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
Agentic Document Extraction: LandingAI & more in 2026
Agentic Document Extraction (ADE) is a specialized form of Optical Character Recognition (OCR) that extracts data from various file types. It combines document processing, data retrieval, structured output generation, and automation to streamline knowledge work. ADE stands out from traditional OCR by its ability to recognize complex document structures, such as tables, flowcharts, and images.
RAG Frameworks: LangChain vs LangGraph vs LlamaIndex vs Haystack vs DSPy
We benchmarked 5 RAG frameworks: LangChain, LangGraph, LlamaIndex, Haystack, and DSPy, by building the same agentic RAG workflow with standardized components: identical models (GPT-4.1-mini), embeddings (BGE-small), retriever (Qdrant), and tools (Tavily web search). This isolates each framework’s true overhead and token efficiency.
Text-to-SQL: Comparison of LLM Accuracy in 2026
I have relied on SQL for data analysis for 18 years, beginning in my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases.
Speech-to-Text Benchmark: Deepgram vs. Whisper in 2026
We benchmarked the leading speech-to-text (STT) providers, focusing specifically on healthcare applications. Our benchmark used real-world examples to assess transcription accuracy in medical contexts, where precision is crucial. Benchmark results Based on both WER and CER results, GPT-4o-transcribe demonstrates the highest transcription accuracy among all evaluated speech-to-text systems.
Top 10 Open Source ASM Software Based on GitHub Stars
Attack surface management tools need four core capabilities: discovering internet-facing assets, prioritizing risks, fixing vulnerabilities, and continuously monitoring your network.
Top 10+ Mobile Device Management Software with Pricing
Companies are expected to maintain an efficient and safe IT environment. In contrast to on-premise work environments, remote and hybrid work require careful IT configuration, with a special emphasis on mobile devices. Mobile Device Management (MDM) software is evaluated on security, ease of use, granular control, scalability, and pricing models.
Mobile Device Management Pricing Comparison: 10+ Products
Understanding pricing structures and other aspects of Mobile Device Management (MDM) software is necessary to make wise decisions. Depending on the pricing structure, mobile device management products can range in price. See how Mobile Device Management pricing is structured and compare MDM products. Price comparison Ranking: From least to most expensive.
20 Digital Twin Applications/ Use Cases by Industry in 2026
As more sectors explore virtualization, digital twin solutions are gaining mainstream traction. According to Deloitte study, the global market for digital twins is expected to grow with 38% CAGR to reach $16 billion by 2023, and the proliferation of IoT technology accelerating this growth.
LLM Latency Benchmark by Use Cases in 2026
The effectiveness of large language models (LLMs) is determined not only by their accuracy and capabilities but also by the speed at which they engage with users. We benchmarked the performance of leading language models across various use cases, measuring their response times to user input.
Vision Language Models Compared to Image Recognition
Can advanced Vision Language Models (VLMs) replace traditional image recognition models? To find out, we benchmarked 16 leading models across three paradigms: traditional CNNs (ResNet, EfficientNet), VLMs ( such as GPT-4.1, Gemini 2.5), and Cloud APIs (AWS, Google, Azure).
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.