
Cem Dilmegani
Cem has been the principal analyst at AIMultiple for almost a decade.
Cem's work at AIMultiple has been cited by leading global publications including Business Insider, Forbes, Morning Brew, Washington Post, global firms like HPE, NGOs like World Economic Forum and supranational organizations like European Commission. [1], [2], [3], [4], [5]
Professional experience & achievements
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. [6], [7]
Research interests
Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity (including network security, application security), data collection including web data collection and process intelligence.
Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.
Education
He graduated as a computer engineer from Bogazici University in 2007. During his engineering degree, he studied machine learning at a time when it was commonly called "data mining" and most neural networks had a few hidden layers.
He holds an MBA degree from Columbia Business School in 2012.
Cem is fluent in English and Turkish. He is at an advanced level in German and beginner level in French.
External publications
- Cem Dilmegani (September 28, 2017). Post-AI Banking: Millions of jobs at risk as banks automate their core functions. International Banker
- Cem Dilmegani, Bengi Korkmaz, and Martin Lundqvist (December 1, 2014).Public-sector digitization: The trillion-dollar challenge.McKinsey & Company
Conference & other event presentations
- Real Estate and Technology, presented by Hofstra University’s Wilbur F. Breslin Center for Real Estate Studies and the Frank G. Zarb School of Business in 2023 and 2024.
- Radar AI session (June 22, 2023): "Increasing Data Science Impact with ChatGPT".
- Generative AI Atlanta meetup (March 10, 2023): Generative AI for Enterprise Technology.
Sources
- Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
- Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
- Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
- Science, Research and Innovation Performance of the EU, European Commission.
- EU’s €200 billion AI investment pushes cash into data centers, but chip market remains a challenge, IT Brew.
- Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
- We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.
Latest Articles from Cem
Top 13 Use Cases of Generative AI in Education
Generative AI is an advanced technology that can rapidly produce lifelike images, text, and animations. In our research, we examined its use across various industries, including healthcare and banking. While other technologies, such as conversational AI and robotic process automation (RPA), are being implemented in education, generative AI is not being properly implemented in education.
Top 5 File Management Examples & Best Practices
This article explains 5 Real-Life File Management examples from 5 industries such as banking, car rental services, finance, insurance, and railroad transportation. In examples, we explain these five companies’ business challenges, their solutions, and their results.
Generative AI Healthcare: 15 Use Cases with Examples
As healthcare systems face rising data volumes, workforce shortages, and increasing demands for personalized care, generative AI is emerging as a critical solution. By generating insights from complex medical data, generative AI healthcare applications offer hospital administrators, clinicians, and researchers new ways to improve decision-making and patient outcomes.
Top 5 Restaurant Chatbots & Use Cases
Restaurant chatbots can be a smart investment for restaurant chains because they provide 24/7 customer service and enhance the workforce by delivering standardized services. We compiled information about the top 5 restaurant chatbots, including their use cases, real-life examples, and best practices for implementing restaurant chatbots.
System Testing vs End-to-End Testing Explained With Examples
Confusing system testing with end-to-end (E2E) testing can lead to duplicate work, overlooked defects, and delayed releases. While both are part of the software testing life cycle, they differ in scope, objectives, and execution. We explain each with a side-by-side comparison and examples to help QA teams choose the right approach for their project stage.
Therapist Chatbots: Top Use Cases & Challenges
The World Health Organization (WHO) reports that one in eight people globally suffers from a mental disorder, impacting about 970 million individuals. Furthermore, approximately 70% of those with mental health issues do not receive treatment across the globe. Therapist chatbots can help close the mental health treatment gap and contribute to a happier society.
Top 50 Content Writing Statistics
The content writing industry has grown in popularity over the years, and recent advancements in AI and LLMs have dramatically transformed the landscape. So, we have gathered 50 content marketing statistics about to make a data-driven presentation about the current state of the content writing industry as a whole.
Synthetic Data vs Real Data: Benefits, Challenges
Synthetic data is widely used across various domains, including machine learning, deep learning, generative AI (GenAI), large language models, and data analytics. According to Gartner, by 2030, synthetic data use will outweigh real data in AI models.
Human Generated Data with Methods
Despite the rise of generative AI tools like ChatGPT and Gemini, human-generated data remains crucial for AI developers. Companies like OpenAI invest heavily in obtaining human-generated data to train their large language models (LLMs). Whether through data collection services or in-house efforts, AI developers require a steady stream of human-generated data.
Top 5 Mortgage Chatbots: Use Cases & Examples
Banks with higher customer satisfaction generate deposits 85% faster than their competitors. One crucial financial procedure impacting client satisfaction is loan processing. Chatbots can simulate mortgage brokers and automate tasks around the clock. However, many banking executives are not fully aware of the specific use cases for loan chatbots.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.