18 Jun 2019 | 09.43 am
AI Priorities Businesses Cannot Ignore
PwC/Analytics Institute joint study
18 Jun 2019 | 09.43 am
PwC has combined with the Analytics Institute for a report that benchmarks the Artificial Intelligence readiness of Irish businesses against their US peers.
PwC partner Darren O’Neill, AI and Data Analytics Leader, commented: “It is clear that AI is less well developed in Ireland when compared to the US. Irish businesses have more work to do to fully invest in and embed AI opportunities into their operations.
“While recognising the potential of AI, Irish business leaders need to have the infrastructure, key talent and, most importantly, the appropriate data to successfully implement AI. The gap between the AI vision and execution remains the largest stumbling block. Organisational leaders know AI is important, but they are reluctant or unable to take actions necessary to make the transformation happen.”
Lorcan Malone, chief executive at Analytics Institute, added: “In our experience, organisations have some way to go to provide the robust, reliable and complete data that is needed. Monitoring data standards, as well as developing systems and processes that make it easier for employees to create usable, labelled data sets for future use is also important.”
The analysis identifies six priorities that should not be ignored for AI success:
1. Clarity: Understand AI and how it fits your business: Ensure you and your people understand AI and its benefits. It can support your business strategy, reduce costs, improve products and services and drive efficiencies at every level.
2. Strategy: Get the AI strategy right; Organise return on investment: Bring together AI, IT and core operation leaders in a structured way to manage priorities, data, strategy and resources and organise the business to measure the return on investment.
3. Data: Locate and label to teach machines: Identify the data sets you need to train AI to solve specific business problems, then prioritise capturing and labelling that data in line with enterprise-wide standards.
4. Skills: Build an AI-ready workforce with an agile mindset: Recruiting and upskilling are just two pieces of the puzzle. You also need to systematically identify how AI is changing job roles and skills; evolve upskilling, performance and compensation frameworks and develop new collaborative processes.
5. Trust: Make AI responsible in all its dimensions: Trustworthy AI requires fairness, interpretability, robustness and security, governance and system ethics. Create roles and establish metrics so all teams are working to build responsible AI.
6. Convergence: Combine AI with analytics, the IoT and more: Many technologies can benefit from AI, but advanced analytics and the Internet of Things will bring sizeable benefits.
Photo: Darren O’Neill (left) with Sheelagh Carroll and Lorcan Malone of Analytics Institute.