
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
Generative AI for Sales Use Cases
Unlike traditional AI in sales applications, which typically focuses on data analysis and pattern recognition, generative AI actively contributes to the sales process by creating content, drafting communications, and improving customer engagement. Explore the future of generative AI for sales, including use cases with examples that align with the steps of a typical selling process.
LLM Fine-Tuning Guide for Enterprises
Follow the links for the specific solutions to your LLM output challenges. If your LLM: The widespread adoption of large language models (LLMs) has improved our ability to process human language. However, their generic training often results in suboptimal performance for specific tasks.
7 Competitive Intelligence Challenges & Solutions
In the current competitive business environment, maintaining an edge over rivals has become essential for strategic decision-making. Despite its critical importance, most organizations struggle with competitive intelligence. Research shows that 90% of Fortune 500 companies already use Competitive Intelligence, but its perceived effectiveness is low.
Top 12 ChatGPT Survey Research Use Cases & Tips
ChatGPT generated gamified survey question
How to Use ChatGPT for Business: Top 40 Applications
With numerous potential use cases for ChatGPT, businesses may struggle to identify the most valuable applications of the technology and face challenges in meeting their unique needs. To get the most from ChatGPT, businesses need the tool fine-tuned to meet their specific needs and objectives.
The Future of Large Language Models
Interest in large language models (LLMs) is rising since ChatGPT attracted over 200 million monthly visitors in 2024.LLMs along with generative AI have an influence on a variety of areas, including medical imaging analysis and high-resolution weather forecasting.
Top 7 Examples of ChatGPT Sentiment Analysis
A study estimated that 80% of companies will adapt to solutions that utilize sentiment analysis in 2023. Sentiment analysis is a Natural Language Processing (NLP) method that classifies texts, images, or videos based on the emotional tone as negative, positive, or neutral.
8 Ways to Use ChatGPT for Test Automation
Maintaining high software quality depends heavily on effective test automation, and with AI and natural language processing tools like ChatGPT, automation approaches are rapidly evolving. We explore ChatGPT’s use cases, integration challenges, and potential benefits for test automation.
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.
Top 10 Tools for Contact Center Automation
Contact centers must automate to remain competitive. Organizations can implement comprehensive end-to-end solutions or deploy targeted workload automation tools like RPA to address specific operational challenges. These technologies reduce costs while improving service quality and agent productivity.
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