In many industries, advancements in technology and data processing capabilities have seen the emergence of artificial intelligence (“AI”). While AI is a broad term that can mean many things, it typically refers to the ability of machines and computers to perform human tasks or imitate human behaviour. Some examples might include an algorithm that predicts the likelihood of a patient contracting an infection, a self-driving car, or a piece of software that automates a set of routine, manual tasks.
Business Intelligence (BI), on the other hand, has been around in some form for a number of decades. BI refers to the tools and processes used to turn large volumes of data into actionable insights. Most organisations today would have some type of BI platform and a team that produces business-critical items such as data warehouses, dashboards, scorecards, and KPIs. These tools enable staff at all levels to understand performance and make informed decisions.
Advancements in technology have also blurred the lines between classic BI and other disciplines like data science, machine learning, and big data. With so many buzzwords, it can confuse users, and within an organisation, BI can mean different things to different people. The challenge many organisations face is how to transition from classic BI to AI and who in the organisation is responsible for that. Whilst defining responsibility is important, the most important thing is to be clear on the purpose; that is, what we are doing and why we are doing it. In its simplest form, irrespective of how it is defined, organisations are trying to use data and technology to solve business problems and improve performance.
When we understand this, the challenge becomes less about transitioning from one technology to another and more about building capability. In terms of capability, the question becomes: does my team or organisation have the right people, technology platforms, and processes to maximise the opportunities which data and technology provide?
Whilst AI is a broad term which can mean many things, it typically refers to the ability of machines and computers to perform human tasks or imitate human behaviour.
When I am hiring, I look for motivated, solutions, and customer-oriented people, not necessarily someone with the best technical skills. Whilst it is important they have solid technical and data skills, the most important thing is to have a solutions-focused attitude and a willingness to learn new technologies and ways of doing things.With respect to technology, it is important to have adaptable and flexible platforms.The cloud provides a great opportunity here as it enables server environments and new technologies to be provisioned quickly. All major vendors now have software as a service (SAAS) offerings, which means there is no need to purchase and maintain physical hardware; this all happens seamlessly behind the scenes. A major benefit of SAAS is that you’re always using the latest version of a piece of software, meaning you always have access to the latest features.
Even when an organisation has access to the latest technologies, there is still no guarantee that staff will want to use them. Most people want their job to be as easy as possible, but they don’t really care what product or technology they use. If a 10-year-old technology is doing everything that is required, then there may be no need to reinvent the wheel. Therefore, it is essential to get stakeholder buy-in from the very beginning before beginning a new project. Transitioning from traditional BI to AI can be challenging. However, when the focus remains on building a high-performing team supported by flexible technology platforms, then the challenge becomes less.
And when each project starts by clearly defining the business problem and gaining stakeholder support early on, then introducing a new technology becomes far easier.
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