Monforte is developing an integrated portfolio of AI governance education and workforce development through the Edifice Training Foundation. It is designed for organizations that need people who can govern AI use, oversee the processes it affects, establish and evaluate safeguards, and exercise sound judgment in everyday work.
At its center is a structured competence catalog: a connected set of courses, workshops, practical laboratories and learning pathways organized around responsibility, application and industry context.
The aim is not to turn everyone into an AI specialist. It is to help people develop the capabilities their responsibilities require.
Beyond a conventional course catalog
Choosing a course is not the same as determining what someone needs to be capable of doing.
Our approach starts with the work: what an organization uses AI to accomplish, which decisions or activities it influences, who is accountable, what authority has been delegated, and what could happen when something goes wrong. Learning needs follow from that context.
The planned portfolio forms the competence-development component of Monforte's broader Edifice approach to operational AI governance. It connects organizational responsibilities to relevant learning, practical assessment and evidence that can support decisions about readiness.
For organizations, that means a foundation for coordinated workforce development rather than isolated training purchases. For professionals, it means a clearer route from existing expertise to the additional judgment and practices needed for AI-enabled work.
Four areas of responsibility
The portfolio is organized around four complementary areas. These are not four ranks of seniority: a person's responsibilities may span more than one area.
Enterprise Governance & Executive Accountability
For boards, executives, business leaders and those who establish organizational direction.
Planned learning addresses governance arrangements, decision authority, investment priorities, risk appetite, ethical judgment, third-party dependencies and significant incidents. It will help leaders determine what to authorize, what to challenge, what evidence to require and when intervention is necessary.
The emphasis is on accountable leadership, not technical familiarity alone.
AI-Enabled Process & Application Accountability
For process owners, application and service owners, and operational managers responsible for the work AI supports.
Planned learning connects AI use to business objectives, process performance, acceptance criteria, human oversight, monitoring and change. It distinguishes responsibility for a business process from responsibility for particular systems, risks, safeguards and authorization decisions.
This area includes purchased and embedded AI, shared services, and agentic workflows in which AI can use tools or take actions within delegated limits.
AI Enablement, Control & Assurance
For engineering, technology, data, security, privacy, legal, procurement, risk, quality, safety and assurance professionals.
Planned learning extends established professional disciplines into AI-specific responsibilities: designing safeguards, evaluating systems and suppliers, managing data and change, maintaining evidence, and assessing whether governance arrangements work in practice.
It also distinguishes using AI to assist an audit from auditing AI systems and AI management systems. These require different learning and assessment pathways.
Workforce AI Practice & Responsible Use
For employees, professional users, reviewers, managers and supervisors who use or oversee AI-assisted work.
Planned learning begins with AI literacy and responsible use, then develops role-specific practices for protecting information, checking outputs, documenting material AI use, recognizing failures and escalating concerns. More advanced pathways will address consequential decisions, elevated access and the supervision of AI agents.
The focus is knowing when to rely on AI, when to verify its contribution and when to stop or seek assistance.
Applied to real work and industry conditions
A shared foundation does not mean identical training for every setting. Reviewing an AI-generated clinical note, governing a lending decision, supervising automated quality inspection and evaluating AI-assisted audit evidence involve different responsibilities and consequences.
The planned scope spans healthcare and life sciences, financial services, manufacturing and food systems, energy and infrastructure, government, education, technology, transport, professional services, commerce and other operating environments. It also addresses functions that cross industry boundaries, including human resources, procurement, finance, customer service, engineering and internal audit.
Common learning will be reused where it genuinely applies. Industry and application-specific learning will be added where professional duties, operating conditions, obligations or consequences materially change what people need to know and do.
Coverage will extend beyond generative AI to decision support, document processing, prediction, automated workflows, robotics and agentic systems. Specific industry offerings will be introduced progressively, not all at once.
Connected learning pathways
The planned portfolio will combine focused courses, leadership and organizational workshops, application modules, industry specializations and practical laboratories. Pathways will connect these elements to a defined role rather than simply accumulate unrelated courses.
Learning will range from foundations and practitioner development to advanced specialist and leadership work, with entry expectations appropriate to the responsibilities involved. AI-focused learning is intended to extend relevant occupational and professional competence, not substitute for it.
Two delivery forms are planned:
Live facilitated learning for discussion, applied exercises, leadership decisions and specialist practice, delivered in person or virtually as appropriate.
Standard blended learning combining preparation and self-paced study with instructor or subject-matter-expert engagement and assessment appropriate to the learning outcomes.
Depending on the offering, participation may be through open cohorts, private organizational groups, enterprise programs or individual development pathways.
Evidence of learning, with clear limits
Attendance, understanding and demonstrated application are different forms of evidence. Our approach is being designed to make those distinctions visible.
Depending on the program, assessment may include knowledge checks, scenario-based decisions, case analysis, observed exercises or practical work products. Each released offering will explain what is assessed and what its completion record represents.
Completing training will not, by itself, establish professional certification or authorize someone to perform a workplace role. Employers retain responsibility for assigning authority and determining readiness for their own operating context.
Refresher learning and periodic review are part of the planned model, so development can respond to changes in technology, responsibilities, applications and organizational requirements.
Development and release
The expanded portfolio will be developed, reviewed and released in stages. This page describes its intended direction and scope; it is not a list of courses currently open for enrollment.
Individual course and pathway descriptions, prerequisites, delivery arrangements, assessment requirements and availability will be published as offerings are approved for release.
Organizations and professionals are welcome to contact Monforte to discuss workforce development needs, areas of professional interest or potential education partnerships.