How to combine data and AI
It is one type of challenge for a data leader to get backing from non-data professionals for a data strategy, but it is another entirely when pushing for data in an AI strategy influenced by non-data leaders. There are multiple nuances that can be easily overlooked by non-data professionals.
Firstly, there must be a high level of safety and strict compliance – which is in an ever-evolving state right now as AI tools rapidly develop and have a reduce barrier to entry. “AI has been around for many years but today’s advances in generative AI creates a new era of innovation,” said Chris Oxborough, PwC UK responsible AI lead. “This is not without risk and raises even more the importance of responsible, ethical and safe use of AI. Organisations need to embed the right controls and guardrails to protect their data and IP. Those who put trust at the forefront of their AI strategy will be the winners and reap the greatest benefits.” With business decision makers educated in the ways AI can be a risk and an essential tool, it will be far easier to demonstrate how and why a combination of data and AI can be pivotal to business growth.
Data leaders need to ensure that there is an ongoing education programme that is suitable for different teams within an organisation to emphasise correct data practices to exceed compliance obligations, maintain safety for the business and its customers and to continually improve data literacy levels to match technological advancements.
To set the foundations for success, an organisation needs to have the right ecosystem and infrastructure in place. This can be a daunting and costly task – particularly for legacy operations – but it will set the roadmap for success for many years to come. “With generative AI becoming a strategic priority for organisations, a robust and secure data ecosystem is the foundation to success,” said Leigh Bates, data and analytics partner, PwC. “Data is an invaluable asset in developing reliable generative AI solutions. Organisations need to understand the breadth and depth of data across the AI lifecycle. This will require robust and efficient data architectures and management practices to instil trust and confidence through high quality data.”
Ultimately, the purpose of financial and time investment into AI for data purposes it to increase economic value through a variety of means. Whether this is through sourcing new audiences, identifying new product opportunities or simply through finding operational efficiencies, AI and data have an incredibly important part to play for modern businesses, particularly at a time of financial squeeze. “Every business is considering how it can deliver economic value using AI,” said Sunil Patel, PwC UK CDO. “When you look closely at the businesses that are succeeding with AI, it will lead back to their data. Data and its underlying foundations are the determining factors to what’s possible and how successful you will be with AI. Managing data was already difficult before AI so it is doubly important now that businesses develop modern data strategies and foundations that can propel their data in an AI age to deliver strategic value.”
There has been an incredible drive towards AI integration in 2023 and this does not seem to be slowing down as 2024 rapidly approaches, which means organisations must undertake policies that embrace AI and promote data. It is not a quick process, but it is essential for all businesses striving to become data-driven and embrace data-led decisions. As the world of data and analytics continues to rapidly change its capabilities, having the correct architecture and an executive-level understanding of regulations is key to achieving long-term success and setting the benchmark for competition in the industry.
