Kjell Carlsson, Ph.D. | Source | Head of AI Strategy at Domino Data Lab
Kjell Carlsson, Ph.D.
Dr. Carlsson is the Head of AI Strategy and Evangelism at Domino Data Lab. Kjell advises enterprises on how to drive business outcomes with artificial intelligence (AI) and data science. He does consulting, workshops, keynote speeches and research on topics ranging from augmented intelligence, automated machine learning, computer vision, advanced analytics and machine learning platforms, MLOps, AI APIs, conversation intelligence, AI-enabled business intelligence, AI in healthcare, best practice for scaling data science and the future of AI/GenAI. Kjell has a unique background spanning strategy and AI including experience leading a product organization for an NLU platform, covering AI as an industry analyst, leading a data science team building AI applications, management consulting experience in the tech and financial sectors, and a Business Economics PhD focused on strategy and quantitative analysis.
He is also the host of Domino's Data Science Leaders Podcast - a podcast for data science teams tackling the world's most important challenges and pushing the limits of what AI can do at the world’s most impactful companies. More here: https://domino.ai/data-science-leaders-podcast.
-
Domino Data Lab
Head of AI Strategy
-
AI Strategy Expert Highlights Gaps in Data Environments for AI Success
Dr. Carlsson emphasizes that many organizations lack integrated capabilities for AI lifecycle management. He advises IT leaders to build platforms that orchestrate and govern AI components. Success should be measured by business and operational metrics, not just data capabilities. "Data isn’t everything when it comes to AI, it is just the beginning." -
Bridging the Language Gap in Generative AI for Enterprises
Dr. Carlsson suggests enterprises adopt a multi-model approach, leveraging diverse AI models for language strengths. He notes, "Fine-tuning models with language-specific data can address gaps." Startups can create specialized LLMs for less common languages, offering cost-effective, precise solutions. The future lies in focused models tailored to enterprise needs, enhancing accuracy and scalability in a multilingual market. -
2025 Workforce Must Master GenAI Literacy, Says AI Expert
Dr. Carlsson emphasizes "GenAI literacy" as crucial for 2025, focusing on using Generative AI to boost productivity. This includes understanding GenAI tools, creating content, and integrating outputs. However, traditional data literacy remains essential. Companies must invest in AI talent to develop GenAI solutions, as GenAI literacy alone isn't enough for transformative capabilities.
-
In my own conversations with with data science leaders, they’re saying in theory, these very ultra-large language models are great for prototyping, and end users want them to write their emails, but in terms of what we’re actually going to operationalize, we’re going to look at smaller LLMs and do additional fine-tuning on top of that, and potentially some human-in-the-loop reinforcement learning to get the level of accuracy we need.
-
Data quality has been extremely important in the realm of data science, machine learning [and] AI since time immemorial. But now more people are aware of it and more people are discussing it in the context of generative AI. If your data quality initiatives are often a silo, just trying to do their own thing and work against historic goals, and are very disconnected from what that outcome is, the likelihood that you're going to get to something successful is much lower.
-
With users across the organization clamoring to leverage generative AI capabilities as part of their daily activities, priority No. 1 for CIOs, CTOs, and CDOs is to enable secure, scalable access to a growing range of generative AI models and enable data science teams to develop and operationalize fine-tuned LLMs tailored for the organization’s data and use cases.
