best practices AI project

best practices AI project

They say that two heads are better than one, and ‘teaming up’ with an AI can increase the productivity and problem-solving capabilities of your team in a variety of business functions. However, just as it can take time to oil the gears of a professional relationship, setting up the right foundations, structure, and direction of an AI application requires careful planning. Here are the four foundational principles for nurturing a healthy AI system to support your business goals.

1) Take a Holistic View Of Your Data

Data is a bridge between the tangible and intangible. AI-enabled data analysis programs translate behaviours and patterns into meaningful codes that can be analysed and understood. As a communication vehicle, data therefore needs to be made accessible to its potential audiences. If the data misses its target audience, the results are effectively meaningless.

When programming an AI application, think about who will be using the results, and why. Ensure that the coding reflects the needs of the end-user and allocate part of the project budget to ensure that this is on point.

2) Ensure That Data Scientists Are Aligned With Business Needs

The possibilities of AI applications are almost limitless, and it’s easy to lose focus during an AI development by investing too heavily into capabilities at the expense of outcomes. This can dislocate a project from its primary goals – which is to support specific business functions. When designing an AI for data collation, cleansing or analysis, keep these business goals at the heart of the project.

3) Create a Sense of Urgency

AI is an often-misunderstood area of software programming, and even among software developers, not everyone is clear about what AI can and cannot be used to achieve. As a consequence, it can be challenging to build a business case for AI applications as an alternative to more conventional data processing and search solutions. AI projects can be pushed to the back of the funding queue, can struggle to find support, and may be under-resourced. As such, many AI data collection projects fail to achieve their full potential.

Creating a sense of urgency and relevancy among stakeholders can help the company to understand the value of AI-driven data processing applications. Use data and statistics to demonstrate the value of AI and its applicability to your business goals, provide the stakeholder with tangible ‘early wins’, and design tight project timescales to infuse the development process with momentum.

4) Ensure The Data Has Structure

AIs are inherently logic-based software programs, so unless there is coherent structure in your data architecture, the AI will struggle to deliver relevant search results. When setting up the data management and search framework, try to ‘think’ like an AI. Base the data structure on rigorously logical patterns and ensure that they are tested and tweaked prior to the launch.

Find Out More

At Flare Solutions, we help businesses deploy custom AI solutions for a range of data management and intelligent search functions. For more information about how AI applications can help you organise and access your data in a more efficient way, or to learn more about the best practices for data cleansing, please contact our software specialists today on 02033977766.

Get in touch if you’d like to know more.