AI Impact in EMEAAdvice from Leaders in Banking,Insurance, Life Sciencesand More

Emerj Artificial IntelligenceIntroduction
Different cultures, industries and markets have varied ways of thinking about AI.

For these reasons, a large part of the focus of the SambaNova sponsored series within  Emerj's AI in Business podcast consists of looking at unique challenges, use cases and adoption
trends seen by leaders from across a range of industries and in the EMEA region in particular.

A great example of one such use case exists in the prevalence of large language model (LLM)
applications across what are called "language-heavy industries" — banking, finance andhealthcare, to name a few.

In the following breakdown of conversations with industry leaders, this white paper will highlight
impactful AI use cases discussed in the respective podcast episodes while closely examining theunique business dynamics taking place beneath AI adoption strategies.

Each episode featured in this podcast series focuses on where AI is impacting businesses andindustries every day. To offer a diverse cross-section of perspectives for the following rundown,
our featured expert guests work for enterprises with operations across the EMEA region.

Yet what the speakers primarily offer are actionable insights for enterprise professionals on thefollowing AI adoption strategies and how they work best in their industry and their corner of theglobal economy:

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AI Impact in EMEA

Expert Insights Across Industries
Episode 1

What Successful AI Adopters Are Doing Differently Marshall ChoySenior Vice President of Product, SambaNova

This series kicks off with SambaNova’s Senior Vice President of Product Marshall Choy,with a global perspective distinguishing between organizations that become successful  AI adopters and those that don't.

When it comes to being a successful adopter of AI, it all starts with a coordinated,company-wide approach. Small groups of what Marshall calls AI "flag wavers" within  
a company aren't going to lead to successful, routine AI adoption. What's needed instead
are collaboration, organization, and, most importantly, education about what AI is andwhat it can do to drive business value - especially at the executive level.

Marshall also discusses his perspective on AI trends and evolution and where he sees  
the implementation of large language models (LLMs).

The commonality among industries leveraging LLMs is that they are usually speech, text,
and/or document-heavy. In particular, banking and financial services are traditionally
thought of as "language-heavy Industries," in Choy's words, where professionals arelooking at AI use cases in both the front end (call centers) and the back end (risk andcompliance) that streamline workflows.

According to Choy, these changes in workflows are leading to shifts in thinking acrossthese "language-heavy" industries toward single large language models, or foundation
models, that deliver organization-wide transformation through AI.

In a recurring theme between many of our conversations featured in this white paper,
Choy emphasizes that such transformation is only possible with long-term strategic
thinking from the early phases of the project.

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AI Impact in EMEA Expert Insights Across Industries
Episode 2

Guiding the Patient’s Treatment Journey with AI Kostas Papagiannopoulos, M.D.

Thoracic Surgeon, Leeds Teaching Hospitals; Honorary Senior Lecturer, Leeds University

Kostas Papagiannopoulos, M.D. of Leeds Teaching Hospital, elaborates on a promising use
case for AI in the medical arena: treatment management.

He discusses how proper data gathering – in cohort with AI – can be used to overcome
subjective and widely variant patient data. Dr. Papagiannopoulous also elaborates on  
the importance of coaxing answers that can be translated into data inputs, such as
training data for machine learning.

Getting answers from data is about eliminating bias and potential errors, doing  a thorough analysis, and guiding people into giving answers that translate into data.  
He gives an example case of patients receiving care for breast-related conditions;  by simplifying the questionnaire for people of different socioeconomic and educational
backgrounds, he was able to obtain the objective data to input into the AI model his teamneeded.

By first taking in the right data at the right points and accounting for human biases andsubjectivity, an AI solution that guides something as complex and dynamic as a treatment
regimen becomes a very real possibility.
Episode 3

Creating a Culture of Innovation for AI Christophe Makni

Head of Business Operations, Migros Bank

hristophe Makni of Migros Bank discusses how financial organizations can greatly
increase their odds of successful AI implementation by encouraging leadership buy-in andeducation.

CAs someone that has worked in evaluating and implementing AI in the at-times-stodgy
world of the banking industry for many years, Christophe gives cogent insights on what
successful financial enterprises are doing – and it all starts with leadership.

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AI Impact in EMEA Expert Insights Across Industries

Episode 4

The Importance and Impact of NLP in Insurance Gero Gunkel

COO, Zurich Customer Active Management (ZCAM), Zurich Insurance

Gero Gunkel of Zurich Insurance talks about the great and wide impact of NLP in theinsurance space via a couple of use cases.

NLP is used in process automation to read, process, and analyze text documents. Anemerging use case for NLP is serving as a sort of "digital analyst," using AI to scan thedocument and actually write up a document summary.

Gero underscores the importance of the latter use case in depth, as such functionality
requires an intricate output produced by advanced, unsupervised AI in the form of agenerative model. In these contexts, Gero also talks about the accessibility of NLP in
insurance and the future of NLP applications in this space.

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Episode 5

Using Knowledge Graphs in Target Discovery Krishna Bulusu

Director of Early Computational Oncology, AstraZeneca

A biologist turned director of a department at one of the largest drug discovery
companies, Krishna Bulusu of AstraZeneca provides fascinating insights on how
knowledge graphs are "waking up" latent data to form valuable insights.

Knowledge graphs are very useful in every stage of the drug discovery journey. This
includes identifying and validating disease-relevant proteins at the first step: a process
called target discovery. Knowledge graphs are particularly useful at this juncture because
they provide a wonderful common platform for bringing together divergent data types andunderstanding their relationship.

A tool that is capable of potentially uncovering relationships in data where none previously
existed or is hard to find becomes tremendously useful in the drug discovery process,given the enormous quality and complexity of data required.

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