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 Design to Code Tools Compared: Detailed Analysis
The design-to-code landscape has transformed with AI-powered tools promising to bridge the gap between visual design and production-ready code. With 82% of developers now using AI coding assistants daily or weekly, the demand for effective design-to-code solutions has never been higher. AI Model Evolution: The Claude 4.
Top 12 RMM Software Tested: Features and Pricing
RMM software components keep business devices secure and efficient, thanks to features like patch management. NinjaOne was named a Leader, while Atera earned Visionary status. We benchmarked the top 3 RMM platforms (NinjaOne, ManageEngine, and Acronis) by deploying them to seven servers across six AWS regions.
Best RAG Tools, Frameworks, and Libraries
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models and separately tested various chunk sizes to determine what combinations work best for RAG systems. Explore top RAG frameworks and tools, learn what RAG is, how it works, its benefits, and its role in today’s LLM landscape.
25 Healthcare AI Use Cases with Examples
Healthcare systems are under growing pressure from rising patient data volumes and increasing demand for personalized care. Healthcare AI applications have emerged as a powerful solution to these problems by optimizing processes, enhancing diagnostic accuracy, and improving patient outcomes.
AI Deep Research: Claude vs ChatGPT vs Grok
AI deep research is a feature in some LLMs that offers users a wider range of search results than AI search engines.
Best 30+ Open Source Web Agents in 2026
We tested 30+ open-source web agents across four categories: autonomous agents, computer-use controllers, web scrapers, and developer frameworks. We ran identical benchmarks using the WebVoyager test suite, which covers 643 tasks across 15 real websites, to measure which tools actually complete multi-step web tasks and which fail when sites use dynamic dropdowns or JavaScript-heavy layouts.
Top 7+ Open Source Firewall Options: Features & Types
Network security statistics reveal that Open source firewalls provide a cost effective solution to network security.
Top 30+ Agentic AI Companies
Though AI agents are being hyped and some companies rebrand their chatbots as agentic tools, there are still a few agents in production. Previously, we benchmarked several capable AI agents over several real-world tasks.
Top Python RPA Tools: Robocorp vs. Selenium
RPA is the third-fastest growing area, following process mining and integration platform as a service (iPaaS). Traditionally dominated by .NET, RPA is expanding with Python-based tools, opening new possibilities for Python developers. Explore the leading Python RPA platforms that enable developers to build effective automation solutions.
15+ Best Open Source Web Crawlers for LLM & AI
Recent advancements in Generative AI are moving modern crawlers beyond raw HTML. Agentic web crawlers now use natural-language prompts to select links, rather than relying on fixed rules. These tools produce token-efficient markdown, making them essential for high-performance RAG pipelines.
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