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 40+ AI Developer Tools for Software Development
We have been experimenting with AI development tools in our code generation and code editing benchmarks for months. We have seen that AI agents like Claude Code are highly capable of software development, achieving ~%90 success rate.
Healthcare Data Encryption: Compliance & Real-Life Use Cases
Healthcare generates 30% of the world’s data, making it a prime target for cyberattacks. Over 40 million U.S. patient records are compromised yearly, often due to weak security measures. Data encryption has become a critical component of healthcare data security to mitigate the risk of data breaches.
Data Encryption: Types, Importance & FAQ
Digital information is constantly being shared and stored on the cloud and connected services. According to IBM, the average cost for a data breach involving 50 million to 65 million records is more than $400 million.
Top 7 Computer Vision Challenges & Solutions
Computer vision (CV) technology is revolutionizing many industries, including healthcare, retail, automotive, etc. As more companies invest in computer vision solutions, the global market is expected to multiply 9 times by 2026 to $2.4 billion.
20+ Application Security Statistics & Trends
We present an analysis of current statistics in the field of application security. Our focus is on providing a clear and concise overview of the latest data, reflecting key trends and insights in this area of cybersecurity. The statistics compiled here are drawn from reputable and up-to-date sources.
Top Digital Transformation Frameworks
Digital transformation is an emerging trend, but some companies may not succeed in it. To achieve success, leveraging a digital transformation framework that serves as a roadmap for your organization can be beneficial.
Meta AI Applications and Research Examples
Rather than existing only in research labs, artificial intelligence now shapes the tools people use to connect, work, and create. Meta AI, formerly Facebook AI, drives this transformation by linking research in vision, language, audio, and robotics with applications that reach billions across its platforms.
Deep Learning in Finance Top 11 Use Cases
Based on our analysis of deep learning applications in finance, we’ve identified 11 key use cases where AI-driven models are making an impact. These examples are drawn from real-world implementations across financial institutions and cover areas such as fraud detection, risk assessment, and investment strategies.
5 Steps from Chatbots to Secure Enterprise AI Agents
Though LLMs are great at text generation, their Enterprise AI agents are being built by leading SaaS vendors to address these issues. These bots need to be able to: While these are table stakes, how they are implemented is important.
Top 12 LLM DLP Best Practices to Prevent AI Data Leaks
Enterprises are investing in large language models (LLMs) and generative AI, making the protection of sensitive data essential. As GenAI adoption grows, the risk of sensitive data exposure or GenAI data risk becomes a critical AI compliance concern for organizations across industries.
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