The Covid-19 pandemic has affected all businesses around the world, and recovering from its effects will be a top priority for the remainder of 2021 and beyond. While some businesses struggle with the new reality, others have seen it as an opportunity to improve their data and analytical assets, operationalise, and update their processes.
The key to business success today is the ability to make better decisions faster. This all hinges on the ability to analyse data, but the analytics to work through AI, and then leverage technology to train algorithms that enhance the decision-making process using ML.
With the sheer volume of data available today, it is beyond human ability to gather, analyse and deliver insight in any meaningful timeframe. Crucially, adoption of AI/ML should not be seen as a replacement for human resources, but rather an augmentation of human ability.
The goal should be to use data and analytics to increase revenue, improve efficiency, and respond to customer/market trends, driving better decisions that create a competitive advantage.
According to Gartner, by the end of 2024, 75% of enterprises will operationalise AI, driving a fivefold increase in streaming data and analytics infrastructures. Grand View Research states that the global AI market size is expected to grow at a Compound Annual Growth Rate (CAGR) of 42.2% from 2020 to 2027. A McKinsey survey reports that, for financial year 2019, 66% of respondents agreed that adoption of AI/ML in their business has helped increase revenue, while 40% cited a decrease in costs with the adoption of AI/ML.
What this all means is that AI/ML is no longer a competitive advantage, but is necessary simply to keep pace with global business. However, it can be challenging to get right, as highlighted by a Deloitte report that states that somewhere in the region of 94% of enterprises face problems when it comes to implementing AI.
Before implementing AI in data analytics, organisations need to look at their data and make sure that they have sufficient data points for the AI to process. Without enough data points, AI will inevitably be biased toward a certain outcome, which means it will not provide meaningful analytical insight.
Quality data is essential in reducing noise and bias in the data, which in turn is essential for more accurate outcomes. It also reduces the computational power required by analytics, and speeds the model training process for ML, if data is clean and relevant from the outset.
It is also important to implement AI in the right place. Not everything needs AI to solve a problem, and an indiscriminate approach will reduce both value and impact. Additionally, organisations need to manage the change to maximise adoption and reduce the amount of confusion that may occur.
The biggest issue is that using AI in data analytics is not just about applying AI models to the data. It also needs an understanding of the data being captured for the analytics purpose while understanding which models would yield the best results. These are not skills that many enterprises necessarily possess in-house, which is why partnering with a reputable technology provider is key.
Maximising value from AI requires enterprises to focus their efforts on the right business lines with the right AI models. An experienced partner can help organisations understand the nuances of data and assist with gaining meaningful insights to drive business capabilities to a competitive advantage. In addition, a technology partner can help organisations understand which areas of business could optimally benefit from the use of AI.
AI/ML-related solutions will always provide an edge to business decision-makers, as they can simulate thousands of models and iterations and understand the risk and returns from each iteration, which is practically impossible without these next-generation technologies. Playing catch-up is now vital, as organisations that have not yet jumped on the AI/ML ‘bandwagon’ will find it increasingly difficult to remain competitive.