Instructors: Ronald Schmelzer, Managing Partner, Principal Analyst, Cognilytica
Kathleen Walch, Managing Partner, Principal Analyst, Cognilytica
Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are transforming the way we work, live, and interact with each other. The emergence of these cognitive technologies is transforming the way public sector agencies and
citizens interact with each other. However, putting AI, ML, and DL into practice and getting beyond the buzzwords, as well as procuring these technologies can be a challenge. This seminar focuses on bringing real world practices, use cases, and methodologies
to make AI projects a tangible success for public and private sector organizations. It will also help procurement professionals to understand why procuring AI is different than procuring traditional application development technology. Attendees will
gain insight into what makes AI projects a success across a wide range of industries, learn about the seven patterns of AI, and learn about emerging cognitive automation and intelligent automation approaches.
The Seven Patterns of AI: Government Success Cases in AI
While artificial intelligence has been around for decades, it’s only recently that machine learning, deep learning, and other cognitive technologies are
at the point where they can be practically realized by governments and organizations alike. This session sets the bar for what AI represents today, and provides definitions and level-setting of terminology. We bust some myths about AI, point to real-world
applications of AI in large scale implementations, and explore how AI will impact our future illustrating what an AI-enabled future will look like.
Organizations of all types want to incorporate AI, machine learning, and cognitive
technologies into their operations and processes to augment human workers, increase efficiencies, reduce costs, and achieve the goals of more intelligent systems. But, figuring out how to successfully run an AI project can be daunting. This session will
help attendees learn how to successfully identify key problem areas that AI technologies can address, the seven primary adoption patterns for AI and cognitive technologies with use cases for each pattern, as well as iterative approaches to AI implementation.
At the end of the session, attendees will leave with an understanding of how to apply one or more pattern(s) to successfully start and complete AI projects.
- The seven primary adoption patterns for AI and cognitive technologies
- Dispelling the Myths around Artificial Intelligence
- The 4-Part AI-Enabled Vision of the Future
- Cognitive technologies and their impact on government agencies
- The new realities of our cognitive technology future
- Matching AI vision to reality
- Key takeaway: Understanding Seven Patterns of AI
Panel: Requirements for Broad AI Adoption in Government
The Federal government is being tasked with adopting AI, but have not been given much guidance on how to do this. Many agencies are being thoughtful
about the ways they are adopting AI but could benefit learning from others about current projects, best practices, and what’s possible with AI. From RPA bots and cognitive automation, predictive analytics, natural language processing, or AI chatbots
to help citizens with various needs, this panel will explore the unique requirements agencies need to address in order to gain broad AI adoption.
Moderator: Tom Suder, President & Founder, ATARC
Anil Chaudhry, Director; Regulatory Audit Systems and Innovation, Office of Trade, U.S. Customs and Border Protection ‘pending agency approval’
Krista Kinnard, Director, AI Center of Excellence, General Services Administration (GSA)
Foundations and Applications of AI for Acquisitions Professionals
The President’s Management Agenda lays the foundation needed to address the critical challenges where Government as a whole, still operates in the past, and AI plays a part in helping move the government forward. However, agencies were not given
directive on how exactly to procure and use AI technologies. In this session we’ll walk through a procurement specific case study outlining in detail steps that were taken around Planning, Procurement price and process, Implementation, Outcomes,
and Lessons Learned.
At the end of the session, attendees will leave with an understanding of how different agencies went about identifying and procuring AI projects.
- The steps different agencies and procurement officials took to procure AI
- Outcomes and lessons learned (both failures and successes)
- Key Takeaway: Walk through Case Study for Procuring AI
Panel: How AI is Changing Federal Procurement
It’s a long-held belief that the government is slower to adopt technology, and in some cases, this is correct, but not for AI. The Federal government is being
thoughtful about the ways they are adopting AI and are keeping up with industry in their adoption of these various technologies. From RPA bots and cognitive automation, predictive analytics, natural language processing, or AI chatbots to help citizens
with various needs, AI is continuing to gain adoption.
This panel will showcase how various agencies are using AI to help automate and improve various processes and how procurement fits into the picture from helping to identity AI technologies,
to market research, to finally picking a company for the AI project.
- Focus: How procurement fits into the AI picture in government
Moderator: Kathleen Walch, Managing Partner, Principal Analyst, Cognilytica
- Mitchel Winans, Special Assistant, Office of Procurement, U.S. Internal Revenue Service (IRS)
- Elizabeth Chirico, Acquisition Innovation Lead, Office of the Deputy Assistant Secretary of the Army (Procurement), Assistant Secretary of the Army for Acquisition, Logistics and Technology
- Keith Nakasone, Deputy Assistant Commissioner, IT Acquisition, Information Technology Category, Federal Acquisition Service, General Services Administration (GSA)
- Scott E. Simpson, Innovation Coach, Department of Homeland Security, Office of the Chief Procurement Officer, Procurement Innovation Lab ‘pending approval’
Best Practices in AI Implementation: A Framework for Successful AI Adoption
Organizations and agencies are looking to put AI into practice, but the main challenge is that AI projects are not like typical application development projects.
In this session, we’ll discuss best practical approaches to running AI projects, the data-centric nature of AI solutions, and the specific requirements for particular AI implementation patterns. We will share a methodology that combines the best
of Agile and CRISP-DM approaches, pitfalls to watch out for with AI adoption while addressing governance, transparency, and ethics concerns, and how to deal with emerging AI threats.
- Data-centric vs. Application-centric AI approaches
- Learning from others: examples of AI projects at scale
- Providing proper documentation while following AI specific project management
- Key Takeaway: Agile/CRISP-DM Methodology Adapted for AI