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
Top 5 AI Network Monitoring Use Cases and Real Life Examples
The integration of artificial intelligence (AI) into network monitoring enhances availability and performance. Here are options for you: AI Network Monitoring Tools ** Reviews are based on Capterra and G2. Vendors are ranked according to their number of reviews *** Free trial periods and pricing are included if the content is publicly shared.
15+ Use Cases & AI Applications of Augmented Reality
Augmented Reality (AR) is a digital media platform that allows the user to integrate virtual context into the physical environment in an interactive, multidimensional way. Implementing AI enhances the AR experience by allowing deep neural networks to replace traditional computer vision approaches, and add new features such as object detection, text analysis, and scene labeling.
AI Web Browsers Benchmark: Complete Selection Guide
We tested 9 AI web browsers, including Perplexity Comet, Arc Max, Microsoft Edge Copilot, and ChatGPT Atlas, across key performance metrics to determine which solutions deliver practical value for different workflows.
LLM Latency Benchmark by Use Cases
The effectiveness of large language models (LLMs) is determined not only by their accuracy and capabilities but also by the speed at which they engage with users. We benchmarked the performance of leading language models across various use cases, measuring how quickly they respond to user input.
10+ Agentic AI Trends and Examples
The future of agentic AI isn’t just about improving tools or streamlining business workflows. It’s about integrating AI deeply and transforming business approaches by restructuring current frameworks. Key takeaways: 10+ agentic AI trends and examples 1.
AutoSys: Key Features and User Insights
Interest in Broadcom’s AutoSys is declining (Source: Google Trends) and it has a lower average rating on review platforms compared to most other workload automation tools.
Compare Top 28 Legal AI Software by Pricing
In the last 2 decades, I worked with enterprises as a consultant and tech vendor to deploy advanced analytics & AI solutions. I looked into more than 50 legal tech companies using generative AI and categorized the leading products.
Data Quality in AI: Challenges, Importance & Best Practices
Poor data quality hinders the successful deployment of AI and ML projects. Even the most advanced AI algorithms can yield flawed results if the underlying data is of low quality. We explain the importance of data quality in AI, the challenges organizations encounter, and the best practices for ensuring high-quality data.
Generative AI in Insurance: 10 Use Cases & 5 Challenges
Generative Artificial Intelligence (AI) emerges as a promising solution, capable of not only streamlining operations but also innovating personalized services, despite its potential implementation challenges. The insurance value chain, from product development to claims management, is a complicated process.
Top 9 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.
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