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
Best 9 Network Monitoring Tools in Windows
We tested network monitoring tools designed for Windows environments to evaluate their performance in real-world deployments. Our goal was to find solutions balancing reliability, usability, and deep integration with Microsoft systems. Protocol Support: Tools needed to handle multiple communication protocols: SNMP, TCP, ICMP, and IPMI.
Large Multimodal Models (LMMs) vs LLMs
We evaluated the performance of Large Multimodal Models (LMMs) in financial reasoning tasks using a carefully selected dataset. By analyzing a subset of high-quality financial samples, we assess the models’ capabilities in processing and reasoning with multimodal data in the financial domain. The methodology section provides detailed insights into the dataset and evaluation framework employed.
World Foundation Models: 10 Use Cases
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.
Scrape Data from Twitter (X.com) with Python
Modern social platforms, such as X.com, employ strict anti-scraping defenses, including CAPTCHA, rate limits, and IP blocking. These security measures make building a custom scraper from scratch both difficult and prone to frequent interruptions.
AI Browser Security Risks: ChatGPT Atlas and Comet
Agentic AI browsers now handle your banking, emails, and private documents. A single malicious link can turn these assistants against you. Recent discoveries in Perplexity’s Comet browser reveal how attackers exploit prompt injection to steal credentials, exfiltrate data, and hijack authenticated sessions.
Will Pi Network make you rich? No, but it can pay cents/hour
Users can invest their time or money into Pi Network. Nothing written here should be construed as investment advice.
Building AI Agents with Composable Patterns
We spent 3 days experimenting with workflows and agent pipelines in n8n, following Anthropic’s and OpenAI’s guides on building effective AI agents.
Top 30+ Industrial AI Agents Landscape to Watch
Industrial AI agents address the limitations of siloed data by autonomously integrating and deriving actionable insights from IoT, controls systems (e.g. SCADA), and connected assets.
Top 6 Log Analysis Software Including Solarwinds in 2026
When servers crash at 3 AM or applications start throwing errors, teams need to quickly understand what went wrong. Log analysis platforms help by collecting scattered log files from different systems and making them searchable in a single location.
Top 30+ NLP Use Cases in 2026 with Real-life Examples
The NLP market reached $34.83 billion in 2026, with projections to hit $93.76 billion by 2032. Healthcare is adopting AI at twice the rate of the broader economy, while the voice recognition market has grown to $22.49 billion in 2026, projected to reach $61.71 billion by 2031. We analyzed 250+ deployments across industries.
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