Cem Dilmegani
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, agentic AI, cybersecurity (including network security, application security) and data including web data.Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and the 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, 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.
Media, conference & other event presentations
- Answers to Korea24's questions on job loss due to AI, Korea24
- 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: 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
AI Data Governance for Ethical Use in 2026
As more companies use artificial intelligence (AI) in their technical operations, it becomes increasingly important to guarantee that AI systems adhere to ethical standards and regulatory restrictions. AI data governance addresses the inherent complexities of AI models by focusing on data transparency, ethical decision-making processes, and mitigating bias.
Top 20 Data Labeling Tools in 2026
Data labeling, the process of annotating raw data (such as images, text or audio), is essential for training ML models to perform tasks like classification and recognition. Here, we introduce top 20 data labeling tools. The top data labeling tools: Ranking: From most to least comprehensive.
Top 50 Deep Learning Use Case & Case Studies in 2026
Deep learning uses artificial neural networks to learn from data. When trained on large, high-quality datasets, it achieves high accuracy, making it valuable wherever you have abundant data and need accurate predictions. Below are real deep learning applications across industries and business functions, with concrete examples.
World Foundation Models: 10 Use Cases & Examples ['26]
Training robots and autonomous vehicles (AVs) in the physical world can be costly, time-consuming, and risky. World Foundation Models offer a scalable alternative by enabling realistic simulations of real-world environments. These models accelerate development and deployment in robotics, AVs, and other domains by reducing reliance on physical testing.
10+ Large Language Model Examples & Benchmark in 2026
We have used open-source benchmarks to compare top proprietary and open-source large language model examples. You can choose your use case to find the right model. Comparison of the most popular large language models We have developed a model scoring system based on three key metrics: user preference, coding, and reliability.
GPU Software for AI: CUDA vs. ROCm in 2026
Raw hardware specifications tell only half the story in GPU computing. To measure real-world AI performance, we ran 52 distinct tests comparing AMD’s MI300X with NVIDIA’s H100, H200, and B200 across multi-GPU and high-concurrency scenarios.
RPA in Food Industry: Top 15 Use Cases in 2026
The food and beverage industry relies on efficient operations both in the kitchen and behind the scenes. Robotic Process Automation (RPA) is streamlining essential back-office tasks, from finance to inventory management, allowing professionals to focus on quality and service.
40+ Back Office Automation Examples: RPA, WLA, AI/ML
Many tasks in IT, HR, and finance are repetitive, data-driven, and labor-intensive, making the back office a great candidate for automation using AI and RPA, as well as workload automation. According to research, RPA in the back office can reduce employee costs by 40%, providing a quick and tangible ROI to organizations.
Python Job Scheduling: Methods and Overview in 2026
Automating repetitive tasks is essential for efficiency, whether you’re running a small script or managing large-scale applications. Python job scheduling enables you to execute tasks automatically at specific times or intervals, thereby reducing manual effort and enhancing reliability.
Zero-Knowledge Proofs: How it Works & Use Cases in 2026
As businesses collect a vast amount of customer data to gain insights, improve their products and services, and monetize their data assets, they can become vulnerable to cyber threats and data breaches. The cost of breaches is rising every year, reaching ~$4.2M per breach.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.