Artificial Intelligence is a complex and often daunting platform. Trying to get your arms around it is challenging, even if you are familiar with it. Overall, however, there are four general top-level domains that, if understood, make deployment and management successful. They are strategy, the ecosystem, regulations, and security (deepfakes and more). These are the areas on the minds of most of the C-level, IT, and developers invested in AI.
This seminar will discuss the many vectors in these areas and make the students aware of the many variables that need to be understood to deploy a successful, optimized, and functioning AI platform within your organization.
This seminar will discuss the many vectors in these areas and make the students aware of the many variables that need to be understood to deploy a successful, optimized, and functioning AI platform within your organization.
WHY SHOULD YOU ATTEND?
This will help you understand the AI landscape. This seminar will explore the AI platform’s major emerging and leading-edge technologies and practices to deploy and manage the AI goals and objectives of your organization. It will dissect these areas and explain what effect each has how they are interconnected and why.AREA COVERED
- AI Vision: The AI vision statement is usually forward-looking and aspirational and reflects the organization’s commitment to leveraging AI to deliver positive and responsible outcomes.
- Business Value Drivers: These drivers represent the ways value is recognized by the organization and are used to ensure candidate AI initiatives are aligned with the goals and objectives of the organization.
- Strategic AI Principles: These guiding principles align the business strategy with the AI strategy and reflect the organization’s overall approach to the use of AI.
- Responsible AI Principles: These guiding principles govern the development, deployment, and maintenance of AI applications to mitigate the potential risks of deploying AI-based applications.
- Performance and Reliability: Once an ecosystem is in place, it must meet a number of criterion to succeed; accuracy, precision, performance and scalability, KPIs, costs, and ROI all need to be working together to ensure a successful outcome.
- Governance and Regulations: The AI platform must not run outside of the regulation and governance landscape. If not implemented early, these can cost monumental time and money to correct later on.
- Security and Privacy: While deep-fakes are at the center of the radar screen for many organizations and public agencies, they are only a part of the security landscape.
LEARNING OBJECTIVES
- How to develop an AI strategy that is both deployable and manageable.
- Creating value and ROI by assessing business value, feasibility, and cost.
- Determining the most important factors that will decide which AI solutions are best for your organization.
- Aligning AI strategy with your existing organizational strategy.
- Where to look and how to decide your approach to AI governance.
- Evaluating initiatives, both locally and globally.
- The deep-fake and security landscape – the threat from dis- and misinformation and why understanding this is of paramount importance in your AI ecosystem.
- Homegrown or vendors. Do you bring development in-house or bring in third parties as partners?
- The knowledge vacuum.
WHO WILL BENEFIT?
- Individuals who are involved in developing and implementing AI for organizations. Including Government/Public Sector, Financial Services, Media, Information Technology, Telecom, Education, Healthcare, Public Safety, Infrastructures, Retail, Utilities, Transportation, and anyone who has responsibility for their organization/entity’s AI plans.
This will help you understand the AI landscape. This seminar will explore the AI platform’s major emerging and leading-edge technologies and practices to deploy and manage the AI goals and objectives of your organization. It will dissect these areas and explain what effect each has how they are interconnected and why.
- AI Vision: The AI vision statement is usually forward-looking and aspirational and reflects the organization’s commitment to leveraging AI to deliver positive and responsible outcomes.
- Business Value Drivers: These drivers represent the ways value is recognized by the organization and are used to ensure candidate AI initiatives are aligned with the goals and objectives of the organization.
- Strategic AI Principles: These guiding principles align the business strategy with the AI strategy and reflect the organization’s overall approach to the use of AI.
- Responsible AI Principles: These guiding principles govern the development, deployment, and maintenance of AI applications to mitigate the potential risks of deploying AI-based applications.
- Performance and Reliability: Once an ecosystem is in place, it must meet a number of criterion to succeed; accuracy, precision, performance and scalability, KPIs, costs, and ROI all need to be working together to ensure a successful outcome.
- Governance and Regulations: The AI platform must not run outside of the regulation and governance landscape. If not implemented early, these can cost monumental time and money to correct later on.
- Security and Privacy: While deep-fakes are at the center of the radar screen for many organizations and public agencies, they are only a part of the security landscape.
- How to develop an AI strategy that is both deployable and manageable.
- Creating value and ROI by assessing business value, feasibility, and cost.
- Determining the most important factors that will decide which AI solutions are best for your organization.
- Aligning AI strategy with your existing organizational strategy.
- Where to look and how to decide your approach to AI governance.
- Evaluating initiatives, both locally and globally.
- The deep-fake and security landscape – the threat from dis- and misinformation and why understanding this is of paramount importance in your AI ecosystem.
- Homegrown or vendors. Do you bring development in-house or bring in third parties as partners?
- The knowledge vacuum.
- Individuals who are involved in developing and implementing AI for organizations. Including Government/Public Sector, Financial Services, Media, Information Technology, Telecom, Education, Healthcare, Public Safety, Infrastructures, Retail, Utilities, Transportation, and anyone who has responsibility for their organization/entity’s AI plans.