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Cem Dilmegani

Cem Dilmegani

Principal Analyst
345 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 enterprise AI and software.

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

Agentic AIMay 7

40+ Agentic AI Use Cases with Real-life Examples

Autonomous generative AI agents execute complex tasks with little or no human supervision. Agentic AI differs from chatbots and co-pilots. Unlike traditional AI, particularly generative AI, which often requires human intervention in complex workflows, agentic AI aims to autonomously navigate and optimize processes thanks to its decision-making capabilities and goal-directed behavior.

AIMay 7

AGI/Singularity: 9,800 Predictions Analyzed

Artificial general intelligence (AGI) is when an AI system matches human cognitive abilities across all tasks. Based on available predictions, quick answers on AGI: Will AGI/singularity happen? AGI is inevitable according to most AI experts. When will the singularity/AGI happen? Recent surveys of AI researchers predict AGI in 2040s.

CybersecurityMay 7

Top 10 Microsegmentation Tools in 2026

Microsegmentation divides networks into controlled segments, limiting the spread of breaches. We researched 11 microsegmentation platforms, verifying vendor claims against official product documentation, cross-referencing release notes, and field notices.

Agentic AIMay 7

OpenClaw Alternatives: Hermes vs ZeroClaw vs PicoClaw

Autonomous AI agents, such as OpenClaw and Hermes agent, automate multi-step tasks that would normally require constant human input. While OpenClaw has become the most widely adopted always-on autonomous agent, many users are seeking alternatives due to its challenging deployment process and complex configuration requirements.

CybersecurityMay 7

Top 6 SaaS Backup Solutions

Many businesses operate under the misconception that their SaaS providers (like Microsoft 365 or Google Workspace) fully protect their data from all threats. While these platforms offer some level of data redundancy, they do not protect against accidental deletion, ransomware, or insider threats.

Enterprise SoftwareMay 7

Top 7 WAN Monitoring Software

We selected WAN monitoring software that offers bandwidth monitoring and traffic analysis, along with real-time tracking of network devices, servers, applications, and infrastructure across wide-area networks. See a comparison of popular WAN monitoring software: Selection criteria We selected WAN monitoring tools meeting these criteria: Top 7 WAN Monitoring Software 1.

AIMay 6

50+ ChatGPT Use Cases with Real Life Examples

ChatGPT reached approximately 1 billion weekly active users in early 2026 roughly 10% of the world’s population. OpenAI surpassed $20 billion in annual revenue for 2025, confirmed by CFO Sarah Friar. OpenAI and Harvard economist David Deming analyzed 1.5 million conversations to find out.

AIMay 6

Tabular Models Benchmark: Performance Across 19 Datasets 2026

We benchmarked 7 widely used tabular learning models across 19 real-world datasets, covering ~260,000 samples and over 250 total features, with dataset sizes ranging from 435 to nearly 49,000 rows. Our goal was to understand top-performing model families for datasets of different sizes and structure (e.g. numeric vs.

Enterprise SoftwareMay 6

Top 7 GoAnywhere Alternatives in 2026

GoAnywhere is a secure file transfer platform used by enterprises to exchange data securely. Whether you’re in search of more advanced features, diverse pricing options, or superior customer service, our curated list of alternatives offers the insights you need to make a well-informed choice.

AIMay 4

Chatbot vs ChatGPT: Differences & Features

Traditional chatbots retrieve pre-written answers from a fixed knowledge base. ChatGPT generates responses from scratch using a large language model trained on broad internet-scale data. That single architectural difference is why they solve completely different problems and why choosing the wrong one costs time and money.