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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

Enterprise SoftwareSep 2

Top 20 RPA SAP Use Cases & Examples in 2026

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.

AISep 2

RPA Generative AI: Top 15 Use Cases in 2026

RPA (robotic process automation) and generative AI are two popular tools in the digital transformation landscape: These two tools are widely used because of their wide-ranging capabilities. RPA’s handling of repetitive tasks & generative AI’s automation through the creation of original content creates an opportunity for businesses to reshape their companies’ operational efficiency.

DataAug 29

Image Data Collection with Best Practices in 2026

Computer vision (CV) is revolutionizing industries, from autonomous vehicles to healthcare, but success depends critically on the collection of high-quality image data. Organizations that implement strategic data collection services can achieve higher accuracy in specialized applications, while poor data strategies lead to biased models and compliance violations.

Enterprise SoftwareAug 29

Top 12 Transactional Email Services in 2026

We analyzed over 30 transactional email services based on their features & dimensions to identify the following providers.

DataAug 25

Data Labeling for NLP with Real-life Examples in 2026

NLP technology is increasingly being used to enable smart communication between people and their devices. Companies like Google, Amazon, and OpenAI have invested billions in NLP technologies that can understand, interpret, and generate human language with remarkable accuracy. However, behind every sophisticated NLP model lies an important foundation: labeled training data.

DataAug 25

Ethical & Compliant Web Data Benchmark in 2026

As enterprises scale their web data operations, compliance, data, and risk executives increasingly evaluate the associated ethical, reputational, and legal risks. We benchmarked 5 leading web data collection services across 3 dimensions and tested each service with more than 20 potentially unethical scenarios.

CybersecurityAug 24

Data Loss Prevention (DLP): Types & 5 Challenges in 2026

The increased mobility introduces risks of data loss or theft, which can lead to severe financial losses and reputational damage for companies. Effective Data loss prevention (DLP) software needs to prevent the unauthorized movement of private data and personally identifiable information (PII) to limit reputational and financial risk.

DataAug 23

6 Web Scraping Challenges & Practical Solutions in 2026

Web scraping, the process of extracting required data from web sources, is an essential tool; however, it is a technique fraught with challenges. See below the most common web scraping challenges and practical solutions to address them.

AIAug 23

Responsible AI: 4 Principles & Best Practices in 2026

AI and machine learning are revolutionizing industries, with 90% of commercial apps expected to use AI by 2025 as AI statistics shows. Despite this, 65% of risk leaders feel unprepared to manage AI-related risks effectively.

AIAug 22

In-depth Guide to Knowledge Graph: Use Cases in 2026

Your organization has data everywhere: customer databases, financial systems, HR records, project files, and emails. But when you need to answer “Which customers bought Product X and also had support tickets last month?” you’re stuck searching multiple systems, copying data to Excel, and hoping you didn’t miss anything. This data chaos costs companies millions.