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
AGI Benchmark: Can AI Generate Economic Value
AI will have its greatest impact when AI systems start to create economic value autonomously. We benchmarked whether frontier models can generate economic value. We prompted them to build a new digital application (e.g., website or mobile app) that can be monetized with a SaaS or advertising-based model.
Federated Learning: 5 Use Cases & Real Life Examples
According to recent McKinsey analyses, the most pressing risks of AI adoption include model hallucinations, data provenance and authenticity, regulatory non-compliance, and AI supply chain vulnerabilities. Federated learning (FL) has emerged as a foundational technique for organizations seeking to mitigate these risks.
Top 5 Open-Source Agentic Frameworks
We reviewed several popular open-source AI agent frameworks, examining their multi-agent orchestration capabilities, agent and function definitions, memory management, and human-in-the-loop features. To evaluate practical performance, we implemented four data analysis tasks on each framework: logistic regression, clustering, random forest classification, and descriptive statistical analysis.
40 IoT Applications & Use Cases with Real-Life Examples
Worldwide annual revenue on IoT in 2033 is expected to be $934B. IoT enables a myriad of different business applications. Knowing those IoT examples and use cases can help businesses integrate IoT technologies into their future investment decisions.
15 Digital Twin Applications/ Use Cases by Industry
As more sectors explore virtualization, digital twin solutions are gaining mainstream traction. According to Deloitte study, the global market for digital twins is expected to grow with 38% CAGR to reach $16 billion by 2023, and the proliferation of IoT technology accelerating this growth.
How AI transforms Cybersecurity: Real-Life Examples
By leveraging machine learning, advanced analytics, and automation, AI enables businesses to enhance their security posture, identify vulnerabilities, reduce response times, and allocate resources more efficiently. However, AI is also a double-edged sword; cyber threats are also evolving due to developments in network security and generative AI.
AI Agent Security: 7+ Tools to Reduce Risk
As AI agents gain autonomy, they introduce new risks, ranging from prompt injection to unauthorized access. Security is a critical aspect of AI agents; we cover AI agent security and highlight the tools designed to address it.
Top 10 Network Planning Tools: Key features & Benefits
Network planning tools help businesses optimize performance, manage resources efficiently, and ensure scalable, reliable network designs for growth and stability.
Top 20 Browser Testing Tools
At AIMultiple, we utilize cross-browser testing tools on a daily basis to determine if our 1,000+ web pages work as intended in various browsers. Based on our experience, we picked the top browser testing tools, which include both open-source and proprietary software.
Synthetic Data vs Real Data: Benefits, Challenges
Synthetic data is widely used across various domains, including machine learning, deep learning, generative AI (GenAI), large language models, and data analytics. According to Gartner, by 2030, synthetic data use will outweigh real data in AI models.
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