As a part of our WealthTech Insight Series (WTIS) and in partnership with Umlaut and IAWMC, last month we published a new research-led whitepaper called: Applying Artificial Intelligence in UK Wealth Management - an Evolutionary Tale. The purpose of this research was to assess the current perception and opinion of UK wealth managers on the potential of AI, its future applications and whether it will have a transformative effect on UK wealth management. Below is an extracted article by Shane Reid, Founder at Umlaut, focused on creating Success with AI.
The implementation of AI or Machine Learning (ML) can be daunting. The first question we are usually asked is: “Can we trust the data?”. In most cases, this is the easiest place to focus on and achieve results. By ensuring ML tools, combined with your staff, are providing accurate data downstream, the AI tools will provide rich data that businesses, staff and clients will trust.
Below are some steps that have been successful for other clients on the ML and AI journey.
- Define specific business problems: Identify the specific business problems that ML and AI can help solve. This could be anything from optimising investment portfolios to improving customer engagement.
- Collect data: Collect high-quality data that can be used for ML and AI analysis. Ensure that the data is accurate, complete, and relevant to the specific business problems identified.
- Build a team: Build a team with the necessary skills to develop and deploy ML and AI models. This could involve hiring data scientists, engineers, and analysts, or upskilling existing staff. Upskilling your staff, or using competent partnerships is not as difficult as first thought.
- Choose the right tools: Choose the right ML and AI tools for the specific business problems identified. There are a variety of tools available, from pre-built solutions to customised models. Keep the initial commitment small during the POC, it will make it easy to pivot.
- Start small: Start with a small project to demonstrate the value of ML and AI. This could involve building a proof of concept or pilot project to test the technology on a small scale.
- Monitor and measure results: Monitor and measure the results of the ML and AI project to ensure that it is delivering the desired outcomes. This may involve developing specific metrics or key performance indicators (KPIs) to track progress.
- Scale up: Once the initial ML and AI project has been successful, scale up the technology across the organisation. This could involve expanding the scope of the project or integrating ML and AI into other business processes.
Implementing ML and AI technology into a business can be a complex process, but starting small and taking a strategic approach can help to ensure success. By identifying specific business problems, collecting high-quality data, building the right team, choosing the right tools, starting with a small project, monitoring and measuring results, and scaling up, businesses can effectively integrate ML and AI technology into their operations and realise the benefits; valuable insights into their clients’ behaviours and preferences, optimise their investment strategies and create more personalised experiences for their customers.
Access more insight by reading the whitepaper here.
