Artificial intelligence (AI) gives organisations clear ways to grow revenue, reduce cost and create new services. Teams that start with the right use cases and strong data habits move from pilots to production faster and with less risk. With solid data foundations, AI can shorten decision cycles, personalise customer experiences and automate routine work, freeing people to focus on higher-value tasks.
Despite the opportunity, many organisations exploring how to leverage the promises of AI find that their initiatives are not actually delivering measurable value – often encapsulated by the frustration, "AI isn't working". This outcome often arises because AI models are fundamentally only as good as the data they are trained on or consume.
AI delivers strong and reliable outcomes when models leverage accurate, timely and well-understood data, with clear measurement. This is especially important when organisations use data as the foundation for AI systems to consume and draw conclusions – identifying trends and outliers, and generating GenBI reports that provide conversational, context-aware and adaptive insights.
The Data Readiness Challenge
The undisputed mantra for high-stakes AI initiatives is the old adage, "garbage in, garbage out". When an AI model is given flawed data, whether it's incomplete, inconsistent, or biased, it will learn and perpetuate those flaws, which in turn result in immense financial and reputational risks.
For example, an AI used to detect credit card fraud might fail to identify new types of fraud if its training data is outdated and doesn't reflect current patterns. Companies that invest in AI systems without having the proper data foundations often prevent the delivery of measurable value, leading to costly AI failures and wasted resources. Another common challenge is trust in the data, and by extension, trust in the results produced by AI. Organisations need clear, auditable measures for data quality, including accuracy, bias control, lineage and explainability, to build confidence in both the inputs and the results.
For CDOs, CTOs and leaders navigating this landscape, three key foundational challenges routinely undermine AI projects:
- Data Silos and Inconsistency: Many organisations have data scattered across different departments and systems in various formats. This leads to data silos, where valuable information is locked away and disconnected from other relevant datasets. For AI, this fragmented data is a major problem, as it requires extensive manual effort to consolidate and standardise before it can be used for training. Inconsistent data formats, units of measurement, or even simple spelling differences can create noise that confuses the model.
- Data Volume, Velocity, and Architectural Limitations: The sheer volume and velocity of data being generated today is staggering. With the rise of IoT devices, social media, and real-time analytics, data is pouring in at an unprecedented rate. Keeping this data clean, validated, and up-to-date in real-time is a monumental task. Legacy architecture (people, process, technology) can serve as a significant bottleneck, preventing modern AI implementation and value realisation. Delayed or outdated data can render an AI model useless, especially in time-sensitive applications like financial trading or supply chain management.
- Governance, Accountability, and Reliability: Data governance is the framework that defines how data is managed throughout its lifecycle. With AI, a lack of clear ownership and accountability for data quality can lead to serious issues. Without proper governance, it's difficult to track where data came from, who is responsible for its accuracy, and how it's being used. This not only impacts model performance but also raises significant compliance and privacy concerns. Poor governance can also lead to hidden security risks, where sensitive information is inadvertently embedded within a model's training data.
Getting your data AI-ready
To give your AI journey the best chance of success, the strategic path is to "get the basics right" by prioritising data governance and quality fundamentals. A well-implemented and supported data strategy, alongside modern data architecture, will enable your business to leverage the right data to drive growth, improve efficiency and innovate.
By establishing strong data foundations across governance, quality, and readiness and platform, organisations can build a reliable infrastructure for effective AI enablement. This can include:
Establishing a Strong Data Governance Framework: Establishing clear data governance policies and procedures is essential to ensure that business users have access to high-quality, trustworthy data. Define clear roles and responsibilities for data ownership and management, and implement policies for collection, storage, and usage to ensure consistency and compliance from the start.
Lifting data quality with automation: Automating data quality assessments and enforcing data quality standards can help maintain the integrity of the data while still allowing business users the freedom to explore and innovate. Utilise automated cleaning and validation tools to efficiently identify and correct errors at scale.
Prioritising Data Literacy and access: Foster a culture where every team member understands the importance of good data quality. Provide training and give business users the tools and platforms they need to become self-sufficient in their data exploration and analysis.
Implementing Continuous Monitoring: Data is dynamic, not static. A continuous feedback loop is critical for monitoring data quality and model performance, allowing teams to retrain models with fresh, clean data, ensuring they remain accurate and relevant over time.
A Pragmatic and Iterative Approach
The smart strategy is an iterative approach. This means avoiding the extremes of trying to “clean everything first” or assuming that AI will “cope” regardless of quality. Instead, disciplined data engineering and AI tools should reinforce each other, prioritising the delivery of value both fast and safely.
This strategic yet pragmatic approach, ensures tangible, measurable business value early and continually. Organisations can select a critical business objective, and then figure out how to reach this objective with technology enablers while touching all the best data practices.
By focusing on governance, quality, and readiness, organisations can transform their data into their most valuable asset, building a reliable infrastructure that supports lasting AI success.
At ClearPoint, we help organisations plan for and implement the data foundations and AI needed for success. Discover how ready your data and structure is for leveraging AI through a proven readiness assessment facilitated by our data AI experts. Talk to us today to discuss how you can start benefitting from AI-leveraged data in your business.
