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Cem Dilmegani

Cem Dilmegani

Principal Analyst
687 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

AIJul 24

AI Fail: 4 Root Causes & Real-life Examples in 2026

Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions.

DataJul 24

AI Data Collection: Risks, Challenges & Tools in 2026

AI builders need fresh, high quality data:  However, data collection comes with its risks. For example, enterprises need to avoid unethical data collection practices and ensure that data is collected ethically to minimize reputational risk.

AIJul 24

Top 30 Affective Computing Applications: Emotion AI Use Cases

Thanks to affective computing, also known as emotion AI, computers start to recognize human emotions based on facial expressions, body language, or voice tone. Technology’s applications in different industries are expanded by significant investment in technology where some managers lack knowledge.

AIJul 24

14 Rossum AI Alternatives / Competitors in 2026

Document processing is crucial in many industries such as finance automation and accounts payable. Alongside accounts payable AI (APAI) solutions, AI-driven Intelligent Document Processing (IDP) vendors such as Rossum capture attention.  In this vibrant market, AP AI market spending is projected to surge to $1.9 billion by the end of 2025.

Enterprise SoftwareJul 23

Cloud Workload Automation: Top Software & Use Cases

Businesses are increasing their flexibility while managing costs by adopting a hybrid cloud strategy. According to Statista, industries have increased their cloud workloads and had an uptick as a response to the COVID-19 pandemic.

DataJul 22

Top 5 RLHF Platforms: Guide & Features Comparison ['26]

As AI adoption grows, with 65% of organizations now regularly using generative AI, selecting the right tools for optimizing AI models has become more crucial than ever. Reinforcement learning from human feedback (RLHF) platforms have emerged as key players in this process.

DataJul 22

Sentiment Analysis Datasets in 2026

Sentiment analysis is a great way to understand the customers’ feelings toward a company and to see if they are associated with sales, investments, or agreements. Ensuring a reliable sentiment analysis depends on many factors, and one of its building blocks is the dataset used to train the models.

DataJul 22

Top 6 AI Data Collection Challenges & Solutions in 2026

AI adoption was slightly lower last year (Figure 1); one reason could be the various challenges in implementing AI. Training data collection has been identified as one of the main barriers to AI adoption. To avoid data-related challenges, businesses are opting to work with AI data collection services.

AIJul 21

Top 6 AI Email Assistant Software in 2026

An AI email assistant supports email management processes and helps enhance productivity by automating responses, scheduling meetings, and managing tasks while providing continuous support. We selected the top 6 AI email assistants below based on their capabilities in drafting messages, offering personalized suggestions, and organizing inboxes.

AIJul 21

Generative AI in Manufacturing: Use Cases & Benefits ['26]

Generative AI is becoming a strategic tool for manufacturers facing challenges such as supply chain disruptions, labor shortages, and rising cost pressures. It helps automate design, predict maintenance needs, and optimize supply chains, while driving efficiency, reducing costs, and speeding up innovation.