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Cem Dilmegani

Cem Dilmegani

Principal Analyst
713 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, automation, cybersecurity (including network security, application security), data collection including web data collection and process intelligence.

Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and 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

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

DataSep 3

Top 10+ Software Testing Best Practices

Detecting software bugs before release is crucial for ensuring project success from user engagement and financial perspectives. The cost of solving bugs in the testing stage is almost seven times cheaper compared to the production stage. However, testing is time-consuming and expensive.

AIAug 4

5 AI Training Steps & Best Practices

AI can boost business performance, but 85% of AI projects fail, often due to poor model training. Challenges such as poor data quality, limited scalability, and compliance issues hinder success. Check out the top 5 steps in AI training to help businesses and developers train AI models more effectively.

Enterprise SoftwareApr 3

Top 10+ Crypto Domain Name Examples

Crypto Domain Name (DNS) providers are gaining popularity, with over 2 million “.eth” domains already registered in 2025. DNS providers allow users to register and manage domain names linked to blockchain networks. These services also offer tools for utilizing decentralized websites for seamless integrations with Bitcoin wallets.

Enterprise SoftwareApr 7

NFT Standards of Top Blockchains

NFTs require some level of standardization at their creation to be compatible with their blockchain.We explore some of the NFT standards.  What is an NFT standard?  Non-Fungible Token (NFT) standards outline how to create NFTs on a particular blockchain.

DataJul 2

Crowdsourced Data Collection Benefits & Best Practices

Data collection is a crucial stage in developing AI/ML models, directly influencing their real-world performance. Whether you work with a data collection service or gather data yourself, it’s vital to execute this process correctly. Here, we explore crowdsourcing, an effective method for data gathering, to help businesses select the best approach for their AI/ML projects.

Enterprise SoftwareSep 10

Top 25 Use Cases / Examples of Intelligent Automation

Traditional automation approaches can be effective in simple tasks but they often rely on rigid, pre-defined rules and are limited in their ability to adapt to changing circumstances. This can lead to inefficiencies and errors, especially when dealing with complex tasks or data.

DataJul 22

Top 6 AI Data Collection Challenges & Solutions

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.

AISep 16

Top 15 Use Cases in AI for Neurology with Examples

Neurological disorders are among the most complex and costly to diagnose and manage, contributing billions in global healthcare expenditures each year.

Enterprise SoftwareOct 9

RPA Pricing Compared Across Leading Vendors

We reviewed leading RPA pricing in detail to identify the lowest RPA vendor license fees: Calculating the total cost of ownership (TCO) for RPA software is complex because there are various factors that affect RPA pricing, such as: See license costs of market-leading solutions for different scenarios with different numbers and types of licenses: RPA pricing

DataJun 27

Top 6 Data Collection Methods for AI and Machine Learning

While some companies rely on AI data collection services, others gather their data using scraping tools or other methods. See the top 6 AI data collection methods and techniques to fuel your AI projects with accurate data: Overview of AI data collection methods 1. Crowdsourcing Online talent platforms, such as crowdsourcing platforms, have various benefits.