Artificial Intelligence (AI) has come a long way over the past few years. Following a period of experimentation, real life use cases are now driving innovation across businesses. AI-enabled workflows are expected to increase from 3% to 25% by the end of the year and the majority of businesses (78%) are deploying AI in at least one business function. But despite a surge in adoption, only 25% of businesses have seen ROI from AI thus far, meaning most organisations aren’t gaining full value from their AI deployments.
Poor ROI is especially damaging in the mid-market, where losses from failed AI projects have a bigger impact compared to a large enterprise where losses might be more easily absorbed. Too often, businesses move quickly into implementation without a clear strategy or abandon AI if the results aren’t immediate, meaning AI can quickly become a money pit. And without a clear way to measure the impact of implementation, it’s nearly impossible to prove its value to leadership and justify investment.
Mapping the path to AI success
But with AI being hailed as a transformative force akin to the internet, and with its capabilities constantly evolving, organisations of all sizes need to cut through the noise and ensure they drive ROI from their AI initiatives. Here are three crucial steps that will set organisations up for AI success:
1. Start small with embedded AI
The journey begins with embedded AI – a type of AI integrated directly into existing processes. These solutions are designed to automate routine tasks and streamline operations, hitting the efficiency sweet spot. The most common examples include data entry, claims processing and fraud detection. But while the use cases are relatively straightforward, embedded AI drives immediate gains in productivity that can impact an entire company. The reduced need for manual intervention means organisations have more time for higher-value tasks and can concentrate on what’s really important.
But most notably, by focusing on specific low-risk areas, such as invoice processing and IT support, organisations gather crucial performance data. These pilot projects serve as proof of concept for AI’s ROI, helping to build the business case for further investment, without investing the time and resources for a significant digital overhaul.
2. Uplevel with applied AI
Once the productivity gains of embedded AI can be shown, the next step on the AI journey is to scale up initiatives. With a solid foundation to build on, applied AI allows for more advanced applications that can adapt to market changes. The main use cases include using AI to drive strategic decision-making or enhance the customer experience through personalised recommendations.
The biggest difference is that applied AI moves past incremental efficiency gains and towards innovation-driven growth. But the impact goes a lot deeper. Applied AI acts as the bridge between the experimental phase of AI and organisational-scale projects, giving businesses the confidence to invest more in AI knowing there’s a clear path to value.
3. Measure Business Outcomes
Before diving into a full-scale business transformation, businesses must prove AI’s value with tangible results and analyse which areas of the business could most benefit from further investment in AI. From the very start of their AI journey, organisations should define specific KPIs, such as reducing processing times, cutting error rates or driving cost savings. These metrics aren’t just box-ticking exercises. They’re essential to gain a deeper understanding of whether AI initiatives are delivering on their promise.
Once KPIs are in place, build out robust monitoring systems to keep track of progress. Using dashboards and analytics tools enables organisations to spot inefficiencies that are hampering success, such as slow response times and inaccurate outputs. By focusing on these measurable outcomes, organisations can continuously refine their AI initiatives, building stakeholder confidence to justify further investment in AI projects.
AI done the right way
The journey towards AI integration might seem daunting, but it doesn’t have to be. Whether an organisation is at the start of its journey or has already implemented some form of AI, a phased approach allows firms to receive the full benefits by going at a pace that suits them.
By starting with manageable impact-driven projects and gradually scaling up, significant cost savings can be made while laying the groundwork for further use cases and innovation. If AI implementation is done right, businesses of all sizes can capitalise on the technology’s efficiency and potential, leaving them in a stronger position to accelerate growth.
