Scaling AI for impact: overcoming challenges to seize the AI opportunity
Article republished with permission from techUK.
Read Blue Hat CEO Tim Palmer’s guest blog on techUK’s AI Adoption Hub for their #SeizingTheAIOpportunity campaign week 2025.
Learn how a pragmatic, phased approach to anomaly detection, using artificial intelligence, benefits from the strengths of unsupervised and supervised learning.
In an era where data-driven decisions are pivotal and we’re swimming in high quality data - how do we find value in all this data?
A pragmatic, phased approach to anomaly detection – whether detecting fraud, system failures or unusual user behaviours - offers a compelling entry point that delivers immediate value while building foundations for more sophisticated uses of your data.
The colossal volume of data flowing through modern enterprises has rendered traditional manual monitoring approaches obsolete. Financial services companies process millions of transactions daily, or SaaS platforms generate terabytes of user interaction data. Hidden within these vast datasets are the irregular patterns that may signal threats, opportunities or system malfunctions – but finding them cannot be achieved by writing rules or inspecting the data. Enter Machine Learning.
About techUK’s AI Adoption Hub: 2025 is the year AI becomes an integral part of everyday life, as organisations across industries prioritise embedding AI into their core strategies and operations. To help promote greater levels of responsible AI adoption, techUK will continue to work alongside its members and key stakeholders across the AI ecosystem, to demonstrate the significant benefits of this technology for both the economy and society. www.techuk.org
Adopting a pragmatic, phased approach
The most successful implementations follow a progressive methodology that balances exploratory power with production-grade precision:
Phase 1: Unsupervised learning
Unsupervised learning algorithms provide an ideal starting point. These models excel at identifying patterns in raw, unlabelled data, surfacing outliers that deviate from normal behaviour without requiring predefined classifications.
This approach offers several advantages for organisations in early adoption stages:
It minimises the initial investment in data labelling and annotation
It surfaces anomalies that human analysts might miss, particularly in high-dimensional data
It provides valuable insights into the nature and distribution of unusual patterns without bias
Phase 2: Analysis & semi-supervised learning
The outputs from unsupervised models create the foundation for the next phase. By presenting detected anomalies to human experts or semi-automated classification tools, organisations can efficiently create labelled datasets that capture domain knowledge and business context.
This curation process serves multiple purposes:
It filters false positives and prioritises anomalies by business impact
It translates abstract statistical outliers into meaningful business categories
It creates the training foundation for more precise supervised models
Phase 3: Supervised learning
With a curated dataset in hand, supervised learning models can be trained to classify anomalies with greater precision and contextual understanding. These models deliver the speed, accuracy and explainability required for production systems.
The benefits at this stage include:
Real-time classification of anomalies with business-relevant labels
Reduced false positives through targeted training on edge cases
Clear detection logic that can be explained to stakeholders and regulators
Continuous improvement through feedback loops
Overcoming implementation challenges
While a phased approach can substantially de-risk AI adoption, it’s worth being aware of some common challenges:
Data quality and accessibility: Fragmented data architectures and legacy systems often impede the creation of unified datasets. Successful organisations prioritise data engineering as a foundation before advanced analytics.
Skills gaps: The shortage of AI talent remains acute across the UK. Progressive organisations are addressing this through combinations of upskilling existing technical staff, strategic hiring, and partnering with specialised consultancies.
Integration with existing workflows: Detection without action creates limited value. Leading implementations ensure anomaly detection outputs seamlessly integrate with operational systems and human workflows.
Governance and explainability: Particularly in regulated industries, organisations must balance detection performance with model transparency. The phased approach naturally builds explainability into the process as human experts validate and categorise initial findings.
Moving forward: Beyond detection to prediction
For organisations that successfully implement this phased approach, anomaly detection becomes not just a security tool but a foundation for predictive capabilities. The patterns identified through anomaly detection often reveal early indicators of emerging trends, customer behaviours, or system issues before they become critical.
As businesses continue navigating digital transformation, this pragmatic path to AI adoption offers both immediate operational benefits and long-term strategic advantages - turning the theoretical promise of AI into practical business impact.
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