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
Demand Forecasting in the Age of AI & Machine Learning
Businesses face different inventory challenges when they are dealing with supply chains. Addressing supply chain issues is paramount. Demand forecasting enables businesses to reduce supply chain costs and achieve significant improvements in financial planning, capacity planning, profit margins, and risk assessment decisions.
Graph Analytics: Top 10 Use Cases & Tools
Analytics is generally used to gain insights from numeric data. However, graph analytics analyzes relationships between entities rather than numeric data. Using graph algorithms and relationships in graph databases, graph analytics solutions uncover insights in fields like social network analysis, fraud detection, supply chain, and search engine optimization.
7 AI Transformation Strategies
AI transformation is the next phase of digital transformation. Businesses are willing to invest in AI technologies to stay ahead of competitors. Digital transformation is a prerequisite for companies to initiate their AI transformation, as digital data is essential for AI training, and digital processes are typically required to deploy AI solutions.
QC Companies: Guide Based on 4 Ecosystem Maps
Quantum computing, which has wide-ranging applications in optimization, research and cryptography, is driven by these organizations: Quantum hardware is an emerging computing technology which relies on complex hardware. As in the early days of personal computing, there are companies specialized on hardware, software and end-to-end solutions.
Quantum Annealing: Practical Quantum Computing
Quantum annealing is a promising quantum technology for companies that have urgent optimization problems which take too long for traditional computers to solve. It can be used to solve optimization problems more effectively than traditional computers. However, it is still mostly used in academia and more R&D is required to build commercial quantum annealers.
Top Digital Transformation Frameworks
Digital transformation is an emerging trend, but some companies may not succeed in it. To achieve success, leveraging a digital transformation framework that serves as a roadmap for your organization can be beneficial.
22 AutoML Case Studies: Applications and Results
Though there is a lot of buzz around autoML, we haven’t found a good compilation of case studies. So we built our comprehensive list of automated machine learning case studies so you can see how autoML could be used in your function/industry.
Machine Learning Accuracy: True-False Positive/Negative
Selecting the right metric to evaluate your machine learning classification model is crucial for business success. While accuracy, precision, recall, and AUC-ROC are common measurements, each reveals different aspects of model performance. We’ve analyzed these metrics to help you choose the most appropriate one for your specific use case, ensuring your models deliver real value.
Python RPA: 8 Benefits for Developers
The intersection of robotic process automation (RPA) and Python can revolutionize the intelligent automation landscape. Even though RPA software bots are useful across a wide range of industries, between 30% and 50% of RPA projects fail.
RPA Developer: Everything You Need to Know
Although plenty of resources online about RPA skills and training exist, job seekers still struggle to understand where to start. We’ve compiled the most comprehensive resource on RPA developer skills, salary, and jobs to help job seekers explore where to start and what they need to do to excel as an RPA developer.
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