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Cem Dilmegani

Cem Dilmegani

Principal Analyst
724 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

AISep 5

Generative AI Healthcare: 15 Use Cases with Examples

As healthcare systems face rising data volumes, workforce shortages, and increasing demands for personalized care, generative AI is emerging as a critical solution. By generating insights from complex medical data, generative AI healthcare applications offer hospital administrators, clinicians, and researchers new ways to improve decision-making and patient outcomes.

DataSep 4

Synthetic Users Explained: Top 7 AI User Research Tools

Traditional research requires weeks of finding participants, scheduling interviews, and analyzing results manually. Synthetic user platforms enable teams to create thousands of realistic user profiles instantly, allowing them to test ideas, messaging, and user flows.

AISep 4

Chatbot Testing: A/B, Auto, & Manual Testing

Achieving chatbot success can be challenging. Claims such as “10 times better ROI compared to email marketing” are only realistic if the chatbot is designed, tested, and implemented effectively. A structured testing process plays a key role in ensuring that a chatbot delivers reliable results.

Agentic AISep 4

Building a No-Code AI Lead Generation Workflow with n8n

I have been reviewing popular AI sales agents, including  AiSDR and Outreach.io. While these platforms support lead management, they are typically focused on broader sales engagement and delivered as commercial packages with costs ranging from $2K to $5K per user per month.

CybersecuritySep 4

RMM Pricing: 10 Products Analyzed

Understanding RMM software pricing models and factors is important for making informed decisions. Remote monitoring and management products may vary based on the pricing structure. Companies can evaluate product prices based on the number of technicians or endpoints they require. Here, we explore the structures of 10 RMM pricing models of software.

Agentic AISep 3

MCP Security: Best Practices and Avoid Common Pitfalls

The model context protocol (MCP), pioneered by Anthropic, is quickly becoming the go-to standard for connecting large language models (LLMs) to the outside world.  But the same simplicity that makes MCP so powerful also makes it risky.

AISep 3

Best ChatGPT Alternatives: Features & Comparison

Based on my analysis of LLM models and their performance in real-world applications, I’ve compiled a list of ChatGPT alternatives covering everything from open-source LLMs to enterprise-ready AI assistants. Whether you’re looking for better accuracy, cost-effective options, or specialized AI chatbots, this guide will help you find the right tool for your needs..

Agentic AISep 3

Best AI Agents for Workflow Automation

We researched the leading AI agent platforms for workflow automation, analyzing their documentation, feature sets, integration capabilities, and publicly available customer implementations.  There are 4 ways to implement AI agents for workflow automation. Top 10 AI Agents for Workflow Automation *Starting price per month ** Reviews are based on Capterra and G2.

AISep 3

Specialized AI Models: Vertical AI & Horizontal AI

While ChatGPT grabbed headlines, the real business value comes from AI built for specific problems. Companies are moving beyond general-purpose AI toward systems designed for their exact needs. This shift is creating three distinct types of specialized AI – each solving different business challenges.

DataSep 3

20 Test Automation Case Studies Demonstrating Business Impact

QA teams struggle with slow, manual testing, which often results in higher costs, longer development cycles, and customer dissatisfaction. Transitioning to automated QA testing is the top priority in the software testing environment. To help decision-makers assess the impact of test automation, we analyze 20 case studies highlighting real-world transformations.