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
Centralizing AI Tool Access with the MCP Gateway
We’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 for connecting AI agents to external tools. OpenAI-compatible and lightweight MCP Gateways Designed to make MCP tools easily accessible to agents and AI clients.
Top 16 UEBA Use Cases for Today's SOCs in 2026
Traditional security measures, such as web gateways, firewalls, IPS tools, and VPNs, are no longer sufficient to defend against modern cyberattacks. Attackers routinely operate using valid credentials that rule-based tools never flag. UEBA systems address this gap by monitoring non-user entities alongside human users, using machine learning to establish behavioral baselines and detect deviations.
Top 5 NGFW Use Cases with Case Studies
The market for next-generation firewalls (NGFWs) is rapidly expanding, with an anticipated compound annual growth rate of ~11% between 2023 and 2028, increasing from $5 billion to $8.6 billion. As organizations seek advanced security solutions to combat evolving cyber threats, NGFWs are becoming essential.
Top 6 Data Collection Methods for AI and Machine Learning
While some companies rely on AI data collection services, others gather their data using scraping tools or other methods. See the top 6 AI data collection methods and techniques to fuel your AI projects with accurate data: Overview of AI data collection methods 1.
Top 5 Alternatives to Tenable Nessus : Features & Comparison
Several notable options are available in the DAST and vulnerability scanning tools market. We selected the top alternatives to Tenable Nessus based on our research and DAST benchmark.
Large Action Models: Hype or Real?
Following the launch of Rabbit, an AI device that can use mobile apps, the term large action models (LAMs) is getting popular. These models move beyond conversation by turning LLMs into “agents” that can connect the siloed, app-driven world without requiring users to click on apps or integrate APIs.
Why Cybersecurity in Sports Is More Important Than Ever
At least 70% of sports organizations have experienced cyber incidents or breaches. With cybercriminals targeting: We explore the unique cybersecurity risks facing the sports industry and examine the strategies sports organizations can employ to protect themselves and their stakeholders from cyber threats.
AI Data Quality in 2026: Challenges & Best Practices
Poor data quality delays the successful deployment of AI and ML projects. Even the most advanced AI algorithms can yield flawed results if the underlying data is of low quality.
Top 5 Open-Source Agentic AI Frameworks in 2026
We benchmarked 4 popular open-source agentic frameworks across 2,000 runs (5 tasks, 100 runs each per framework), measuring end-to-end latency, token consumption, and architectural differences. Agentic AI frameworks benchmark We examined how the frameworks themselves influence agent behavior and the resulting impact on latency and token consumption.
Building a No-Code AI Lead Generation Workflow
AI sales agents promise automated prospecting and outreach, but most are bundled as full sales engagement platforms costing significantly once onboarding, data enrichment, integrations, and premium support are added. After reviewing leading AI SDR and lead generation tools and building hands-on workflows, I found that many teams need something simpler, more flexible, and more cost-effective.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.