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
RPA vs. API: What Are Their Differences?
The abundance of automation technologies in the market today is one of the main catalysts encouraging 89% of companies to adopt a digital-first strategy.
Top 7 Speech Recognition Challenges & Solutions
Speech recognition technology has significantly advanced in areas like generative AI, voice biometrics, customer service, and smart home devices. Despite rapid adoption, implementing this technology still poses various challenges. Here, we outline the top 7 challenges and best practices for overcoming them: 1.
Sentiment Analysis Stock Market: Sources and Challenges
Making accurate predictions regarding stock prices is challenging as the stock prices move depending on factors like interest rates, corporate governance, investors’ risk aversion, market trends, and firm investments. However, understanding the market psychology with sentiment analysis stock market might give a clue about future stock price movements.
Top 10 AI in Fashion Use Cases & Examples
Creative bottlenecks, inefficient supply chains, and rising consumer expectations are pushing fashion brands to seek smarter solutions. According to McKinsey, generative AI can offer a path forward by adding up to $275 billion to operating profits in the fashion, apparel, and luxury sectors until 2028.
5 Unsuitable Processes for RPA
RPA adoption is rapidly increasing in businesses from different sectors. From banking to automotive, RPA’s potential to automate error-prone, repetitive, and time-consuming tasks can save companies time, money, and human resources that can be spent better elsewhere.
Model Retraining: Why & How to Retrain ML Models?
Only ~40% ML algorithms are deployed beyond the pilot stage. Such low rate of adoption can be explained with the lack of adaptation to new trends and developments such as economic circumstances, customer habits and unexpected disasters like Covid-19.
Robotic Process Automation (RPA) in Aviation
The aviation sector, as a whole, can gain from the adoption of RPA tools in areas including airport administration, ticket sales, and aircraft navigation. The aviation industry is ripe for automation thanks to the never-ending flow of data that could be turned into actionable insight.
Compare Top 5 MLOps vs DataOps Differences
Inspired by DevOps practices, MLOps and DataOps have emerged as critical methodologies for ensuring seamless machine learning and database operations. While both share roots in automation and operational efficiency, the debate around MLOps vs DataOps highlights their distinct roles in IT workflows.
20 Test Automation Case Studies Demonstrating Business Impact
QA teams struggle with slow, manual testing, which often results in higher costs, longer development cycles, and customer dissatisfaction. Transitioning to automated QA testing is the top priority in the software testing environment. To help decision-makers assess the impact of test automation, we analyze 20 case studies highlighting real-world transformations.
Top 7 Sentiment Analysis Challenges
Words are the most powerful tools to express our thoughts, opinions, intentions, desires, or preferences. However, the complexity of human languages constitutes a challenge for AI methods that work with natural languages, such as sentiment analysis. Explore sentiment analysis challenges and ways to improve sentiment analysis accuracy: Top 7 challenges in sentiment analysis 1.
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