1. Day » Tuesday

Session 1: Introduction to Artificial Intelligence and Effective Command Writing

Purpose:
Learning the basic principles of effective results from artificial intelligence and creating the framework for safe use of artificial intelligence.

Content:

• What artificial intelligence can do and its limitations,
• The possibility of artificial intelligence being mistaken, confirmation of information, and the use of master prompts,
• Comparison of good and bad command examples,
• Four-part command structure: Purpose, Context, Output and Border,
• Creating clear and understandable commands to artificial intelligence, as if it were describing a job to a newcomer but competent intern,
• A brief demonstration of the results of the same command in different AI tools.

Data Traffic Light:

• Green: Data that can be easily shared, such as general information and public content,
• Yellow: Data that can be shared by extracting identity information,
• Red: Data that should not be shared, such as personal data, commercial secrets, and customer information.

Participant Application:
The participant creates a command for a task from their own business process and develops it using a four-part structure.

Output:
An improved command and a list of green, yellow and red data prepared for its own company.

Session 2: Daily Workflows and Office Automation

Purpose:
Making recurring office jobs faster and more efficient with artificial intelligence support in everyday business processes.

Content:

• Creating an action list from meeting notes or audio recordings,
• Preparation of administrator summary from long documents (Gemini Notebook),
• Understanding incoming e-mails in a foreign language and drafting responses,
• Preparing the Excel formula with ChatGPT, explaining the formula, and testing it on Excel,
• Convert tables within PDF to editable tables,
• Voice-writing and the use of artificial intelligence with a phone camera.

Participant Application:
The participant prepares a command template that can be reused for one of their own routine jobs.

Output:
Personal command template.

2. Day » Saturday

Session 3: Sales, Marketing and Content Production

Purpose:
Making sales and marketing processes from market research to content production faster and more efficient with artificial intelligence.

Content:

• Target market preliminary research and resource control; verification from the source of an information or number presented by artificial intelligence,
• Creating different content from a single product introduction: LinkedIn sharing, product description, and customer email,
• Preparing product description in Turkish, English and German,
• Preparing a follow-up email after the fair,
• Simple visual production.

Participant Application:
The participant prepares three different messages for the same product or service, aimed at three different target audiences.

Output:
Three different messages for your own product or service.

Session 4: Customer Experience and CRM

Purpose:
Ensuring faster, consistent and systematic management of customer demands.

Content:

• Classification of customer messages by topic, priority and related department,
• Preparing response drafts for customer messages,
• Identify the most common customer problems and create process improvement suggestions,
• Creating a customer assistant prototype that responds based on company documents,
• Testing the assistant with different scenarios: the normal question, the question at the border, and the question whose answer is not found in the documents.

Participant Application:
The participant prepares a customer assistant design card for his/her company: the purpose, the documents he/she will use, the answer format, and what he/she should not do.

Output:
Customer assistant design card.

3. Day » Tuesday

Session 5: Logistics, Supply Chain and Export Operations

Purpose:
Get quick and accurate insights from operation data and make data analysis processes more effective.

Content:

• Checking data quality before starting data analysis: identifying incomplete, repetitive, and inconsistent records,
• Analyzing sales trends on a country and product basis,
• Do not analyze delivery delays,
• Identify risky products in terms of stock,
• Creating simple sales trend forecasting and assessing why these forecasts should be interpreted carefully,
• Create a single comparison table from three different supplier offerings.

Participant Application:
The participant identifies a question from his or her job and first looks for an answer to the question: "Is the data in my hand enough to answer that question?"

Output:
A correctly generated data question and initial analysis.

Session 6: Human Resources and Data-Oriented Management

Purpose:
Making written work in recruitment and employee management processes faster and more efficient with artificial intelligence support.

Content:

• Do not prepare a job advertisement,
• Creating an interview question set and assessment score table,
• Pre-assessment via example (imaginary) CVs,
• The real CVs are personal data; what should be considered when uploading to AI tools, and the risk of artificial intelligence-induced bias,
• Determining the main themes and general satisfaction trend from open-ended responses in employee surveys.

Participant Application:
The participant prepares a job ad and assessment score sheet for a position in his or her company.

Output:
Job advertisement and evaluation score table.

4. Day » Saturday

Session 7: Financial Systems and Decision Making

Purpose:
Accelerate routine financial affairs and address management decisions in a more systematic framework.

Content:

• Creating a maturity tracking table from PDF invoices,
• Don't do simple budget deviation analysis,
• Establishing a decision framework with artificial intelligence for a strategic decision,
• First of all, determining the criteria of decision,
• Comparison of options according to the set criteria,
• Listing incomplete information that may change the decision,
• Highlighting the approach by which artificial intelligence does not make decisions, structure the decision-making process.

Participant Application:
The participant determines a decision from his or her own business and creates a decision matrix with a list of criteria with artificial intelligence support.

Output:
Decision matrix prepared for its own decision.

Session 8: Applied Workshop – Transform Your Own Business (Project Presentation)

Purpose:
To ensure that each participant leaves the training with a concrete AI usage example and implementation plan that they can implement in their own company after training.

Content:

• Determining a business idea that can be improved by artificial intelligence in the participant's own company,
• Evaluation of ideas in terms of impact and ease of application,
• Choosing the idea that has the highest impact and is easiest to implement,
• Converting the selected idea into a sample plan over a single-page worksheet,
• A collective response to the out-of-cover questions accumulated throughout education.

Output:
A 30-day pilot implementation plan.

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Our Expectations from Participants

Prior to Education

In order to achieve maximum efficiency before AI training for SMEs, participants are expected to complete the following preparations before training:

• Filling out the registration form (roll, artificial intelligence usage frequency, ChatGPT account type and Google account information),
• Creating a ChatGPT account (free account is sufficient),
• Once logged into the Gemini Notebook with a Google account,
• To participate in training by computer (phone or tablet is not enough for applications).

During Training

Due to the application-oriented progress of AI training for SMEs, participants are expected to pay attention to the following issues:

• To actively participate in the practice sections,
• Copying commands contained in the ready command document to adapt them to their own business processes
• To communicate unintelligible or plugged topics through the chat section,
• If possible, use a second screen or connect to the meeting over the phone so that you can use the computer screen for applications,
• Not upload real company data, customer information, or personal data to artificial intelligence tools during training. All applications will be carried out through content that does not contain imaginary data or personal information,
• When working with real data on the Gemini Notebook Not using the "I liked" and "I didn't like" buttons. This will be discussed on the first day of training, as well as the first session.

Mini Presentation Between Two Weekends

During the period between the first and second week, participants are expected to experience what they have learned in their own business process:

• Applying at least one of the commands learned in AI training for SMEs in their own business,
• To briefly share the resulting result at the beginning of the second week.

Educator: Duhan Onat KARADAYI

Duhan Onat Karadayı graduated in Statistics from Middle East Technical University (METU) in 2026 and is pursuing a master’s degree in Innovation Project Management at the Czech Technical University in Prague. He completed a long-term data science and statistics internship at the Human Resources Office of the Presidency of the Republic of Türkiye and a summer internship in TÜBİTAK’s Strategic Planning and Management unit. During his undergraduate studies, he assisted with graduate-level work in the Department of Statistics and was a founding member of the METU Artificial Intelligence Society.

Combining public-sector data science experience with an academic background in statistics, Karadayı works on preparing large datasets for analysis, statistical modelling and decision-support systems. His AI work brings together data analytics and process automation to help companies realise their operational potential. He also contributes to project management, supported by his postgraduate studies. Since joining İnovakademi in 2025, he has worked on needs analysis, data analytics, project management and AI-supported process improvement, addressing operational bottlenecks through end-to-end data pipelines built with machine learning methods.

Please contact for detailed information.

To register for training, please use the form below.

Gökhan EROL
e: gerol@inovakademi.com
g: +90 (533) 761 19 34
t: +90 (216) 709 73 32