Why companies must be ruthless with their AI prioritisation

The past few years saw companies aggressively pursuing investments in AI. Every company surveyed in a recent MIT report stated that they plan to increase their AI spending over the next year, with half saying it will increase by over 25%. 

So, how can companies ensure they are keeping pace with this AI innovation race, whilst being conscious of finite resources? The answer is not necessarily to reduce spending, as that risks falling behind the competition, but rather to be ruthless with AI prioritisation. Organisations need to ensure they are avoiding a scattergun approach when it comes to AI investment, ensuring they focus on the products that will generate the biggest impact - either by increasing efficiencies, or just as important, boosting revenue.

Invest in solid data foundations

The first area of prioritisation, which will affect all further AI adoption, is to ensure that solid data foundations are built. Without these, companies will simply not be able to deal with the vast amount of data processing demands, nor the quality of data governance necessary to support rapid AI innovation. This involves investing in fit-for-purpose data architecture, such as a data intelligence platform. Data intelligence platforms revolutionise data management by using AI models to automatically analyse all data across an enterprise and how it is used. This ultimately empowers every employee, even those in non-technical roles, to understand how to best store, use and leverage data. 

This is a crucial step, particularly as 40% of CDOs, CTOs and CIOs state that the biggest difficulty they face with their data and AI platforms is the training and upskilling of staff. Clearly, it is vital to equip those in technical roles with the skills needed to drive innovation, whilst ensuring safety and security. However, by ensuring that there is effective data intelligence throughout an entire organisation, companies can put themselves in the best possible position to firstly, choose the correct AI products to invest in, but then also develop and implement them in the most efficient way possible. 

Choose between offence and defence

When it comes to choosing which project to focus on, there has to be a balance struck between pursuing quick wins and going after more complex tasks that use up more resources, but could offer significant benefits. But also, is whether to focus on ‘offensive’ or ‘defensive’ use cases. Much of the talk around generative AI’s impact has been focused on ‘defensive’ use cases that cut costs or reduce risk, such as the automation of repetitive tasks that otherwise drain resources or are prone to mistakes. However it is only one piece of the puzzle; 70% of CIOs, CDOs and CTOs report it’s very important for AI projects to reduce costs, but the same percentage also put equal importance on enabling revenue generation. There are many AI use cases that are more ‘offensive’ in nature, being used to drive revenue, and companies must ensure they also prioritise these if they are to be successful moving forward.

For example, companies can use AI to analyse vast amounts of data in order to identify new markets and untapped opportunities, thus greatly increasing potential revenue. With regards to retention, a crucial aspect of driving revenue, AI can also be used to analyse subtle changes in customer behaviour. This can allow teams to proactively solve customer problems, thereby reducing churn. And, finally, AI models can offer significant value when it comes to forecasting. By identifying subtle patterns and trends that may be missed by a human, AI can support data-driven decision making across a range of areas, from optimising stock, to fine-tuning production schedules. This list is by no means exhaustive, which just goes to show the profound effect that AI is having on driving revenue for businesses across every industry.

Know which products to pursue

How to balance between the two types of AI use case will depend on differing industries and geographies. For example, companies must weigh up whether the potential costs of noncompliance or a data breach are greater than the potential upside of onboarding a new customer to drive revenue. This is where teams can develop scorecards to help determine what should be focused on, which include three main categories: strategic importance, feasibility and tangible ROI. This gives a result that balances how important a use case is to meeting business goals, how achievable it is given current infrastructure and how easy it is to measure success. By including all areas of the business, this scorecard can give a strong indication as to where money and time should be spent.

Advancements in generative AI have laid bare the impact that its improvements to productivity and efficiency can have on both cost savings and on the ability to drive revenue growth. However, this has created a reality whereby the risk of getting something wrong has increased exponentially, and the upside of getting them right has gone up just as much. As a result, the effective prioritisation of where to spend precious resources and on which products has become one of the most important factors to long term, sustainable growth for businesses all over the globe.