Following the initial hype and excitement about the promise of Generative AI (GenAI) and the subsequent race to test and roll out the latest GenAI tools, many business leaders are now sceptical about their ability to deliver on their earlier promise due to a lack of concrete business results.
It’s perhaps inevitable that this disillusionment would happen as reality sets in. After all, expectations were set unrealistically high with wild claims of ‘immediate ROI’ or the power to ‘revolutionise business tomorrow’. With so much promise combined with the accessibility of GenAI tools, businesses were quick to jump on the GenAI bandwagon, often without first considering what they hoped to achieve or even why they were doing it in the first place.
However, this lack of clear use cases or alignment with broader business objectives is only part of the story. Many leaders are also pointing to difficulties with data quality and alignment, and integration with existing IT architecture as powerful barriers, preventing them from achieving the full benefits of GenAI.
It’s well known that data quality is the foundation for good business outcomes but with GenAI, the stakes are even higher, and organisations will need a modern and robust data infrastructure that enables them to scale GenAI and deliver concrete results. Seamless integration of data and applications is fundamental for businesses aiming to implement and benefit from GenAI technologies. GenAI relies heavily on large, high-quality datasets to generate accurate, meaningful outputs, and this data must be well-integrated, accessible and up to date. Data silos can also hinder the effectiveness of GenAI by limiting the availability and comprehensiveness of the information required for training and deployment.
The good news is that as those early GenAI prototypes and tools have evolved and matured, important lessons have been learned about how to get this right. Join us on Friday 15 November 2024 at 09:00 GMT for the Chief Disruptor Forum, Delivering Tangible Results from Enterprise-wide Generative AI, and hear from our panel of experts on how to generate concrete results as you move from GenAI pilot projects to enterprise-wide programmes.
AGENDA
09:00 Welcome and introduction
09:05 Guest speakers to cover:
- Introduction: exploring why so many leaders believe GenAI has fallen off its pedestal
- Beyond the hype: what tangible and intangible benefits can organisations realistically expect to achieve from their GenAI initiatives?
- What are organisations' main legacy challenges and strategies to overcome them?
- What are the benefits of data and application integration?
- How to develop a robust data and integration strategy
- How to overcome data silos and get your data ready for GenAI
- Addressing ethical and security challenges
- How to develop a GenAI governance strategy
- Practical strategies to overcome the AI skills shortage
- Exploring the different methodologies for assessing GenAI ROI (scorecard versus cost-benefit analysis versus proof of concept)
0935 Audience Q&A
09:50 Summary and next steps from the panel
10:00 Close
HEAR FROM
Natalia Konstantinova Lead Architect in AI, BP
Natalia Konstantinova is a great enthusiast with over 15 years' experience in the application of Natural Language Processing, Artificial Intelligence, IT and machine learning technologies to real world problems.
She is currently a Staff Data Scientist at BP and her role is to develop standards and best practices to accelerate the adoption and implementation of AI enabled solutions, and doing so with the right level of compliance and standards within BP.
Natalia got her PhD from the University of Wolverhampton and worked in various fields such as machine translation, ontologies, information extraction, dialogue systems and chat bots.
Natalia is a strong believer that modern technology can transform businesses and our everyday life.
Dominic Wellington, Enterprise Architect, SnapLogic
Dominic Wellington is an enterprise architect at SnapLogic, with over two decades of enterprise IT experience. His experience spans data platforms, AI for IT Operations (AIOps), and observability, and he has had a pioneering role in the transition from data centre automation to cloud computing. Today he works on enabling seamless access to business value across data, apps, and APIs for large enterprises, including with Generative AI.
Carl Dalby, Group Head of AI / Digital, His Majesty's Government and All-Party Parliamentary Group on AI and Guest Lecturer, University of Aberdeen
Susan Saunders, Senior Enterprise Architect, WPP
Susan Saunders is a Senior Enterprise Architect specialising in Strategy, Architecture, and AI Governance. She leads Architecture Governance at WPP, co-facilitating the Architecture Community for a major creative media and advertising organisation. Susan's career began as an architect for a major pharma company's supply chain, transitioning from various IT roles. In her current role, she has designed and deployed centralised architecture governance, emphasising agility and maintaining a simplified IT application portfolio. Her approach balances centralised and democratised governance, managing multiple tasks simultaneously while focusing on a risk-based approach that includes legal considerations.
