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Cem Dilmegani

Cem Dilmegani

Principal Analyst
360 Articles
Stay up-to-date on B2B Tech
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, 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

Media, conference & other event presentations

Sources

  1. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
  2. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
  3. Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
  4. Science, Research and Innovation Performance of the EU, European Commission.
  5. EU’s €200 billion AI investment pushes cash into data centers, but chip market remains a challenge, IT Brew.
  6. Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
  7. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

Latest Articles from Cem

DataMar 6

Synthetic Users Explained: Top 7 AI User Research Tools

Traditional user research takes weeks: recruiting participants, scheduling sessions, and manually coding transcripts. Synthetic user platforms compress that timeline to hours by generating AI-driven personas you can interview, survey, and test against without the logistics.

CybersecurityMar 6

Top 7 Open-Source DLP Software

While open-source DLP software offers viable solutions for data protection, larger enterprises often turn to closed-source DLP software solutions for enhanced centralized key management and cloud-native deployment options. Below are the top five open-source DLP tools, evaluated for detection accuracy, deployment complexity, and community support.

AIMar 6

20 Chatbot Companies To Deploy in 2026

With 200+ chatbot platforms on the market, the choice isn’t obvious. The right vendor depends on three things: how your team wants to build (drag-and-drop vs. code), which systems you need to connect to, and how much conversation volume you’re actually handling. We compared the 20 most widely used chatbot platforms for building production applications.

CybersecurityMar 6

Demilitarized Zone (DMZ): Examples & Architecture

A Demilitarized Zone (DMZ) network is a subnetwork containing an organization’s publicly accessible services. It serves as an exposed point to an untrusted network, often the Internet. DMZs are used across various environments, from home routers to enterprise networks, to isolate public-facing services and protect internal systems.

AIMar 6

Generative AI ERP Systems: 10 Use Cases & Benefits

Enterprise resource planning (ERP) software helps businesses see the process across different departments so they can make smarter decisions faster. Generative AI, alongside technologies like RPA, has the potential to enhance ERP processes.

Enterprise SoftwareMar 6

Top 20 RPA SAP Use Cases & Examples

SAP is one of the oldest and most valuable ERP systems, with ~ €31 billion in revenue. Though an ERP suite offering automation in many areas, most SAP processes are manual and repetitive, such as accounting processes, transaction management, and reporting.

AIMar 6

Large Language Model Training

Integrating existing LLMs into enterprise workflows is increasingly common. However, some enterprises develop custom models trained on proprietary data to improve performance for specific tasks. Building and maintaining such models requires significant resources, including specialized AI talent, large training datasets, and computing infrastructure, which can increase costs to millions of dollars.

AIMar 6

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.

Enterprise SoftwareMar 5

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.

CybersecurityMar 5

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.