Are you AI ready?
Successful adoption of AI brings various commercial benefits for businesses, with trust and cost efficiency being among the key factors. If you want to reap the benefits of AI to help your business increase efficiencies, enhance decision-making, build brand trust, and mitigate risks, among other advantages, then these considerations from Tim Bowes are crucial for successful AI adoption. They enable your business to embrace the benefits of AI effectively.
AI implementation and data quality
AI is high on most organisations’ agenda, as they seek to understand how it can be harnessed for the benefit of the organisation and what efficiencies and savings it can bring. Given AI models are just another consumer of large volumes of data, then it is important to understand that the same basic principles apply to AI implementation as per other data transformation activities. The quality of the data that is being consumed should be understood and be within acceptable tolerances for the outputs to be trustworthy and reliable.
Top considerations for AI adoption
Quality
The adage of rubbish in rubbish out still holds very true when it comes to the implementation of any data models that will be used for AI or Machine Learning (Linear Regression (LR), Large Language Models (LLM), Deep Neural Networks (DNN) etc). The quality of the data sourced to feed into these models must be thoroughly understood, with biases identified and accounted for. Without this understanding, the outputs of the models cannot guarantee good quality and should not be relied upon for making business decisions.
Security
Security is just as crucial as the quality of data. When integrating generative AI within your organisation, imposing restrictions on the data accessible to the AI model is vital to prevent the use of personally identifiable information (PII). While granting access to HR policy information is sensible, exposing sensitive HR data is not advisable.
Training data
The data used to train models is of paramount importance and should be transparent and well-documented. Current commercial generative AI tools such as Copilot and Gemini are black boxes at present as the training data has not been made available. This lack of transparency makes biases, recency, and relevance unknown factors that could significantly distort the outputs.
Validation
Trust in the outputs of the models must be gained and maintained. Being able to validate the outputs of the models through understanding the inputs, the context which they were generated, and the results of decisions AI has made. Crucially human input and oversight must be part of the validation process.
Use case adoption
AI evaluation frameworks need to be established and implemented to assess the risks associated with potential AI use cases. Low-risk, efficiency-gaining use cases should be prioritised initially, allowing the organisation to build understanding and trust in AI.
Implementation
AI use cases must be developed and tested in safe environments, away from production and core sensitive data and processes, this is typically facilitated using data labs with close collaboration between Business SMEs and data experts.
Accountability
Business leaders need to understand and take accountability for both the opportunities and risks involved. Clear communication about the intended outcomes, the safety measures implemented, and the integration of fairness and ethics to ensure inclusivity for all are essential.
What this all means…
So, what does this all mean? Well, those used to working in the landscape pre the AI boom, will not be surprised to hear that strong Data and AI Governance must be incorporated throughout AI use case evaluation. implementation and across ongoing model tuning and validation. Robust management and governance are the key to trust worthy data and the same is true for AI implementation. Policies must clearly set out the principles as to how AI will be adopted across the organisation with process to implement assure the above topics.
If you’d like to know more about how we are supporting our clients with data management and our AI readiness assessments, get in touch we’d be happy to chat through any of your questions.