Enterprise AI Implementation Specialist
200 Hours / 4 Months, Part Time Program
This part-time Enterprise AI Implementation Specialist Program features expert instruction, hands-on projects, and a real connection to the industry to get graduates hired.
AI is reshaping every industry and every profession. According to the World Economic Forum, 92 million jobs may disappear, and 170 million new roles are expected to emerge by 2030. The need to implement AI in large organizations is imminent and meaningful: AI implementation specialists are needed to make sure that employees and companies benefit from AI while safeguarding data.
Wawiwa’s reskilling program prepares participants to become job-ready Enterprise AI Implementation Specialists – professionals capable of leading AI adoption in the business from functional diagnosis to actual deployment.
Deep Focus on the Most Needed Expertise in Tech
Graduates will become indispensable to organizations by bridging business needs with AI solutions across the enterprise. They’ll design, implement, and optimize AI workflows, systems, and integrations that drive smarter decisions and business impact.
Open Doors With Industry Connections
Equip students to succeed in a rapidly expanding field with help from our network of AI experts, instructors, hiring partners, and alumni. The Enterprise AI Implementation Specialist curriculum was created in collaboration with the industry’s leading companies to make our programs as relevant as possible to the local industry’s needs, assuring graduates will be job-ready as soon as the program ends.
Guide Students to Deliver a Professional Project
Throughout the program, participants gain practical experience by completing large-scale projects that reinforce the concepts and tools learned in each module. They diagnose organizational needs, identify high-impact AI opportunities, and design end-to-end enterprise AI solutions. They build AI workflows, assistants, or agentic systems, while applying architecture, security, and governance best practices. They also use AI tools throughout the process to design and prototype solutions faster and more effectively. The program culminates in a final project, where participants deliver a complete enterprise AI solution – including diagnosis, architecture, a working prototype, governance framework, ROI model with KPIs, and an executive presentation and demo.
Who Is the Program For?
- Business analysts
- IT professionals
- Operations managers
- Digital transformation leaders
- Consultants
- Tech-savvy professionals transitioning into AI roles
No prior coding background is required!
How the Program Works
200 total learning hours designed for skill-building:
- 150 academic hours – live classes with an expert instructor and guided practice
- 50 independent project work hours – hands-on practice and real-world project development to apply and reinforce learned skills
We Develop the Soft Skills that
Enterprise AI Implementation Specialists Need
Technical skills matter just as much as soft skills for an Enterprise AI Implementation Specialist. You need both to succeed.
Problem-solving
Looking at challenges with a clear mind and finding practical ways to move forward
Teamwork
Collaborating with others, leveraging each person’s strengths, and respecting responsibilities
Communication
Explaining thoughts and work simply, asking the right questions, and keeping everyone aligned
Adaptability
Adjusting quickly when priorities, tasks, and tools change, and staying effective through it all
Critical Thinking
Challenging information and assumptions, asking questions, and making sound decisions
At Wawiwa, learners build soft skills naturally throughout the program. Because our training is hands-on, students learn by doing – working in teams, solving real problems, and practicing how professionals operate in real workplaces. This approach helps them develop strong technical abilities and the essential soft skills needed to thrive on the job from day one.
Top Notch Professionals
This unique program was built by a professional team made up of the leading experts in AI, with vast knowledge and experience in training too.
Eran Lasser
CEO of Wawiwa
Eran is a tech education entrepreneur with over 20 years of experience. Eran founded and managed four IT training companies: John Bryce Training (Israel), TRIG (China), JB-IQsoft (Hungary), KocBryce (Turkey). Eran also partnered to establish DAN.IT Education (Ukraine), Techub (Georgia), and more. In addition, Eran managed Mentergy, which provides e-learning and distance learning solutions. Over the years, he was responsible for the reskilling of more than 50,000 individuals now working as tech professionals.
Daniel Anderson
Chief Training Officer
Daniel is responsible for updating state-of-the-art topics in the company’s tech training programs, and for maintaining its training methodologies. Daniel was the Chief Trainer at the Israel Defense Force’s Tech Training Center and is a graduate of the IDF training process. He develops and delivers a wide range of programming courses, and is a Full-Stack Developer and Data Scientist.
Oded Israeli
Head of AI Programs
Oded is an AI evangelist since the onset of the AI revolution in 2022. He boasts over two decades of expertise in marketing, strategy, and product management across both large enterprises and successful startups. Oded also advised Fortune 500 companies as a Strategy Consultant at the Boston Consulting Group, and founded several companies as an entrepreneur. Oded holds LL.B. and LL.M. degrees (cum laude) from Tel Aviv University and an MBA from INSEAD Business School.
Program Syllabus
Objective
Build foundations in AI thinking, solution architecture, and implementation planning – even without a technical background.
Topics Covered
- AI fundamentals for enterprises
- Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings – practical business implications
- From business idea to executable AI roadmap
- Process modeling and workflow mapping
- Enterprise AI architecture patterns and documentation standards
- Data flows and integration logic
- Build vs buy decisions
- Prompt engineering for structured systems
Hands-On
- Translate a business case into an AI architecture diagram
- Design an AI workflow and implementation canvas
Objective
Diagnose organizational needs and identify high-impact AI opportunities across core business functions.
Topics Covered
- Business diagnostics frameworks and process analysis
- Use-case libraries by department: Marketing, Sales, HR, Finance, Customer Support, Operations, Product
- Root Cause Analysis (RCA) and bottleneck identification
- ROI estimation and prioritization matrix for AI initiatives
- AI readiness assessment and data sensitivity checks
- Change management basics for adoption
Hands-On
- Run a discovery workshop and build an AI opportunity map
- Create an ROI hypothesis and quick-win shortlist
Objective
Build practical solutions using vibe coding and automation platforms, with enterprise-ready documentation and handoffs.
Topics Covered
- Automation design patterns (Zapier/Make/n8n) and system thinking
- Internal assistants and workflow using the leading vibe coding platforms
- Knowledge assistants fundamentals (RAG when needed)
- Human-in-the-loop workflows and escalation paths
- Evaluation basics: tests, edge cases, and quality checks
- Deployment and documentation for handoff
Hands-On
- Build an end-to-end automation that improves a real business process
- Build an internal assistant prototype and document it for deployment
Objective
Design and implement agentic workflows that handle complex tasks reliably and safely.
Topics Covered
- Agent design patterns and tool usage
- Orchestration and multi-step workflows
- Memory, context, and knowledge access patterns
- Monitoring and evaluation of agent outputs
- When to use agents vs simpler automation
Hands-On
- Build a single-agent workflow for a business function use case
- Extend to multi-agent collaboration with evaluation checkpoints
Objective
Operate AI solutions safely in real organizations with security guardrails, governance, testing, and stability practices.
Topics Covered
- Zero Trust AI security principles and data handling
- Prompt safety, injection awareness, and guardrails
- Testing checklists and evaluation workflows
- Governance models: committees, policies, and review cycles
- Operational stability: monitoring, incident basics, and maintenance routines
Objective
Lead adoption inside the organization through stakeholder alignment, champions, rollout strategy, and measurable impact.
Topics Covered
- Stakeholder mapping and resistance management
- Building an internal AI champions network
- Rollout strategy: communications, enablement, and training
- Adoption and impact metrics
Objective
Deliver a complete enterprise AI solution: diagnosis, architecture, prototype, governance, ROI, and executive presentation.
Project Deliverables
- Organizational AI diagnosis and opportunity selection
- Solution architecture and implementation roadmap
- Working prototype (automation, assistant, or agentic workflow)
- Security and governance pack
- ROI model and success KPIs
- Executive presentation and demo
What Do Students Have to Say?
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