Certified AI Leadership Professional

The Certified AI Leadership Professional (CALP) program is a practical program designed to equip professionals, managers, and future leaders with the essential skills to use AI copilots for business productivity and career growth. Covering Generative AI, prompt engineering, custom GPTs, and responsible AI use, CALP prepares participants to integrate AI tools into their daily work, support digital transformation, and drive meaningful impact in the AI-driven workplace. Through real-world examples, hands-on workshops, and guided practice, participants learn how to use AI tools to improve daily tasks, support smarter decisions, and boost workplace productivity. The program concludes with a Capstone Project, helping participants apply AI copilots to their own job roles or team workflows.

No technical background is required. Basic digital skills and familiarity with business tools will enhance your learning experience. Familiarity with tools (optional): experience with Microsoft 365, ChatGPT, or similar AI tools is helpful. Workplace knowledge (optional): understanding of daily business tasks and workflows will help you apply AI copilots effectively.

How you'll learn

• Instructor-Led Training (ILT): Participate in interactive, classroom-based sessions led by experienced industry professionals for an immersive learning experience.
• Virtual Instructor-Led Training (VILT): Join live online classes from anywhere, offering flexibility without compromising interactivity and mentorship.

• Professionals & Team MembersHR, finance, sales, operations, and support staff aiming to improve their work with AI copilots.
• Junior & Mid-Level EmployeesIndividuals looking to boost productivity, automate tasks, and stay relevant in the AI era.
• Supervisors & Future LeadersThose who want to develop AI skills to enhance team performance and career growth.

• Use AI copilots to boost productivity and automate tasks.
• Apply prompt engineering for better AI results.
• Create simple custom GPTs for your work needs.
• Identify AI use cases to improve business outcomes.
• Enhance your role and career with practical AI skills.

Program Hours

16 Hours

Intensive Option

4 full days, with each day lasting 4 hours.

4-Week Option

4-week program, with 1 session per week, each session lasting 4 hours.

8-Week Option

8-week program, with 1 session per week, each session lasting 2 hours.

Upon successful completion of the Certified AI Leadership Professional (CALP) program, participants receive an official Certificate of Achievement issued by Epsilon AI Learning — USA, recognizing proficiency in AI copilots, prompt engineering, and AI-driven business applications. The program is delivered in alignment with international standards, including ISO 21001 and ISO 9001 — with Epsilon AI Global, the parent company, also certified to ISO/IEC 27001:2022 for information security — and follows globally recognized frameworks such as IACET and CPD. All graduates receive a digitally authenticated certificate containing a unique Certificate ID and personal EPSILON ID for instant, secure online verification — ideal for professional portfolios, LinkedIn profiles, and employer validation. A prestigious hardcopy certificate is also issued with advanced security and branding features: a unique serial number and verification code, a gold-embossed seal and authorized EPSILON AI signatures, and UV-printed security elements for enhanced authenticity and protection against forgery.

• Attend a minimum of 80% of live instructional sessions.
• Achieve a minimum score of 80% on the final examination.
• Successfully complete and present the Capstone Project.

• Introduction to the AI Era: how AI evolved from rule-based systems to machine learning, deep learning, and generative AI and Copilots; what a Copilot is in business terms (an always-on assistant for thinking, writing, analysis, and decision support embedded in daily tools).
• Why AI is critical now for executives: competitive pressure, cost/speed/quality advantages, and the strategic risk of ignoring AI versus controlled experimentation.
• The Copilot Ecosystem for Business Leaders: general-purpose language models (ChatGPT, Claude, Gemini), embedded Copilots in productivity suites, and domain-specific copilots; how to choose tools based on security/compliance fit, language support (Arabic/English), integration, and cost.
• Strategic Framework — the "AI Business Opportunity Lens": cost reduction, revenue increase, risk reduction, and experience improvement; mapping AI to the value chain and simple ROI thinking for executives.
• Role of LLMs in Business: how Large Language Models power knowledge-work automation, decision support, and content generation at scale, and act as the "engine" behind copilots and enterprise AI assistants.
• Group Activity (AI in Your Industry) and Individual Exercise (Personal AI Opportunity Canvas): identifying sector pain points and completing a one-page opportunity canvas with concrete use cases to explore.

• How Copilots think — the executive-friendly view: how a generative model responds to instructions, why vague instructions produce vague answers, and the difference between asking a question and giving role + context + task + constraints.
• The Epsilon Prompt Framework — PURPOSE, PERSONA, PARAMETERS, PRECISION, PROCESS, POLISH — with bad vs. good prompt examples for email writing, board memos, policy drafting, and market analysis.
• Hands-On Tools Exploration: guided overview and comparison of ChatGPT, Microsoft 365 Copilot, Notion AI, and GitHub Copilot, and when to use each for content creation, data analysis, coding & automation, and knowledge management.
• Hands-On Block 1 (Prompt Improvement Drills) and Block 2 (Functional Prompt Packs): rewriting naive prompts using the Epsilon Framework and building 8–10 role-based "problem cards" for Finance, Marketing/Sales, HR, Operations, Legal, and Government/Policy.
• Managing hallucinations and bias: typical failure modes (confident but wrong answers, made-up data or sources, ignored constraints) and practical ways to reduce risk (asking for source reasoning and uncertainty, limiting tasks to text transformation, using checklists).
• Building your Personal Prompt Library / Playbook: documenting at least 5 high-impact prompts (name, business situation, exact prompt, expected output format, common issues and fixes), plus daily operational and strategic/managerial prompts.

• Where Copilots live in a company: three main integration patterns — frontline tools (email, chat, office apps), internal portals and knowledge bases, and integration in line-of-business systems (CRM, ERP, core banking, HR systems).
• Mapping process and injecting AI: mapping an existing process step-by-step (e.g., complaint handling, invoice processing, hiring, tender response) and identifying "AI insertion points" for assistive rather than fully automated roles.
• Security, Privacy & Compliance: guidelines executives must insist on — never pasting highly sensitive data into public tools without policy, preferring enterprise versions with privacy commitments, keeping audit trails, and applying human-in-the-loop review for critical decisions.
• AI Workflow Redesign Framework: a step-by-step method to redesign workflows for speed, accuracy, and scalability by identifying current steps, highlighting manual/repetitive tasks, and introducing AI copilots at key points.
• Workshop (Design an AI-Enhanced Process) and Mini Roadmap: mapping a chosen process, marking where Copilots could draft/summarize/extract/suggest, defining a small pilot, involving IT/legal/compliance/operations, and setting success metrics over a 60–90-day timeline.

• Regulated sectors — what makes them different: why regulation is tighter (citizen rights, financial stability, data sensitivity), typical regulators and their concerns, and common fears (bias, discrimination, opaque decisions, security breaches).
• Government use cases: low-risk cases (policy summaries, classifying and routing citizen complaints, summarizing legislative documents, multilingual communication) and higher-risk cases handled carefully (automated eligibility decisions, risk profiling, predictive policing).
• Financial Services & Fintech use cases: loan application triage and document checking, plain-language client communications, market-news analysis and risk summarization, support chatbots/voicebots — plus red lines and controls (sign-off, explainability, logging for audits).
• Ethics & Bias Workshop: common ethical issues in generative AI (biased training data, stereotypes, unequal performance across languages/groups) worked through case scenarios (hiring shortlisting, credit scoring, prioritizing complaints) to identify harms, stakeholders, and safeguards.
• AI Usage Policy & Operational Governance and building your own "Responsible AI Checklist": responsible usage rules, data-privacy boundaries, validation/review processes, and escalation for critical decisions aligned with compliance, ethics, and organizational policies.

• Introduction to Custom GPTs: what custom GPTs and private AI assistants are, the difference between public AI tools and internal AI assistants, and their business value (standardization, knowledge retention, process automation).
• Business use cases: HR assistant (policy Q&A, onboarding support), sales assistant (proposal drafting, client responses), internal knowledge assistant, and operations support tools.
• Designing a Custom GPT: defining purpose, target users, inputs & outputs, and business rules, and integrating company knowledge, documents, and standard workflows.
• Hands-On Exercise (Build a Custom GPT): each participant identifies a real business challenge, designs a simple AI assistant, and defines its workflow, scope, and expected outcomes — leaving with a functional custom GPT concept, documented use case and ROI, and an internal deployment idea.

• What voice & conversational AI actually do: the distinction between classic IVR menus, fixed-flow chatbots, and modern conversational AI and voicebots backed by large models, plus typical components (speech-to-text, intent understanding, response generation, text-to-speech).
• Business cases for Voice AI: customer service (first-line FAQ support, status checks, after-hours coverage), internal uses (call transcription, meeting notes, voice entry, field use), and accessibility and inclusion.
• Designing a conversation flow: greeting and expectation setting, clear options and questions, graceful handling of confusion, and smooth escalation to a human — with an exercise mapping conversation paths, common intents, escalation points, and end conditions.
• Arabic, dialects, and multilingual design: challenges of dialects and code-switching between Arabic and English, designing prompts and flows robust to language variation, and deciding which parts stay in Arabic vs. English.
• Business & ROI view and integration: realistic time savings, cost components, soft benefits, a simple payback-period calculation for a pilot, and combining Voice AI + Copilots + CRM systems within a customer-experience strategy.

• From raw data to executive insight: what traditional BI does well, where AI adds value (natural-language queries, automated explanations of drivers and anomalies, simple forecasting), and realistic expectations that AI helps interpretation but does not fix bad data.
• Types of questions executives should ask with AI: descriptive ("What happened?"), diagnostic ("Why did it happen?"), predictive ("What is likely to happen next?"), and prescriptive ("What should we do?"), with examples across sales, churn, operations, staffing, and compliance.
• AI + Decision Intelligence in practice: using AI for scenario analysis, what-if simulations, and decision recommendations, and enhancing dashboards with AI-generated insights and automated explanations, plus a plain-language mini demo.
• Hands-On (Designing an AI-Augmented Dashboard) and Data Storytelling Exercise: choosing a reporting area, defining core KPIs and monthly management questions, sketching a dashboard layout with an AI insight area, and turning a hypothetical insight into a 3-minute executive story.

• Why AI projects fail: common patterns such as "cool POC, no follow-through", no owner after the pilot, no change in process, and IT-only or business-only initiatives — discussed through anonymized real examples of failures and successes.
• Readiness check (Where are you now?): a short guided questionnaire on data readiness, technical capacity, leadership support, culture, and governance maturity, with participants scoring themselves to find their weakest dimension.
• Department-level AI integration: applying AI across HR, finance, sales, and legal by identifying key processes, mapping AI opportunities, and aligning with KPIs.
• Building a realistic AI roadmap: translating ideas into 2–3 quick wins, 1–2 strategic initiatives, and parallel governance/education steps, defining sponsor, core team, timeline, success metrics, and key risks for each initiative.
• Change management, communication & governance: addressing staff fears ("Will this take my job?"), framing AI as an assistant not a replacement, offering training, and defining a simple governance model (executive sponsor/steering committee, AI lead, legal/compliance involvement, approval process).

• Capstone Project presentations (approx. 2 hours): each participant or small team presents their AI initiative — business problem and opportunities, chosen AI use cases, process map and integration points, sector/regulatory considerations, and change management and roadmap (8–10 min presentation + 3–5 min Q&A).
• Panel feedback & improvement suggestions: instructors share common strengths and best practices (clear business outcome, realistic pilot, good risk controls, strong communication plan) and give gentle critique on plans that are too ambitious or too vague.
• Future of AI for leaders: near-future trends to watch (multimodal models, more autonomous agents handling multi-step tasks, tighter regulations and required documentation, growing expectations from employees and customers) and how to stay up to date without being overwhelmed.
• Personal Action Plan & commitments: each participant writes a one-page "90-Day Action Plan" (key initiative, implementation steps, stakeholders, KPIs, and risks), followed by certification, group photo, and an explanation of the alumni community and next steps.