One of the biggest benefits AI brings is its potential to empower people and provide solutions to their needs. While AI tools are being deployed across a diverse range of businesses, many of them fail because they weren’t built in a human-centric way, leaving the original problem unsolved.
Businesses constantly seek to attract customers, grow revenue and reduce costs. While organisations invest in AI platforms, true return on their investment is only reflected after practicing a holistic view of the whole ecosystem and analysing what are the needs and behaviours of both end users and those delivering the service, as well as what is the problem the tool will help to solve. This should be the main principle companies consider to anchor an AI system.
With over six in 10 organisations (61%) planning to increase their AI spending in the next year, according to a recent report from McKinsey, integrating AI into processes successfully at scale will depend on keeping humans meaningfully in the loop.
When designing an AI system it is crucial for organisations to ask themselves questions such as what are the business’s goals, what does a great journey look like, and what talent, processes and technologies are needed to deliver a quality experience. By formulating these questions, companies have the opportunity to tailor the experience to a specific individual rather than broader group, while having a complete and real context and understanding on how the tool will address a problem and user needs. Without this information, even the most advanced system can cause teams to struggle to implement the tool, and therefore its adoption can stall.
In the rush to introduce AI, companies tend to make a common error: attempt to replace human expertise rather than create a tool that supports their teams. The result? Distrust in the system, as experts can’t verify immediately the reasoning behind a generative text produced.
In human-centric fields, evidence from Stanford University and the Cabinet Office suggests AI is best received and most effective as an assistant rather than replacing human interaction.
For instance, experts such as frontline engineers in heavy industries lose hours every week retrieving and reviewing operational data manually, such as logging into portals, adjusting date ranges and building reports. By the time they finish these tasks, they haven’t been able to start working on the actions that require their expertise.
Hence the use of AI, only when it is well-designed, appears as a tool that can shift how analysts spend their time, allowing them to focus on what only humans can do: expertise, creativity, relationship-building, and decision-making based on data. It’s essential to keep in mind that AI tools can be used to present relevant data to professionals, but humans are the ones responsible for deciding what to do with that information, with the evidence and authority to question the system’s output.
When AI is introduced, it should be easy for teams to make use of it by being able to see where the information came from, who provided it, and subsequently give feedback that allows the system to improve. These insights are therefore crucial, as they allow teams to refine system instructions and update the contextual knowledge necessary for the tool to fit business goals and deliver a quality experience.
Through this approach, AI can deliver structured outputs such as categorised decisions, clear labels and intuitive data visualisations, enabling analysts to detect errors earlier and apply their own expertise to drive better results. Far from being just a regulatory requirement under the EU AI Act, showing the source and making errors easy to flag helps teams decide when to rely on an answer, integrating the system seamlessly into their daily workflows.
When we built Ombo, for example, we created a system in which disclosure was present from the start, inspiring confidence in the information users were reviewing. Through clearly labelled AI-generated answers and a clear route to a person when an answer needed checking or a decision required judgement, employees could trust the guidance the tool was providing, as they had access to key context such as information source, who they should reach in case of clarification, and decision tracing.
Human expertise is essential to anchoring an AI system in the real world. While these tools can learn patterns and probabilities to provide better outputs, human judgement is more valuable when navigating real-world context and nuance.
The best cases of AI use are those in which humans are brought into the loop when they’re needed, ensuring that each output produced by a Machine Learning system aligns with the organisation workflows, culture and standard procedures. It’s our job as experts to figure out when and how often that should be to mitigate risk.
A successful AI deployment occurs when the tool is used by experts as an assistant that allows them to spend less time on operational tasks, and more on applying their expertise on areas where it’s needed. We should measure whether the tool reduces routine work without compromising the quality of decisions. Clear sources, effective review and feedback help teams use it with confidence.