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 35+ Generative AI Tools by Popularity & Category ['26]
At AIMultiple, we constantly use generative AI technologies: We built dozens of custom GPTs and benchmarked the latest AI tools such as AI agents, cloud GPUs, and e-commerce AI video generators.
DAST Software Pricing Comparison: Burp Suite, Nessus & More
With over 20 DAST tools on the market, selecting the most suitable one can be challenging due to their different features and pricing options. We’ve compiled publicly available information on vendors’ pricing strategies, making it easy to get an overview and estimate the likely costs you may face.
Multi-Agent Communication with Google's A2A in 2026
Agent2Agent (A2A) Protocol is an open standard for communication and collaboration between AI agents.Though it’s new, it’s gaining attention, especially since it works well with MCP, which is becoming the industry standard. A2A is expected to become the go-to protocol for multi-agent communication.
Top 10 Use Cases of Conversational AI in Retail in 2026
Modern retail faces challenges in providing personalized customer service at scale. Customers expect instant responses, 24/7 support, and tailored shopping experiences. Conversational AI addresses these challenges by enabling personalized interactions, improving customer satisfaction, and automating various online retail operations.
AI HR Analytics: Use Cases, Benefits & Challenges in 2026
The integration of AI HR analytics into human resource management is revolutionizing how organizations optimize their workforce and make data-driven decisions. By leveraging artificial intelligence, machine learning, and predictive analytics, HR professionals can move beyond traditional practices to enhance employee experience, operational efficiency, and align HR strategies with overarching business goals.
Centralizing AI Tool Access with the MCP Gateway in 2026
Source: Jahgirdar, Manoj In this article, I’ll walk through the evolution of AI tool integration, explain what the Model Context Protocol (MCP) is, and show why MCP alone isn’t production-ready. Then we’ll explore real-world gateway implementations between AI agents and external tools.
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.
Deep Learning in Healthcare: 12 Real-World Applications
The computing capability of deep learning models can enable fast, accurate, and efficient operations in patient care, R&D, and insurance. Key deep learning in healthcare includes: IDC claims that: Patient Care 1.
30+ Low-Code/ No-Code Statistics in 2026
We explore key statistics that highlight the growing influence of low code/no code solutions across industries, shedding light on their impact, adoption rates, and potential for shaping the future of technology.
Generative AI in Retail: Use Cases, Examples & Benefits
Retail businesses strive to enhance customer experiences and loyalty. This requires producing attractive content in various formats, effective marketing efforts, and exceptional customer service. With generative AI, retailers can resolve most of these issues through automation, particularly by enhancing their ability to analyze customer data for more personalized customer experiences.
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