The opportunities presented by AI are phenomenal, but organisations need well-structured, reliable data foundations backed by strong data governance to fully unlock its promise. As the saying goes, “garbage in, garbage out”, and therefore the quality of your organisation’s data determines the power of AI when it comes to deriving insight and making decisions.
So how do we ensure powerful data? Getting your data ready for AI shouldn’tbe a single, big bang event but an ongoing process,starting with an understanding of your key use cases and an assessment of your readiness for AI. This can then progress iteratively with small proofs of value to test your data quality and infrastructure while building support from the wider business.
As mentioned, an AI maturity assessment is key in understanding where you are now and where you want to be. This can be split into five broad pillars: AI governance, data management, people, process and technology. Let’s break those pillars down into more digestible steps.
Step 1: Is your data governance framework robust to ensure data quality, privacy and compliance is embedded in AI systems?
The importance of a robust data governance framework can’t be overstated and ensures data quality, privacy, and compliance is embedded in the development of AI systems, while ensuring humanity is retained and decisions are driven by ethics and societal considerations. Transform have experience in this area, working with the National Citizen Service (NCS), in collaboration with data ethics committees, to ensure that young people’s identities were obfuscated and AI model bias reduced when predicting attrition rates for programme attendees.
Step 2: Does your data management system enable you to make decisions based on accurate information?
Data management is what allows organisations to make decisions based on accurate, consistent and accessible information. This is critical for reducing the risk of errors and biases in AI models while improving their accuracy and efficiency, all while ensuring that models comply with data privacy regulations. Transform have worked with Health Education England to combine, clean and deduplicate data to create modelling datasets in readiness for AI; in addition, we have identified significant variables for use in AI predictive models to forecast potential junior doctors’ attrition.
Step 3: Are you bringing your people along to establish an AI-native culture?
It's vital that your people are brought along with your organisation on the AI journey. Siloed pockets of data literacy can be effective on specific tasks, but the goal is to work towards an AI-native culture where AI skills development is key across the organisation.
Step 4: Are you ready to move your processes from use-case by use-case to innovation-driven?
Similarly, at the beginning processes may be focused on specific business outcomes, but with maturity they need to move from operational to a self-service model driven by innovation.
Step 5: Do you understand how your technology and infrastructure can drive your AI journey?
Finally, technology and infrastructure are key drivers of AI maturity. Transform are tech agnostic, meaning we can deliver AI capability in line with existing tech stacks at whatever point you are in your AI journey, from tooling driven by small POCs to large-scale infrastructure implementations to facilitate a self-service model for any IT environment.
The bottom line? Getting your data ready for AI is a critical step in any AI workflow and will allow you to unlock the full potential of your data. We hope these steps help you along the way, but if you need a little more help from a friend, Transform are standing by with our AI Impact Assessments.
Reach out.