Cem Dilmegani
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
- Cem Dilmegani, 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.
Media, conference & other event presentations
- Answers to Korea24's questions on job loss due to AI, Korea24
- 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: 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
No-Code AI: Benefits, Industries & Key Differences
No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background.
AI Agents: Operator vs Browser Use vs Project Mariner
AI agents are increasingly marketed as end-to-end digital workers, but real-world performance can vary widely depending on the task, tools, and execution environment. To understand what these systems can genuinely deliver today, we conducted hands-on benchmarking across practical business scenarios.
RAG Evaluation Tools: Weights & Biases vs Ragas vs DeepEval vs TruLens
Failures in Retrieval Augmented Generation systems occur not only because of hallucinations but more critically because of retrieval poisoning. In such cases, the retriever returns documents that share substantial lexical overlap with the query but do not contain the necessary information.
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 10+ AI Agents in Healthcare: Use Cases & Examples
AI agents in healthcare are intelligent, autonomous systems that support clinicians, automate routine work, and personalize patient care by delivering data-driven insights, improving diagnostic accuracy, and enhancing both operational efficiency and patient support. We previously explained healthcare AI use cases. This article lists the AI agents for healthcare that automate workflows in clinical operations.
Local AI Agents: Goose, Observer AI, AnythingLLM
We spent three days mapping the ecosystem of local AI agents that run autonomously on personal hardware without depending on external APIs or cloud services. Our analysis categorizes the leading solutions into five key areas, based on hands-on testing across developer agents, automation tools, productivity assistants, frameworks, and local runtimes.
10+ Epic LLM/ Conversational AI/ Chatbot Failures
Building chatbots that understand natural language remains difficult. Many fail at basic tasks or produce responses that users mock online. AI keeps advancing, and chatbots might eventually match human conversation skills. Until then, their mistakes offer valuable lessons.
Top Open Source UEBA Tools & Commercial Alternatives
At their core, UEBA solutions aim to identify patterns in data, whether from real-time streams or historical datasets. Open source UEBA tools After reviewing the documentation of each open-source UEBA framework/tool, I listed leading open-source behavior analytics technologies that provide standard SIEM-like capabilities (e.g., alerting, MITRE ATT&CK threat intelligence framework, API-based ingestion from data sources).
DAST: 7 Use Cases, Examples, Pros & Cons
DAST’s ability to mimic real-world cyberattacks and expose vulnerabilities in real time makes it a valuable asset in the cybersecurity toolkit. As seen in the graph, the popularity of DAST has increased significantly in the last five years.
Enterprise Generative AI: 10+ Use Cases & Best Practices
Generative AI (GenAI) presents novel opportunities for enterprises compared to middle-market companies or startups including: However, generative AI brings challenges unique to large organizations. For example: Explore our practical enterprise AI use cases to learn how large companies can build, deploy, and govern their own generative AI models effectively.
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