In collaboration with Lara Fox, Managing Director, Objective
AI is quickly becoming a powerful tool for organisations of all shapes and sizes. Used in the right way, it can help drive operational efficiencies, reduce costs and enhance customer experiences. AI can also reduce errors by taking on repetitive and time-consuming tasks, often performing them more accurately than a human can.
However, many AI and analytics initiatives fail because data foundations are weak. As a result, time and money are spent on tools and pilots that never deliver real value, while competitors move faster and get more from their data.
AI systems are only as good as the data that powers them. In the rush to adopt AI, many organisations overlook the need for data preparation before putting it to work. Without a solid foundation of clean, reliable data, AI models will struggle to deliver meaningful outcomes. Before AI can add value, a business’s data needs to be structured, trusted and accessible.
The challenge of being data-ready
Many organisations have data scattered across multiple systems, from spreadsheets and CRM platforms to finance tools and SaaS applications. Teams waste time searching for the right information, while reports take too long to produce. Inconsistent metrics and reporting can also mean that board-level decisions are made using inaccurate or outdated data.
Poor data quality often comes from duplicate records and missing fields. Organisations that fail to address these issues before using AI will struggle to generate reliable insights and risk undermining trust in AI-driven decisions. Without a central reporting database, businesses often rely heavily on spreadsheets or the knowledge held by a small number of key individuals.
When data is inaccurate or incomplete, AI and analytics cannot be effective. Addressing these issues early frees up time later and avoids spending money on tools that are incapable of delivering real value or return on investment.
Getting data AI-ready
The first and most important step for organisations considering AI is to ensure their data is high-quality and well structured. Resolving data issues upfront improves confidence in insights and decisions, while creating a solid platform for sustainable growth.
Before working on the data itself, organisations need clarity on what they want AI to achieve. Is the goal to improve efficiency, automate manual processes, or support forecasting and decision-making? Defining objectives early provides focus and a clearer direction of travel.
AI struggles when data is trapped in silos. Organisations should start by mapping where their data lives. Whether in HR systems, CRM platforms, finance tools or elsewhere. Using data platforms, APIs and integration tools can help bring this information together and create a single, trusted view of the business.
Strong data governance is equally important. This means establishing clear ownership for maintaining data quality, defining who can access information, and ensuring compliance with regulations. For AI-driven insights to be useful, the underlying data must be trusted, transparent and supported by clear audit trails.
Getting the best out of AI
AI performs best when data is structured, well labelled and consistently categorised. This involves standardising formats, adding clear labels and documenting data properly. While this can be labour-intensive, the long-term value of having accurate, consistent data that AI can analyse far outweighs the initial effort.
Equally important is investing in people. Improving data literacy across the organisation and helping teams understand how their data is used encourages better habits and more informed decision-making. AI insights are only valuable if people know how to interpret and act on them.
Organisations that focus on clear outcomes, improve data quality, put governance at the forefront and invest in their people will be best placed to unlock the full value of AI and use it to move the business forward.
Data will never be perfect
For organisations who are worried about messy data or being left behind, it’s important to remember that data will never be perfect. AI readiness isn’t about having more data or building a giant database; it’s about ensuring the data you do have is connected, relevant and accessible.
The organisations that use AI most effectively are those that start small with pilot projects, learning what data really matters. They use these insights to improve systems, refine processes and apply data in more meaningful ways. AI readiness is not a one-off exercise, but an ongoing discipline.
Addressing inconsistent data early frees up time, improves confidence in decision-making, reduces risk and creates a strong foundation for better reporting, sustainable growth and successful AI adoption.
