Artificial Intelligence (AI) and Machine Learning (ML) are becoming increasingly mainstream. The reason for this is because when applied in an organisational context effectively they can significantly enhance business outcomes. Utilising data and AI at a business level to drive the overall value of the customer is very different from using it at the tactical level to optimise a channel or clicks and warrants considerable upfront planning.
Machine Learning is the process whereby machines learn to predict outcomes or find patterns from a large number of examples (big data). Algorithms are fed data and, like humans, learn from their errors to improve their performance. Traditionally this was done with easy to interpret models such as linear regression, where cause and effect are clear. As computational resource has become cheaper and readily available via the cloud, more complex models such as decision trees can find thousands of combinations of input features to predict outcomes. AI tends to refer to algorithms called neural networks. Neural networks are able to identify non-linear patterns, often unclear to humans, to create highly accurate and nuanced predictions. The holy grail of AI will be general purpose AI whereby machines can learn to perform tasks without set objectives, however this is still a long way off. Currently AI algorithms can be automated to detect patterns, continuously learn, understand complex problems and make decisions at speed.
1) When a business thinks of AI as a route to augmenting human Intelligence. By using more accurate predictions, forecasts and pattern or anomaly detection to understand customers, business and the wider market companies are able to leverage their strategic thinking to achieve competitive advantage.
2) When AI and automation are used to optimise operations, reducing inefficiencies, wastage and ultimately reducing cost.
3) When AI is used to leverage data from a variety of different sources and industries to create a value greater than the sum of its parts, applicable to a variety of end users. This can result in finding value in data as intellectual property. This can be open source data, for example, weather data, or could include partner data, such as mobile phone data or property data.
Seven steps that need to be considered in every AI project
1. Diagnosing if AI is right for the business objective
Diagnosis is the first big hurdle in any new project. The process must begin with a problem-solving situation and assess whether data science is the right solution for the objective. Appropriate projects are ones where
AI can potentially deepen understanding or add value with speed, accuracy or automation, such as product development, customer insight or even day-to-day operations.
The next consideration is how mature the business is in terms of its data management. The general consensus is that an organisation does not necessarily need to have reached a level of maturity in data management and governance before considering AI, because in many cases it is possible to outsource to a third party. This does depend on whether the company is looking to adopt the technology in an internal-facing or external, customer-facing capacity. For the latter, a certain level of maturity needs to have been reached, otherwise there are a number of risks. This could include unexplainable black box outcomes for customers which may not be GDPR compliant or the risk of reputational damage. However, for innovation or general hypothesis-based experiments, it is not necessary. The best way to drive value from AI is to focus on the transformation that is required within the business and agree on objectives and outcomes that need to be resolved.
So, start with the position of answering key questions. If AI is the best solution use it. But using it for the sake of it is rarely useful and often an expensive and worthless exercise.
