Over the last few decades, AI has moved from the domain of science fiction into an integral part of our everyday lives with real world applications. Global C-suite leaders are recognising the potential and future possibilities of AI; in fact, according to Gartner research, adoption of AI tripled in 2018, with 37 per cent of firms already implementing AI in some form.
However, this is not to say that implementing AI within the enterprise is an easy task, and there are a number of hurdles that companies will need to vault in order to reap the benefits - from lack of standardisation, to complex tooling, lack of quality data and shortage of skills. However, there are also simple measures organisations can take to better prepare for the imminent AI revolution.
Four steps to AI readiness
For AI to be successfully adopted, organisations need to consider these four-level strategies:
- Come together and learn the fundamentals of AI
In order to increase the chances of AI having a valuable impact on all departments, organisations should aim to have some basic knowledge and understanding of the technology.
To harness the real power of AI, organisations can’t just go to an AI consulting firm and ask them to optimise your profits. Building an AI strategy must start with those at the managerial level to spread awareness of the technology and its potential. This doesn’t mean everyone has to be or become an AI expert, but to introduce them and highlight how AI can bring valuable change to organisations. So, in order to succeed with AI, a strategy must be communicated throughout the organisation; this, in turn, will help avoid confusion or conflicting approaches around implementation.
- Selecting the right issue to address
The main objective for organisations running AI projects should be to help solve core problems, opportunities or challenges. The problems that AI can solve could be in areas organisations have not thought about venturing before, even if it is beneath their noses.
AI is now being used to contribute to various organisational goals and provide solutions to problem areas. Firstly, these include using AI as a tool to help streamline businesses and improve profitability. Secondly, to assess what areas of the organisation generates revenue at a low profit margin – these revenue streams could provide fertile ground for automation and acceleration via AI. Thirdly, to help organisations review their costs and pinpoint the ones they would like to reduce. AI can provide better understanding to what generates costs and identify areas that could be optimised or changed to reduce them. Fourthly, to implement a tried and tested AI model that is able to reduce the margin of error delivered by employees. Lastly, to manage repetitive and mundane tasks that take up employees’ time and can be demoralising.
- Run through your data inventory
Despite data being a key contributor to driving AI solutions, C-level executives are still reluctant to implement solutions from data sets. According to a KPMG survey, 67 per cent of CEOs have admittedly ignored insights from data analysis and have instead made decisions based on their own intuition and experience. Organisations need to embrace data-driven strategies so that data streams can be used to reduce or eliminate organisational problems.
However, sometimes finding enough valuable or suitable data is not possible. If that’s the case, organisations will need to consider how they can create, find or even purchase this data. The mere act of looking for data can help spark helpful practices that yield exactly what organisations need to get an AI project off the ground.
- Choosing the correct tools
The final step is choosing the right tools. Previously, AI tools were used towards academic research and as a proof of concept. Now, new technologies are emerging from AI that are allowing organisations to build AI models faster and without having to overspend. Since software and hardware used for AI is going through rapid development, organisations need to ensure the solutions they choose are scalable and future proof; this will help ensure that high maintenance costs are avoided.
Adopting an operational AI platform approach will allow organisations to interact with AI faster, more effectively and in a more visual way than drafting code. This reduces the complexity, while also automating certain tasks so that data scientists and IT experts can focus on innovating. Importantly, it also ensures that projects are auditable, repeatable and scalable, helping to make AI more enterprise-friendly.
AI for all, not for the few
Adopting AI will not be an easy task. However, organisations that explore the unchartered waters of AI will sharpen their competitive edge, lower operating costs and innovate faster. Adopting these practical steps will help organisations to get started, helping them to plan strategically for the future.
With AI set to make a big impact in every industry, it is important organisations choose the right tools that will allow increasing collaboration across the entire network and increasing delivery of overall efficiency. Operationalising AI will enable organisations to execute AI projects at scale and at speed from start to finish and help support all tasks across the AI project workflow. Ultimately, this will help organisations to create a level playing field by putting AI in the hands of the many, rather than the few.
Luka Crnkovic-Friis, CEO and co-founder, Peltarion