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
Test Automation Documentation with Best Practices
Test automation is vital for ensuring the quality and reliability of applications in software testing and development. Businesses and QA teams are transitioning from manual testing to automation testing as it can: What often goes overlooked is the role of effective documentation in maximizing the benefits of test automation.
Top 10 ERP AI Use Cases & Case Studies
Enterprise resource planning (ERP) systems help organizations manage core business processes such as finance, operations, and human resources within a single platform. As business processes grow more complex and data-driven, companies are increasingly integrating AI capabilities, such as machine learning and conversational AI, into ERP systems to automate tasks, improve decision-making, and increase efficiency.
AI Utilities: Top 15 Use cases & case studies
AI adoption can help utilities streamline operations, optimize resource management, enhance customer interactions, and develop new digital services. Learn the real-life examples of AI utilities: AI utilities use cases & real-life examples Energy 1.
Top 6 Open Source Sensitive Data Discovery Tools
The following tools are selected based on GitHub activity and sorted by GitHub star count in descending order. They cover the main use cases for sensitive data discovery: metadata cataloging with lineage, agentless scanning, and API-based detection of PII, PCI data, and credentials at rest. Read more: Sensitive data discovery & classification tools, DLP software.
Top 10 Multi-Factor Authentication (MFA) Solutions
Multi-factor authentication ensures that only authorized users can access accounts, sensitive information, or apps.
Top 10+ Multi-Factor Authentication (MFA) Use Cases
Our research on multi-factor authentication (MFA) solutions shows that the leading software is effective in adaptive authentication, biometric authentication (Fingerprint/Face ID), and push notifications.
Multi-Factor Authentication (MFA) Pricing and Plans
Listed MFA pricing and plans vary based on several factors which increase costs: *$1,500 annual contract minimum. How to select the right MFA plan? An MFA solution that is sufficient for individual usage may not be suitable for a large enterprise with several customers, partners, and business consumers.
Top 10 Application Security Tools: Features & Pricing
The global application security market was valued at USD 10.65 billion in 2025 and is projected to reach USD 42.09 billion by 2033, with a 18.8% CAGR, driven by surging attacks on web and mobile applications, cloud-native adoption, and regulatory requirements, including the EU Cyber Resilience Act.
Wu Dao 3.0: China's Version of GPT-5
When the US cut off China’s access to advanced chips, the Beijing Academy of Artificial Intelligence faced a choice: complain about restrictions or work around them. They picked the second option. Wu Dao 3.0, launched in July 2023, throws out the playbook. No massive trillion-parameter models competing for headlines.
Top 25 Synthetic Data Use Cases
Synthetic data is gaining widespread popularity and applicability across industries, including machine learning, deep learning, and generative AI (GenAI). Synthetic data offers solutions to challenges such as data privacy concerns and limited dataset sizes. It is estimated that synthetic data will be preferred over real data in AI models by 2030.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.