The inconvenient truth is that most big data projects fail to deliver the expected return on investment. In fact, Gartner predicts that only 15% of data projects utilising AI in 2021-2022 will be successful.

Companies are spending more than ever on data and analytics projects, often using cutting-edge AI and machine learning tech – but many of them don’t generate the ROI that the business expected. In fact, a recent ESI ThoughtLab study of 1,200 organisations found that companies are generating an average ROI of just 1.3% from AI data projects, while 40% don’t generate a profit at all.

There are lots of reasons why this happens. Sometimes, the expectations of data projects are too high. But more often, companies embark on data projects without a clear strategy and without appropriate skills and resource to replicate the benefits of a pilot project at scale. AI projects require time, expertise and scale to deliver a decent ROI.

This might come as a surprise to some early project teams. Building a proof-of-concept AI data project can be relatively easy – if you have a team of skilled data scientists, a small project could be ready to test in a few months. The challenge comes when organisations try to scale up those prototypes to work in an enterprise setting.

If your data scientists don’t have the appropriate software development skills, then you could end up with a machine learning model that works in principle but isn’t fully integrated into workflows and enterprise operations – meaning it’s not collecting, sharing or analysing the intended data.

Enterprises need to ensure that they have the skills needed to make machine learning models work within their business. This might mean creating an app or integrating machine models with existing sales platforms.  

When a global online home retailer developed a machine learning model to improve the efficiency of logistics, they soon realised that this was only the first step. Data scientists had created a model that was able to predict which warehouse and logistics carrier would be the most efficient for individual projects based on the product size and likelihood of sale in a particular region.

Our development team was able to help  take the project to the next step, by creating ways to integrate this model into existing systems and automate the data collection process. The result is a system that can advise the business which proportion of a product to store in a particular warehouse, and which carrier to use to cut 5% from shipping costs, for example.

To increase your chances of creating positive ROI from data-enabled AI projects, organisations need to ensure they have the right skills in project teams – in addition to data scientists, you will need engineers, process owners and strong DevOps.

Second, ensure that you are measuring ROI over an appropriate timescale. The upfront costs involved in scaling data projects can result in flat ROI in the short-term. Data preparation, technology costs and people development are substantial expenses, and it takes an average of 17 months to show ROI, with firms surveyed by ESI showing a return of 4.3% at this stage.

Third, are you measuring the right things to accurately measure ROI? Capturing the cost savings from automated processes and data availability only tells half the story. By incorporating machine learning into the transformation of enterprise supply chains, logistics and product development, companies can drive increased revenue, market share, reduced time-to-market and higher shareholder value.

To find out more about how you can realise higher ROI from data investment, download our free Playbook here.