Artificial intelligence (AI) has become a buzzword in today’s business world, promising everything from efficiency to predictive insights. Yet, implementing AI is not always straightforward.
Companies, including those outside tech-heavy industries, often worry about the costs, risks, and complexities associated with it. Ultimately technology should serve people rather than overwhelm them. But how can we implement AI to work for us?
Embrace AI as a tool, not a threat
AI’s role isn’t to replace people, but to support them. The key is to view AI as an assistant – handling repetitive, data-intensive tasks, freeing your team to focus on higher-value activities like client engagement and managing complex cases. For instance, brokers can use AI to automate affordability checks or pre-populate application forms, saving time for tasks requiring personal expertise. Conveyancers might find AI useful for automating routine tasks like generating title reports, allowing more time to focus on intricate legal matters. Think of AI as a tool that enhances capabilities, not as a substitute for human expertise – like a dishwasher that simplifies tasks but still requires human oversight.
Build trust with incremental steps
One of the biggest challenges companies face with AI is trust. Thanks to its portrayal in the media and popular culture AI is often perceived as unpredictable. When deploying AI start small, measurable projects that allow us to build confidence in the technology. For instance, automating meeting summaries and task management doesn’t seem as impressive as fully automated decision-making, but these smaller initiatives demonstrate AI’s usefulness without risking critical operations. Small, practical applications slowly build a foundation of trust for larger projects.
Use data wisely
AI is only as effective as the data it receives. Rushing into AI often means overlooking the need for structured, high-quality data. Without it, AI models struggle to produce reliable outputs.
Start by defining the problem clearly and collecting relevant data to understand the scope. For instance, if conveyancers spend excessive time on repetitive questions, quantify this and collect data to assess the impact. This empirical approach ensures AI solutions are grounded in addressing genuine issues. Before diving into AI, ensure your data is accurate, relevant, and properly organised.
Focus AI strategy on your needs
AI is a broad field, encompassing everything from machine learning to chatbots. It’s important not to get carried away by trends. Companies should adopt AI that suits their specific needs, rather than following hype. Continually ask, “What problem does this solve?” If there’s no clear answer, revisit the technology and assess if it genuinely aligns with your goals. AI implementation should be purposeful and practical, tailored to your organisation’s goals and context.
Demystify AI
Fear of the unknown is a common barrier to AI adoption, as employees often worry that AI might take over essential functions. Actively involving stakeholders from the outset can address these concerns. Gather feedback on existing challenges and explain how AI tools support, rather than replace, their work. Training sessions and forums to discuss AI help dispel myths and identify early concerns. If AI is introduced as a helpful assistant, rather than a replacement, people are more open to it.
For example, using AI to automate routine tasks like organising documents—not to replace case managers. Once people see that AI simplifies rather than replaces their work, they’re more open to embracing it.
Build AI solutions around real-world testing
Before committing to a new AI project, conduct a series of tests to validate the solution’s effectiveness. Use a hypothesis-driven approach: define what we expect the AI to achieve, and then we test these expectations against real-world scenarios.
This testing phase includes a cost-benefit analysis, helping us understand whether the technology’s benefits justify its costs. Many promising ideas don’t survive this phase, but this is a critical step to ensure that your chosen tech is genuinely beneficial.
Maintain human oversight and feedback loops
AI should support, but human oversight remains essential. Automated systems can handle routine tasks, but complex decisions require a human touch. Keep humans in the loop by allowing your team to review and adjust AI processes as needed. Feedback loops help assess whether AI meets its goals and reassures teams of their essential role in AI workflows.
Communicate the long-term benefits
AI’s true potential lies in the efficiencies it creates over time. Communicating these long-term benefits helps your team understand AI’s value, encouraging a proactive approach. Implementing automated remortgaging solutions, for instance, initially required time and training but has since reduced processing times and improved accuracy.
Implementing AI without fear is possible with a strategy that centres on transparency, practicality, and a people-first approach. By treating AI as a supportive tool rather than an all-powerful solution, organisations can harness its strengths while addressing employee concerns.
AI should enhance our digital transformation journey—bringing balance to processes making them digital when it can be, and personal when it matters most.
Rui Sousa is solutions architect at Movera
