When approaching an AI project, there are many paths that can be taking to get to the desire endpoint. Vendors are innovating and creating valuable solutions but understanding who and which solution to use can be a real headache. Moreover,

novel use cases on unique assets may require novel, self-built, solutions. Our panellists will help you through this process, discussing:

● Balancing cost with control: Buy to cut development time by 40%? Build for increased control over customization and long-term cost savings? ● Dissecting the impacts on speed to operationalization

● Do talent gaps in the face of AI-specific developer shortages make consultants and vendors a necessity?

● How to strike the hybrid sweet spot

75% Interactive for Maximum Learning

Take a break from the PowerPoints with this interactive session and further your conference takeaways by actively participating and learning realistic ways to adapt your AI strategy long after the summit ends. This workshop will help you map your vision – reflecting on what you’ve heard over the past two days and work on key takeaways you can communicate to your executive team tomorrow. The opportunity to learn from your peers will provide critical and insightful industry perspectives.

The Interactive Workshop Agenda: 

1. Introductory overview 
2. Attendees divided into teams and provided a case study assignment 
3. Using the provided prompts, teams will brainstorm and develop their ideas 
4. Team leads to present ideas to the wider group, providing key takeaways

Building and Sustaining a Centralized AI Capability

Operators are increasingly looking to leverage AI across their operations by developing out a centralized AI capability. Perhaps the summit has inspired you to start this process, or perhaps you’re looking to develop your current organizational capability. This workshop will help you navigate the challenges and various paths which can be taken in not only building out a centralized AI capability but building out a capability which is sustainable and consistently delivers value to your organization.

The Workshop will focus on:

● Opportunities and challenges with adopting a centralized functional model for AI

● Hype vs reality in AI and positioning a data science team for long-term success

● Approaches on deciding whether to re-use, rent, build or buy an AI solution

● Experiences in development & change management for predictive AI models that improve performance in commercial & supply chain operations

● Developing credibility of recommendations and sustaining long-term performance of AI models

75% Interactive for Maximum Learning

Take a break from the PowerPoints with this interactive session and further your conference takeaways by actively participating and learning realistic ways to adapt your AI strategy long after the summit ends. This Workshop will help you map your vision – reflecting on what you’ve heard over the past two days and work on key takeaways you can communicate to your executive team tomorrow. This is an opportunity to learn from your peers and will provide critical and insightful industry perspectives. 

The Interactive Workshop Agenda: 

1. Introductory overview 
2. Attendees divided into teams and provided a case study assignment 
3. Using the provided prompts, teams will brainstorm and develop their ideas 
4. Team leads to present ideas to the wider group, providing key takeaways 

Building and Sustaining a Centralized AI Capability

Operators are increasingly looking to leverage AI across their operations by developing out a centralized AI capability. Perhaps the summit has inspired you to start this process, or perhaps you’re looking to develop your current organizational capability. This workshop will help you navigate the challenges and various paths which can be taken in not only building out a centralized AI capability but building out a capability which is sustainable and consistently delivers value to your organization.  

The workshop will focus on: 

● Opportunities and challenges with adopting a centralized functional model for AI

● Hype vs reality in AI and positioning a data science team for long-term success

● Approaches on deciding whether to re-use, rent, build or buy an AI solution

● Experiences in development & change management for predictive AI models that improve performance in commercial & supply chain operations

● Developing credibility of recommendations and sustaining long-term performance of AI models