“If a machine is expected to be infallible, it cannot also be intelligent.” — Alan Turing’s prescient observation takes on new urgency as artificial intelligence (AI) systems evolve from simple tools to autonomous agents capable of pursuing complex goals. Today’s technology leaders face an unprecedented challenge: building teams that can ensure these increasingly powerful systems remain both capable and controlled.
According to a global survey by the Institute of Electrical and Electronics Engineers (IEEE) , titled “The Impact of Technology in 2025 and byond,” AI is set to be the most critical technology in 2025. Most enterprise leaders (91 percent) believe there will be a generative AI ‘reckoning,’ as public fascination and perception is set to shift to a greater understanding of, and expectations for, what the technology can and should do.
In 2025, AI adoption is predicted to increase, with 33 percent of leaders excited to implement GenAI into small projects and 24 percent continuing to find practical uses for AI . However, to enact these strategy adjustments, a deeper understanding of the fundamentals and challenges of AI is required.
Technical prowess meets alignment expertise
Today’s technology leaders seek candidates who understand the intricate art of AI scaffolding –the practice of building programmatic frameworks that enable AI systems to form and execute complex plans while maintaining appropriate boundaries.
Consider a strategic planning AI. Through chain-of-thought prompting, it might break down a market expansion strategy into discrete steps, each validated against company values and risk parameters before execution. This structured reasoning process ensures the AI remains within operational bounds while delivering valuable insights.
Advanced scaffolding techniques have become essential skills. Professionals must implement ‘Tree-of-Thought’ methodologies that allow AI systems to explore multiple reasoning paths simultaneously – imagine an AI evaluating different approaches to a sensitive customer service issue, maintaining clear audit trails of its decision-making process while ensuring responses align with company values. These systems require sophisticated memory management to maintain context without unauthorised goal drift – a critical safety feature when handling sensitive business operations.
Recent incidents where AI systems have exhibited unexpected behaviours underscore the importance of value alignment expertise. The most sought-after candidates demonstrate mastery of constitutional AI approaches and reinforcement learning from human feedback. At one major tech company, inadequate oversight of an AI system led to it developing subtle but problematic biases in customer interactions – a situation that proper constitutional guardrails could have prevented.
The AI safety talent gap challenge
The shortage of AI safety talent is particularly acute because these roles require a sophisticated understanding of both technical systems and human values. Traditional computer science education rarely covers crucial aspects like goal decomposition and strategy planning. This gap became starkly apparent when a financial services AI began optimising for short-term metrics at the expense of long-term customer relationships – a classic example of inadequate goal structure design.
Organisations need professionals who understand how to implement proper scaffolding techniques such as recursive task decomposition. For instance, when an AI system manages supply chain optimisation, it should break down complex decisions into monitored sub-tasks, each checked against environmental and social responsibility metrics before implementation.
Building safety-first AI teams for 2025
For organisations just beginning their AI journey, building safety-first capabilities does not necessarily require immediately hiring top AI safety talent. Start by training existing technical teams in basic safety principles and scaffolding techniques. Smaller organisations can partner with AI safety consultancies or join industry consortiums focused on responsible AI development.
When hiring dedicated AI safety professionals, look for candidates with experience in implementing oversight mechanisms in complex systems. Certifications in AI ethics and safety, such as the IEEE’s AI ethics certifications[i], can indicate valuable expertise. More importantly, seek professionals who can demonstrate practical experience in implementing safety measures in production environments.
The future demands professionals skilled in meta-learning frameworks – systems that can safely acquire new capabilities while maintaining alignment with core values. For example, a customer service AI might learn new response patterns from interactions, but only after validating them against established ethical guidelines and customer satisfaction metrics.
Looking ahead: the safety imperative
Approaching 2025, organisations must recognise that building safe agentic AI requires a workforce skilled not just in development, but in safety and alignment. Success depends on finding and nurturing talent that can design and implement robust scaffolding while ensuring transparent and interpretable reasoning processes.
Organisations should create specialised roles focused on AI safety and alignment, developing clear career paths that emphasise safety expertise. This includes establishing internal centres of excellence for AI safety, where teams can develop and share best practices in scaffolding and oversight mechanisms.
The future of AI safety talent
The development of agentic AI systems presents both unprecedented opportunities and risks. Organisations that succeed will be those that prioritise building teams capable of ensuring these systems remain aligned with human values and interests through sophisticated scaffolding and oversight mechanisms.
For business leaders, the time to act is now. Begin by assessing the organisation’s current AI safety capabilities. Identify gaps in expertise and develop a concrete plan for building or acquiring necessary talent. Leaders need to consider both technical capabilities and ethical understanding when evaluating potential team members or training programs.
As agentic AI systems become more powerful and autonomous, the need for talent that can ensure their safe development and deployment will only grow. Leaders must invest now in building teams with the expertise to implement proper safety measures, alignment techniques, and robust scaffolding methodologies that will form the foundation of responsible AI development.
Eleanor Watson
Eleanor Watson is AI Ethics Engineer and AI Faculty at Singularity University and member of the IEEE.
