Maximising value from AI in 2026

AI is proving transformational to organisations. It's driving real time success in unlocking data-driven insights, boosting productivity, and elevating the customer experience.

AI is able to personalise recommendations based on customer profiles and where they live, deliver segmentation for targeted marketing - aiding the automation of email campaigns and chatbot responses - and provide predictive analysis that helps to predict customer behaviour.

AI success thwarted by inaccurate data

A big issue that impacts on the effective implementation of AI is data decay. Customer contact data lacking regular intervention degrades at 25 per cent a year as people move home, die and get divorced.

This is compounded by the fact that 20 per cent of addresses entered online contain errors, including spelling mistakes, incorrect house numbers, and invalid postcodes.

Stop AI hallucinations

Poor customer data can mean AI 'hallucinations', which leads to flawed results. This is because granting an AI tool access to inaccurate customer data can result in nonsensical outputs, or worse, biased and incorrect decision making

For example, with AI having access to incorrect customer data, such as an inaccurate name or address, poor personalisation could be delivered, which will have a negative impact on sales and the customer experience.

Strengthen data verification practices

It's essential to have data verification processes in place at the point of data capture, and when cleaning held data in batch, to ensure AI has accurate data to work with. This usually involves straightforward, cost-effective improvements to the data quality process.

Obtain an address lookup or autocomplete tool

Using an address lookup or autocomplete service at the customer onboarding stage is a good option. They automatically provide the correct address as the user begins to enter theirs. This enables them to select an option that is correct, easily recognisable, and correctly formatted for their country location.

Gathering accurate address data in this way avoids the potential for mistakes caused by fat finger syndrome, and there's a reduction in the number of keystrokes required when typing an address by up to 81 per cent. This speeds up the path to checkout and diminishes the likelihood of a basket being abandoned - supporting the delivery of a sale and a standout customer journey.

There's similar technology that enables real time verification of email and phone data at first contact, strengthening critical datasets and enhancing AI performance.

Remove duplicate data

Having duplicate data on customer databases is a significant issue. It's not uncommon for businesses to experience data duplication rates of 10 to 30 per cent, which is often caused when errors in contact data collection take place at different touchpoints and when two departments merge their data. This duplication causes confusion for AI applications.

Using an advanced fuzzy matching tool is a good way to eliminate duplicate data. Such a service can merge and purge even the most complex records to create a 'single user record', which delivers an optimal single customer view (SCV) from which AI can derive insights.

Data cleansing 

An important part of the data cleaning process, and therefore in supporting efforts with AI, is data suppression or data cleansing, because these services highlight those customers who have moved or are no longer at the address on file. These tools should have access to the National Change of Address (NCOA) database, which is available across the UK, the US, and other regions, because it identifies customer address changes, and supplies their updated address details.

Along with removing incorrect addresses data cleansing services should include deceased flagging. This prevents the distribution of mail and other communications to those who have passed away, which can cause distress to their friends and relatives.

Therefore, implementing suppression strategies helps organisations to reduce costs, preserve trust, stop fraud, and improve AI performance.

SaaS data quality platform

Today, with evolving technology, there are software-as-a-service (SaaS) data quality platforms on the market that can cost-effectively collect accurate addresses and wider customer contact data in real time at the onboarding stage, as well as cleaning held data in batch, worldwide. As SaaS solutions, they are easily accessible and require no coding, integration, or user training.

In summary

The benefits that AI can bring in helping to drive revenue and provide a competitive edge over rivals in 2026 and beyond is huge. However, this can only take place if the AI has access to high quality data. Without it organisations must be prepared to experience AI 'hallucinations' which leads to unreliable predictions and therefore bad outcomes. By adopting best practice data quality procedures it's possible to unlock the full potential of AI, leading to increased sales and a standout customer experience.