The evolution of artificial intelligence (AI) has entered a new phase, one defined not just by systems that generate content, but by systems that can autonomously plan, decide and execute actions to achieve defined goals with minimal human intervention; this is known as Agentic AI.
These systems have the potential to manage complex workflows, improve productivity, accelerate speed-to-value, enhance operational efficiency, and enable faster, more informed decision-making. Agentic AI can move businesses from reactive problem-solving to proactive, opportunity creation.
This is important because in today’s fast-paced, data-driven world, the ability to act decisively and intelligently at scale is a critical competitive advantage. Organisations that embrace Agentic AI as a strategic partner can relieve their employees of repetitive or operational tasks, enabling them to focus on creativity, strategy, and innovation.
Yet, realising the full potential of Agentic AI goes beyond technology alone. It requires leadership that drives responsible adoption, a workforce equipped with the skills and mindset to collaborate effectively with AI, and organisatonal governance frameworks that ensure its use is ethical, transparent, and accountable.
Clear metrics to measure outcomes, a culture that encourages seamless collaboration between humans and AI, and operational guardrails that balance innovation with risk management are all critical. When these elements come together, organisations can create an environment where AI amplifies human capabilities, drives tangible business value, and builds a lasting competitive advantage.
Many organisations across industries are already exploring Agentic AI to drive efficiency and enable smarter decision-making and several businesses are now also transitioning from pilot projects to full- scale enterprise implementations. Autonomous agents are, for example, optimising supply chains in manufacturing, automating routine customer service in retail, analysing complex datasets in finance, supporting clinical decisions in healthcare, and monitoring infrastructure in energy and telecoms.
For businesses just starting on this journey, a strong first step is to identifying tasks or processes where autonomous AI can deliver tangible business value. These often include repetitive, data-heavy, or decision-intensive activities, such as operational workflows, large-scale data analysis, customer service interactions, or elements of strategic planning. From there, leaders can launch a small, focused pilot using accessible AI platforms or pre-built agents to test assumptions, rapidly gain insights, and measure outcomes. This controlled approach can allow teams to observe AI performance within existing processes, collect real-world data, and uncover technical, operational, or cultural challenges.
Insights from this pilot can then refine the approach, define clear success metrics, and determine where scaling AI will deliver the most value. Throughout the process, it is essential to ensure that strong governance, ethical stewardship, leadership support, and comprehensive employee training aspects are in place to drive adoption and maximize impact.
To support this, the quality of leadership has become even more important, where leaders are expected to create clarity of purpose, articulating why AI matters, how it solves real business problems, and how it creates value for customers and employees. A human-centred approach, grounded in empathy, judgement, and ethics , is also key as it can ensure that AI augments human capabilities while addressing workforce concerns and creating opportunities for reskilling and development.
Furthermore, responsible AI is no longer a technical consideration; it is a core leadership mandate. This require executives to understand and actively manage risks related to bias, data privacy and security, while establishing clear accountability and embedding ethical principles into AI design, deployment, and decision-making.
Equally important is inclusive leadership. Without deliberate action, AI can widen inequality if access, skills, and opportunity remain concentrated. Leaders therefore have a responsibility to democratise access to AI, invest in broad-based skills development, and ensure diverse perspectives shape AI systems.
When done well, this not only drives better and more innovative and impactful business outcomes, but can also generate societal value by expanding opportunities, strengthening trust, and promoting more inclusive economic growth.
'Writes in her personal capacity', Vinoliah Martin - Technology Executive, Board Member, PhD researcher.
Share
