Turning ENEC engineers into AI builders

Executive Summary

Cognit DX delivered a highly practical AI capability-building program for ENEC that combined technical training, individual assessment, hands-on engineering work, and business use case development. The program went beyond classroom learning: it produced 15 internal AI builders and five business-relevant AI projects selected from ENEC leadership priorities.

Client Overview

Emirates Nuclear Energy Company (ENEC) is the organization responsible for implementing the UAE Peaceful Nuclear Energy Program. For this engagement, Cognit DX delivered a specialized AI for Coders and Engineers program for a selected cohort of ENEC engineers in Abu Dhabi.

Program Objective

The program was designed to go beyond traditional AI training by transforming a selected group of engineers into internal AI champions, builders, and applied innovation leads capable of identifying, developing, and scaling AI use cases within the organization.

The objective was not only to teach participants the fundamentals of artificial intelligence, machine learning, generative AI, and agentic AI, but also to enable them to build practical, business-relevant AI solutions connected directly to ENEC’s internal priorities.

Program Structure

The program was delivered in three main phases, allowing Cognit DX to establish a clear baseline of participant capability, deliver practical and technical training, and then measure progress through applied project work.

• Pre-Training Evaluation — 2 Days
• Intensive Technical Training — 10 Days
• Post-Training Project Evaluation — 2 Days

Phase 1: Pre-Training Evaluation

The program began with a detailed two-day pre-training evaluation. Cognit DX conducted a dedicated one-hour, one-on-one interview with each of the 15 participants to assess their existing technical capability and readiness for the program.

• Assessment Areas
• Python programming capability
• Previous coding experience
• Awareness of artificial intelligence and machine learning
• Familiarity with large language models
• Understanding of AI agents and agentic platforms
• Experience with AI development tools and platforms
• Previous project work and GitHub repositories

Each participant was asked to present examples of previous work, including GitHub repositories where available. Based on these interviews and reviews, Cognit DX assigned a pre-training evaluation grade that served as a baseline for measuring progress at the end of the program.

Why this mattered

This pre-assessment ensured that the training was grounded in the real capabilities of the participants and allowed Cognit DX to tailor the learning experience to the group’s technical level.

Phase 2: Intensive Technical Training

The core training phase was delivered over 10 full training days, divided into two weeks. Each day followed a practical rhythm: approximately three hours of instructor-led learning in the morning, followed by approximately three hours of hands-on mini-project work in the afternoon.

Week 1: Artificial Intelligence and Machine Learning

The first week focused on the foundations of artificial intelligence, machine learning, and applied data science. Participants were introduced to the key concepts, tools, and techniques required to build AI-powered solutions.

• Python for AI development
• Machine learning fundamentals
• Prediction models
• Regression models
• Classification and segmentation
• Search and optimization concepts
• Clustering, including K-means
• Working with AI development platforms
• Introduction to Hugging Face
• Building practical AI projects

Week 2: Generative AI and Agentic AI

The second week focused on generative AI, large language models, retrieval-augmented generation, AI agents, and agentic development frameworks.

• Large language models
• Prompt engineering for technical use cases
• Retrieval-Augmented Generation (RAG)
• AI agents
• Agentic workflows
• LangChain
• LangGraph
• AI application architecture
• Building generative AI and agent-based solutions
• Learning Model

Participants were not only learning AI concepts; they were building with AI every day. The morning sessions introduced the tools and methods, while the afternoon mini-projects immediately converted the learning into practice.

Phase 3: Post-Training Project Evaluation

The post-training phase was designed to evaluate progress and convert learning into tangible business value. On Day 13, the 15 participants were divided into five groups of three engineers each. Each group was assigned a business-relevant AI project.

These projects were not selected randomly. Before the program began, Cognit DX worked with ENEC leadership to identify a list of AI use cases and internal priorities that the organization wanted to explore. From this list, five projects were selected and assigned to the participant groups.

Each group spent a full day working on its assigned project under trainer supervision. Following this project day, participants continued working on their solutions over a two-week period, enhancing functionality, improving stability, strengthening technical implementation, and engaging with the trainer for guidance and feedback.

Final Project Showcase

The final post-evaluation day took place on-site and was designed as a formal project showcase. The audience included Cognit DX leadership, the trainer, the participants, and senior ENEC leadership. Two senior directors from ENEC attended the final presentations.

Each group presented its project for approximately 20 minutes, covering

• The business problem addressed
• The AI solution developed
• The technical approach used
• The tools and platforms applied
• The potential value to the organization
• The next steps required to scale the solution internally

ENEC leadership was highly impressed by the quality and relevance of the projects. Senior directors expressed their intention to incubate the projects internally and sponsor them for further development and implementation.

Key Differentiator: Training Plus Business Use Case Development

The ENEC program was designed to deliver more than a training outcome. It combined capability building with applied innovation.

In many organizations, AI training and AI implementation are treated as two separate investments. A company may first train its employees, and later hire an external vendor to identify and build AI use cases. Cognit DX combined both objectives into one integrated program.

The result was a model that did not simply teach participants about AI; it enabled them to build AI solutions for their own organization. Rather than only delivering knowledge, Cognit DX transferred capability.

Core Takeaway

The program did not only provide the organization with AI solutions; it taught ENEC’s engineers how to build them.

Key Impact

• 15 engineers trained as AI champions and AI builders
• 14-day structured capability-building journey
• 2 days of pre-training technical assessment
• 10 days of intensive hands-on AI, ML, generative AI, and agentic AI training
• 2 days of post-training project evaluation and presentation
• Five business-relevant AI projects developed by participant teams
• Projects selected from ENEC leadership’s own priority use case list
• Senior leadership engagement during the final project showcase
• Internal sponsorship interest for further incubation and implementation

Outcome

The final outcome was not limited to trained participants. The engagement produced both new internal AI capability and five practical AI projects that could be further developed and scaled inside the organization.

This made the program a strong example of Cognit DX’s ability to design and deliver applied AI capability-building programs that combine training, technical enablement, business relevance, and measurable outcomes.

Relevance for Government and Enterprise AI Enablement

This case study is particularly relevant for government and large enterprise stakeholders seeking to build internal AI capability rather than relying only on external vendors. The model creates internal champions, produces practical use cases, and builds the organizational confidence required to move from AI awareness to AI implementation.

Source note: ENEC is identified on its official website as Emirates Nuclear Energy Company, responsible for implementing the UAE Peaceful Nuclear Energy Program.