Using AI to Build Durable High Value Added Data Services

Businesses which want to deploy AI to build new value added data services (VADS) need to start by identifying which data will be of most value to which stakeholders. You can start with first principles by surveying your customers (and/or relevant target stakeholders) to find out what types of data they are interested in and, ideally, what types of insight they would be prepared to pay a premium for. The results of this research will support the writing of a strong business case for your AI-generated VADS. 

Selecting the right service 

At the VADS shortlisting stage, it is important to consider three questions: 

1. Data asymmetry: Is it possible to access unstructured/siloed data that holds insights which your competitors or clients currently cannot access? 
2. Friction in Decision Making: Is there a high-value process which is hampered by demanding lots of staff time to read, extract or synthesise information today? 
3. Willingness to Adopt/Pay: Is the end result painful enough that users will either pay a premium for the solution, or heavily increase their platform usage if we solve it? 

If you are intent on rapid ‘Return on AI investment’, it is important to look out for the following characteristics: high transaction volumes, heavy reliance on manual data synthesis, structured or semi-structured data availability, and a high cost associated with human error. The best early use cases for AI that GaiaLens has seen are those where the AI can act as a high-speed ‘first pass’ analyst, reducing time-to-insight massively. 

Reporting, compliance and customer experience use case hotspots  

The type of use cases most likely to be worthy of building a successful VADS for include reporting, compliance and customer experience. This is so because, in all these cases, they demand synthesisation of huge amounts of unstructured text and strict business logic.  

Sectors drowning in complex, unstructured documents see the highest immediate value. For example, in financial services we build AI systems for parsing ESG reports, earnings calls and regulatory filings for investment signals. In healthcare, common use cases include correlating diagnostics data and amalgamating or summarising patient records.  

Build a wall around your data 

When shortlisting which value added data services to prioritise for development, it is worth asking the following questions: 

1. Is this data exclusive to your business, or is it easily ‘scrape-able’ by competitors? 
2. Does the data decay or age rapidly? 
3. Can we legally and ethically use this data for the planned commercial purpose? 
4. Is the data clean and structured enough to train or ‘ground’ an AI model on without huge remediation costs? 

It’s critical to identify whether the existing data assets you are planning to put to work are sufficiently differentiated and proprietary in nature. Every enterprise generates ‘data exhaust’, essentially a byproduct of everyday operations.   

Within that operational exhaust are patterns and signatures of activity, which together help your AI system learn your business systems inside out. It might show specific business processes, reveal proprietary partnerships in action, and other closed loop user interactions. The resulting richness of this data may well make the difference between reliably delivering unique insights to your VADS users, or failing to generate unique value added data or insights. If the output looks like it can be located easily from a third party broker, or scraped from the internet via an LLM, your AI project will ultimately fail.  

Data quality focus 

The quality, as well as quantity, of underlying data in general is also key to ensuring your VADS makes it into full production and adds consistent value. Data specialists such as GaiaLens provide a foundational layer for these projects – ingesting, structuring, standardising and enriching messy data, and ensuring the data feeding the AI is reliable, clean and ready for enterprise usage. 

Let’s look at these ‘AI data readiness’ steps in a little more detail: 

1. Ingestion: this involves creating automated connectors to disparate internal and external datasets 
2. Normalisation & Cleansing: Standardising formats, handling missing values, and deduplication 
3. Vectorisation & Structuring: converting text into ‘embeddings’ for LLMs or structured tables for analytics. For embeddings you need to translate raw text into numerical ‘vectors’ that capture semantic meaning. The resulting vectors allow machines to understand the context and relationships between words. The easiest way to do this is through the use of an embedding API. 
4. Governance: applying metadata, access controls, and compliance tagging. There are functional tags for operational management to help identify resources involved such as monitoring, reporting etc. Classification tags make the level of sensitivity of the data clear – helping to enforce data protection policies. Accounting tags are used to tie data to a cost centre, project or service.  
5. Serving Layer: exposing clean data via robust APIs or secure querying environments.  

Data governance and explainability  

Data Governance also ensures that PII (personally identifiable information) is masked, anonymised or excluded from AI processing altogether. It enforces data retention polices and allows identified users with permissions to view specific outputs relevant to their role and seniority.  

All of the above is critical to assuring the provenance and auditability which underpins data trust. So, if a user challenges the resulting AI-generated insight, the system must be able to instantly point to the exact source document, tag and other meta data that generated it.  

Auto drift detection 

In addition, through MLops and DataOps pipelines, organisations which are embarking on VADS building projects, must implement automated drift detection to flag when incoming data formats are different from what has gone before. This capability enables firms to continuously monitor model accuracy, detect anomalies and quarantine bad data immediately, before it compromises the service.   

‘Bonus’ insights 

During the course of building VADS, there are often ‘bonus’ insights to be found which you do not even plan for. Unsupervised machine learning models and clustering algorithms are excellent at finding non-obvious correlations across massive, disparate datasets – patterns a human analyst would never think to look for. LLMs can also parse years of forgotten, unstructured data like customer support tickets to extract new, monetisable feature requests or market trends. 

Hybrid AI architecture 

AI architecture and delivery model choices is also worthy of consideration when you are planning to build VADS. GaiaLens generally uses a hybrid approach for scaling enterprise AI systems. Retrieval Augmented Generation (RAG) is often combined with Knowledge Graphs to provide the highest accuracy results and grounded answers. AI agents can be layered on top to orchestrate complex, multi-step workflows. Streaming architectures are bolted on only when real-time updates are strictly necessary.  

Probabilistic vs Deterministic systems 

LLMs are ideal for synthesis, human-like text generation e.g. email messaging and summarising reports. They are probabilistic in nature – highly dependent on training data and context. While deterministic systems apply rules based on logic uniformly – generating outputs based on these immoveable rules: 

Role of AI agents 

AI agents can be configured to act as autonomous orchestrators. These agents can take a high level user prompt and break it into a logical sequence of sub tasks. We’ve all read our favourite LLM cycling through these sub tasks. They may query multiple databases, and then run calculations, synthesise the results and format the final actionable insight, all without human hand holding.   

AI-powered data-based services in action 

It might all seem very complex to get VADS designed, built and into production but the rewards for getting these services right are already beginning to show themselves both within large businesses and with their customers. We are seeing major traction in ‘AI co-pilots’ embedded within SaaS platforms where users pay a premium for intelligent workflow automation. 

A good example is BlackRock’s AI-enabled commentary capability within Aladdin Wealth. Rather than replacing the adviser, it acts as a co-pilot by synthesising portfolio analytics, house views and client-specific preferences into concise, usable commentary. That allows advisers to spend less time assembling material manually and more time delivering tailored guidance to clients. 

The US-based bank and credit card provider Capital One shows two AI co-pilot patterns in action: the first is an internal servicing co-pilot that helps contact centre agents answer customer queries more quickly and accurately. The other is its AI-powered car-buying assistant, which goes beyond search to help users navigate choices and complete parts of the process. In both cases, the AI is adding value by reducing friction, accelerating response times and improving the quality of the customer experience. 

What makes these examples commercially significant is that AI capability is being embedded directly into existing financial workflows where it can improve adviser productivity, strengthen customer engagement, and make higher-value services easier to deliver at scale. 

Bridging the ‘insight to action’ gap 

It is possible, then, to bridge the gap between using a VADS to generate insight and using it to help take informed action to the user’s advantage. The AI output should not just be a dashboard. It must integrate directly into the user’s workflow (e.g., Jira, Salesforce, trading platforms) and include a ‘click to execute’ or ‘click to approve’ actions based on the insight. 

So, when we are putting KPIs in place for our new VADS, it’s important to step beyond traditional engagement metrics into:  

1. Daily or Weekly Activity Usage, as well as, 
2. Time Saved / Throughput: Measurable reduction in time-to-decision 
3. Task Success Rate: Percentage of AI outputs accepted by the user without edits 
4. Revenue Impact: Upsell rates for premium AI tiers or increased retention rates. 

The AI commercial and competitive advantage 

Right now, firms that are building Value Added Data Services for their staff, partners and customers, are able to command a premium for these services, deriving new revenue streams and differentiating their company from the pack.  

So, there has never been a better time to begin identifying which VADS can bring you exponential commercial, as well as competitive, advantage. Revenues will flow to those which can offer the best quality data and build strong service offerings around that data provision.