Applied Digital, Data & AI

Chairpersons welcome and opening remarks. 

• How to move your project from “experimentation” to “live”. 
• Turning AI into ROI – understanding why you need AI. 
• Top tips for moving your pilot to the next stage.

• Discussing how AI is creating significant value across industries.  
• How does AI serve to benefit both small and medium business, as well as large enterprises? 
• What are the implications of ethical AI when trying to digitalise services? 
• A future focus, what are the next steps for AI led digital transformation? 

• How to build AI systems collaboratively. 
• The five stages of explaining AI in practice. 
• Overcoming hesitation to deploy AI-powered solutions. 

• The rapid rollout of the augmented workforce due to labour shortages – what does this currently look like across enterprises?  
• Aiding employees’ skills and capabilities with AI, ML and intelligent automation to help build a digitally enabled and efficient workforce.   
• Discussing the idea that an augmented workforce can boost employee satisfaction due to automation of repetitive tasks and more time for innovation.  
• It takes two – tackling the idea that humans are being replaced by technologies.  

Delving into how decision-making and reward-based training is being used to train AI models in the same way humans learn through trial-and-error. 

• 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? 

• How humans make decisions 
• What is noise & bias? 
• Can AI help? Example use case 
• Things to watch out for 

• Saving time and resources by automating repetitive, information heavy processes.  
• Starting, stopping and altering automation in real-time – how AI automation keeps optimising throughout the chain.  
• Automating with improved accuracy, speed and reliability.

• The new capabilities of RPA and AI combined for complex business tasks.  
• How do you ensure information exchange is consistently accurate?  
• What IA looks like across industries.  
• As many view IA as the future of work, how quickly will this come into play on a mass scale? 

• The current work within the NLP (Natural Language Processing) landscape – examples of real-life use-cases.  
• What industries could benefit from utilising NLP capabilities?  
• The next steps and progressions that are needed to break barriers.

• 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. 

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

View day 2 content here:

• Optimised Data & Analytics
• Transformational AI
• Human-centred Approaches to DTX