Demystifying data and AI
To set the scene and establish some shared understanding, Susannah started by clarifying some key definitions that are often thrown around in discussions about data and AI. She emphasised the importance of a shared understanding to foster inclusive conversations and keep barriers to entry low.
What is data?
Data is generated by human action, collected through things like user input, sensors, logging, and calculations. Data could be in the form of numbers, words or symbols but most importantly it is something that can be made useful through analysis, which is in turn interpreted to reveal patterns, trends, and relationships, ultimately leading to meaningful information or knowledge.
What is information?
Information is data that has been “made useful” or given a purpose, through analysis, transformation, collation or any other form of processing. Information is processed and organised, able to provide context and meaning, and enables decision making.
What is AI?
At its core, AI is a series of algorithms with roots in statistics and probability. AI algorithms analyse data to identify patterns and relationships, which can then be used to make predictions, classify new data, or generate new content.
What is an algorithm?
The definition of an algorithm is “a set of instructions to be followed in calculations or other operations.” This applies to both mathematics and computer science. So, at the essential level, an AI algorithm is the programming that tells the computer how to learn to operate on its own. AI algorithms often work by taking in training data that helps the algorithm to learn. How that data is acquired and is labelled marks the key difference between different types of AI algorithms.
Driving value
Susannah challenged the traditional view of data as a mere byproduct of business operations. Instead, she encouraged organisations to treat data as a valuable asset in its own right - something to be nurtured and managed with intention.
Collecting huge quantities of data isn’t the goal, she pointed out. The aim should be to unlock value from that data. Organisations that do well with AI, according to Stanford’s AI Index, are those that treat their data with this mindset.
Critically, Susannah warned against rushing into AI investments without addressing the state of your data. AI systems rely on high-quality data - bad data will only lead to worse outcomes when amplified by AI. If you don’t understand what data you have, what it’s telling you, and where the gaps are, and where your insights are, what are you hoping to get out of your AI implementation? AI isn’t a magic wand, it won’t fix your data, it will only amplify and increase your existing data issues (whilst costing you more money).
If we invest in AI without first investing in our data, the ROI will simply never exist.
Data foundations for AI success
Susannah acknowledged that despite massive investments in AI in recent years, most organisations struggle to turn data into measurable ROI.
The problem, she said, isn’t the AI models themselves but the poor state of the underlying data and how it is managed, governed and understood.
When data foundations are strong, organisations can:
- Better understand their business.
- Define strategic direction.
- Identify gaps.
- Modernise infrastructure.
- Build trust in their data.
- Shape a strong data culture.
- Secure long-term success.
To deploy successful AI, organisations must redefine their data foundation as a strategic differentiator and a critical success factor.
To bring this to life, she referenced Maslow’s Hierarchy of Needs. AI is the top of the pyramid - equivalent to self-actualisation - but without a solid base, which is food and shelter in Maslow’s hierarchy, and clean data in our hierarchy, you’ll never reach the top.
Data transformation journey
To help firms get started, Susannah introduced a structured view of the data transformation journey – an end-to-end approach that encapsulates all the essentials for using data well within an organisation. This framework encourages organisations to identify where they are, where they want to go, and what they’re missing.
By using this model, teams can take stock of their current capabilities and map out a clear path to AI-readiness, rather than jumping into solutions without foundational clarity.
Checklist for impactful AI
Drawing on her experience with clients, Susannah shared a checklist of actions that can lead to more meaningful and impactful AI initiatives:
Understand your why.
Speak to users.
Define the problem to be solved.
Understand your data.
Define data requirements.
Build or refine your data foundation.
Define your evaluation and success criteria.
Explore solution options.
Implementation.
AI should solve real problems for real people. Data is generated by people (whether that’s users or customers) so it is a direct reflection of human behaviours and needs. Speaking to users will help organisations to better understand their data, and their data will help them to better understand their users – these two things have to coexist to be successful.
Unlocking the real value of AI
Susannah wrapped up the session by reinforcing a few key messages:
Massive AI investments haven’t paid off for many companies because they’ve overlooked the importance of strong data foundations.
To see ROI from AI, start with data - structure it, understand it, and treat it as a critical asset.
Don’t aim for AI “self-actualisation” if you haven’t yet secured your organisation’s basic data needs.
Data is not a byproduct; it’s a core strategic asset. And without a solid foundation, no amount of AI magic will deliver real impact.
Know where you stand
Understanding your organisation’s AI maturity is the first step to scaling it effectively and responsibly. Our AI Maturity self-assessment helps you do just that, giving you a clear view of your current strengths and areas to improve, and providing a tailored roadmap to guide your next steps.
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