AI is everywhere and no organization can afford to ignore it. It has caught the imagination of businesses and there is a frenzy to invest in AI according to Gartner’s research on buying behavior which shows 92% businesses are planning to investing in AI-powered tools in 2024. Gartner also predicts that the AI market will experience strong growth until 2027 registering an average growth rate of 19%.
Bouyed by innovations in cloud computing, machine learning, and big data analytics, business are empowered to experiment and deploy AI applications at a rapid pace. The dominant role of AI is becoming increasingly evident in the way AI influences how humans interact with software and devices to create unique experiences.
Designing and developing an AI product is complex and requires domain expertise, technical understanding and an intuitive understanding of human needs. Even as companies are in a tearing hurry to find a foothold and consolidate its position with AI software, it must be accompanied by deep analysis and strategic planning. Developing a successful AI product is more than a technology initiative—it requires comprehensive understanding of the business problem, the nuances of its applications along with insights into what the AI injection is likely to create.
Indeed, a great idea is not sufficient to ensure the product success without a comprehensive understanding of the market, a well-defined value proposition and the right data to fuel the AI model. It requires aligning the technology with business goals, securing stakeholder buy-in, and continuously refining the product to meet evolving customer needs and expectations.
NewVision has had a head-start in helping customers across industries to develop AI products and solve business problems. Our methodology to build the right product comprises three key stages: Strategy which includes Planning; followed by the Discovery of the right product; and Development strategies aligned with those goals.
This blog will explore the first phase of strategy and planning wherein we evaluate the idea by defining the AI product’s vision, and goals and identifying the problem to establish a solid foundation. Based on our experience and understanding, here is a quick guide through each step of the process during this stage.
Laying the Foundation with Problem Identification
The AI development journey must begin by pinpointing a specific problem with analysis to understand how AI can address a certain problem, such as enhancing customer experience with optimized process or ensure higher savings with real-time insights. This is crucial to determine which AI technology to use—whether it’s machine learning, natural language processing or computer vision.
At NewVision a key part of the problem identification process entails interviewing stakeholders to align business goals and priorities, and then create a business model canvas based on these interactions. It provides a comprehensive overview of the challenges, and the current and future need of the organization and facilitates assessment from multiple perspectives. The value proposition of the product should stand strong on its own, making a compelling case for the integration of AI, but without relying solely on it.
Gathering Data: The Backbone of AI
Once the problem is defined, the next crucial step is gathering data. As the saying goes, a model is only as good as the data it is trained on. This means the data must be relevant to the problem, free from biases and comprehensive enough to cover various outcomes.
Data typically comes in two forms: structured and unstructured. Structured data is well-organized and easily searchable such as, a spreadsheet with columns for names and addresses. Unstructured data is more complex that includes social media posts, transcript from a customer service call, data from sensors and logs, etc.
Often unstructured data requires lot of preparation from systematic gathering and aggregation to data cleaning, labeling, transforming, normalizing to validation and augmentation. Given that the quality of the algorithm is directly proportionate to the quality of data, data preparation is a key part of the AI development strategy.
Our experience finds that investing time and effort in data preparation is worth every bit of effort.
Selecting the AI Technology
After data preparation, selecting the right technology such as machine learning, natural language processing (NLP) or augmented reality is crucial. This must be aligned with the business objective, such as, if the objective is to achieve sentiment analysis then NLP will be more beneficial than machine learning, as NLP can detect deeper insights and nuanced meanings, such as sarcasm.
Similarly, businesses wanting to convert audio calls to text for record keeping will be well-served by implementing speech recognition technology. Key considerations during this evaluation include the following.
Data Availability and Quality: The amount of data available as well as the diversity of data is crucial to train the model. High-quality data that is comprehensive, consistent, and relevant to the algorithm is needed to ensure high accuracy in the model.
Scalability: Important to ensure AI workload can access vast compute and storage resources. Whether a modular architecture is best suited and whether the product will require continuous integrations and improvements.
Portability: Ensuring the product functions seamlessly across platforms including different clouds, in the core, edge, and end points is important to future-proof it and have embedded adaptability.
Building AI with Precision and Purpose
Developing a successful AI product is a complex journey that requires careful planning and execution. Forrester estimates that in 2023 organizations lost billions of dollars in AI initiatives due to poor quality of data. By clearly identifying the problem, gathering the right data, and selecting the appropriate technology, organizations can create innovative AI tools to thrive and dominate in the market.
As organizations struggle to scale projects, the role of partners in designing and developing will become increasingly prominent. If you want to stand out in the competitive landscape with a compelling AI product and want to know how NewVision can help you get it right, write to us at contact@newvision-software.com
