
Cem Dilmegani
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 12 Patch Management Software with Pricing & Features
Businesses increasingly depend on various technologies, from specialized applications to complex networks. This technological dependence increases security vulnerabilities, opening doors for potential exploitation. See the best patch management solutions to protect your IT infrastructure: *Based on the total number of reviews and average ratings (on a 5-point scale) from leading software review platforms.
The Industrial AI Agent Landscape: 30+ Platforms to Watch
Industrial AI agents address the limitations of siloed data by autonomously integrating and deriving actionable insights from IoT, controls systems (e.g. SCADA), and connected assets.
Agentic AI Architecture for Industrial Systems
Agentic AI allows natural language interaction with industrial systems, enabling users to query data and receive actionable insights. We will outline a reference architecture designed for industrial environments, describe how task-specific agents and tools can be orchestrated. We will also explore current state of natural language interfaces (NLIs) in industrial systems.
12 Reasons AI Agents Still Aren't Ready
For all the bold promises from tech CEOs about AI agents “joining the workforce” and driving “multi-trillion-dollar opportunities,” the reality is far less inspiring. What we currently have are not autonomous agents, but glorified chatbots dressed in fancy packaging; mostly mimicking scripts. Give them the same task twice, and you’ll often get wildly different results.
Multi-GPU Benchmark: B200 vs H200 vs H100 vs MI300X
We benchmarked NVIDIA’s B200, H200, H100, and AMD’s MI300X to measure how well they scale for Large Language Model (LLM) inference. Using the vLLM framework with the meta-llama/Llama-3.1-8B-Instruct model, we ran tests on 1, 2, 4, and 8 GPUs. We analyzed throughput and scaling efficiency to show how each GPU architecture manages parallelized, compute-intensive workloads.
AI Identities: The Role of Agentic Systems in Governance
Agentic AI systems are rapidly emerging in enterprise environments. To govern them safely, each agent needs to be recognized as a first-class identity with its own credentials, permissions, and audit trail.
Best 17 AgentOps Tools: AgentNeo, Langfuse & more
AgentOps is emerging as a key discipline in IT operations, focusing on the deployment, management, and optimization of autonomous agents. Today, I will introduce AgentOps and the leading tools shaping it as a new discipline within IT operations.
Agents.md: The README for Your AI Coding Agents
This guide introduces AGENTS.md, an open specification changing how AI agents interact with software projects. Unlike traditional README.md files, which are written for humans and often leave gaps, AGENTS.md provides clear, machine-readable instructions that both people and AI agents can follow with precision.
Best AI Agents for Workflow Automation
We researched the leading AI agent platforms for workflow automation, analyzing their documentation, feature sets, integration capabilities, and publicly available customer implementations. There are 4 ways to implement AI agents for workflow automation. Top 10 AI Agents for Workflow Automation *Starting price per month ** Reviews are based on Capterra and G2.
AI Agents for Competitive Intelligence: Tools and Applications
The competitive intelligence landscape is shifting rapidly. MarketsandMarkets projects the global AI agent market will grow from USD 5.1 billion in 2024 to USD 47.1 billion by 2030, at a CAGR of 44.8%. Traditional methods, quarterly reports, and manual research are being replaced by AI agents that continuously track competitors, delivering insights in real-time.
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