Cem Dilmegani
Cem has been the principal analyst at AIMultiple for almost a decade.
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, automation, cybersecurity (including network security, application security), data collection including web data collection and process intelligence.
Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and 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 (September 28, 2017). 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
Conference & other event presentations
- 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 (March 10, 2023): 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 5 Technologies Improving Insurance Fraud Detection
According to the FBI, insurance fraud (excluding health insurance) costs more than $40 billion annually in the U.S. alone.(Insurance Fraud. FBI. Accessed: February/11/2025.) Technological tools such as artificial intelligence (AI), the Internet of Things (IoT), machine learning, and blockchain can be used by insurers to more effectively detect and prevent insurance fraud.
Top 20+ AI Chip Makers: NVIDIA & Its Competitors
Based on our experience running AIMultiple’s cloud GPU benchmark with 10 different GPU models in 4 different scenarios, these are the top AI hardware companies for data center workloads.
Audio Annotation
A subset of data annotation, audio annotation, is a critical technique for building well-performing natural language processing (NLP) models. These models offer numerous benefits to organizations, including analyzing text, speeding up customer responses, and recognizing human emotions. In this article, we take a deep dive into audio annotation to understand its importance for businesses.
What is Composite AI? Business Impact
Artificial intelligence (AI) has opened new capabilities for businesses with a diverse set of use cases across sectors. 78% of organizations now use AI in at least one function Composite AI integrates multiple AI techniques into unified solutions that can address complex business problems more effectively than any single approach.
IoT Agriculture: Use Cases & Technologies
With the global population projected to reach approximately 10 billion by 2050, the need for increased agricultural production has become more pressing than ever.
Top 11 Freemium & Free RPA Providers Compared
We explored the top free and freemium RPA providers, comparing their offerings, limitations, and ideal use cases to identify the most suitable option for business needs. * Products are listed based on the number of reviews they have within their respective groups. ** Community editions are free plans without time restrictions.
Top 10 Use Cases of IoT Manufacturing
The market for IoT manufacturing is expected to be valued $400B by 2026. The ability to collect and analyze accurate data from connected IoT devices and sensors in real-time provides manufacturers with unprecedented insights into their industrial processes.
Data Quality Assurance with Best Practices
Optimal decisions require high-quality data. To achieve and sustain high data quality, companies must implement effective data quality assurance procedures. See what data quality assurance is, why it is essential, and the best practices for ensuring data quality.
Top 13 Training Data Platforms
Data is an essential part of the quality of machine learning models. Supervised AI/ML models require high-quality data to make accurate predictions. Training data platforms streamline data preparation from collection to annotation, ensuring high-quality inputs for AI systems.
How To Scrape Real Estate Data With Python
Scraping real estate data is more complicated than it looks. Zillow quickly blocks bots with PerimeterX, while Redfin requires piecing property details from scattered DOM elements. Every platform employs its own defenses, making reliable extraction a challenge without the use of proxies or APIs.
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