Agentic artificial intelligence (AI) represents a new frontier in AI, defined by systems that can autonomously make decisions, take action and drive outcomes with minimal human intervention.
Designed to tackle complex, multi-step challenges, agentic AI leverages cutting-edge technologies like large language models and advanced reasoning capabilities to navigate dynamic environments and deliver results.
Unlike traditional AI, which operates within rigid, predefined rules, agentic AI is adaptive and continuously learns from experience − empowering organisations to unlock greater efficiency, innovation and competitive advantage.
As of 2025, agentic AI is transitioning from experimental stages to practical applications across various sectors:
Enterprise adoption: Organisations like Ernst & Young (EY) have integrated agentic AI into their operations. EY’s agentic platform assists 80 000 tax professionals with data collection and compliance tasks, enhancing productivity without reducing workforce size.
Financial services: Agentic AI is utilised in banking for fraud detection, customer service and algorithmic trading, offering real-time decision-making capabilities.
Retail and e-commerce: Companies are deploying AI agents to manage inventory, personalise customer experiences and streamline supply chains, increasing efficiency and customer satisfaction.
Scientific research: In fields like chemistry and biology, agentic AI aids in hypothesis generation, experiment design and data analysis, accelerating the pace of discovery.
Despite these advancements, system reliability, ethical considerations and integration with existing workflows remain. Ensuring transparency, accountability and alignment with human values is critical as agentic AI systems become more prevalent.
To safeguard against the challenges of agentic AI, companies need to prioritise system reliability, ethical responsibility and seamless integration into their operations. This starts with rigorous testing, continuous monitoring and regular updates to ensure systems perform reliably across diverse scenarios.
Building in redundancy and human oversight mechanisms helps mitigate risks of failure or unexpected outcomes.
Building in redundancy and human oversight mechanisms helps mitigate risks of failure or unexpected outcomes. At the same time, adopting an AI ethics framework, forming cross-functional ethics committees, and conducting fairness and bias audits are critical steps to ensure AI systems align with human values, respect privacy and avoid reinforcing harmful biases.
Equally important is ensuring agentic AI integrates smoothly into existing workflows. Companies should map out how human-AI collaboration will work, provide adequate employee training, and roll out AI systems in phased pilots to identify and resolve challenges early.
Transparency and accountability must also be built into the process, using explainable AI techniques, clear decision-making documentation, and defined chains of responsibility. By combining strong governance with a culture of ethical awareness and technical diligence, organisations can harness the benefits of agentic AI while minimising risks and maintaining stakeholder trust.
A company is ready to adopt agentic AI when it has strong foundations in strategy, technology, data management, governance and organisational culture.
This means the company doesn’t just have technical capabilities and the right leadership mindset, operational frameworks and risk management in place. It should already have experience with simpler forms of AI (like analytics or machine learning), well-governed data practices, and a track record of successfully integrating digital innovations into its workflows.
Without these, adopting autonomous and adaptive AI systems can introduce chaos, inefficiency, or even reputational harm.
Five maturity measurements
Strategy and leadership alignment: The company has a clear, well-communicated AI strategy aligned with business goals. Leadership actively sponsors AI initiatives, understands opportunities and risks, and allocates appropriate resources.
Data maturity: The organisation has high-quality, well-governed and accessible data. It knows where its critical data lives, has strong privacy and security controls, and has experience using data to drive decisions.
Technology and infrastructure readiness: The company has scalable and flexible IT systems, cloud platforms, APIs and integration tools supporting agent-based AI. It is also experienced with earlier-stage AI models like predictive analytics or machine learning.
Governance and risk management: Formal governance structures are in place for AI − including ethics policies, compliance checks, bias monitoring and accountability frameworks. The company is prepared to manage risks such as bias, transparency and regulatory compliance.
Culture and skills: The workforce is digitally literate, open to innovation and trained in AI basics. There is a culture of collaboration between technical teams and business units, and employees are empowered to work alongside AI agents without fear of job loss or disruption.
Agentic AI offers transformative benefits to businesses by enabling systems that can autonomously manage complex tasks, make decisions and adapt to changing conditions with minimal human oversight.
This advanced capability drives significant efficiency gains, reduces operational costs and accelerates innovation by automating multi-step processes that once required manual intervention. Businesses can improve decision-making speed, optimise workflows, and deliver more personalised and responsive customer experiences.
By freeing human teams to focus on high-value, strategic activities, agentic AI boosts productivity and strengthens a company’s competitive-edge in an increasingly dynamic market landscape.
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