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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

AINov 27

Large Language Model Training

While using existing LLMs in enterprise workflows is table stakes, leading enterprises are building their custom models. However, building custom models can cost millions and require investing in an internal AI team.

AINov 27

LLM Fine-Tuning Guide for Enterprises

Follow the links for the specific solutions to your LLM output challenges. If your LLM: The widespread adoption of large language models (LLMs) has improved our ability to process human language. However, their generic training often results in suboptimal performance for specific tasks.

AINov 27

Large Language Model Evaluation: 10+ Metrics & Methods

Large Language Model evaluation (i.e., LLM eval) refers to the multidimensional assessment of large language models (LLMs). Effective evaluation is crucial for selecting and optimizing LLMs. Enterprises have a range of base models and their variations to choose from, but achieving success is uncertain without precise performance measurement.

CybersecurityNov 27

Top 5 SaaS Backup Solutions for MSPs

Many businesses operate under the misconception that their SaaS providers (like Microsoft 365 or Google Workspace) fully protect their data from all threats. While these platforms offer robust infrastructure and some level of data redundancy, they do not protect against accidental deletion, ransomware, or insider threats.

Agentic AINov 26

40+ Agentic AI Use Cases with Real-life Examples

Autonomous generative AI agents execute complex tasks with little or no human supervision. Agentic AI differs from chatbots and co-pilots. Unlike traditional AI, particularly generative AI, which often requires human intervention in complex workflows, agentic AI aims to autonomously navigate and optimize processes thanks to its decision-making capabilities and goal-directed behavior.

Agentic AINov 26

Open Operator: A Free Alternative to OpenAI's Operator

Early in 2025, OpenAI announced Operator, a new research preview of ChatGPT that serves as an agent for repetitive activities. It can browse for plane tickets, book a table, or shop online, and execute daily digital tasks for you on its own.

AINov 26

ChatGPT Apps SDK Tutorial: Build Custom AI Apps

Following OpenAI’s introduction of the Apps SDK, we built an app that recommends the best software vendors using AIMultiple’s database. When users ask about agentic AI, CRM, or observability tools, etc., the app searches our articles and returns ranked results.

DataNov 26

Top 3 Prolific Alternatives

Prolific is a popular AI data collection service that offers a crowdsourcing platform for AI data seekers. Our research identified some drawbacks of working with Prolific from the perspectives of its customers and workers.

AINov 26

Top 5 IBM Watson Competitors

Businesses use conversational AI to handle customer questions at scale and cut down wait times. IBM’s WatsonX Assistant is a popular choice, but with over 200 platforms available, it’s not the only option – and it might not be the right one for you.

Agentic AINov 26

Compare 50+ AI Agent Tools

We’ve spent the past few months testing AI agents in real-world scenarios – not just reading marketing materials, but actually using these tools to see what works and what doesn’t. Despite the hype around “autonomous AI,” most tools today are co-pilots, not autopilots.

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