The core ingredient for Artificial Intelligence deployments is data. This can include structured data, such as tables, databases, and forms as well as unstructured data such as photos, videos and documents. Many organisations assume that AI projects centralise around structured data and this has historically been the case. However, with the advances of AI systems over the past 5 years, there are now huge opportunities to leverage images, video, audio and documents and unlock operational efficiencies.
1. Understanding Organisational Objectives:
Before embarking on an AI project, it is imperative to ensure that there is synergy with the overarching business objectives. As part of a feasibility study, we conduct in-depth consultations with key stakeholders to understand the unique challenges, goals, and operational expectations of the business. By gaining situational awareness of the organisations operations and strategy, we can tailor our AI guidance to address specific pain points and drive tangible business outcomes.
2. Evaluating Economic Viability
In addition to technical considerations, we understand that AI initiatives must demonstrate economic viability and return on investment (ROI). Our feasibility studies encompass rigorous cost-benefit analyses, where we quantify the potential value generated by AI adoption against associated implementation costs. By evaluating factors such as cost savings, revenue enhancement, and competitive advantage, we provide clients with a clear understanding of the financial implications of AI integration. Through transparent and data-driven assessments, we empower organisations to make informed decisions regarding their AI investment strategies.
3. Defining Use Cases and Scenarios
AI is a versatile tool with diverse applications across various domains. In collaboration with our clients, we identify and prioritise potential AI use cases that align with their business objectives. Whether it's enhancing customer experience, streamlining operations, or driving predictive analytics, we leverage our domain expertise to build compelling AI scenarios tailored to the client's industry and requirements. By defining clear use cases, we ensure that AI solutions are purposeful and directly contribute to organisational success.
4. Conducting Technical Feasibility Analysis
While the conceptualisation of AI use cases is crucial, it is equally essential to assess their technical feasibility. Sendient’s team of AI experts conducts comprehensive technical assessments, evaluating factors such as algorithm suitability, computational requirements, and scalability potential. Through prototyping and split testing, we validate the feasibility of proposed AI solutions, iteratively refining our approach to align with technical constraints and opportunities. By leveraging cutting-edge AI technologies and methodologies, we ensure that the proposed solutions are not only feasible but also future-proofed for scalability and adaptability.
5. Identifying Data Anomalies and Inconsistencies
When building out a data strategy, it is essential to identify and rectify anomalies and inconsistencies that could compromise the integrity of AI models. Sendient’s data scientists utilise advanced data profiling techniques and anomaly detection algorithms to uncover irregularities such as missing values, duplicates, outliers, and inaccuracies. By systematically addressing data anomalies, we ensure that the resulting AI models are built on a solid foundation of accurate and consistent data.
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