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 enterprise AI and software.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
- Hosted on American Variety Radio, answering Court Lewis' questions on AGI on April 25th, 2026.
- Answered Korea24's questions on job loss due to AI on November 5th, 2025. Recording available on: 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
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.
20 Chatbot Companies To Deploy in 2026
With 200+ chatbot platforms on the market, the choice isn’t obvious. The right vendor depends on three things: how your team wants to build (drag-and-drop vs. code), which systems you need to connect to, and how much conversation volume you’re actually handling. We compared the 20 most widely used chatbot platforms for building production applications.
SCADA Systems: Comparison of Top 10 SCADA Software
Supervisory Control and Data Acquisition (SCADA) systems form the operational backbone of industrial infrastructure. But choosing a platform is a long-term commitment. These systems often run for 10 to 20 years, and the full cost of owning one is high. This makes the choice hard to reverse.
30+ Industrial AI Agents 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.
VPN Benchmark of Top 5 VPN Providers
We tested six leading consumer VPN services, measuring throughput against a clean baseline, packet drops over 15 minutes per provider, CPU and RAM use on macOS, and VPN connection behavior under load. Our test on VPN providers show that best VPN services change based on different needs.
Best 12+ Android Emulators in 2026
Android emulators let you run Android apps and games on PC, Mac, and browsers. Different emulators excel in different use cases. Below is a list of the top Android emulators categorized by their strengths from gaming to app development, security testing, and everyday Android app usage.
7 Useful AI Transformation Strategies in 2026
Before choosing how to transform AI, leaders need to know where to start. We analyzed the Anthropic Economic Index (March 2026 release), mapping over 1 million real-world Claude interactions across 3,260 occupational tasks to the standard APQC Process Classification Framework (PCF).
Top 12 Patch Management Software Including NinjaOne
Unpatched systems are low-hanging fruit for attackers. A single outdated server or forgotten workstation becomes an entry point. Patch management software automates the process of finding, testing, and deploying updates before vulnerabilities turn into breaches.
Top 3 Prolific Alternatives in 2026
We analyzed Prolific’s top alternatives by comparing participant ratings, crowd size, payment terms, and verified platform data. The right alternative depends on whether you’re a worker looking for tasks or a research buyer looking for data the tradeoffs differ significantly between the two.
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.
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