The AI Opportunity Hiding Underneath Enterprise Data
- 10Pearls Editorial Team
- 10 min read
Summary
Traditional predictive systems often depend on manually engineered features that can limit how much context they extract from complex enterprise data. Transformer-based predictive models are capable of learning from richer sequences and relationships, creating new opportunities to improve risk detection, forecasting, and decision-making.
Enterprises are collecting more data than ever and investing heavily in AI, yet predictive systems still rely on human-defined signals. While these models can uncover patterns on their own, their analysis of the problem is often shaped by the signals and features humans choose. That leaves a larger question: if enterprises already have the data, where is the next major AI opportunity?
In his latest Forbes Technology Council article, Imran Aftab captures the opportunity simply, “What lies beneath the AI iceberg is not unused data, but unused context.”
Why more complex data requires a different approach
In the case of traditional predictive systems, data scientists will identify key variables, turn raw data into features, and train machine learning models on those choices. As enterprise data becomes more complex and interconnected, relying on manual feature engineering becomes harder to maintain. As behaviours shift and relationships multiply, teams must continually revisit features, thresholds, and assumptions to keep models aligned.
The predictive potential of transformers
Generative AI has largely entered organisations in the UK and Europe through capabilities like content generation, copilots, search, and workflow automation. However, the transformer architectures that power these tools can do much more than generate content.
Transformer-based predictive modelling offers a different approach, providing a richer picture of how a system behaves by learning from complex sequences and context, rather than relying on a predefined set of features. This enables organisations to make better use of the complexity their data already contains.
In practice, that richer context and ability to identify subtle relationships across data can translate directly into tangible business value, with applications in many different industries in the UK and Europe, such as sharper anomaly detection, earlier risk identification, better equipment forecasting, and deeper patient insights.
For enterprise leaders, this represents an important shift in how they think about predictive AI.
The question is moving from how much data an organisation can collect to how effectively its models can understand the relationships within it.
Read the full article.
The question is moving from how much data an organization can collect to how effectively its models can understand the relationships within it.
Read the full article.
Insights
AI/ML
LLM in Healthcare: Use Cases, Risks & Deployment
A guide to LLM in healthcare, covering use cases, model selection, risks, HIPAA compliance, and what organizations need for deployment.
AI/ML
AI Governance Checklist: 9 Controls to Enforce
AI now writes a large share of production code, and it does not just change how software is built, it...
AI/ML
How AI Is Reshaping Healthcare Cybersecurity Threats
AI is helping both sides of healthcare cybersecurity. Attackers use it to speed up reconnaissance, write convincing phishing and deploy...
AI/ML
AI for Credit Unions: Use Cases, Agents and ROI
A practical guide to AI ROI at institutions under $1B, covering high-payback use cases, real integration costs, NCUA expectations, and...
AI/ML
AML Transaction Monitoring and KYC: From Manual to AI
Traditional KYC and AML processes are struggling to keep up with modern financial crime. Learn how automation and AI are...
AI/ML
How Enterprises Can Scale AI Without Losing Accountability
AI now writes a large share of production code, and it does not just change how software is built, it...
AI/ML
Build vs Buy AI Talent: A CTO’s Guide
Hiring an AI team is only one way to build capability. This guide helps CTOs weigh the trade-offs between hiring,...
AI/ML
The AI Opportunity in Enterprise Data
Enterprises have more data than ever, but traditional predictive approaches may not be suitable to capture the context within it....
AI/ML
AI Pilot Failure: Why Enterprise AI Pilots Don’t Scale
AI pilot failure often stems from execution gaps. Discover why enterprise AI pilots struggle to scale, and what it takes...
AI/ML
Understanding the uses of AI in energy sector
This blog explores how AI is transforming the energy sector, the opportunities it offers in various energy domains, and what...