Due to growing scope of AI solutions, there is a high demand to develop and integrate AI solutions in various domains. When there is a request for AI based solution for certain use case, it is important to analyse if AI is required to solve the usecase. Once that is clarified, the clients have to be informed on the challenges, complexity, resource constraints for building one. Performance, accuracy constraints, security, changes, scalability and reliability of the solution have to be discussed.

The demand for AI solutions is undoubtedly growing every day, but it’s important to evaluate whether an AI solution is genuinely necessary or if a manual or pre-existing approach would suffice. We shouldn’t adopt AI everywhere just because it’s trendy. I follow a four-step strategy: 1. Formulate the problem mathematically. 2. Identify a solution. 3. Assess if the solution is optimized in terms of cost, energy, and use case. 4. Deploy the solution.

There are various different ways to deal with this scenario but the ones which I would suggest are below: 1) Just don't flow with the hype of AI in the situation. If things can be done by choosing AI for one microservice, we should continue with that and don't put the usage of AI in everything. 2) Secondly, it is very important to understand what exactly the client needs. The new gen. AI solutions lack the proper fulfilment of exact solutions, so we should exactly fulfill the needs with exaggeration of AI 3) Finally, it's very important to understand the complete flow of what the system is performing and how the system is performing, so we should also put this factor into account to understand the solution.

Managing high client demands for AI solutions requires clear communication and a structured approach: 1. Begin with discovery sessions to understand client needs, clarify expectations, and explain AI’s capabilities and limitations. 2. Define the project scope with SMART goals and prioritize deliverables based on business impact. 3. Maintain ongoing communication through updates and feedback, involving clients in critical decisions. 4. Use agile methodology for iterative development, delivering incremental value and adapting to changes. Since AI projects often begin with an unclear scope, regular client communication is crucial to ensure alignment and smooth adjustments throughout the process.

The demand for AI solutions is indeed significant across industries. However, many clients may not fully grasp the complexities of AI development, deployment, and maintenance. Clients often have high expectations regarding AI capabilities without understanding the trade-offs and requirements of implementing these solutions. Role of Engineers and Stakeholders. Engineers and stakeholders have a pivotal role in bridging this knowledge gap: 1. Phased Explanation of AI Development. 2. Communicating AI Limitations. Understanding Business Value Action for Clients For effective AI implementation Invest in skilled professionals with expertise in MLOps, LLMOps, and related fields. Encourage collaboration between technical teams and business units.