AI Workflow Automation
A 12-clock-hour certificate from AI America LLC. Learners build, configure, connect, test, and document four working AI systems for nonprofit and small-organization operations.
Credential details
Credential name: AI Workflow Automation
Credential type: Certificate, issued by AI America LLC
Training provider: AI America LLC, registered with the Ohio State Board of Career Colleges and Schools, Certificate of Registration #2340
Total clock hours: 12 (4 hours of live instruction plus 8 hours of instructor-supervised, graded build labs)
Delivery: Four 60-minute live instructional sessions plus four 2-hour instructor-supervised labs, delivered virtually
Typical duration: 4 weeks
Instructor: Rahul Desai, Founder and Head of School
Prerequisites: None. Learners need a work computer, an account on the AI and automation tools used in their workplace, and permission from their employer to build in those tools.
Assessment: Four graded labs (25 points each) plus a live capstone demonstration. Passing score is 80 of 100.
Completion document: Certificate of completion showing the participant's name, the credential name "AI Workflow Automation," and the date earned
Contact: techcred@aiamerica.co
What learners build
This certificate teaches learners to build, configure, connect, test, and document working AI systems used in day-to-day nonprofit and small-organization operations. Every learning objective is assessed through a submitted, graded build. No credit is awarded for concept discussion alone.
Each learner leaves with four functioning builds:
A reusable AI content agent with a system prompt, an attached style guide, templated outputs, and an image-generation step
A structured contact-interaction database with a connected intake form and AI note parsing
A voice-to-task pipeline that transcribes a spoken note, structures it with AI, and writes a task record
A multi-step automation with an AI step, an error-handling branch, and a test log that turns one source document into three channel-specific formats
Learning objectives
On completion, the learner can:
Build and configure a reusable AI content agent with a written system prompt, an attached style-guide file, and at least three templated output formats.
Configure an image-generation and editing step inside that agent to produce a branded infographic draft from a text brief.
Build a structured contact-interaction database with defined fields and connect an intake form that writes new records.
Configure an automation that parses free-text or voice interaction notes into structured database fields.
Build a voice-to-task pipeline that captures a spoken note, transcribes it, structures it with an AI step, and writes a task record.
Build a multi-step automation that includes an AI step, an error-handling branch, and a test log, and that transforms one source asset into at least three channel-specific formats.
Document each build in a one-page runbook so another staff member can run and maintain it.
Deploy and test each build with sample data and record pass or fail results in a test log.
Technical skills taught
Every skill below is practiced hands-on in a supervised lab and verified through a submitted, graded build.
Agent configuration: writing and versioning a system prompt, attaching reference files, and defining templated outputs for a custom AI agent.
Image-generation step: adding an image-generation and editing step to an agent and controlling it with prompts and settings.
Form-to-database integration: mapping form fields to database fields so each submission writes a new record.
Automation and integration: building triggers, actions, and field mappings between applications in Zapier or Make, Power Automate, or Google Apps Script.
Speech-to-text integration: connecting a hosted speech-to-text model to convert voice notes into text.
Structured data extraction: configuring an AI step that returns named fields (task, priority, due date, sentiment) that downstream steps can write to a database.
Error handling and testing: adding error-handling branches, running sample data, and recording pass or fail results in a test log.
Technical documentation: writing a one-page runbook so another staff member can run and maintain each build.
Course outline
Each module is one 60-minute live session followed by a 2-hour instructor-supervised, graded lab. Sessions and labs run virtually or on site at the employer's choice.
Module 1: Build a reusable AI content agent (1 live hour + 2 supervised lab hours)
Live session: How a custom AI agent is assembled: system prompt, attached style-guide file, and templated output specifications. Configuring an image-generation step for infographic drafts. Version control and testing of agent behavior.
Supervised lab: Configure a custom agent (ChatGPT custom GPT, Gemini Gem, or Copilot Studio agent). Write a system prompt, attach a style-guide file, define three templated outputs (for example a newsletter section, a social caption set, and a donor update), and generate one infographic draft using an image step.
Deliverables: Exported agent configuration, three generated assets, one infographic draft with the prompt and settings used, one-page runbook.
Module 2: Build a structured contact-interaction database with AI intake (1 live hour + 2 supervised lab hours)
Live session: Database schema design for a contact-interaction log: contact record, interaction record, linked fields, status, date, next step. Mapping an intake form to fields. Configuring an AI step that parses free-text notes into structured fields.
Supervised lab: Create the database (Airtable, Notion, Google Sheets, or Microsoft Lists). Connect an intake form (Airtable or Notion form, Microsoft Forms, or Google Forms). Build an automation that takes a free-text note and writes contact, topic, sentiment, next step, and date into the correct fields.
Deliverables: Database schema, working intake form, test log showing three notes parsed into records, one-page runbook.
Module 3: Build a voice-to-task capture pipeline (1 live hour + 2 supervised lab hours)
Live session: The voice-to-structured-data pattern: capture, transcription, AI structuring, write to destination. Failure points (silent audio, format errors, missing fields) and how to handle them.
Supervised lab: Assemble a pipeline (Zapier or Make, Power Automate, or Google Apps Script) that takes a voice note, transcribes it with a hosted speech-to-text model, uses an AI step to extract task, priority, and due date, and writes a task record to the destination database.
Deliverables: Working pipeline, run log for two voice inputs, one-page runbook.
Module 4: Build a multi-step automation with an AI step, error handling, and content fan-out (1 live hour + 2 supervised lab hours)
Live session: Multi-step automation design. Inserting an AI step. Building an error-handling branch. Writing and reading a test log. The fan-out pattern: one source asset transformed into several channel-specific formats.
Supervised lab: Build a multi-step automation (Zapier or Make, Power Automate, or Google Apps Script) that includes at least one AI step and an error-handling path, and that transforms one source document into at least three formats (email draft, social caption, short web text). Run it against sample data and record results.
Deliverables: Working automation with error branch, test log, one-page runbook.
Assessment and grading
Each of the four labs is scored out of 25 points, for 100 points total. The minimum passing score is 80, and no single lab may score zero.
Build is present and functions when run: 10 points (submitted build, instructor run)
Required elements configured correctly, such as the system prompt, schema, or error branch: 6 points (configuration export or screenshots)
Connected to the correct data source or destination: 4 points (records written during test)
Tested with sample data and results logged: 3 points (test log)
Documented in a one-page runbook: 2 points (runbook)
Capstone demonstration: In the final session, each learner demonstrates all four builds in a 15-minute screen share and runs the Module 4 automation end to end with sample data while the instructor observes. The demonstration is required for certificate issuance.
Certificate: The certificate attests that the named learner built, configured, connected, tested, and documented a reusable AI content agent, a structured contact-interaction database with AI intake and note parsing, a voice-to-task pipeline, and a multi-step automation with an AI step and error handling, and demonstrated each build with sample data. It shows the participant's name, the credential name "AI Workflow Automation," and the date earned.
Who this credential is for
Operations, program, development, and communications staff at nonprofits and small-to-medium organizations who want to turn day-to-day AI use into working, documented systems.
All labs use common nonprofit and SMB-organization tasks (donor and contact logs, program updates, task capture, content production) and standard commercial tools, not any one organization's proprietary systems.
The credential is open to any employer. AI America issues the same certificate, under the same rubric, to any learner who passes.
Learners keep their four builds and four runbooks as a portfolio of working systems they can demonstrate to any employer.
In 2026, AI America has delivered related hands-on AI workflow training to staff at firms including Cintrifuse, the Cincinnati Business Committee, The Port (Port of Greater Cincinnati Development Authority), and Grote Enterprises.
Employer demand for these skills
Employers increasingly seek staff who can build and run AI-enabled workflows. Published labor market research shows:
Job postings that require AI skills offer 28% higher salaries, about $18,000 more per year, and 51% of 2024 postings requiring AI skills were outside IT and computer science occupations.
(Source: Lightcast, Beyond the Buzz, July 2025)Workers with AI skills earned a 56% average wage premium in 2024, and the skills employers seek in AI-exposed jobs are changing 66% faster than in other jobs.
(Source: PwC, 2025 Global AI Jobs Barometer)66% of business leaders say they would not hire someone without AI skills.
(Source: Microsoft and LinkedIn, 2024 Work Trend Index)92% of nonprofits now use AI in some capacity, but only 4% have documented, repeatable AI workflows. Lack of training is the top barrier cited by nonprofits not yet using AI (48%).
(Source: Virtuous and Fundraising.AI, 2026 Nonprofit AI Adoption Report)
This certificate targets that gap. Each learner leaves with four working, tested, and documented workflows.
For employers using TechCred
Employers applying to Ohio's TechCred program may reference this page as the third-party training provider documentation for this credential. The credential name on the application must read exactly "AI Workflow Automation." TechCred determines credential eligibility and reimbursement; AI America does not.
For current program rules, see the Ohio Department of Development's TechCred page.
Provider information
AI America LLC is a private training provider registered with the Ohio State Board of Career Colleges and Schools, Certificate of Registration #2340. Pricing for this credential is the same for all customers regardless of TechCred participation.
Recognition as a TechCred eligible training provider does not constitute endorsement, recommendation, approval, or certification by the TechCred Program, the State of Ohio, or any of its agencies.
Email us at techcred@aiamerica.co