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
Top 10 Enterprise Job Scheduler Software
AIMultiple vetted top job scheduling software based on the features, pricing, and market presence metrics of leading solutions. Follow the links below to see our rationale for each selection: Enterprise job scheduling software market leaders **Ratings are based on B2B review platforms.
GraphQL vs. REST: Top 4 Advantages & Disadvantages
Picking between GraphQL and REST isn’t about which one is “better,” it’s about which fits your specific project. Both handle API requests differently, and understanding these differences helps you avoid performance bottlenecks and unnecessary complexity.
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.
Agentic AI for Cybersecurity: Real life Use Cases & Examples
Agentic AI systems don’t just follow fixed rules; they can make decisions, take actions, and support security teams across SecOps and AppSec. We examined how these agents detect threats, automate routine tasks, and assist developers during software security checks.
AI in Government: Examples & Challenges
AI in government is no longer a hypothetical or early-stage experiment. Public institutions are moving from isolated pilot projects to large-scale and systemic adoption of AI across core government functions: from social services and healthcare to transportation, public safety, and administrative operations.
Multimodal Embedding Models: Apple vs Meta vs OpenAI
Multimodal embedding models excel at identifying objects but struggle with relationships. Current models struggle to distinguish “phone on a map” from “map on a phone.” We benchmarked 7 leading models across MS-COCO and Winoground to measure this specific limitation. To ensure a fair comparison, we evaluated every model under identical conditions using NVIDIA A40 hardware and bfloat16 precision.
Top 5 Facial Recognition Challenges & Solutions
Facial recognition is now part of everyday life, from unlocking phones to verifying identities in public spaces. Its reach continues to grow, bringing both convenience and new possibilities. However, this expansion also raises concerns about accuracy, privacy, and fairness that need careful attention.
eCommerce Technologies Use Cases & Examples
The eCommerce sector continues to expand by ~10% each year as more consumers shift their purchasing habits online and seek faster and more convenient digital experiences.This growth is also accompanied by increasing competition, making it essential for businesses to understand how technology is shaping customer expectations.
Top 40+ 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. LLMOps tools aim to address these challenges by providing structured processes for fine-tuning, deployment, monitoring, and governance.
Best LLMs for Extended Context Windows
We analyzed the context window performance of 22 leading AI models by testing them using a proprietary 32-message conversation that includes complex synthesis tasks requiring information recall from earlier in the conversation. Our findings are interesting. Smaller models often beat their larger counterparts, and most models fail well before their advertised limits.
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