AI Research Intern Review

• Investigate current AI technologies including LLMs (e.g., ChatGPT, Claude, Google Gemini), computer vision, automation platforms (e.g., Zapier, Chatfuel), and design/writing tools (e.g., Notion AI, Rytr, Copy AI) for asset-related use cases.
• Map AI to Asset Management Domains: Explore AI applications across key asset management pillars:
• Data: Improve data governance, unstructured data interpretation, and reporting via tools like ChatGPT, TextFX, and NoteFolio.
• Risk: Analyze predictive risk modeling using AI-driven diagnostics (e.g., DeepAI, Quillbot, Bright Eye).
• People & Process: Enhance workflows, training, and collaboration through tools like Taskade, Mindgrasp, and Gleap.
• Maintenance: Research predictive maintenance and digital twin applications using platforms such as Dialogflow, Balloonary, and MarsAi.
• Strategic Alignment: Use insights from AI-generated reports and dashboards (e.g., Notion AI, Plai, POPAI) to support ISO 55000-aligned decision-making.
• Evaluate Use Cases: Identify real-world use cases for integrating AI into asset-heavy sectors such as utilities, transportation, energy, and infrastructure. Benchmark AI-driven asset management tools like IBM Maximo, SAP IAM, and Uptake, alongside new-gen solutions.
• AI Tool Assessment: Cross-reference the provided list of tools with potential PAM applications. For example:
• Chatbot/LLM Tools: ChatGPT, Claude, Perplexity – for automated asset helpdesks, document analysis, and stakeholder communication.
• Productivity Tools: Zapier, Dialogflow, Bright Eye – for process automation and alert systems.
• Design & Visualization: Midjourney, Canva, Lucidpic – for visual asset planning and communication.
• Writing & Reporting: Rytr, Grammarly, Jenni AI – for asset documentation and reporting workflows.

Yes. I was responsible for key sections of research and report writing

During this internship, I developed a deeper understanding of how Artificial Intelligence can be applied to Physical Asset Management (PAM). I gained practical experience evaluating AI tools such as SparkPredict, IBM Maximo, GE APM, and Amazon Rekognition. I strengthened my research, analytical thinking, and technical writing skills while learning to frame AI use cases within real-world asset-heavy sectors. I also became more familiar with frameworks like ISO 55000 and the principles of data governance and AI ethics.

Sufficient enoughto get the job done

Purely online but encouraged team work

I enjoyed working on structured research problems and translating technical information into accessible insights. Collaborating within a group taught me the importance of task coordination and the value of diverse perspectives. I realised I am drawn to roles that combine technical analysis with strategic planning. This experience also confirmed my interest in emerging technologies and their application in infrastructure and utility sectors.

Yes