AI Strategies

Chairpersons welcome and opening remarks.  

Fearing that disruptive technologies would replace workers; it is now clear that these innovations are there to aid employees’ skills and capabilities – realising them from automated tasks and giving them the time to work on new projects. Here we will look at the role of AI and ML in helping to build a digitally enabled and efficient workforce.

• Using AI and ML to gain real-time intelligence, in turn creating a strong competitive advantage.  
• Creating a holistic technology approach to your digital transformation strategy.  
• What new services are expected to appear in this space? 

• Looking at how to truly embed AI into internal processes to enhance the teams’ capabilities, not disrupt the workflow.  
• Giving employees the training and tools they need to be developers, not just users.  
• Fitting technology to the solution, not the other way around. 

• Building a comprehensive business strategy, how to embed AI into this from the outset. 
• Enabling decisions based on real-time intelligence. 
• Utilising ML solutions to aid processes and consumer experiences. 

• Protecting data from cyber hacks through AI-based security solutions. 
• The potential to scan malware in real-time. 
• The ability to react autonomously to everchanging threats.  

• How AI/ML algorithms can be utilised to find network traffic and unusual patterns in a growing complex digital world. 
• The current barriers hindering the capabilities of AI and ML use in cyber security. 

Looking at NLP (Natural Language Processing) progressions like OpenAI for enhanced user experiences and automated responses.  

• How language modelling is enhancing workforce and service capabilities, as well as increased data literacy.  
• What are the challenges that need to be considered here?  
• Accelerating processes with deep learning and neural networks – looking at the expansion of deep learning at scale across complex tasks.  

A deep-dive into the current deep learning use-cases. Where do we go next with the capabilities deep learning provides?  

• As low-code and no-code technologies can be quickly rolled out, why haven’t these been adopted sooner?  
• Closing the AI skills gap – where to start? 
• While these technologies create a quick fix, are they a long-term solution? 

• Creating a holistic strategy that includes AI, BI, ML and IoT processes and capabilities.  
• A shit towards simplifying the vendor landscape to limit the overlap here – is the next step converging data warehouses and data lakes? 
• Building partnerships across solutions and sectors as the key to an optimised eco-system.  

View Day 2 Content Here:

Applied Data & Analytics – Data Optimisation | Big Data | Intelligent Decision Making | Data Storage | Industry Use Cases

Transformational AI – Personalization | Edge AI | Future Progressions | Innovations | Industry Use-Cases