AI in 2025: 10 Trends to Watch Out
AI Trend 1: Agentic AI
Agentic AI is redefining how autonomous systems operate, enabling them to independently make decisions and execute actions to achieve specific goals. These systems are particularly invaluable in high-stakes scenarios such as disaster response, where timely decisions can save lives, and real-time analytics, where rapid data processing drives competitive advantages.
Why It Matters
Agentic AI combines advanced techniques such as memory retention, environmental sensing, strategic planning, and adaptive learning. By 2028, Gartner predicts that 15% of daily work decisions will be autonomously made by agentic AI systems, up from virtually none in 2024. This evolution marks a paradigm shift in operational efficiency and decision-making across industries, heralding a new era of productivity and innovation.
Key Use Cases
AI Trend 2: AI Governance Platforms
As AI becomes integral to decision-making, ensuring its ethical and responsible deployment is paramount. AI governance platforms are emerging as essential tools for organizations to maintain control over their AI initiatives. These platforms provide robust frameworks to monitor, regulate, and fine-tune AI systems, ensuring alignment with societal values, corporate ethics, and regulatory compliance.
Why It Matters
AI governance addresses critical issues such as bias, transparency, accountability, and ethical use of technology. With AI systems influencing areas like hiring, lending, and healthcare, ensuring fairness and equity is no longer optional. By 2028, organizations leveraging these platforms are expected to achieve 30% higher customer trust ratings, signalling their commitment to responsible AI practices.
Key Use Cases
AI Trend 3: Disinformation Security
The rise of sophisticated AI tools has intensified the challenge of disinformation, presenting risks across industries and societies. Disinformation security, a cutting-edge discipline, leverages AI and digital forensics to detect, analyse, and mitigate the spread of fake news, deepfakes, and malicious impersonations. These tools utilize machine learning models trained on vast datasets to distinguish between authentic and manipulated content, providing a proactive defence mechanism against emerging threats.
Why It Matters
Adversaries are increasingly exploiting AI for social engineering, fraudulent activities, and targeted misinformation campaigns, creating significant challenges for organizations and governments alike. The financial and reputational damages caused by unchecked disinformation can be profound. Robust disinformation security measures are no longer optional; they are essential to maintaining trust, safeguarding information integrity, and ensuring organizational resilience. By 2028, half of all enterprises are expected to adopt advanced tools and frameworks specifically designed to address disinformation risks, reflecting the growing recognition of this pressing issue.
Key Use Cases