls for organizations to implement controls around data provenance, security, and transparency when integrating AI with technologies like blockchain or PETs. Additionally, sectoral regulations such as HIPAA (for healthcare IoT) mandate strict privacy and security controls. Organizations must conduct impact assessments, ensure ongoing monitoring, and document technical and organizational measures, especially when deploying AI in conjunction with these emerging technologies. Two concrete obligations include: (1) implementing technical and organizational measures for data quality, traceability, and cybersecurity (e.g., under the EU AI Act), and (2) conducting regular impact assessments and maintaining documentation of risk controls (e.g., per NIST AI RMF). Failure to address these obligations can result in regulatory penalties, reputational harm, or systemic risks.
Ethical & Societal Implications
Emerging tech drivers amplify both the benefits and risks of AI adoption. They can exacerbate privacy concerns, increase attack surfaces, and deepen digital divides if not equitably deployed. PETs may help mitigate some privacy risks but could limit the utility of AI in certain contexts. The convergence of these technologies can challenge traditional accountability mechanisms and complicate consent, transparency, and explainability. Societal impacts include shifts in labor markets, potential for surveillance, and questions of digital trust. Addressing these implications requires proactive governance, inclusive design, ongoing stakeholder engagement, and consideration of unintended consequences such as algorithmic bias, exclusion of marginalized groups, and the risk of over-reliance on automated systems.
Key Takeaways
Emerging tech drivers accelerate and shape the adoption of AI across sectors.; They introduce new governance, security, and interoperability challenges.; Frameworks like the EU AI Act and NIST AI RMF impose concrete controls for these technologies.; Failure to address risks can result in regulatory penalties and systemic harms.; Ethical and societal impacts require ongoing vigilance and adaptive governance.; Combining these technologies can magnify both benefits and risks in AI applications.; Proactive impact assessments and robust documentation are critical for compliance.