
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
Agentic Document Extraction: LandingAI & more
Agentic Document Extraction (ADE) is a specialized form of Optical Character Recognition (OCR) that extracts data from various file types. It combines document processing, data retrieval, structured output generation, and automation to streamline knowledge work. ADE stands out from traditional OCR by its ability to recognize complex document structures, such as tables, flowcharts, and images.
How to Measure AI Performance: Key Metrics & Best Practices
Measuring AI performance is crucial to ensuring that AI systems deliver accurate, reliable, and fair outcomes that align with business objectives. It helps organizations validate the effectiveness of their AI investments, detect issues like bias or model drift early, and continuously optimize for better decision-making, operational efficiency, and user satisfaction.
LLM VRAM Calculator for Self-Hosting
The use of LLMs has become inevitable, but relying solely on cloud-based APIs can be limiting due to cost, reliance on third parties, and potential privacy concerns. That’s where self-hosting an LLM for inference (also called on-premises LLM hosting or on-prem LLM hosting) comes in.
Top 30+ Agentic AI Companies
Though AI agents are being hyped and some companies rebrand their chatbots as agentic tools, there are still few agents in production. Previously, we benchmarked some of these capable AI agents over several real-world tasks. In this article, we identified key agentic AI companies that live up to the hype.
Compare Best AI Agents in Customer Service
AI agents powered by large language models (LLMs) can respond to customer queries in natural language, interpret context, and generate human-like responses. These agents can process and synthesize large volumes of information from sources such as knowledge bases. We compiled a list of the best use cases for the top 4 customer service AI agents.
Model Context Protocol (MCP) and Its Importance
Model Context Protocol (MCP) is an open protocol that standardizes how applications, databases and tools provide context to LLMs. More simply, it enables applications to connect to AI models, helping to achieve standardized results. Why is it important? MCP servers are becoming more popular because of their integration capabilities with AI systems.
Compare 10+ Open Source Security Audit Tools
Previously, I explained 30+ security audit tools based on their specializations. To compare vulnerability scanning, web application scanning, and security automation & simulation capabilities of the best free open-source auditing tools, I spent several hours going through the documentation and watching demos of these tools.
Top 7 Enterprise eCommerce Platforms: Features & Pricing
Enterprises need these eCommerce capabilities: We selected the top 7 enterprise eCommerce platforms based on their capabilities listed above.
Python Job Scheduling: Methods and Overview
Automating repetitive tasks is essential for efficiency, whether you’re running a small script or managing large-scale applications. Python job scheduling allows you to execute tasks automatically at specific times or intervals, reducing manual effort and improving reliability. Here are the different job scheduling methods in Python, ranging from simple to advanced solutions, along with their pros and cons.
Top 15+ Auto Dialer Software: Types, Features & Pricing
Auto dialer software (aka automated call software) automatically dial telephone numbers pulled from a list and connect either to the customer or a live agent, or play a recorded message. This helps automate key contact center functions like call routing.
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