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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

Agentic AINov 21

How ACP Enables Interoperable Agent Communication?

We’re starting to see GenAI move toward standardization, similar to how HTTP transformed the internet in the early 1990s. Just as HTTP enabled the rise of the World Wide Web, new protocols are emerging.

AINov 20

Future of Deep Learning according to top AI Experts in 2026

Deep learning currently delivers the best results for many AI applications. But there’s debate about its ultimate potential. Geoffrey Hinton believes deep learning will eventually solve all problems, while other scientists point to fundamental flaws without clear solutions.  As interest grows among researchers, developers, and the public, breakthroughs are likely.

Enterprise SoftwareNov 20

Top 4 ERP AI Use Cases & Case Studies in 2026

Enterprise resource planning (ERP) systems help organizations manage and connect processes in finance, operations, and human resources. The ERP market is expected to reach ~$52bn in 2024. As ERP systems handle more complex processes, traditional ERP becomes insufficient.

Enterprise SoftwareNov 20

Top 5 File Management Examples & Best Practices in 2026

This article explains 5 Real-Life File Management examples from 5 industries such as banking, car rental services, finance, insurance, and railroad transportation. In examples, we explain these five companies’ business challenges, their solutions, and their results.

AINov 20

Bias in AI: Examples and 6 Ways to Fix it in 2026

Interest in AI is increasing as businesses witness its benefits in AI use cases. However, there are valid concerns surrounding AI technology: AI bias benchmark To see if there would be any biases that could arise from the question format, we tested the same questions in both open-ended and multiple-choice formats.

Enterprise SoftwareNov 19

Top 10 VisualCron Alternatives in 2026

VisualCron stands out for its Windows-based job scheduling capabilities. However, during our review of VisualCron, we experienced that its Windows focus may be insufficient for businesses with complex IT scenarios and hybrid cloud requirements. We selected the top alternatives to VisualCron based on the features, pricing, and market presence metrics of leading solutions.

Enterprise SoftwareNov 19

Top IoT Cloud Benefits, Challenges & Platforms in 2026

In an IoT ecosystem, devices communicate effortlessly through the cloud. This is much simpler than using traditional, physical servers tucked away in an office. The cloud is popular for handling IoT data because it’s easy to access, can grow fast (scalable), and helps recover data after disasters.

Enterprise SoftwareNov 19

UiPath Pricing: RPA Pricing Models Explained in 2026

UiPath is one of the most popular RPA vendors, but RPA pricing structure can be complex. We examined 10,000 different price combinations to help business and tech leaders understand UiPath’s pricing and get a high ROI RPA solution from their UiPath partnership.

AINov 19

Top 10 Edge AI Chip Makers with Use Cases in 2026

The demand for low-latency processing has driven innovation in edge AI chips. These processors are designed to perform AI computations locally on devices rather than relying on cloud-based solutions. Based on our experience analyzing AI chip makers, we identified the leading solutions for robotics, industrial IoT, computer vision, and embedded systems.

DataNov 18

Guide To Machine Learning Data Governance in 2026

In this article, we explain machine learning data governance. We explain its key principles, benefits, use cases, best practices, and our future expectations of data governance.