Implementing AI: 3 Takeaways from AI in Practice Training
AI training day sees experts explore ways to improve data practices and develop multistakeholder organizational strategies
At a Glance
- Experts at an AI training event highlighted key steps to implementing AI including tailoring approaches to business needs.
- Also covered were ways to improve data management and create an adaptable strategy with multistakeholder input.
The rush to implement AI in the wake of ChatGPT and the generative AI wave has seen businesses attempt to reposition themselves in a bid to stay competitive globally.
At a training event approved by the UK Government's Office for Artificial Intelligence and hosted by Informa Tech, the parent division of AI Business, and The AI Summit London, experts from different industries came together to discuss and explore potential approaches to implementing AI.
Among the takeaways were three practical considerations:
There’s no one-size-fits-all approach. Identify what processes fit your business, its needs and the needs of its customers. Also, consider your risk appetite, but be mindful.
Good data usage comes from good data management. Work towards improving and aligning processes and approaches on governance, security and biases.
Adopt an approach that’s adaptable, easy to define and able to refer back to. Create a cross-functional approach that allows for multistakeholder input that works towards your goals across the organization but is easy to adapt when new solutions or problems arise. Getting the C-suite on board is hard but a necessary part.
The session was led by Leon Gordon, founder and CEO of Onyx Data (pictured below). He said that everyone should have a data and AI strategy in place.
“It's here to stay and we need to we need to embrace it, but in a structured, secure and governed way.”
1. No one-size-fits-all approach
Throughout the event, conversations flowed not on how to build systems, but on how organizations should position themselves to best prepare to effectively implement them.
Organizations from small to large attended, reaching a consensus that not every approach would suit everyone. Recognizing a business’s maturity is the best way to work out where it is in its data journey.
What might work for a large corporation may not apply to a startup. Attendees outlined their experiences from experiments and use cases and the group talked through potential opportunities and technical approaches to takeaway for their own work.
Gordon said: “AI can be adopted across industries, across functionalities and has a plethora of use cases available across the board. But it's not a one-size-fits-all approach.
“You have to be mindful of where your business is going, what the appetite for risk is of the organization and potentially doing proof of concepts (POC) as opposed to overarching, fully encompassing projects. Go for that POC which delivers, delivers well in time, at a good cost and then use that as the flagship to go across the organization to build out your bigger projects.”.
2. Good data usage comes from good data management
Attendees undertook assessments to explore their wider approaches to using data. They encompassed four components:
Data management
Ethics and biases
Data quality
Data governance and security
The group discovered that, as referenced in point one, not one single data strategy works for every business, but the business benefits are clear for those who take the time to develop a data-driven culture.
A major point that arose was that using spreadsheets to house and manage data could be determinantal to a business’ effectiveness. But Gordon pointed out that not all spreadsheets are bad.