Key Moments:
- Professor Alexiei Dingli outlined a hands-on AI readiness framework at AIBC Euro‑Med 2025.
- The approach emphasizes aligning teams, data quality, and structured benchmarking to convert operational issues into measurable value.
- Dingli stressed that technology adoption must begin where practical needs meet tangible benefits, advising careful vendor selection and process controls.
Expert Insights on AI Adoption
During the first day of AIBC Euro‑Med 2025, Professor Alexiei Dingli from the University of Malta introduced a detailed strategy for using artificial intelligence to address and resolve persistent operational hurdles. His framework is tailored to align data, team coordination, and swift, effective decision-making, designed specifically for real-world operational scenarios.
Professor Dingli brings a wealth of experience in artificial intelligence, with a career spanning over twenty years. His credentials include prestigious research awards, leadership roles such as the former Head of the Department of AI at the University of Malta, and numerous contributions to national AI initiatives and industry projects. His work has received recognition from organizations such as the European Space Agency and WIPO.
Context is king for AI agents. Which means that there’s going to be a huge premium for the individuals, teams, and companies that are able to best design systems to give agents the best context to do their work.
Knowledge work has always been a relatively messy space. If you go…
— Aaron Levie (@levie) September 2, 2025
Building a Robust AI Readiness Strategy
Professor Dingli categorized AI as a broad collection of technologies, each with varying strengths. He stressed that optimal outcomes hinge on carefully matching AI tools to specific business challenges, and on the importance of high-quality data over mere technological trends. He warned against seeing “agentic AI” as a universal fix, emphasizing that while important, it still requires extensive validation before it can be widely implemented.
| Key Elements of Dingli’s Framework |
|---|
| Target easily achievable projects that resolve immediate issues |
| Prioritize data quality, structure, and governance |
| Insist on benchmarking and thorough pilot testing before full-scale implementation |
| Implement rigorous controls to manage risks and potential failures |
| Use a structured checklist to assess organizational preparedness for AI integration |
He noted, “There is no magic behind AI; when systems carry billions of parameters, the complexity is real, but so are the limits without the right process and data.” Dingli further remarked, “We can do everything with AI, but we can’t do anything,” highlighting the necessity of practical expectations and carefully scoped projects.
AI’s Business Impact and Vendor Considerations
The keynote illuminated the transition of AI from laboratory proof-of-concept to reliable commercial application, surpassing human capabilities in selected tasks given suitable governance. Professor Dingli provided a relatable example: by automating large-scale invoice processing, companies can minimize manual workload, reduce error risk, and refocus staff on higher-value activities, all while achieving notable cost savings and rapid returns on investment.
When evaluating external solutions, Dingli advised focusing on a vendor’s demonstrated delivery, platform scalability, robust data architecture, compliance readiness, and sound financial models, warning against reliance on superficial branding or undifferentiated “AI-washed” solutions. His “Aim, Benchmark, Control” checklist provides a portable method for decision-makers in finance, operations, and compliance functions. Dingli stated, “We need to quantify the opportunity cost of the status quo; if it is not broken, it may still be losing money,” challenging leaders to identify hidden inefficiencies stemming from legacy manual processes and slow AI adoption.
Looking Ahead: Day Two at AIBC Euro‑Med 2025
The momentum generated by Professor Dingli’s session is set to continue with day two of the event. Attendees can anticipate further discussions and practical guidance around scaling AI responsibly, with dedicated sessions focusing on compliance, risk, technological innovation, and operational strategy across gaming, blockchain, and digital assets.
- Author
Daniel Williams
