Feature
Research Report
Designing AI-First Systems for Customer Access
As AI systems integrate into business operations, the quality and relevance of their training data is critical. Large-scale, global datasets have driven AI progress but often miss market, industry and user-specific nuances.
Locally sourced AI training data — collected from specific regions, industries or operations — improves model accuracy, reduces bias and aids regulatory compliance.
Table of Contents
- The Problem With Mass AI Datasets
- Why Industry Data Changes Everything
- Proprietary AI Training Data as a Competitive Moat
- 4 Operational Hurdles for Proprietary AI Models
- Best Practices for Training AI on Proprietary Data
The Problem With Mass AI Datasets
Large publicly available datasets often lack contextual relevance, causing models to misinterpret user intent, struggle with local regulations or introduce biases affecting certain groups. This can result in inefficiencies and risks in regulated or customer-focused industries.
By contrast, locally sourced data:
- Enhances model accuracy
- Adapts more easily to local rules
- Reduces bias
Generic datasets lack the specificity needed to deliver insights for particular industries or markets. And AI output quality depends entirely on training data quality. Poor or irrelevant data weakens performance and can cause biased or flawed decisions with real-world consequences.
Locally sourced AI training data offers tailored models aligned to business needs. Investing in proprietary data pipelines can provide competitive advantages by tailoring AI to specific customers, employees and market conditions.
Related Article: Is Your Data Good Enough to Power AI Agents?
Why Industry Data Changes Everything
Many businesses still use generic AI models trained on massive internet-scraped datasets, which handle broad use cases but lack industry-specific knowledge for complex applications. These models may generate acceptable outputs but miss critical nuances in fields like law, medicine, finance or manufacturing.
Models trained on general internet data reflect global trends, not local regulations or market habits. For example, a multinational company's chatbot might fail to understand regional communication styles or product specifics, negatively affecting customer experience. Even advanced AI may misinterpret technical language without domain-specific training (e.g., confusing medical terms like "hypertension" and "hypotension," or missing machine fault distinctions in manufacturing).
A company selling herbs and spices is a good example of this: generic models recognize common spices (like Oregano and Thyme) but lack knowledge of rare ingredients (like fenugreek and ras el hanout) and uses. Proprietary data — like product catalogs, expert descriptions, common customer inquiries — can provide far more accurate AI responses.
Proprietary AI Training Data as a Competitive Moat
AI models perform best when trained on data reflecting their intended use. Proprietary datasets (like the herbs and spices dataset mentioned above) from customer interactions and industry sources produce precise, actionable outputs, unlike generic data which may include irrelevant answers and patterns.
Businesses leveraging unique datasets create AI models hard for competitors to replicate, strengthening market position via proprietary AI solutions tailored to their customers.
“Proprietary datasets are unique to an organization, which means they can provide unique insights and predictive power," said Stepan Solovev, CEO of SOAX. "This differentiates their AI capabilities from competitors that rely on generic data sources.”
Locally sourced data improves personalization by enabling AI to understand regional dialects, industry jargon and customer-specific needs. "Public datasets are often too broad and may not reflect the specific language, culture or market conditions related to your business..." noted Steve Fleurant, CEO of Clair Services. "Training an AI chatbot on local customer interactions, it can understand regional and industry-specific slang, meet specific needs and provide a personalized experience.”
Sourcing training data locally also enhances compliance with regional laws and controls sensitive information, reducing legal risks and boosting consumer trust — a boon for companies following data privacy regulations and AI governance frameworks that require strict control over data use.
A prime example of locally sourced AI training data in action is BloombergGPT, an AI model trained on financial news, regulations and industry data. With its specialized knowledge, it outperforms general AI in finance-related tasks like risk assessment and market analysis due to domain-specific training data.
4 Operational Hurdles for Proprietary AI Models
AI effectiveness depends on both data quality and volume. Small or biased datasets produce inaccurate or skewed results. Enterprises must implement data governance practices to clean, validate and refine their training data.
The first issue many organizations run into is disorganized data. Businesses think they have tons of useful data, said Ilia Badeev, head of data science at Trevolution Group, but when they try to use it, they realize half of it is incomplete, duplicated or just plain wrong. "Fixing that takes time. It takes a system that continuously cleans, updates and verifies information. It can't be a one-time effort."
Using proprietary data also raises privacy issues. Data privacy regulations and industry-specific regulations like HIPAA demand strict usage, storage and protection standards. To maintain compliance and protect privacy, businesses often must:
- Apply data anonymization
- Regulate user access roles
- Implement consent mechanisms
- Utilize lineage tracking
- Regularly schedule audits
Roman Eloshvili, VKTR contributor and founder of ComplyControl, added that organizations should also communicate why data is collected and how it will be used — but also only collect the data necessary for AI training.
Training AI on locally sourced data is only the first step. Integrating AI models into IT and analytics infrastructure adds another layer of complexity. Companies must ensure compatibility between AI systems, databases and software, which may require investments in scalable architecture, data pipelines and APIs to avoid bottlenecks.
Building proprietary-data-based AI also demands resources for:
- Data collection
- Storage
- Labeling
- Preprocessing
- Computing power
- Skilled personnel (data scientists, engineers, compliance specialists)
Though costly, this investment often yields competitive advantages.
Related Article: Why Bad Data Is Blocking AI Success — and How to Fix It
Best Practices for Training AI on Proprietary Data
Launch a Controlled Pilot Before Scaling
Using large untested datasets risks unknown biases and inefficiencies. Starting with a pilot project using a limited, high-quality dataset helps refine training methods and identify issues before scaling.
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“Open-source AI models can only be general-purpose and will work great on general-purpose tasks," said Dr. Steve Anning, head of AI at Friday Initiatives. "But to realize the benefits, users don’t want to spend their time localizing general-purpose outputs. The productivity gain of AI models will only be gained from localized outputs.” Incremental approaches help enterprises fine-tune models, validate results and ensure compliance before broad application.
Establish Data Governance From Day One
Before feeding data into AI models, Fergal Glynn, CMO at Mindgard, advised stripping personal identifiers like names or locations.
"It helps maintain the required privacy," he explained. "Plus, audit regularly to catch potential risks at the early stages to figure out hidden biases in training data.” Embedding ethical AI practices helps reduce compliance risks and ensures fairness and security.
Combine Proprietary and Public Data Strategically
While locally sourced data enhances AI accuracy and relevance, it may have limited scope or volume. Adopting hybrid models can work around this limitation, combining proprietary information with publicly available or third-party datasets maintains specificity and adds broader contextual knowledge.
Collaborate With Cross-Functional AI Expertise
Effective use of localized data requires expertise in data science, machine learning (ML) and industry applications. Businesses should collaborate with internal AI teams, third-party vendors, or academic partners — like universities or researchers — to refine training and ensure responsible AI practices.