Should you own your
Generative AI model?
3 considerations to guide your decision
Generative AI is the hottest topic in technology — and with goodreason. Generative AI has the potential to radically transform the workof enterprises and governments, but their needs are vastly different fromthe consumer use cases that have been gaining headlines.
Most importantly, before embarking on this AI journey, organizations
have a critical decision to make: whether to use a model built andowned by a vendor, or a model that is owned and controlled by thecustomer organization. Based on decades of cumulative experience,
we believe this decision should be informed by three fundamental
considerations.

1

What are the laws, regulations, andexpectations of your industry and customers?
In many industries, transparency and explainability of model training isalready required. Both GDPR and FINRA Rule 3110 (Supervision) requiretransparency and explainability in the decision-making process, whether
manual or automated.
However, in all industries, being able to document the model’s training
lineage and model weights is essential to retain customer trust, facilitateaudits, and help identify potential biases or ethical concerns. As anexample, model governance and lack of explainability were cited as thehighest concerns among 84 percent of financial executives responding toa LendIt annual survey on AI.

Which of the following concerns does your organization haveabout adopting AI for credit risk?

46%

Model governance/
regulator
requirements

38%Black-box models are
hard to interpret andexplain

29%

Implementationdifficulty

26%

23%

OtherNo Concerns

Source: Lendit and Brighterion, “AI perspectives: Credit risk and lending,” 2022.
*Responses do not total 100% as more than one answer may apply

1

AI Model Ownership: 3 Critical Considerations

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2How accurate does your model
have to be?
Achieving the highest levels of accuracy requires ‘finetuning’—that is, additional training specific to the industry
or organization. For instance, the word ‘quarter’ means
something very different in banking than it does in sports.
Fine-tuning also reduces the chance of dangerous“hallucinations” (when an AI response sounds confidently
correct but is not factual)—less important in recreational
AI tools, but potentially catastrophic in those focused onenterprise tasks.
Since shared models are trained on massive public
datasets, a single model can solve dozens of consumer
tasks with strong general accuracy but is far less accurate
for domain-specific or company-specific tasks.

However, almost by definition, the most important data
for fine tuning is also the most valuable and sensitive—
customer data, product design information, competitive
intelligence, financial or patient data, personnel, supplier,or legal material.
Passing this data to a shared model means that the resultant
model would not be owned by the originating organization,and potentially exposes an organization to concerning
security issues and privacy breaches.

The Power of Domain-Specific Training
Bloomberg developed BloombergGPT—a model
tailored for the financial industry that focuses on
financial natural language processing tasks, such assentiment analysis. This delivered benchmarking scoresfor financial tasks 27-33 points ahead of models
trained with general-purpose data alone.11 – Bloomberg Professional Services, March 30, 2023, “Introducing BloombergGPT, Bloomberg’s 50-billion
parameter large language model, purpose-built from scratch for finance,” https://www.bloomberg.com/
company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/

2AI Model Ownership: 3 Critical Considerations

Copyright © 2023 SambaNova Systems, Inc. All rights reserved.

3Should you use a toolor build an asset?

In the 20th century, organizations were valued
primarily on their capital and personnel. Recently,
we have understood more clearly the value ofintangible assets like brand, reputation, and market
position. Looking toward the future, how organizations
retain, understand, and analyze their data will be adifferentiator critical to market value.
Specific AI tools (point solutions) may increase
efficiency of specific tasks such as writing or data
retrieval, but will not be able to fundamentally shift theBuilding Asset Value
A manufacturing company that builds complex
systems relies on a highly skilled but aging
workforce. A major concern for leadership is theincreasing difficulty of training and upskilling new

business model underpinning the organizations thatteam members.

use them. The value created will mainly benefit theTraining an LLM with the company’s own private
data would help to transfer this knowledge, retain
top talent and expertise, and support a variety ofbusiness processes such as supply chain optimization, system design, manufacturing optimizationand field service. Most importantly, the company
will be able to leverage Generative AI to acceler-vendors of the shared model, not its users.
By contrast, organizations that invest in core AI assets—using foundation models and dedicated systems tobuild capability and value—will experience myriad
benefits across the organization, and retain value
for themselves, their customers, and their partners.
Generative AI offers a strategic opportunity for theenterprise to build core IP with large language models(LLMs) that have been adapted with the company’s
data, unlocking long-term competitive business value
across a wide range of workflows.

ate onboarding and upskilling of new employees.
The resulting LLM is an IP asset that grows in value,
intelligence, and relevance over time—increasingorganizational capability, delivering greater value
across multiple domains, and increasing competitive advantage and differentiation for the organization, its customers, stakeholders, and investors.

Conclusion
Deploying Generative AI requires careful considerationof model development and ownership. Understanding
and owning your AI model is critical to transparency,
explainability, customer trust, and meeting laws andregulations.
Looking forward, organizations may be defined by how
well they adopt and utilize Generative AI—and building
AI as an asset will create substantial and increasing ROI
over time. To reap maximum value, these models should
be developed as critical business assets—utilizing stateof the art technology and foundation models, while also
being fine-tuned, refined, deployed, and owned by
the organization.
Learn more at SambaNova.ai

About SambaNova Systems
Customers turn to SambaNova to quickly deploy
state-of-the-art AI capabilities to meet thedemands of the AI-enabled world. Our purposebuilt enterprise-scale AI platform is the technology
backbone for the next generation of AI computing.
We enable customers to unlock the valuablebusiness insights trapped in their data. Our flagshipoffering, SambaNova Suite, overcomes thelimitations of legacy technology to power the large
complex foundation models that enable customers
to discover new services and revenue streams, andboost operational efficiency.

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AI Model Ownership: 3 Critical Considerations

Copyright © 2023 SambaNova Systems, Inc. All rights reserved.