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
Recommendation Systems: Applications and Examples
Recommendation systems benefit both businesses and customers by using data to personalize experiences. They help boost sales, increase customer loyalty, and reduce churn by simplifying choices and keeping users engaged. We benchmarked three Python recommendation libraries: LightFM, Cornac BPR, and TensorFlow Recommenders, using the same implicit-feedback dataset and identical preprocessing steps.
Top AI Website Generators Benchmarked in 2026
To find the most helpful prompt-to-website creator, we benchmarked the following tools: If you need to learn about no-code AI website generator tools, you can follow the links: Benchmark results We conducted this benchmark using the latest versions of the tools available as of January 2025.
Mobile AI Agents Tested Across 65 Real-World Tasks
We spent 3 days benchmarking four mobile AI agents (DroidRun, Mobile-Agent, AutoDroid, and AppAgent) across 65 real-world tasks using an Android emulator with applications such as calendar management, contact creation, photo capture, audio recording, and file operations.
Top 9 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.
Top LLMOps Tools & Compare them to MLOPs
The rapid adoption of large language models has outpaced the operational frameworks needed to manage them efficiently. Enterprises increasingly struggle with high development costs, complex pipelines, and limited visibility into model performance.
Compare 9 Large Language Models in Healthcare
We benchmarked 9 LLMs using the MedQA dataset, a graduate-level clinical exam benchmark derived from USMLE questions. Each model answered the same multiple-choice clinical scenarios using a standardized prompt, enabling direct comparison of accuracy. We also recorded latency per question by dividing total runtime by the number of MedQA items completed.
Top Image Recognition Tools Compared in 2026
We evaluated the real-world performance of top cloud image recognition tools for object detection tasks by benchmarking their default API configurations across 5 classes using 100 images. This included contrasting performances, analyzing features, and comparing service offerings in relation to pricing. Benchmark Results Performance overview at IoU=0.
Agentic CLI Tools: Claude Code vs Cline
Agentic CLI tools are AI coding tools that can create and delete files, run commands, plan, and execute the coding of the entire project. We tested the leading tools in 20 real-world web development scenarios to see which one truly delivers a production-ready website.
AI Data Quality in 2026: Challenges & Best Practices
Poor data quality hinders 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. We explain the importance of data quality in AI, the challenges organizations encounter, and the best practices for ensuring high-quality data.
DGX Spark vs Mac Studio & Halo: Benchmarks & Alternatives
NVIDIA’s DGX Spark entered the desktop AI market in 2025 at $3,999, positioning itself as a “desktop AI supercomputer”. It packs 128GB of unified memory and promises one petaflop of FP4 AI performance in a Mac Mini-sized chassis. See the benchmark results on value and performance compared to alternatives: Competitive analysis: DGX Spark vs.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.