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Cem Dilmegani

Cem Dilmegani

Principal Analyst
688 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

AIDec 5

Embedding Models: OpenAI vs Gemini vs Cohere in 2026

The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever. We benchmarked 11 leading text embedding models, including those from OpenAI, Gemini, Cohere, Snowflake, AWS, Mistral, and Voyage AI, using ~500,000 Amazon reviews.

Enterprise SoftwareDec 5

A General Guide to Internet of Everything (IoE) in 2026

The Internet of Everything (IoE) connects things, data, people, and processes using sensors and communication systems, going beyond just device connectivity to a fully integrated ecosystem. IOE solutions create value on healthcare, smart cities, retail, smart homes and industrial processes areas.

CybersecurityDec 5

A Comprehensive Overview of Top 5 ZTNA Open Source Components

As businesses move towards remote and hybrid work environments, implementing zero-trust network access (ZTNA) solutions can support businesses’ cybersecurity efforts. ZTNA open-source tools offer a cost-effective way to authorize access at each layer, securing remote access to resources.

AIDec 4

Handwriting Recognition Benchmark: LLMs vs OCRs [2026]

OCR achieves over 99% accuracy on typed text in high-quality images. However, handwriting remains challenging due to variations in style, spacing, and irregularities.

Agentic AIDec 4

AI Memory: Most Popular AI Models with the Best Memory

AI models can remember earlier parts of a conversation, but their memory capacity varies wildly. Interestingly, smarter models often have worse memory. We tested 23 popular large language models to see which ones actually remember information during long conversations.

Enterprise SoftwareDec 4

Top 7 Workload Automation Tools ['26]: Vendor Benchmark

We compare 7 workload automation tools. In our screening, we used three criteria. 100+ employee size on LinkedIn, 3M+ funding or revenue and reference from a Fortune 500 company.

Enterprise SoftwareDec 4

Top 7+ Hybrid Cloud Job Schedulers in 2026

Enterprises should leverage both the cloud and on‑premises systems to meet diverse data-storage and compute requirements. AIMultiple presents seven workload automation solutions for cloud automation, each distinguished by their specific functionalities and benefits. Follow the links on the vendors for our rationale. *Ratings are based on B2B user review platforms.

AIDec 4

AI Adoption in Manufacturing: Insights from 100 Companies

Our analysis of the top 100 manufacturing companies by revenue from the Forbes Global 2000, spanning automotive, industrial equipment, chemicals, consumer electronics, and more across 15 countries, reveals two clear patterns in how manufacturers approach artificial intelligence. We evaluated three key metrics across all 100 companies: AI partnerships, open-source contributions, and AI initiative outputs.

AIDec 4

Generative AI Ethics: Concerns and How to Manage Them

Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, filed a lawsuit against Perplexity, alleging that the company illegally and knowingly copied Britannica’s human-verified content and misused its trademarks without permission. Explore what generative AI ethics concerns are and best practices for managing them. 1.

AIDec 4

20 Strategies for AI Improvement & Examples in 2026

AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift over time when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.