Antes de tudo, é importante deixar claro os limites da IA hoje em dia. Por mais que ela já esteja atuando de uma forma "quase humano", ainda será necessário muito desenvolvimento pra que ela possa de fato passar em um teste de Turing, o qual serve como um teste cego para que as pessoas possam definir se do outro lado estão falando com uma IA ou uma pessoa real. Além disso, vale ressaltar também que a IA não é passível de eventual alucinação, dado que ela nada mais que estatística e probabilidade, e, por isso, podem acontecer certos casos improváveis de vez em quando. Em resumo: o combinado não sai caro 😎

When clients start expecting miracles from AI projects, managing risk becomes essential. It’s crucial to set clear, realistic goals from the beginning and have an open conversation about what’s achievable and what’s not. Taking an iterative approach with regular check-ins helps keep expectations in line with real-world progress, allowing for adjustments as needed. It’s also important to ensure your clients have a solid understanding of what AI can and can’t do, which can be achieved through sharing resources or offering a workshop. This approach helps maintain a balanced and realistic outlook on the project’s potential.

1. You need to explain the limitations, potential challenges, and the scope of what the project can realistically achieve. 2. Engage in thorough discussions to understand the client’s business objectives, pain points, and what they hope to achieve with the AI project. 3. Propose starting with a smaller, manageable pilot or PoC. 4. Use an agile or iterative development approach. This allows for continuous feedback, adjustments, and refinements throughout the project lifecycle, helping to manage expectations as the project evolves. 5. Communicate about scope creep, where clients might request additional features or changes that were not part of the original agreement. 6. Ensure that any changes to the project scope are agreed upon.

Managing unrealistic client expectations in AI projects requires more than just technical skills It demands empathy & education! Clients often lack a full understanding of AI so be patient & help them bridge their knowledge gaps Create an environment of transparency; clearly outline your project roadmap & highlight potential roadblocks Maintain ongoing touchpoints with your clients; by keeping them informed they'll see the progress & understand the challenges in real time And when you face a problem, make sure to always provide a potential solution outlining the pros & cons so your client feels they are a part of the decision making process By following these steps you'll alleviate many of the risks of unrealistic client expectations

You need clear communication, thorough planning, and proactive risk management when dealing with a client with high expectations. 1. Begin by having an open discussion with the client about the capabilities and limitations of AI. 2. Divide the project into smaller, manageable phases with clear milestones. 3. Conduct a thorough risk assessment at the beginning of the project, identifying areas where client expectations may not align with technical realities. 4. Use AI-driven predictive analytics to forecast potential project challenges and outcomes based on historical data. This can be achieved by implementing AI tools to monitor project progress in real time.