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
OCR Benchmark: Text Extraction / Capture Accuracy
OCR accuracy is critical for many document processing tasks and SOTA multi-modal LLMs are now offering an alternative to OCR.
Figma MCP Server Tested: Figma to Code
Figma has announced the beta release of its Dev Mode MCP server, which connects design files directly to AI coding tools like Cursor, Windsurf, and Claude Code. The server uses Model Context Protocol (MCP) to provide design context to Large Language Models, enabling code generation that reflects both design specifications and existing codebase patterns.
Top 10+ RMM Software with Pricing & Features
Based on our RMM benchmark, user reviews & feature comparisons, here are the top RMM software. See why we selected them by following the links below: RMM software components help keep networks secure and efficient, thanks to features like patch management.
IT Documentation Benchmark & Review
We evaluated the leading IT documentation platforms to assess their performance, features, and usability. Our benchmark measured document creation workflows, editor capabilities, search accuracy, and relationship management across real-world scenarios, including error code searches, PDF content indexing, and command syntax preservation. Benchmark findings *NinjaOne auto-saves drafts but doesn’t display them separately.
Top 8 Observability Software with Pricing and Feature Comparison
Observability platforms promise complete visibility across distributed systems, but selecting the right one is hard when every vendor claims they do everything. We analyzed the top 8 observability software by looking at their documented capabilities, public pricing, verified customer reviews, and enterprise reference cases.
Best Database Performance Monitoring Tools: Top 5 Platforms Compared
Database issues cause application failures. A memory spike crashes your server. A slow query times out user requests. We analyzed six database monitoring platforms and benchmarked three of them extensively on MySQL and MongoDB by installing them from scratch, running identical workloads, and documenting every step of the setup and monitoring experience.
Large Language Model Training
While using existing LLMs in enterprise workflows is table stakes, leading enterprises are building their custom models. However, building custom models can cost millions and require investing in an internal AI team.
LLM Fine-Tuning Guide for Enterprises
Follow the links for the specific solutions to your LLM output challenges. If your LLM: The widespread adoption of large language models (LLMs) has improved our ability to process human language. However, their generic training often results in suboptimal performance for specific tasks.
Compare 45+ MLOps Tools
Machine Learning Operations (MLOps) brings DevOps principles into machine learning to simplify workflows from model development to deployment and maintenance.
Agentic CLI Tools Compared: Claude Code vs Cline vs Aider
Agentic CLI tools are command-line–based AI coding agents that combine large-language-model reasoning with the structure and reliability of terminal workflows. Unlike passive autocomplete tools, an agentic CLI actively plans and executes multi-step tasks: reading your repository, generating or editing files, running tests, resolving Git history, and orchestrating shell commands, all through natural-language prompts.
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