Gartner on Data Trust and AI, FPT Corporation's Vertical AI in Vietnam, and Street.co.uk's Planning Innovations

At the Gartner Data & Analytics Summit in Sydney, analysts cautioned against trusting AI that relies on questionable data, with Carlie Idoine and Gareth Herschel emphasizing the importance of data quality for AI effectiveness. Erick Brethenoux highlighted the distinction between generative AI and AI agents, pointing out the prevalent confusion due to vendors capitalizing on generative AI’s profit potential. He noted the practical benefits of AI agents in resource-efficient, parallel task execution and the challenges of autonomy in software agents. Luke Ellery addressed AI economics, explaining that Microsoft’s Copilot provides perceived productivity gains rather than significant time savings, suggesting that generative AI can transform business models through long-term investments. Ellery advised categorizing AI initiatives by value type and aligning with organizational goals for better strategic outcomes. (Source)

Vietnamese tech company FPT Corporation is making significant strides in the realm of domain-specific AI by developing tailored solutions for diverse industries such as healthcare, manufacturing, and finance. Emphasizing the next major AI wave, which focuses on Vertical AI, FPT employs technologies like xMainframe to modernize outdated systems, particularly in Japan, enhancing efficiency and reducing operational costs. Notable innovations include the I2 platform for manufacturing data analysis and IvyChat for financial automation. The company’s approach involves organizational transformation through AI-assisted workforces, positioning it as a leader in creating practical, differentiated value across various sectors. This strategic focus includes new partnerships, as seen with Thailand's Sunline and Buzzebees, fostering deeper technological and business collaboration. (Source)

Gartner's recent analysis indicates a bleak outlook for agentic AI, with over 40% of projects predicted to be canceled by 2027 due to high costs and unclear business value, despite the proliferation of these solutions in enterprises. The issue is compounded by 'agent washing,' where vendors falsely market AI products with limited autonomy. Meanwhile, fears grow over AI's failure to justify hefty investments, as shadow AI usage bypasses enterprise protocols, compromising data security. Academic studies criticize the cognitive limitations in AI, revealing users may become less critically engaged, highlighting concerns over AI's role in business and knowledge development. (Source)

Estate agency software provider Street.co.uk has partnered exclusively with Planda, an AI-driven platform that provides detailed planning potential reports for properties. This collaboration enables Street.co.uk agents to offer free, automated Planning Potential Reports when creating property valuations, giving them a significant competitive advantage. These reports utilize billions of planning records to forecast the likelihood of obtaining planning permission for home improvements and offer AI-generated recommendations tailored to each property. Co-founder of Street Group Heather Staff highlights that these reports allow agents to justify valuations more effectively and engage in deeper conversations beyond standard comparables. Brandon Johnson, CEO of Serac Group & Planda, emphasizes the unmatched precision and speed of Planda’s planning intelligence. Agents receive a free preview report, with options to upgrade and resell to homeowners. (Source)

Newly available AI agents are poised to significantly enhance app modernisation, a development welcomed by developers navigating rapid software evolution. Charlotte Dunlap from GlobalData highlights that AI agents transcend intelligent process automation by supporting autonomous, goal-driven applications capable of adaptation and reasoning. Companies are increasingly shifting from deterministic workflows to AI agents that execute complex tasks using memory and decision-making capabilities. Key considerations for enterprises adopting AI agents include mature machine learning and natural language processing technologies, comprehensive implementation frameworks, low-code development tools, and standardised protocols like MCP and A2A for improved interoperability. These protocols facilitate better integration and collaboration in multi-agent systems, democratising the deployment of AI agents across diverse DevOps teams. (Source)