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
AI Explained: Trends and Applications by Industry
Artificial Intelligence (AI) allows computers to learn from experience, adapt to new inputs and perform human-like tasks. Most of the AI examples you’ve heard about –from chess-playing machines to self-driving cars–rely heavily on deep learning, a subfield of AI.
Timestope is possibly a big waste of time for its users
Timestope claims to have the aim of decentralizing crypto ownership by helping users earn from the advertisement served to them. Please read our disclaimer about investment related advice before reading our analysis.
Compare Top 5 MLOps vs DataOps Differences
Inspired by DevOps practices, MLOps and DataOps have emerged as critical methodologies for ensuring seamless machine learning and database operations. While both share roots in automation and operational efficiency, the debate around MLOps vs DataOps highlights their distinct roles in IT workflows.
Human Generated Data with Methods
Despite the rise of generative AI tools like ChatGPT and Gemini, human-generated data remains crucial for AI developers. Companies like OpenAI invest heavily in obtaining human-generated data to train their large language models (LLMs). Whether through data collection services or in-house efforts, AI developers require a steady stream of human-generated data.
Customer Engagement Automation: 5 Tools & Examples
Businesses face rising customer expectations and limited resources, especially as 80% of customers now value their experience as much as the product itself.Meeting these expectations requires clever use of customer engagement automation tools, which we divided into two categories: individual and comprehensive tools.
Data Versioning: Top 3 Benefits & Best Practices
Companies rely on AI/ML models to make business decisions. Effective AI/ML models require high-quality data to make accurate predictions about future conditions. That’s why data is called the new oil for which successful companies need their own refinery.
Top 4 Free ITAM Software
IT asset management software comes with different pricing plans including paid, free, and open source options. See our rationale for below recommendations by following the links on product names. Businesses can choose from free or open-source software to manage their assets.
Top 10 Healthcare Analytics Use Cases with Examples
The $28 billion healthcare analytics marketis transforming how providers, payers, and life sciences organizations compete, and companies that move now can seize the advantage. By delivering solutions that drive predictive care, reduce costs, and optimize operations, analytics unlocks new revenue streams and strengthens customer loyalty in a healthcare industry racing toward data-driven performance.
Cloud LLM vs Local LLMs: 3 Real-Life examples & benefits
In 2025, Cloud LLMs and Local LLMs are transforming business operations with unique advantages. Cloud LLMs, powered by advanced models like Grok 3, o3, and GPT-4.1, offer exceptional scalability and accessibility. Conversely, Local LLMs, driven by open-source models such as Qwen 3, Llama 4, and DeepSeek R1, ensure superior privacy and customization.
Sentiment Analysis: Steps & Challenges
Sentiment analysis is growing in popularity as it turns raw, unstructured text data into interpretable insights for business through sentiment analysis. However, tangible use cases for sentiment analysis and the fundamental steps of this method may not be clear.
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