Cem Dilmegani
Cem's work at AIMultiple has been cited by leading global publications including Business Insider, Forbes, Morning Brew, and Washington Post, global firms like Deloitte and 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 enterprise AI and software.Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and the 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, 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.
Media, conference & other event presentations
- Hosted on American Variety Radio, answering Court Lewis' questions on AGI on April 25th, 2026.
- Answered Korea24's questions on job loss due to AI on November 5th, 2025. Recording available on: Korea24
- 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: 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 7 Open Source RPA Tools
Open source RPA (Robotic Process Automation) can still play an important role in the future of automation. Its main advantages are transparency, flexibility, and the absence of licensing costs. Below, we list six open-source RPA tool options and link to their source code. We compare seven open-source RPA tools on license, language, platform, and maintenance…
Generative AI Ethics: How to Manage Them
Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, filed a lawsuit against Perplexity, alleging that the company illegally and knowingly copied Britannica’s human-verified content and misused its trademarks without permission. Explore what generative AI ethics concerns are and best practices for managing them. AI models learn patterns from…
Recommendation Systems: Applications and Examples
We examined the main types of recommendation systems, key concepts, and real-world applications, and benchmarked LightFM, Cornac BPR, and TensorFlow Recommenders using AUC, Precision@10, and Recall@10. These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques. Additionally, these libraries implement machine learning models to analyze…
Top 13 GAN Use Cases
While GANs pioneered many early generative AI applications, particularly in image synthesis and style transfer, most consumer-facing generative AI tools today rely on diffusion-based architectures or related approaches such as flow matching and diffusion transformers (DiT). However, GANs remain important in specific domains, such as super-resolution, face restoration, the generation of synthetic tabular or healthcare…
Top 10 Cloud Workload Automation software & 7 Use cases
Although 71% of companies run public cloud data warehouses, integrating fragmented multi-cloud environments while controlling cloud costs poses a dual challenge. However, cloud workload automation can orchestrate the data pipelines feeding these platforms to optimize both connectivity and resource efficiency. Explore the differences between cloud and hybrid workload automation, the top tools, and use cases…
AI Fail: 10 Root Causes & Real-life Examples
Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions. By identifying and addressing the underlying issues, companies can mitigate the risks associated with AI and ensure that it is used safely…
AI Ethics Dilemmas with Real Life Examples
Though artificial intelligence is changing how businesses work, there are concerns about how it may influence our lives. This is both an academic/societal problem and a reputational risk for companies; no company wants to be undermined by data or AI ethics scandals that damage its reputation. Explore insights into ethical issues that arise with the…
The Future of Large Language Models
See the future of large language models by comparing approaches, such as self-training, fact-checking, and sparse expertise, that could address LLM limitations. LLMs access external sources during conversations instead of relying on training data. The model queries external databases, retrieves current information, and provides citations. Limitation: Still makes errors. Citations don’t guarantee accuracy; models sometimes…
Enterprise Generative AI: 11 Use Cases & Best Practices
Generative AI (GenAI) presents novel opportunities for enterprises compared to middle-market companies or startups, including: However, generative AI brings challenges unique to large organizations. For example: Explore our practical enterprise AI use cases to learn how large companies can build, deploy, and govern their own generative AI models effectively. The web is full of B2C…
8 Network Monitoring Use Cases with Real-Life Examples
Network monitoring is one of those things that IT teams notice when it is missing. When it works well, problems get caught before users know anything is wrong. When it is absent, a minor connectivity issue can quietly escalate into a major outage. See our examples below to show how organizations are actually putting these…
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