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
8 AI Code Models Benchmarked: LMC-Eval
More than 37% of tasks performed on AI models are about computer programming and maths.
AI Center of Excellence (AI CoE): Meaning & Setup
The adoption of artificial intelligence (AI) is increasing as companies try to capture value from enterprise AI applications. However, according to an IBM survey, challenges such as limited AI expertise, increasing data complexity, and lack of tools for AI development hinder AI adoption for enterprises. To reduce AI project failures, organizations need a dedicated unit to oversee AI initiatives.
The Future of Large Language Models
Interest in large language models (LLMs) is rising since ChatGPT attracted over 200 million monthly visitors in 2024.LLMs along with generative AI have an influence on a variety of areas, including medical imaging analysis and high-resolution weather forecasting.
Top 13 AI Cybersecurity Use Cases with Real Examples
We present the major AI cybersecurity use cases, each followed by a real-world example demonstrating its impact. Given the widespread adoption of AI and cybersecurity solutions across diverse industries and regions, we aim to provide examples that reflect this global and cross-sector relevance.
Model Context Protocol (MCP) and Its Importance
We have been testing 8 Model Context Protocol (MCP) servers across web search and extraction, as well as browser automation tasks, in this guide we will explain what MCP is and how it acts as a universal connector to use tools via client-side architecture.
Top 5 SAP BTP Automation Platforms
SAP BTP platform combines data and analytics, AI, application development, automation, and integration in a unified environment. It is a platform optimized for SAP applications in the cloud. The platform is significant in its reach, given that SAP customers account for 87% of total global commerce.
Image Classification: Applications & Best Practices
Around 1.72 trillion 1 photos are taken every year. Many are used to train digital solutions, such as self-driving systems powered by image recognition and computer vision (CV) technologies.
Top 11 Voice Recognition Applications & Examples
If you’ve used virtual assistants like Alexa, Cortana, or Siri, you’re likely familiar with speech recognition and conversational AI. This technology enables users to interact with devices through verbal commands by converting spoken queries into machine-readable text. The number of voice assistant users in the U.S.
Top 10 Application Security Metrics: Why Do They Matter?
Application security metrics are essential for providing a clear, quantifiable overview of an organization’s security posture. They assist organizations with: With 25% of all breaches directly coming from application vulnerabilities, monitoring security metrics is necessary. We highlight 10 metrics to help organizations measure, mitigate, and optimize their application security efforts.
Top 10 Endpoint Detection & Response (EDR) Tools
Endpoint Detection and Response (EDR) solutions, also known as endpoint detection and threat response (EDTR), provide real-time monitoring, threat detection, and response capabilities at the endpoint level, enabling organizations to hunt for threats proactively. The EDR tools market is characterized by a multitude of vendors offering a variety of functionalities and features.
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