The WCTC Applied AI Lab offers foundational artificial intelligence training designed to equip employees with essential AI knowledge and practical skills. This hands-on program provides participants with a clear understanding of AI fundamentals, machine learning concepts, and real-world business applications to help your organization leverage AI effectively.

This introductory program provides employees with foundational AI knowledge and hands-on experience with generative AI tools. Participants will learn practical applications for enhancing daily productivity and apply best practices for prompt engineering. The course demystifies artificial intelligence and empowers teams to integrate AI tools into their workflows confidently.

LEARNING OUTCOMES

• Gain foundational understanding of AI concepts and technology
• Identify different types of AI and how machines learn
• Explore how generative AI tools enhance productivity in real-world work settings
• Practice effective prompt engineering techniques with tools like Claude, ChatGPT, and Gemini
• Apply AI use cases for common business tasks
• Incorporate AI into daily workflows

INCLUDES 1 TRAINING SESSION

• What is AI and types of AI
• How machines learn
• Real-world business applications and use cases
• Benefits and ethical considerations
• Overview of generative AI tools (Claude, ChatGPT, Gemini)
• Principles of effective prompts and prompt engineering activity
• Use cases for common business tasks
• Incorporating AI into daily workflows

For More Information Contact:

Sarah J. Buszka, MPA Director, Applied AI Lab

262.691.5580 | sbuszka1@wctc.edu wctc.edu/ai

The WCTC Applied AI Lab recognizes that successful AI integration requires strategic planning and alignment with organizational objectives. This comprehensive program equips leaders with the tools and frameworks needed to assess AI readiness, identify opportunities, and develop actionable AI strategies that drive measurable business outcomes.

This executive-focused program combines strategic planning frameworks with practical tools to help leaders develop actionable AI strategies aligned with organizational objectives. Participants will learn to assess AI readiness, prioritize projects, and create comprehensive implementation plans. The course provides a structured approach to AI integration that addresses technical, cultural, and operational considerations.

LEARNING OUTCOMES

• Assess the AI readiness of your business
• Develop an AI vision that aligns to your organization's strategic objectives
• Identify key areas where AI can drive growth and efficiency
• Develop a strategic approach to integrating AI into business processes
• Prioritize AI projects based on impact and feasibility
• Create actionable implementation plans including resource requirements and success metrics
• Define strategies for risk mitigation and change management

INCLUDES 1 TRAINING SESSION

• AI readiness assessment and current vs. future state analysis
• Building your AI vision through SWOT analysis
• Identifying and defining root causes to organizational problems
• AI project prioritization using strategic frameworks
• Creating AI strategy canvas to align projects with business objectives
• Defining success metrics, timelines, and KPIs
• Risk mitigation strategies and governance frameworks
• Building resilience through scenario planning
• Determining first steps: build, buy, or hire

For More Information Contact:

Sarah J. Buszka, MPA Director, Applied AI Lab

262.691.5580 | sbuszka1@wctc.edu wctc.edu/ai

The WCTC Applied AI Lab offers comprehensive AI policy consulting services designed to help organizations establish safe, effective, and enforceable artificial intelligence governance frameworks. As companies navigate AI adoption, having a clear, well-structured policy is critical. Our consulting service conducts a thorough assessment of your existing AI policy, identifies gaps and risks, and delivers actionable recommendations to support responsible AI adoption across your organization.

This consulting engagement provides a detailed evaluation of your existing AI policy draft, identifies gaps in governance, data protection, and risk management, and delivers an improved policy tailored to your organization's specific needs. Our expert consultants review your policy against current best practices and frameworks for AI governance, assess alignment with your industry requirements and confidentiality needs, and provide clear recommendations with sample language for policy enhancement.

Key Deliverables

• Comprehensive gap analysis report identifying strengths and gaps in current policy
• Assessment of policy clarity, completeness, and enforceability
• Evaluation against current AI capabilities, risks, and best practices
• Stakeholder consultation and input gathering
• Prioritized recommendations for policy improvements
• Revised, fully edited AI policy addressing identified gaps
• Clear, plain-language policy ready for employee communication

Scope of Services

• Policy Review and Gap Analysis: Detailed review of existing AI policy against best practices frameworks for AI governance. Assessment of clarity, enforceability, and accessibility. Identification of missing sections (e.g., escalation protocols, training requirements, audit procedures, governance structures, data classification)
• Stakeholder Consultation: Virtual or on-site meeting with key stakeholders (HR, IT, Legal, Operations). Discussion of current and anticipated AI usage, data sensitivity considerations, departmental needs, and policy concerns
• Policy Improvement Recommendations: Prioritized list of recommended changes including governance structures, risk management clauses, data classification systems, acceptable use definitions, human review requirements, compliance monitoring, and incident response procedures
• Revised Policy Delivery: Improved and fully edited AI policy that addresses identified gaps, uses clear and accessible language, and is ready for employee communication and implementation

Typical Engagement Overview

• Duration: 2-6 weeks from signed agreement to policy delivery
• Meetings: One 60-minute stakeholder consultation session (virtual or on-site)
• Deliverables: Gap analysis report and revised, enhanced AI policy
• Customization: Services can be tailored to your organization's specific industry, size, and risk profile
• Support: Includes recommendations for implementation, employee training, and policy communication

Common Areas We Address

• Governance structure and accountability mechanisms
• Escalation protocols and incident response procedures
• Data classification systems and protection frameworks
• Departmental guidance (HR, Finance, Engineering, etc.)
• Training and awareness program requirements
• Approved AI tools lists and security requirements
• Risk management and compliance monitoring procedures
• Enforcement frameworks and accountability measures
• Human oversight requirements and output verification
• Policy maintenance and continuous improvement processes

Why Conduct an AI Policy Review?

• Ensure your policy addresses current AI capabilities and emerging risks
• Protect proprietary information and sensitive data from unauthorized AI disclosure
• Establish clear governance and accountability structures
• Reduce liability and compliance risks
• Empower employees to use AI safely and effectively
• Build stakeholder confidence in your AI governance approach
• Create a foundation for successful AI adoption across your organization

For More Information Contact:

Sarah J. Buszka, MPA Director, Applied AI Lab

262.691.5580 | sbuszka1@wctc.edu wctc.edu/ai

The WCTC Applied AI Lab, in partnership with WCTC Corporate Training Center's Lean Six Sigma experts, offers a unique program that bridges traditional continuous improvement methodologies with modern AI automation capabilities. This comprehensive training equips continuous improvement teams and leaders with the skills to identify waste, map processes, and systematically integrate AI solutions that eliminate inefficiencies and drive measurable productivity gains in transactional and operational environments.

This co-taught program combines Lean principles with AI automation strategies to help organizations identify improvement opportunities and implement AI-driven solutions. Participants learn to apply enterprise mapping, waste identification, and process analysis techniques while developing the ability to recognize where AI can enhance workflows. The course provides practical frameworks for translating Lean insights into actionable AI projects, including the Lean-Adapted AI Systems Discovery Canvas that connects organizational decisions to AI prediction opportunities.

IMPORTANT NOTE: This program requires coordination between AI Lab instructors and Lean Six Sigma specialists. Additional planning time is needed to customize content to your organization's specific processes and industry context. We recommend scheduling a discovery meeting 6-8 weeks prior to delivery to align curriculum with your operational priorities.

Learning Outcomes

• Understand fundamental Lean principles and their application to AI-enabled process improvement
• Identify and eliminate waste in transactional and operational workflows using the 8 Wastes framework
• Apply enterprise mapping techniques to assess organizational capability and identify improvement opportunities
• Recognize AI use case opportunities within existing business processes
• Understand AI automation categories: Robotic Process Automation (RPA), Generative AI, and Process AI
• Translate process steps into structured digital requirements for AI implementation
• Apply the Lean-Adapted AI Systems Discovery Canvas to connect business decisions with AI solutions
• Quantify improvement opportunities using capacity analysis and productivity metrics
• Package process documentation and requirements for AI development teams
• Foster a culture of continuous improvement and responsible AI adoption

Includes Customized Training Program

• Continuous Improvement Foundation: Lean principles, waste identification (8 Wastes - DOWNTIME), Kaizen facilitation, process mapping (SIPOC, value stream mapping, flowcharting), enterprise mapping with capability assessment, capacity analysis and productivity metrics
• AI Fundamentals: What is AI and types of AI, how machine learning works (training vs. inferencing), real-world AI applications in manufacturing and transactional environments, benefits and challenges of AI investment
• Waste Elimination with AI: Data-driven insights and factory optimization, AI-powered quality control and predictive maintenance, inventory management and process automation, identifying AI solutions for specific waste types
• AI Opportunity Identification: Recognizing automation opportunities within workflow maps, understanding automation categories (RPA, Generative AI, Process AI), translating Lean pain points into AI use cases, prioritization tools for automation-ready processes
• Lean-Adapted AI Systems Discovery Canvas: Mapping organizational mission to critical decisions, identifying core processes and associated wastes, determining prediction needs to support better decisions, applying human judgment to AI-generated predictions, systematic exploration of AI-powered systems
• Prompt Engineering and Practical Application: GUIDE framework (Give context, Use clear language, Include instructions, Demonstrate with examples, Edit prompts), hands-on exercises with business challenge scenarios, brainstorming AI applications using generative AI tools
• Digital Process Documentation: Translating process steps into structured digital requirements, packaging deliverables for AI development teams (process maps, requirements documentation, KPIs), creating effective handoff packets for technical implementation
• Change Management and Sustainment: Lean leadership and change management principles, addressing barriers to AI adoption (data quality, culture, employee concerns, security), standard work documentation for digital processes, post-implementation validation and control planning

Program Customization and Coordination

• Pre-Program Discovery: 60-90 minute consultation to understand your organization's current state, continuous improvement maturity, and AI readiness
• Content Customization: Course examples and exercises tailored to your industry, processes, and specific operational challenges
• Instructor Coordination: Co-taught by AI Lab expert and Lean Six Sigma specialist requiring additional preparation and alignment time
• Recommended Timeline: Schedule discovery meeting 6-8 weeks before desired delivery date to allow adequate customization time
• Flexible Delivery: Can be delivered as intensive multi-day workshop or segmented into modules over several weeks
• Typical Program Length: 9-12 hours depending on depth of coverage and organizational needs

For More Information Contact:

Sarah J. Buszka, MPA Director, Applied AI Lab

262.691.5580 | sbuszka1@wctc.edu wctc.edu/ai

The WCTC Applied AI Lab offers a critical workshop designed to help organizations navigate the complex landscape of AI ethics, legal compliance, and responsible AI governance. As AI adoption accelerates, understanding legal obligations, mitigating risks, and implementing ethical AI practices has become essential. This program equips leaders, legal teams, HR professionals, and decision-makers with the knowledge and frameworks needed to develop institutional AI strategies that protect your organization while enabling responsible innovation.

This interactive workshop addresses the evolving AI regulatory landscape and provides practical guidance for implementing responsible AI frameworks within your organization. Co-developed with legal experts, the program covers high-risk AI use cases, regulatory requirements, real-world litigation examples, and the ten foundational pillars of responsible AI. Participants learn to assess AI risks, develop compliant policies, and create institutional strategies that balance innovation with accountability. The session includes case study analysis and an interactive workshop component where participants can address their organization's specific AI ethics challenges.

Learning Outcomes

• Understand the current AI regulatory landscape including GDPR, state-level legislation, and emerging federal frameworks
• Identify high-risk AI use cases in talent management, products and services, and educational settings
• Recognize legal implications of AI deployment including bias, discrimination, privacy violations, and copyright issues
• Apply the Ten Foundational Pillars of Responsible AI to organizational decision-making
• Develop institutional strategies for AI governance and policy development
• Assess vendor accountability and liability considerations in AI tool procurement
• Create policies that address acceptable use, data privacy, intellectual property, and human oversight
• Implement frameworks for transparency, ethical AI use, and employee training requirements
• Navigate copyright issues and fair use considerations in AI systems
• Build an AI roadmap that prioritizes impact, complexity, and risk mitigation

Includes Comprehensive Workshop

• AI Policy Background: Timeline of global AI regulations, U.S. federal and state legislation, EU AI Act, NIST AI Risk Management Framework, Executive Orders on trustworthy AI
• High-Risk AI Use Cases: Products and services (data privacy, algorithmic bias, security risks), Talent management (recruitment, hiring, performance evaluation, ADEA compliance), Educational settings (academic integrity, student privacy, intellectual property protection)
• Real-World Legal Cases: Workday discrimination lawsuit and liability implications, CVS settlement on AI-powered hiring tools, Anthropic copyright litigation, Disney cases on AI-generated content
• Ten Foundational Pillars of Responsible AI: Transparency and openness, data quality and control, integrity and accountability, equitable access, AI Golden Rule, privacy and security, ethical and safe use, human-in-the-loop requirements, AI literacy and training, values alignment
• Copyright and Intellectual Property: Copyright requirements and human authorship standards, AI-generated content ownership, fair use exceptions and limitations, protecting employee and organizational IP
• Institutional Strategies: Developing AI-approved tool lists, creating clear and consistent AI use policies, integrating AI into support services, monitoring impact on outcomes and compliance
• Essential AI Policies: Acceptable use policy, data privacy and security policy, intellectual property policy, transparency and disclosure policy, quality control and human oversight policy, ethical AI use policy, employee training requirements, incident response procedures
• AI Roadmap Development: Mapping existing AI use cases, brainstorming new applications, risk heatmapping, prioritizing by impact and complexity, build vs. buy decisions, implementation and governance planning
• Interactive Workshop: Guided problem-solving session to address participant-specific AI ethics and compliance challenges

Who Should Attend

• Executive leadership and senior management
• Legal counsel and compliance officers
• Human resources professionals
• IT and technology leaders
• Risk management and privacy officers
• Academic administrators and faculty (higher ed only; not K12)
• Procurement and vendor management teams
• Anyone responsible for AI governance, policy development, or implementation oversight

For More Information Contact:

Sarah J. Buszka, MPA Director, Applied AI Lab

262.691.5580 | sbuszka1@wctc.edu wctc.edu/ai