Building a strategy for the effective use of AI

Start with the ‘Why?’

When identifying the use case for AI, it is essential that you set a target measure of success. Any strategy for putting Artificial Intelligence (AI) to work to improve your business needs to start by establishing a collective understanding and agreement of what you are trying to achieve and for whom.

Discovery phases often start inside customers’ data teams. In large enterprises, this is likely to mean speaking with the organisation’s CDO (Chief Data Officer).

However, to ensure systems are built to deliver meaningful business value, GaiaLens often meets with a number of departmental heads. This includes Product Delivery, Customer Service or Customer Success teams, amongst others. It helps define the most pressing business requirement which AI might address.

The core goal of any system must be fully defined and agreed upon at different levels of the organisation. That must include the end-user right up to the C-suite exec signing off on the budget.

Collecting, cleaning and preparing the data

Most AI initiatives lead from there naturally to an exploration of the underlying data needed. They might say, “We have a data warehouse full of these regulatorily-required records. But we are not really using those records for anything (apart from holding them for regulatory compliance purposes) right now.”

They might ask for our thoughts on how to make all that data queryable and downloadable by their customers. In-house decision-makers might use intelligence from other queries to make better business decisions.

Our job right at the start of any engagement is to help shape a solution. We also work out how to organise that data so that it can be queried and form the basis of a valuable solution for employees and customers alike.

Find the right datasets

Any successful AI pilot will need to first explore what datasets need to be collected, extracted and checked for quality. Data gaps will need to be filled, duplications removed, and mislabelled data corrected. Unit errors must also be checked during initial Exploratory Data Analysis (EDA) work.

Unstructured data, including PDFs, must be logged, tagged and structured so that data can be extracted via a query. Very often, once the data has been cleaned, it is consolidated into a single database. This creates a single version of the truth using uniform labelling.

Two of the more vital skills for ensuring data can be reliably turned into insights are AI data science and engineering. Key characteristics of any dataset are its structure, recency and depth.

You also need to consider the size of the dataset for a pilot. If you have too much data, select a sample of that data, extract and test it for completeness and accuracy. Look at making it ready for querying and extraction by an appropriate Large Language Model (LLM). The dataset must be large enough to ensure that the selected model being used is reliable.

Data processing and management

Beyond the initial collection and organising of data, it’s important to establish robust, sustainable data operations. The following questions need to be answered:

1. Must the data support real-time querying, or is batch processing sufficient?
2. How will the data be secured and anonymised?
3. How will you control access?
4. How will we classify data by sensitivity, particularly when mixing proprietary, personal, and public source data?
5. How will we meet regulatory and compliance obligations, not just at the point of use but throughout the data’s lifecycle—including data disposal?

AI pilots are underpinned by sound, well-thought-out data governance, as well as disciplined data science.

Data use and analysis

Ensuring AI systems provide accurate, up-to-date and meaningful data is a key part of our work during AI pilots. It’s important to validate results against agreed ‘ground truths’. It is also important to ensure your underlying datasets are not biased or misleading.

You must also build in ‘human in the loop’ review cycles. This prevents drift and ensures against ‘hallucinations’. These can emerge if queries are probing data which isn’t complete or isn’t present in the dataset. In addition, AI systems need to be built as living systems that require continuous learning, evaluation and recalibration as new data comes in.

Most of the AI initiatives we are shaping for customers have an intention to improve the efficiency of specific tasks. They often enable specific job types to be more productive. When this is the case, naturally, the people who are invested in a new system making their life easier will want to check its outputs very closely during pilots.

Explainability and observability are increasingly essential. They enable stakeholders to understand why an AI system produced a given output. It ensures that results remain traceable, defensible, and compliant with regulatory expectations. Above all, the analysis phase should be governed by clear KPIs linked to the original use case. This ensures that AI systems deliver measurable improvements.

Model selection

GaiaLens sometimes advises on which model to use according to the scale of the task and the type of data being analysed. Model selection is also an economic decision. For example, if you are only expecting a handful of in-house managers to query the system each month, an enterprise ChatGPT license is unlikely to be the most cost-effective option. Alternatively, you may choose to deploy several different models to perform specific tasks.

A good deal of the work we do is associated with remaining compliant. Our job often enables automation of legal or regulatory reporting. We do the database building, training of the models, and offer an end-to-end, outsourced, dashboard front-ended service if preferred.

For many companies, there is a nervousness around sharing company confidential data with a public model. GaiaLens is therefore increasingly involved in configuring what’s called ‘closed loop’ chatbots. It allows new information to come into the chatbot, but stops underlying data leaking out of the model.

Building workflows and enabling integrations

Often, the systems we are building need to be integrated into existing enterprise systems. Indeed, if we are dealing with customer data, we are likely to be integrated with an organisation’s CRM system. If the AI solution is linked to customers’ supply chain, we may need to integrate with their enterprise SCM (Supply Chain Management) system.

Summary

Right at the start of shaping any Proof of Concept, we establish with the customer what is the desired outcome(s). What would good look like for them? What user experience are they looking to deliver? It is important to define goals which reach beyond efficiency into adding value to a key stakeholder and to the bottom line.

If the AI pilot can evidence both value and the promise of additional revenue, then the company will invest in putting that AI system into production. It will also be willing to continue investing to nurture, enhance and improve it. Additionally, it will expand the number of users who derive value from it. That can set a good precedent for tackling the next challenge for which AI may offer a solution.

GaiaLens began life as an AI platform built for some of the most complex, regulated datasets and frameworks in finance. Today, the company is bringing that experience to the wider enterprise market including the utilities sector. It helps organisations transform fragmented and incomplete data sets into structured, high integrity data they can actually use, whether to serve customers better, meet tightening data governance rules, automate reporting and workflows, or run business processes more efficiently.

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