Designed to help corporate leaders integrate AI into their business processes strategically, ethically, and sustainably, this four-day program offers a holistic roadmap , from data governance and natural language reporting to AI compliance frameworks, ethical principles, and the establishment of a customized AI ecosystem for organizations . Each day is a standalone module focused on its specific area of expertise; participants can participate throughout the week or just on the days they are interested in. The goal is to help executives gain clarity in the transition to AI-enabled business models, improve decision-making quality, and develop the digital leadership skills of the future.
Day 1: Artificial Intelligence-Assisted Data Management Training
"Transition from Chaos to Strategy"
December 15 / 10:00 – 16:00 / Online Applied Workshop & Case Analysis
The rapidly growing volume of data in organizations has become an invisible chaos, creating uncertainty in decision-making and operational inefficiency. To gain a competitive advantage in the age of artificial intelligence, transforming this chaos into an organized, fluid, and strategic data architecture is no longer a choice; it's a necessity. This training is a comprehensive professional transformation program that analyzes the root causes of data chaos, simplifies processes, and aims to establish an AI-powered insight mechanism.
Participants learn critical concepts such as the data lifecycle, integration architecture, synchronized insight generation, KPI frameworks, and real-time data flow through practical examples. At the end of the training, they create a "Data Lifecycle Prototype" and "ETL-Based KPI Design" specific to their organization, completing the program with tangible outcomes and applicable methodologies.
Educational Content
Chapter 1: The Data Lifecycle: From Raw Data to Strategic Value
The processes of collecting, cleaning, structuring, and integrating data into decision-making mechanisms are at the heart of organizational efficiency. In this section, participants:
• Selection of reliable data sources
• Standardization and accuracy control
• GenAI-powered insight extraction
• Learn the processes of transforming meaningful data into action.
Implementation: Each participant designs a “Data Lifecycle Model” specific to their own organization.
Part 2: Data Pipelines and Synchronous Insight Generation
Managing data from distributed systems within a consistent and fluid architecture is the key element that accelerates decision-making processes. In this section:
• Creating integration architecture
• Source–target matching
• Inconsistency detection and data cleaning techniques
• Artificial intelligence applications are discussed in instant KPI production.
Implementation: Participants design and produce an ETL-based KPI for their own processes.
Method and Duration
1 full day – 4 modules (90 min x 4)
Workshops are conducted using a conversation format, practical KPI design, case studies and interactive learning techniques.
Educational Achievements
✓ Diagnosing the root causes of data chaos
✓ Artificial intelligence-supported data collection and processing techniques
✓ Placing the corporate data lifecycle within a professional framework
✓ Ability to design KPIs and indicators
✓ Creating applicable data management prototypes in a short time
✓ Strengthening the data-driven approach in strategic decision-making processes
Day 2: The New Language of Thought: Strategic Thinking and Productivity Program with Artificial Intelligence
"Advanced AI literacy and hands-on productivity for professionals"
December 16 / 10:00 – 16:00 / Online Applied Workshop & Case Analysis
In the age of artificial intelligence, winning competition isn't just about knowing the technology; it's about learning how to think with it. Modern generative AI models are no longer just tools that execute commands; they're "intelligence partners" capable of analyzing, establishing context, and supporting strategic thinking. This training is designed to provide professionals with a deep understanding of the structure, logic, and potential of this new language.
Through this program, participants acquire a wide range of powerful skills, from understanding AI thinking to professional use of advanced prompt strategies, from data processing and reporting scenarios to decision-support models. The training is complemented by comprehensive applications, sample scenarios, and a personalized productivity toolkit that combines theory with practice.
Educational Content
Chapter 1: Understanding Artificial Intelligence — The New Architecture of Thinking
In this section, participants learn how artificial intelligence “thinks,” what parameters it works with, and what types of inputs produce better quality results.
• The logic of artificial intelligence and the learning cycle
• Factors affecting model behavior: context, sampling, intuition
• AI as “Intelligence Partner”: Human-machine shared intelligence model
• Basic principles and rules of prompt science
• Descriptive, analytical, creative and operational inquiry methods
• Professional prompt architecture based on role, context, task and format
• Chain of Thought and persona prompting techniques
Applied study: Comparing the results by solving the same problem with different prompt strategies.
Part 2: Turning AI into Productivity — Applied Strategies
In this section, participants learn how to effectively use artificial intelligence in efficiency, speed, decision support and reporting processes through real business scenarios.
• Analysis, reporting, content production and planning with artificial intelligence
• Solving complex tasks with a multi-step thinking chain
• Techniques for creating a “Corporate Prompt Library”
• Professional ways to deal with AI errors, loss of context, and data bias
• Ethics, compliance, and artificial intelligence usage responsibilities
• Safe use within the framework of KVKK, GDPR and AI Act
• Work culture with AI: continuous learning individual and learning organization
Hands-on work: Participants design AI-powered solutions for their own business processes and create a feedback loop.
Method and Duration
1 full day – 4 modules (90 min x 4)
• Seminar – lecture
• Case study
• Real-time application
• “Design Your Own Prompt Library” workshop
Educational Achievements
At the end of the training, participants:
• Will be able to understand the working logic of artificial intelligence at a professional level in a non-technical language,
• Use artificial intelligence not as a tool but as a thinking and productivity partner,
• Will be able to create productivity sets specific to their daily business processes,
• You can increase the speed of reporting, analysis and planning by 5–10 times by using the right prompt strategies,
• Will be familiar with corporate AI usage ethics and compliance standards,
• They will be able to solve multi-step complex tasks efficiently with artificial intelligence.
Day 3: Digital Reflex Design with Analytical Editing
"Artificial Intelligence-Supported Dynamic Data Analysis, Visual Reporting and Cognitive Autonomy Training"
December 17 / 10:00 – 16:00 / Online Applied Workshop & Case Analysis
Organizations are no longer simply collecting data, but gaining competitive advantage by instantly interpreting it and integrating it into their business processes. In this new era, where artificial intelligence has become an "intelligence partner," managers need analytical systems that accelerate decision-making and develop data-driven reflexes. This training equips participants with the skills to design next-generation digital reflex models that analyze data signals in real time, generate insights, and trigger automated actions.
The training teaches how to position AI not just as a tool but as a cognitive business partner. Participants develop their own enterprise prototypes through practical applications in pattern recognition, exploratory analytics, data-driven flow design, automation logic, and dynamic performance indicators. By the end of the program, each participant will have created a "business-ready digital reflex cycle" for their unit, generating tangible value in decision-making and operational efficiency within the organization.
Educational Content
Chapter 1: Analytical Fiction: The Journey from Data Signal to Insight
In this session, participants will learn the analytical mindset that transforms data into meaningful information and the exploratory analytics techniques supported by artificial intelligence. We will focus on patterns, biases, meaningful relationships within data, and decision-oriented analytical models.
Achievements:
• Understanding analytical modeling logic
• Applying pattern recognition techniques
• Analyzing data and generating insights with artificial intelligence
• Establishing an analytical framework that supports strategic decisions
APPLICATION:
Each participant designs a custom insight generation cycle that suits their role.
Chapter 2: Digital Reflex: Data – Decision – Action Chain
This section teaches how to create "digital reflexes" within an organization by developing data-driven automation models. Structures that trigger automated actions from analytical outputs, trigger logic, feedback loops, performance indicators, and cognitive decision models are discussed.
Achievements:
• Artificial intelligence-supported automation design
• Creating rules that trigger action from data signals
• Dynamic KPI and performance indicator development
• Establishing a customized cognitive decision cycle
APPLICATION:
Participants develop a cognitive autonomy prototype for their own teams.
Method and Duration
1 full day – 4 modules (90 min x 4)
• Seminar + Interview + Case Study + Live Applications
• At the end of the training, each participant:
He/she creates his/her own Digital Reflex Prototype.
Educational Achievements
✔ Positioning AI as an “intelligence partner”
✔ Learning digital reflex logic that automates data analysis
✔ Establishing an insight production cycle and decision support system
✔ Designing business unit-specific action automations
✔ Producing ready-to-implement models that will increase corporate efficiency
Day 4: How to Build Your Own Artificial Intelligence Ecosystem
“Applied Design Program for Professionals”
18 December / 10:00 – 16:00 / Online Applied Workshop & Case Study
Organizations’ competitive strength no longer depends solely on the data they possess, but on their ability to operate this data within an AI-driven ecosystem. This program enables professionals to redesign their organization’s data and process architecture from a strategic perspective, aiming to build AI-based digital capacity tailored to each business unit. Participants learn how the processes of collecting, processing, correlating, generating insights, and transforming insights into automated actions function as an integrated system.
This program teaches, through practical examples, how AI technologies can be formally embedded into workflows, at which stages human–machine collaboration creates value, and how an organization can build its own AI ecosystem from the ground up. By the end of the training, each participant develops a “ready-to-use” Data Lifecycle & AI Ecosystem Prototype for their company.
Training Content
1st Module: The Anatomy of an AI Ecosystem
Core Structures That Form Digital Intelligence
This session deconstructs how an AI ecosystem works. It examines how data management, integration layers, analytics engines, decision mechanisms, interaction models, and adaptive learning cycles connect to one another, explaining how the system evolves into a living structure that constantly observes, calculates, interprets, and improves itself.
Key Takeaways:
• Understanding the relationship between data, analytics, integration, and decision mechanisms
• Identifying the components that form corporate “digital intelligence capacity”
• Grasping how an ecosystem evolves like a living organism
2nd Module: Designing the Ecosystem — Logic, Flow & Structure
A Roadmap for Setting Up AI-Based Systems
In this module, participants analyze their organization’s data flows, operational cycles, decision-making processes, and automation levels to create an “ecosystem map.” This map forms the basis for determining where AI creates value and at which points it should be integrated into corporate processes.
Key Takeaways:
• Identifying strengths and weaknesses of the existing digital capacity
• Assessing the maturity of internal data cycles from an AI perspective
• Determining organization-specific AI integration areas
Method & Duration
One full day – 4 modules (90 min x 4)
• Seminar + Conversation + Case Study + Live Applications
At the end of the program, each participant completes:
Applied Workshop: Data Lifecycle Prototype
Participants model the data flows of their own business units and create a micro AI-supported ecosystem prototype. This application provides a transferable draft for their organizations.
Key Takeaways:
• Creating an organization-specific Data Lifecycle model
• Designing practical AI integration points
• Developing rapid prototypes for special processes
Training Outcomes
✔ Understands the core components of an AI ecosystem
✔ Can analyze the digital structure of their organization at a high level
✔ Understands how data, integration, analytics, and decision layers come together
✔ Builds the strategic framework needed to establish an AI ecosystem
✔ Transfers an applicable AI-based data management prototype to their organization
🎤 Training Manager: Dr. Serkan Gursoy
Dr. Serkan Gürsoy holds a PhD in Science and Technology Policy. He specializes in data ecosystems, artificial intelligence management, and digital intelligence strategies. His research, which began during his doctoral studies at Middle East Technical University, focuses on modeling thought and norms in digital environments, the dynamics of interaction with artificial intelligence, and the digitization of corporate intelligence, including employee experiences, with a unique theoretical and applied framework.
Dr. Gürsoy, who transforms his academic knowledge into practical applications and develops information ecosystems, data management infrastructures, and artificial intelligence-based decision support mechanisms, teaches undergraduate and graduate courses on big data management, data-driven entrepreneurship, analytical management, and data processing processes at universities.
With his scientific approach, strategic vision, and practical experience, Dr. Gürsoy focuses on increasing the digital intelligence capacity of institutions, developing a data-based decision-making culture, and establishing AI-supported ecosystems that enable sustainable growth.
-
✍️ Participation Details
-
Program Fee:
-
Full Program (4 Days):
Participation fee for the complete 4-day program is 25,000 TL + 20% VAT per person. Attendance will take place via Zoom. All summit materials and the participation certificate are included in the fee. -
Modular Online Participation (Per Day):
The participation fee for the modular, per-day option is 7,500 TL + 20% VAT per person. Attendance will take place via Zoom. All summit materials and the participation certificate are included in the fee. -
To register, please fill out the registration form at the link below and send us the payment receipt after completing the transfer to the specified bank account. A 10% group discount is applied for groups of 4 or more participants.
-
📢 Important Note:
-
Participation is limited.
You can secure your place by completing the registration form.
https://www.groupbsp.co/form -
Contact Information:
-
Phone: (+90) 530 281 09 66
Phone: (+90) 530 281 09 88
E-mail: info@groupbsp.com -
✍️ Bank Account Details
BSP GROUP KİŞİSEL GELİŞİM DANIŞMANLIK LİMİTED ŞİRKETİ
GARANTİ BBVA
Account No.: 6290285
IBAN: TR36 0006 2000 7460 0006 2902 85 -
✍️ Online Payment Link:
Some of the Organizations We Provide Organization and Training Consultancy Services to
