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
Top 10 VisualCron Alternatives in 2026
VisualCron stands out for its Windows-based job scheduling capabilities. However, during our review of VisualCron, we experienced that its Windows focus may be insufficient for businesses with complex IT scenarios and hybrid cloud requirements. We selected the top alternatives to VisualCron based on the features, pricing, and market presence metrics of leading solutions.
Top IoT Cloud Benefits, Challenges & Platforms in 2026
In an IoT ecosystem, devices communicate effortlessly through the cloud. This is much simpler than using traditional, physical servers tucked away in an office. The cloud is popular for handling IoT data because it’s easy to access, can grow fast (scalable), and helps recover data after disasters.
Guide To Machine Learning Data Governance in 2026
In this article, we explain machine learning data governance. We explain its key principles, benefits, use cases, best practices, and our future expectations of data governance.
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
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.
Python Job Scheduling: Methods and Overview
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.
Top 5 Restaurant Chatbots & Use Cases in 2026
Restaurant chatbots handle the repetitive stuff, taking orders, booking tables, answering “are you open? “so your staff can focus on actual service. They work 24/7 and deliver consistent responses, which matters when you’re operating multiple locations.
Top 5 AI Network Monitoring Use Cases and Real Life Examples
The integration of artificial intelligence (AI) into network monitoring enhances availability and performance. Here are options for you: AI Network Monitoring Tools ** Reviews are based on Capterra and G2. Vendors are ranked according to their number of reviews *** Free trial periods and pricing are included if the content is publicly shared.
15+ Use Cases & AI Applications of Augmented Reality
Augmented Reality (AR) is a digital media platform that allows the user to integrate virtual context into the physical environment in an interactive, multidimensional way. Implementing AI enhances the AR experience by allowing deep neural networks to replace traditional computer vision approaches, and add new features such as object detection, text analysis, and scene labeling.
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