
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
Top 20 Blockchain in Supply Chain Case Studies
Blockchain technology is gaining popularity as a solution to long-standing problems in supply chain management. By offering a decentralized and tamper-proof method for recording transactions, blockchain can address issues related to traceability, transparency, and trust among supply chain partners.
Top 15 HR Automation Case Study in Different Sectors
Disruptive technologies such as artificial intelligence (AI) and robotic process automation (RPA) can dramatically reduce the time spent on repetitive and non-strategic HR tasks. However, according to PwC’s HR technology survey, one of the top reasons preventing HR leaders from adopting these technologies is the lack of compelling use cases.
Top 15 Strategies for AI Improvement & Examples
AI systems achieved remarkable milestones (e.g., exceeding human performance in image and speech recognition); however, AI progress is slowing down as scaling yields fewer benefits. Additionally, AI and ML models degrade over time unless they are regularly updated or retrained.This makes it critical to utilize all levers to improve AI models continually.
Top 4 Facial Recognition Data Collection Methods
Despite the controversies surrounding this technology, the facial recognition systems (FRS) market continues to grow. Facial recognition applications are everywhere, from helping improve mental disorder diagnoses to finding fugitives. Developing and improving these systems requires facial data, which sometimes can be challenging to obtain due to security and privacy-related concerns of people.
Automotive APIs: 7 Use Cases, Databases & Solutions
McKinsey claims that the overall market for automotive software and related electronic components will grow by 7% CAGR from 2020 to 2030.. In 2010, on average a car included 10 million lines of software code, while it ran on 100 million lines of code in 2018.
Top 30 Workload Automation Case Studies
In this article, 30 workload automation case studies are compiled.
30 Intelligent Automation Case Studies / Success Stories
One of the most effective ways to understand how new technology can benefit your organization is through reading case studies of successful implementations. For this purpose, we have aggregated case studies about intelligent automation from numerous sources. You can filter or sort them by industry (e.g.
Hospitality Chatbots: Top Use Cases and Case Studies
According to PwC, the hospitality industry can charge the second-highest premium for excellent customer experience, with a 14% margin. Fast and easy-to-engage digital channels are part of the excellent customer experience. Engaging with many customers 7/24 via live agents is not an efficient strategy for the hotels.
AI Text Generation: Top 16 Use Cases & 4 Case Studies
Generative AI, a subset of artificial intelligence, allows for creating new content, such as text, code, images, designs, and videos, by learning from and building on existing data. Explore how generative AI can be used to generate content in the form of text via 4 use cases and 2 case studies of AI text generation..
Top 6 Use Cases of Generative AI in Banking
Gartner identified generative AI as a top technology trend for the banking and investment industry in recent years. Generative AI’s contributions to data privacy, fraud detection, and risk management can be critical to financial services companies. Key use cases include fraud detection, customer service automation, credit risk assessment, and document processing.
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