Enterprise AI Training and Workshops

Give executives, business leaders, architects, developers and cross-functional teams a practical framework for making better enterprise AI decisions.

AInDotNet provides enterprise AI training and working workshops for medium-to-large businesses and government organizations—especially organizations operating in Microsoft-centric environments.

Sessions can be tailored to the organization’s objectives, industry, AI maturity, technology environment and intended participants.

Topics may include AI opportunity discovery, business-value evaluation, technology selection, enterprise architecture, governance, prototype planning, production readiness and the practical use of Microsoft AI technologies.

These are not generic presentations about the future of artificial intelligence.

The objective is to help participants understand what AI can realistically do, identify where it may create value and establish a disciplined path from AI interest to production implementation.

Request an Initial Fit Discussion

Move Beyond Generic AI Awareness

Most organizations do not suffer from a lack of AI information.

Their executives, employees and technical teams are already surrounded by product announcements, vendor demonstrations, social-media claims and predictions about how AI will transform their industry.

The problem is converting that information into sound organizational decisions.

Teams still need to determine:

• Which business problems are worth addressing
• Which AI opportunities have meaningful value
• Which use cases are technically feasible
• Whether the necessary data is available
• How AI should fit into existing workflows
• Which technologies are appropriate
• What should remain deterministic
• Where human review and approval are required
• How prototypes should be evaluated
• What is required for production operation
• How security, governance and accountability should work
• Which initiatives should be funded, delayed or stopped

AInDotNet’s enterprise AI training and workshops focus on those practical decisions.

Training, Workshops and Working Sessions

Training and workshops serve related but different purposes.

Enterprise AI Training

Training develops a shared understanding of enterprise AI concepts, technologies, opportunities, risks and implementation requirements.

It may be appropriate when an organization needs to:

• Educate executives or business leaders
• Prepare managers to participate in AI initiatives
• Give technical teams a broader enterprise AI perspective
• Establish a common vocabulary
• Correct unrealistic expectations
• Compare different types of AI capabilities
• Understand Microsoft AI technologies
• Prepare participants for opportunity discovery or planning
• Align business and technical stakeholders

Training may include presentations, examples, demonstrations, structured discussion and question-and-answer periods.

Enterprise AI Workshops

Workshops move beyond education and apply structured methods to the organization’s own situation.

Participants may work together to:

• Identify business problems and improvement opportunities
• Examine existing workflows
• Generate potential AI use cases
• Define expected business value
• Evaluate technical feasibility
• Identify data requirements
• consider security and governance constraints
• Score and prioritize opportunities
• Select candidates for prototypes
• Define prototype success criteria
• Examine architecture alternatives
• Establish an AI Innovation Team
• Create an initial implementation roadmap

A workshop may produce an opportunity inventory, scoring results, architecture decisions, team recommendations or other defined working outputs.

The distinction is important:

Training helps people understand enterprise AI.

A workshop helps an organization apply that understanding to decisions and action.

Who Enterprise AI Training Is For

AInDotNet can adapt training and workshops for different organizational roles.

Executives and Senior Leaders

Executive sessions focus on the decisions leaders must make rather than low-level technical implementation.

Topics may include:

• Where enterprise AI creates value
• How to distinguish useful capabilities from hype
• AI strategy and investment priorities
• Business ownership and accountability
• Organizational readiness
• Governance and risk
• Build-versus-buy considerations
• Vendor and technology evaluation
• Prototype, MVP and production distinctions
• Measuring business value
• Establishing an AI initiative portfolio
• Creating an AI Innovation Team

The objective is to help leaders sponsor and govern AI initiatives without requiring them to become data scientists or software developers.

Business Leaders and Subject-Matter Experts

Business participants understand the work, rules, exceptions, customer needs and operational consequences that AI systems must address.

Training and workshops can help them:

• Recognize practical AI opportunities
• Describe workflows and business problems
• Identify repetitive and decision-intensive tasks
• Define acceptable outcomes
• Explain exceptions and failure consequences
• Participate in opportunity scoring
• Evaluate proposed AI-assisted workflows
• Define human-review requirements
• Test prototypes and provide structured feedback
• Distinguish an impressive demonstration from a useful business system

Subject-matter expertise is essential because AI cannot define the organization’s business meaning or acceptable risk on its own.

Enterprise and Solution Architects

Architecture-focused sessions can address:

• Enterprise AI architecture
• Capability-first design
• System boundaries and responsibilities
• Deterministic software versus probabilistic AI
• Application and data integration
• APIs and capability libraries
• Retrieval-augmented generation
• AI assistants and agents
• Identity and access control
• Human-in-the-loop patterns
• Validation and evaluation
• Observability and auditability
• Model and vendor abstraction
• Cloud, on-premises and hybrid deployment
• Production-readiness requirements

These sessions examine AI as part of a complete enterprise system—not merely as a model, prompt or chatbot.

Software Developers and Technical Teams

Technical training can help experienced application developers extend their existing capabilities into enterprise AI.

Topics may include:

• AI application patterns
• C# and .NET AI development
• Azure OpenAI and Azure AI services
• ML.NET and ONNX
• Semantic Kernel
• Microsoft Copilot and Power Platform considerations
• Model APIs and SDKs
• Structured outputs and validation
• Prompt and context management
• Retrieval and enterprise search
• AI-assisted development
• Evaluation and automated testing
• Security and access control
• Logging, monitoring and cost measurement
• Integrating AI into existing applications
• Moving prototypes toward production

The purpose is not to teach every available AI tool. It is to help technical teams understand where different capabilities fit and how to build dependable enterprise systems around them.

Security, Governance, Legal and Compliance Teams

AI initiatives frequently stall when governance participants are consulted only after a prototype has already been selected or developed.

Focused sessions can help these groups examine:

• Data classifications
• Permitted and prohibited uses
• Identity and authorization
• Sensitive-data handling
• Model and vendor considerations
• Human approval requirements
• Audit trails
• Output validation
• Retention and logging
• Intellectual-property concerns
• Regulatory and policy requirements
• Incident response
• Deployment and monitoring controls

The objective is not to eliminate all risk. It is to identify the relevant risks early enough to influence the design and investment decision.

Cross-Functional AI Innovation Teams

A cross-functional AI Innovation Team brings together the perspectives needed to discover, evaluate and govern enterprise AI initiatives.

Participants may include:

• Executive sponsors
• Business-unit leaders
• Process owners
• Subject-matter experts
• Enterprise and solution architects
• Application-development leaders
• Data and analytics professionals
• Security and governance representatives
• Infrastructure and cloud teams
• Project and program managers
• Quality-assurance professionals
• Operational support teams

AInDotNet can help the team understand its responsibilities, apply structured decision methods and establish a repeatable process for advancing the right initiatives.

Enterprise AI Workshop Options

Workshops can be customized, but common engagement types may include the following.

Enterprise AI Executive Briefing

A focused session for executives and senior leaders who need a practical understanding of enterprise AI opportunities, limitations, risks and investment decisions.

A briefing may address:

• Current enterprise AI capabilities
• Common misconceptions
• AI strategy and portfolio decisions
• Business-value expectations
• Governance and accountability
• Prototype and production distinctions
• Microsoft AI considerations
• Recommended organizational next steps

This format emphasizes decision-making rather than technical implementation.

AI Opportunity Discovery Workshop

A structured workshop for identifying potential AI applications across departments, workflows and business processes.

Activities may include:

• Business-process discussion
• Pain-point identification
• Workflow analysis
• Repetitive-task discovery
• Decision and exception analysis
• AI capability mapping
• Opportunity brainstorming
• Initial value and feasibility screening
• Opportunity-inventory development

The purpose is to produce a broad but organized inventory of possible AI opportunities—not to approve every idea for implementation.

AI Opportunity Scoring and Prioritization Workshop

A working session that helps an organization compare competing AI opportunities using defined criteria.

Evaluation criteria may include:

• Expected business value
• Workflow fit
• Technical feasibility
• Data availability and quality
• Implementation cost and complexity
• Security and governance risk
• Operational burden
• User adoption requirements
• Time to useful evidence
• Consequences of incorrect output
• Strategic alignment

The result is a more defensible prioritization than selecting projects based on enthusiasm, organizational politics or vendor pressure.

AI Innovation Team Workshop

A workshop for establishing or strengthening the cross-functional team responsible for enterprise AI discovery and oversight.

Topics may include:

• Team composition
• Roles and responsibilities
• Decision rights
• Opportunity intake
• Scoring and prioritization
• Meeting structure
• Conflict resolution
• Approval gates
• Prototype oversight
• MVP evaluation
• Production-readiness decisions
• Portfolio capacity
• Reporting and reassessment

The objective is not to create another committee. It is to create a practical decision-making structure that brings the necessary perspectives into AI initiatives.

Enterprise AI Architecture Workshop

A working session for architects, development leaders, security teams and technical stakeholders.

Topics may include:

• Current-state technology environment
• Target AI capabilities
• Application and data boundaries
• Capability decomposition
• APIs and integration
• Model and service selection
• Retrieval and knowledge architecture
• Deterministic processing
• Assistants and agentic orchestration
• Human approval points
• Identity and authorization
• Evaluation and validation
• Logging and observability
• Deployment and production operations

This workshop can help establish shared architectural direction before individual teams create incompatible or disconnected AI solutions.

AI Prototype and MVP Planning Workshop

A planning session for organizations with a defined AI opportunity that may be ready for validation.

Participants may define:

• The business problem
• Target users
• Current workflow
• Proposed AI-assisted workflow
• Prototype boundaries
• Required data
• Technical assumptions
• Quality requirements
• Evaluation datasets
• Error categories
• Acceptable false-positive and false-negative rates
• Cost, latency and throughput expectations
• Human-review requirements
• Security constraints
• Continuation or stop criteria

The output can provide a clearer basis for a prototype or MVP engagement.

Production-Readiness Workshop

A workshop for organizations attempting to move an AI prototype toward dependable production operation.

The session may examine:

• Architecture
• Security
• Identity and authorization
• Data protection
• Testing and evaluation
• Failure handling
• Human escalation
• Logging and auditability
• Performance
• Cost controls
• Monitoring
• Deployment
• Support ownership
• Change management
• Rollback
• Governance approval

The objective is to identify the gap between a functioning demonstration and a supportable production system.

Microsoft Enterprise AI Workshop

A Microsoft-focused workshop for organizations that already depend on technologies such as:

• C# and .NET
• SQL Server
• Microsoft Azure
• Azure OpenAI
• Azure AI services
• Microsoft 365
• SharePoint
• Dynamics 365
• Power Platform
• Microsoft Copilot
• ML.NET
• ONNX
• Semantic Kernel
• Existing Microsoft business applications

The workshop can help the organization determine how AI fits into its existing technology environment without assuming that the entire stack must be replaced.

Microsoft technologies may be the natural starting point, but they are not treated as the only possible answer. Other platforms may be considered when they provide a better business, technical, security or economic fit.

Applying the AInDotNet Enterprise AI Frameworks

Training and workshops may draw from AInDotNet’s practical enterprise AI frameworks.

Enterprise AI Engineering Methodology

The Enterprise AI Engineering Methodology connects three major responsibilities:

1. Decide — Identify and select worthwhile AI initiatives.
2. Architect — Define how approved systems should work, integrate, validate and remain governed.
3. Build — Prototype, evaluate, implement and operate the system.

This helps organizations avoid beginning development before they have made the necessary business and architectural decisions.

Enterprise AI Operating Model

The Enterprise AI Operating Model provides a repeatable system for:

• Discovering AI opportunities
• Scoring and prioritizing initiatives
• Selecting prototype candidates
• Advancing initiatives through prototype, MVP and production stages
• Reassessing projects as new evidence becomes available
• Stopping weak projects
• Expanding strong projects
• Managing an enterprise AI portfolio

The operating model treats AI investment as an evidence-based pipeline rather than a collection of disconnected experiments.

Enterprise AI Architecture

The Enterprise AI Architecture framework examines the complete system required around AI capabilities.

This may include:

• Business strategy
• Work and workflow definition
• Capability realization
• Data and integration
• Security and governance
• Validation and observability
• Deployment and operations
• Human accountability

These frameworks can give workshop participants a common structure for decisions while allowing the content to be adapted to the organization’s circumstances.

Workshop Formats

Enterprise AI training and workshops may be delivered as:

• Four-hour focused sessions
• Eight-hour full-day sessions
• Twelve-hour extended workshops
• Sixteen-hour multi-session engagements
• Customized multi-session programs
• Executive briefings
• Technical training
• Facilitated cross-functional working sessions
• Remote sessions
• On-site sessions when appropriate
• Hybrid engagements

A four-hour session may be appropriate for focused education or a bounded working topic.

Longer or multi-session engagements allow more time for organizational context, stakeholder participation, structured exercises, discussion, scoring, architecture analysis and documented outputs.

The correct format depends on the objective, number of participants, range of roles and amount of organization-specific work required.

How Workshops Are Customized

A private enterprise workshop should reflect the organization receiving it.

Customization may consider:

• Industry
• Organizational size
• Government or commercial requirements
• AI maturity
• Existing applications and data
• Microsoft technology investments
• Security and compliance requirements
• Current AI initiatives
• Executive objectives
• Participant roles
• Business processes
• Proposed use cases
• Available workshop time
• Desired decisions and deliverables

Before a customized workshop, AInDotNet may conduct planning discussions and request relevant background information.

The workshop should begin with enough shared context to support productive decisions without consuming the entire session explaining basic terminology.

What Participants May Receive

Depending on the engagement, participants may receive or work with:

• Presentation materials
• Enterprise AI frameworks
• AI opportunity-discovery prompts
• Opportunity inventories
• Structured worksheets
• Business-value questions
• Feasibility-assessment criteria
• Scoring models
• Prioritization matrices
• Architecture diagrams
• Prototype-planning templates
• Governance questions
• Production-readiness checklists
• Role and responsibility guidance
• Recommended next steps
• Workshop findings or summary documentation

Specific deliverables should be defined before the engagement.

A training presentation does not automatically include client-specific analysis or written recommendations. Those outputs require an appropriately scoped workshop, assessment or consulting engagement.

What Makes These Workshops Different

Business Problems Before AI Tools

The starting point is not:

“Where can we install a chatbot?”

The starting point is:

“What work needs to be improved, what capability is required and how will we know whether the result is valuable?”

Tools and models are selected after the organization understands the problem.

Capability-First Technology Selection

Not every problem requires an LLM, generative AI or an autonomous agent.

An appropriate solution may use:

• Conventional software
• Business rules
• Workflow automation
• Predictive machine learning
• Document-processing services
• Search and retrieval
• LLMs
• AI assistants
• Narrowly controlled agents
• Human judgment
• A combination of these capabilities

The objective is to select the simplest combination that meets the business, quality, security and operational requirements.

Architecture and Governance From the Beginning

Security, integration, validation, human approval, observability and operational ownership should not be postponed until after a successful demonstration.

Workshops introduce these considerations early enough to affect opportunity selection, prototype design and investment decisions.

Existing Teams and Systems Still Matter

Enterprise AI does not require an organization to abandon its experienced employees, existing applications or Microsoft technology investments.

Business experts still define meaning and acceptable outcomes.

Architects still define boundaries and system responsibilities.

Developers still implement deterministic business capabilities and integrations.

Security and governance teams still establish controls.

AI becomes another capability within the enterprise—not a reason to discard everything the organization already knows.

Evidence Before Large Investment

A prototype should test important technical assumptions.

An MVP should test whether the solution produces useful value in a bounded operating environment.

Production development should proceed only when the accumulated evidence justifies the additional investment.

Training and workshops help participants understand these distinctions and define appropriate decision gates.

Potential Workshop Outcomes

Depending on the selected workshop, the organization may leave with:

• A shared enterprise AI vocabulary
• More realistic expectations
• An inventory of potential AI opportunities
• A prioritized list of candidate initiatives
• Defined evaluation criteria
• Recommended prototype candidates
• An AI Innovation Team structure
• Clarified roles and decision rights
• Initial architecture direction
• Identified data and integration requirements
• Security and governance questions
• Prototype or MVP success criteria
• Production-readiness findings
• An initial enterprise AI roadmap
• Defined next steps

A workshop does not guarantee that a proposed AI initiative should continue.

Discovering that an opportunity lacks sufficient value, usable data or acceptable risk characteristics can be a valuable result—especially if that conclusion prevents a much larger unsuccessful investment.

Training Is Not the Same as an Assessment

Training develops knowledge.

A workshop applies structured methods through participant discussion and exercises.

An Enterprise AI Opportunity Assessment goes further by conducting client-specific discovery, analysis, scoring and documented recommendations.

Consulting and advisory services may then help the organization address strategy, architecture, governance, program design or implementation decisions over a longer period.

An organization may begin with training and later proceed to a workshop or assessment. Another organization may already have sufficient internal knowledge and begin directly with an assessment.

The correct starting point depends on the decisions the organization needs to make.

Why AInDotNet

Extensive Enterprise Application Experience

Enterprise AI must operate within real applications, databases, workflows, security boundaries and organizational constraints.

AInDotNet brings decades of enterprise application architecture and development experience to AI education and planning.

Microsoft-Centered Expertise

AInDotNet specializes in organizations that depend on C#, .NET, SQL Server, Azure, Microsoft 365, SharePoint, Dynamics 365, Power Platform and related technologies.

Training connects AI concepts to the technologies and teams the organization already uses.

Business and Technical Translation

Enterprise AI decisions require participation from people with very different responsibilities.

AInDotNet helps connect executive objectives, business workflows, architecture, development, data, security, governance and production operations.

Practical Frameworks

Training and workshops use structured enterprise AI frameworks, exercises and decision methods.

The purpose is not simply to create enthusiasm. It is to give the organization a more repeatable way to identify, evaluate, architect and implement valuable AI capabilities.

Technology-Neutral Decision-Making

AInDotNet has a strong Microsoft focus, but no single vendor, model or product is automatically the correct answer.

Recommendations consider business fit, quality, security, cost, integration, operational complexity and long-term supportability.

Request an Initial Fit Discussion

If your organization is considering enterprise AI training, an executive briefing or a working workshop, request a short initial fit discussion.

The purpose of the conversation is to:

• Understand the organization’s objectives
• Identify the intended participants
• Discuss the organization’s current AI maturity
• Determine whether training, a workshop, an assessment or another service is appropriate
• Identify a potential topic and format
• Establish whether there is a reasonable mutual fit

Public presentations, webinars and existing introductory materials may sometimes be available without charge.

Private workshops, customized training, organization-specific exercises, preparation, facilitation and written deliverables are paid professional services.

The initial fit discussion does not include detailed training, client-specific opportunity analysis, formal scoring, architecture recommendations or a customized implementation plan.

Request an Initial Fit Discussion

Frequently Asked Questions