AIMultipleAIMultiple
No results found.
Cem Dilmegani

Cem Dilmegani

Principal Analyst
706 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, automation, cybersecurity (including network security, application security), data collection including web data collection and process intelligence.

Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and 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

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 11

WLA Migration: Best Practices & Vendor Approaches

Organizations are increasingly reconsidering their existing workload automation tools, with many actively exploring or transitioning to new solutions. Migrating to a newer workload automation system can provide immediate gains in scalability, reliability, and integration capabilities. We define workload automation (WLA) migration, best practices, what to pay attention to, and the differing approaches from different vendors.

AISep 24

Top 5 AI Gateways for OpenAI: OpenRouter Alternatives

The growing number of LLM providers creates significant API management hurdles. AI gateways address this complexity by acting as a central routing point, enabling developers to interact with multiple providers through a single, unified API, thereby simplifying development and maintenance.

Agentic AIAug 19

Multi-Agent Communication with Google's A2A

Agent2Agent (A2A) Protocol is an open standard for communication and collaboration between AI agents.Though it’s new, it’s gaining attention, especially since it works well with MCP, which is becoming the industry standard. A2A is expected to become the go-to protocol for multi-agent communication.

AISep 15

AI Video Pricing: Compare Runway, Synthesia & Invideo AI

AI video pricing can differ significantly across platforms, influenced by factors such as output quality, customization options, and features. As more businesses and creators turn to AI for efficient video production, understanding these pricing models becomes essential.

Agentic AIJun 12

Multi Agent Systems: Applications & Comparison of Tools

Multi-agent systems(MAS) enable distinct AI agents to work together to achieve complex objectives. Every AI agent in the system possesses its specific characteristics and responsibilities that contribute to a greater goal. MAS provides a distinctive approach to managing multi-step tasks and enhancing efficiency.

DataJul 25

MLSecOps: Top 20+ Open Source and Commercial Tools

AI is a key technology used in the security software landscape, yet what is often overlooked is the fact that AI itself is becoming an increasingly vulnerable attack surface, due to technical challenges: To protect their machine learning models, companies are using enterprise-grade AI safety frameworks (e.g.,  Anthropic’s Constitutional AI) and increasingly adapting MLSecOps tools.

Agentic AISep 30

4 Agentic AI Design Patterns & Real-World Examples

Agentic AI design patterns enhance the autonomy of large language models (LLMs) like Llama, Claude, or GPT by leveraging tool-use, decision-making, and problem-solving. This brings a structured approach for creating and managing autonomous agents in several use cases.

Agentic AISep 15

Top 10+ AI Agents in Healthcare: Use Cases & Examples

We previously explained healthcare AI use cases. In this article, I listed the best AI agent examples for healthcare that automate workflows in clinical operations: Examples of AI agents in healthcare General-purpose healthcare agents Automates multiple business tasks (e.g., scheduling, medical coding, office operations), but not focused on diagnostics or deep clinical applications.

AISep 24

Top Vector Database for RAG: Qdrant vs Weaviate vs Pinecone

Vector databases power the retrieval layer in RAG workflows by storing document and query embeddings as high‑dimensional vectors. They enable fast similarity searches based on vector distances.

Agentic AIJul 25

From Traditional SaaS-Pricing to AI Agent Seats

Companies are increasingly shifting to dynamic pricing models, allowing buyers to choose their costs based on specific needs and usage patterns.

...678910...