
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
10 Steps to Developing AI Systems
IBM identifies the top AI adoption challenges as concerns over data bias (45%), lack of proprietary data (42%), insufficient generative AI expertise (42%), unclear business value (42%), and data privacy risks (40%).These obstacles can hinder AI implementation, slow innovation, and reduce the return on investment for organizations adopting AI technologies.
Control-M for Enterprise Workload Automation
Control-M is BMC Software’s workload automation solution that orchestrates application and data workflows across mainframe, cloud, and hybrid environments through a centralized interface. The platform manages complex workflow dependencies, provides end-to-end visibility into production processes, and integrates with major cloud services, data platforms, and DevOps tools.
Toloka AI Review & Its Top Alternatives for RLHF
Toloka AI is a popular name in the Reinforcement Learning from Human Feedback (RLHF) and AI data services spaces. If your business is considering an RLHF or AI data partner like Toloka AI, our research can provide valuable guidance.
Compare 10+ LLMs in Healthcare
Large language models (LLMs) are increasingly being applied in healthcare to support clinical tasks such as medical question answering, patient communication, and summarizing medical records.
WhatsApp HR: Top 25 Use Cases for Human Resources
HR teams are under financial strain This lowers their recruitment capacity, training and development opportunities, and retention capabilities. We recommend that HR departments with limited budgets consider using WhatsApp to automate some processes. We explained why WhatsApp is suitable for HR professionals and highlighted the top 25 related categories for use across HR tasks.
Generative AI ERP Systems: 10 Use Cases & Benefits
Enterprise resource planning (ERP) software helps businesses see the process across different departments so they can make smarter decisions faster. Generative AI, alongside technologies like RPA, has the potential to enhance ERP processes.
6 Risks of Generative AI & How to Mitigate Them
With industries prioritizing generative AI for innovation and automation, its potential grows. However, risks of generative AI like accuracy and ethical concerns remain. Addressing these challenges is key to ensuring AI benefits humanity.
Chatbot vs ChatGPT: Differences & Features
The first chatbot emerged in the 60s and became commercial in the late 2000s, but it never matched today’s popularity due to ChatGPT. However, its success shouldn’t be generalized, as it’s a specific chatbot type not suitable for all business processes.
Top 10 Vector Database Use Cases
Processing, storing, and retrieving vast amounts of information rapidly and efficiently is paramount for businesses. Vector databases are a critical emerging technology in addressing this demand. Unlike traditional databases, vector databases focus on high-dimensional vector data, offering unique advantages for certain use cases.
Top 40+ LLMOps Tools & Compare them to MLOPs
LLMs are growing rapidly, but development and fine-tuning remain expensive. LLMOps tools help reduce these costs by streamlining LLM management.To better understand the landscape, we’ve also prepared a detailed comparison of LLMOps and MLOps tools to highlight how they differ in capabilities, focus areas, and workflows.
AIMultiple Newsletter
1 free email per week with the latest B2B tech news & expert insights to accelerate your enterprise.