Artificial intelligence (AI) has become a buzzword in today’s business world, promising from efficiency to predictive insights. However, implementing AI is not always easy.
Companies including industries with heavy external technologies often worry about the costs, risks and complexities associated with it. Ultimately, technology should serve people, not overwhelm them. But how do we implement AI to work for us?
Embrace AI as a tool, not a threat
The role of AI is not to replace people, but to support them. The key is to think of AI as an assistant – handling repetitive data-intensive tasks that unlock your team’s focus on high-value activities such as customer engagement and managing complex cases. For example, brokers can use AI to automate affordability checks or pre-filled application forms, saving time on tasks requiring personal expertise. Transporters may find that AI can be used to automate routine tasks, such as generating title reports, allowing more time to focus on complex legal matters. Think of artificial intelligence as a tool for enhanced functionality rather than a replacement for human expertise – like a dishwasher that simplifies tasks but still requires human supervision.
Build trust through incremental steps
One of the biggest challenges facing companies and AI is trust. Because of its portrayal in media and popular culture, it is often considered unpredictable. When deploying AI launches small-scale measurable projects, it enables us to build confidence in the technology. For example, automation of conference summary and task management doesn’t seem impressive like fully automated decision making, but these smaller plans prove the practicality of AI without risking critical operations. Small practical applications have slowly established a foundation of trust for large projects.
Use data wisely
AI is only as effective as the data received. Crushing into AI often means ignoring the need for structured high-quality data. Without it, AI models can hardly produce reliable output.
First, clearly define the problem and collect relevant data to understand the scope. For example, if the transporter spends too much time on repetitive issues, quantify this issue and collect data to evaluate the impact. This empirical approach ensures the foundation of AI solutions to solve real problems. Before entering AI, make sure your data is accurate, relevant and organized.
Focus AI strategies on your needs
AI is a vast field, including everything from machine learning to chatbots. It is important not to be taken away by the trend. Companies should adopt AI that suits their specific needs, rather than following the hype. Continuously ask, “What problem does this solve?” If there is no clear answer, revisit the technology and evaluate if it is aligned with your goals. AI implementation should be purposeful and practical, tailored to the goals and background of your organization.
Get rid of the mysterious AI
Fear of the unknown is a common obstacle to AI adoption, as employees often worry that AI may take over basic functions. These issues can be addressed by active stakeholders from the outset. Gather feedback on existing challenges and explain how AI tools support rather than replace their work. Training courses and forums discussing AI can help eliminate myths and identify early concerns. If AI is introduced as a useful assistant rather than a replacement, people will be more open.
For example, use AI to automate routine tasks such as organizing documents, rather than replacing case managers. Once people see AI simplifying rather than replacing their work, they will be more willing to accept it.
Building AI solutions around real-world testing
Before a new AI project is launched, a series of tests are conducted to verify the effectiveness of the solution. Use a hypothesis-driven approach: define what we expect from AI to achieve, and then we test those expectations with reality.
This testing phase includes a cost-benefit analysis, which helps us understand whether the benefits of the technology justify its costs. Many promising ideas don’t survive this stage, but it’s a critical step to make sure the technology you choose is really beneficial.
Maintain human supervision and feedback cycles
AI should support it, but human supervision is still essential. Automated systems can handle routine tasks, but complex decisions require human touch. Keep humans cycled by allowing your team to review and adjust the AI process as needed. Feedback loops help evaluate whether AI meets its goals and ensures a team’s important role in the AI workflow.
Convey long-term benefits
The real potential of AI lies in the efficiency it generates over time. Communicating these long-term benefits helps your team understand the value of AI and encourages proactive approaches. For example, implementing an automated recovery solution initially requires time and training, but has since reduced processing time and improved accuracy.
Through strategies centered on transparency, practicality and human-first approaches, it is possible to implement fearless AI. By viewing AI as a support tool rather than an all-around solution, organizations can take advantage of their benefits when addressing employee concerns.
AI should enhance our digital transformation journey – where possible, and where most important, the process of digitalization is implemented.
Rui Sousa is Mover’s Solutions Architect
