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
10 AI Coding Challenges I Face While Managing AI Agents
From what I’ve observed, AI agents are most effective in the early, exploratory stages of development, such as: testing ideas, drafting solution paths, or helping clarify technical direction. They streamline discovery, but their limits become clear when work requires steady judgment, strong context awareness, or long-term strategic reasoning.
Compare 50+ AI Agent Tools in 2026
We spent the last quarter testing AI agents across coding, customer service, sales, research, and business workflows. Not reading vendor marketing,g actually using these tools daily to see what delivers and what’s hype. Despite talk about “autonomous AI,” most tools today are co-pilots, not autopilots.
Building AI Agents with Composable Patterns in 2026
We spent 3 days experimenting with workflows and agent pipelines in n8n, following Anthropic’s and OpenAI’s guides on building effective AI agents.
Top 30+ Industrial AI Agents Landscape to Watch in 2026
Industrial AI agents address the limitations of siloed data by autonomously integrating and deriving actionable insights from IoT, controls systems (e.g. SCADA), and connected assets.
Top 10 Open-Source CSPM Tools for Data Security in 2026
Open-source cloud security posture management (CSPM) tools enable continuous monitoring, assessment, and management of your cloud environments, such as Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). Discover open-source CSPM tools, compare their usability and features: Market presence Tools are sorted based on GitHub stars in descending order.
Deep Learning in Healthcare: 12 Real-World Applications
The computing capabilities of deep learning models enable fast, accurate, and efficient operations in patient care, R&D, and insurance. Key deep learning in healthcare includes: IDC claims that: Patient Care 1.
Top 5 ZTNA Open Source Components in 2026
ZTNA is replacing VPNs in many organizations as part of a broader move toward zero-trust security. ZTNA open-source tools offer a cost-effective way to authorize access at each layer, securing remote access to resources.
Top 13 Open Source SIEM Tools in 2026
Unlike commercial SIEMs such as ManageEngine Log360, open-source SIEM tools commonly lack core SIEM capabilities, including event correlation, log analytics, alerting, or require combining with other tools. However, several network defensive tools (e.g., intrusion detection and prevention systems and network monitoring tools) can be used as SIEM tools with fine-tuning.
Top 6 Log Analysis Software Including Solarwinds in 2026
When servers crash at 3 AM or applications start throwing errors, teams need to quickly understand what went wrong. Log analysis platforms help by collecting scattered log files from different systems and making them searchable in a single location.
Top 30+ NLP Use Cases in 2026 with Real-life Examples
The NLP market reached $34.83 billion in 2026, with projections to hit $93.76 billion by 2032. Healthcare is adopting AI at twice the rate of the broader economy, while the voice recognition market has grown to $22.49 billion in 2026, projected to reach $61.71 billion by 2031. We analyzed 250+ deployments across industries.
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