Cem Dilmegani
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
- Cem Dilmegani (September 28, 2017). Post-AI Banking: Millions of jobs at risk as banks automate their core functions. International Banker
- Cem Dilmegani, Bengi Korkmaz, and Martin Lundqvist (December 1, 2014).Public-sector digitization: The trillion-dollar challenge.McKinsey & Company
Conference & other event presentations
- Real Estate and Technology, presented by Hofstra University’s Wilbur F. Breslin Center for Real Estate Studies and the Frank G. Zarb School of Business in 2023 and 2024.
- Radar AI session (June 22, 2023): "Increasing Data Science Impact with ChatGPT".
- Generative AI Atlanta meetup (March 10, 2023): Generative AI for Enterprise Technology.
Sources
- Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
- Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
- Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
- Science, Research and Innovation Performance of the EU, European Commission.
- EU’s €200 billion AI investment pushes cash into data centers, but chip market remains a challenge, IT Brew.
- Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
- We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.
Latest Articles from Cem
RAG Frameworks: LangChain vs LangGraph vs LlamaIndex
Comparing Retrieval-Augmented Generation (RAG) frameworks is challenging. Default settings for prompts, routing, and tools can subtly alter behavior, making it difficult to isolate the framework’s impact. To create a controlled comparison, we replicated the same agentic RAG workflow across LangChain, LangGraph, and LlamaIndex, standardizing components wherever possible.
Top 5 Open Source Database Monitoring Tools
Commercial database monitoring tools often promise polished user interfaces and dedicated enterprise support. Open-source solutions are increasingly chosen for their transparency, cost-effectiveness, community-driven innovation, and flexibility. We’ve analyzed both approaches to understand the current landscape.
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. Our analysis examines two key indicators of AI maturity: Methodology 1.
Building Personal AI Agents + 18 Agent Platforms and Tools
We spent the two days experimenting with real-world demos and tools to build personal AI assistants that can handle your tasks, such as scheduling meetings, managing notes, or sorting through emails. We will dive into three main approaches to building and using personal AI assistants, with real-world examples for each: 1.
Building AI Agents with Anthropic's 6 Composable Patterns
We spent 3 days experimenting workflows and agent pipelines in n8n according to Anthropic’s and OpenAI’s guides on building effective AI agents. We are going to distill down everything we have learned to give you a guide to build functional AI agents in your LLM projects.
Low/No-Code AI Agent Builders: n8n, AgentKit, make, Zapier
We spent three days setting up and configuring AI agent workflows using the free tiers of popular low/no-code tools, including n8n (self-hosted), make, and Zapier, and evaluated OpenAI’s AgentKit based on its official documentation.
Top 3 MFT Platforms With Distinctive AI Features
We analyzed AI MFT vendors based on criteria including customer reviews, protocol support, and documented AI capabilities. Identify platforms that match your infrastructure requirements and budget. These platforms represent different approaches to AI in MFT from autonomous operations to predictive SLA monitoring to conversational analytics.
Top 6 Database Monitoring Tools: Features & Challenges
The performance of your database directly dictates the health of your applications and the satisfaction of your customers. Database Administrators (DBAs), DevOps, and SRE teams rely on specialized monitoring software to prevent outages, tune inefficient queries, manage costs, and ensure continuous availability.
Top 8 Observability Software with Pricing and Feature Comparison
Observability platforms promise complete visibility across distributed systems, but selecting the right one is hard when every vendor claims they do everything. We analyzed the top 8 observability software by looking at their documented capabilities, public pricing, verified customer reviews, and enterprise reference cases.
Best 7 AI Testing Platforms for QA
We evaluated AI testing platforms embedded with AI agents; most were overhyped Selenium/Playwright with marketing. A few were capable of writing/maintaining test cases or visual testing, though even these tools still have notable limitations. From these, we selected 7 platforms and categorized them by their primary focus areas.
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