My real life experience: I was building a solution for a client using ML techniques but in the following meeting he said he expected outputs from a mathematical model. My solution: I acknowledged the inputs that he gave while explaining why I used ML techniques to solve the problem. I laid out a few points like generalization over different features, continuous refinement with more data. Later I noted his concerns and dived deep into existing research as per his requirements. The important step that I took is I scheduled the call in the following week so that I can have some time and they also didn't feel any lag. While addressing their fundamental problem I also included the aspects of my ML approach in later stages of the project.

When an algorithm's output conflicts with stakeholder expectations, the key is to navigate the situation with transparency, data-driven reasoning, and collaboration. First, analyze the discrepancies—are they due to flawed assumptions, biases in data, or unrealistic expectations? Clearly explain the logic behind the algorithm’s decisions while actively listening to stakeholder concerns. If necessary, refine the model, adjust parameters, or incorporate feedback without compromising accuracy. Aligning business goals with AI outcomes requires balancing technical integrity with stakeholder trust, fostering a solution-oriented dialogue that bridges gaps between technology and expectations.

In my experience it is always a good call listening to stakeholders. Explain your thought process, and how the algorithm came to the conclusion it did. Take some time and understand their point of view, and identify what went wrong. More often than not, the issue is the result of a miscommunication. After identifying the issue, explain alternative approaches that can quickly be implemented, and will satisfy all expectations.

Clarify stakeholder expectations by discussing assumptions and key metrics. Explain the algorithm’s logic transparently and provide data-backed insights. Identify gaps between expectations and reality, then adjust parameters if feasible. Offer alternative solutions or refinements to align outputs with business goals while maintaining accuracy. Keep communication open and solution-focused.

The Key is to assess the reason. in terms of project planning basic agenda is to assess the project requirements and build a optimised solution for the proposed problem statement. Analyse the delta between the observed and expected data and reason for the deviations. Assess whether the proposed solution is aligned with the project requirements. Often an unexpected output can be seen as a better solution for the problem. Once you have collected the data. Build clear and active communication with all the stakeholders to explain the benefits and drawback it provides. Propose a fall back model in case things went sideways. Mutually agree to a conclusion which will be better suited for the project in terms of cost estimation and deadlines.