As AI is based on data, the starting point is to look at your data. Do you have enough data of the right quality? Can you buy more data? Can you buy off-the-shelf pre-learned data + machine learning models?

Have you used Business Intelligence and dashboards? Your AI journey needs these essential building blocks in place or you risk building projects on shaky foundations.

Often we see AI complimenting dashboards as a way to get better predictions and forecasts. These building blocks – data, BI, dashboards, dashboards + AI provide a solid foundation on which to build customer facing applications.

Figure 39. Roadmap to AI

The first step to implementing AI itself is to showcase results through small-scale “phase 1” projects - ‘edge cases’ that can then become core. In general, it’s best to start AI projects with a small scope and aim for ‘soft’ outcomes, such as process improvements, customer satisfaction or financial benchmarking.

Expect initial AI projects to produce lessons that will help with subsequent, larger experiments, pilots and implementations. Where a financial target will be a requirement to start the project set the target as low as possible at first, understand what you’re trying to accomplish on a small scale, and only then pursue more impactful benefits.

Key questions that leaders need to carefully consider when deciding where to use AI include:

•              What intervention will have the most impact with the least effort and risk?

•              Do we have the right data, skills and infrastructure?

•              Should this be outsourced?

Example starting points are:

•              Automating a process or program that traditionally requires a lot of staff resources and is of medium importance to the business. 

•              Relieving staff of labour-intensive tasks that are subject to human error

•              Improving an existing solution by adding machine learning capabilities

•              Solve a big pain point for a customer, for example by using a chatbot