Steps to Overcome Common AI Application Development Obstacles

Posted by Joice Thomas on May 21st, 2021

 AI application development processes naturally involve certain inevitable obstacles irrespective of the industries. It does not matter whether this AI-led process undertakes in the manufacturing industry or an IT industry, the mandatory journey from a feasible idea to a final product is ladened with obstacles. Within the cycle of AI application development, you can feel like it is quite an easy thing to construct, train an algorithm, despite intermittent challenges to acquire the appropriate data and anonymizing while adhering to all the relevant regulations! However, to every challenge, there exists a customized solution! Hence, it takes the collaborative efforts of people, underlying processes as well as the platform used in your organization to create that ultimate product. Let us see how you can overcome common obstacles during the AI application development cycle, and adopting steps catapulting your project to a successful completion!

1. Liberate Data Scientists

True. An AI application development process takes a greater investment of your team’s precious time. What you can do is optimize all those functions that your dedicated team is spending most of the time performing respective duties. So,  why you have hired your data scientists?  For the job to do, right? You hired them as they are specialists in the development of machine learning models and related algorithms. But, do you know as per the research, these data scientists spend 80% of their timings in work like sourcing data, cleaning, and organizing them for projects they are doing? However, your data scientists are doing that work which can be outsourced to some third party to save their golden time for AI-solutions work. 3/4^th of data scientists admit that their data collection process is a mundane, least favorite part of the job, and, after spending so much time in it, it is just the 20% quality data that they get at the end. So, for 20% data collection, they end up valuable time on the process not required. The solution is, liberate your data scientists from this work and outsource to 3^rd party. Let your data scientists focus on their dedicated area,i.e.AI solutions and development.

2. Rely on High-Quality Data Sources

 As a leader to your AI application development team, do you realize where you lack in providing them with quality data sources? Do you rely on a short-cut, open-source for data access/usage? You think using crowdsourced data or open-source data can reduce your expenses, or it can help you in cost reduction, the reality is, it will end up costing you much more than expected! These data types may be readily available to you, but they cannot match the same quality that carefully curated data sets can provide. Moreover, these short-cuts, open-source data are error-prone, lots of omissions required while your development team uses them, and plenty of inaccuracies surface, too. Pitfalls are there of open-source data as algorithms tend to replicate and you may invite more competition in the space from other entrants. So, the solution step for this obstacle is to go for reliable, licensed data sets custom created for you.

3. Experienced AI-Professionals

You may hire several AI professionals direct from a college campus, or those having minimum experience in the domain, but, you need to balance your team with higher-experienced in the same. AI professionals with good subject knowledge, having prior work experience in cutting edge technology and digital tools, are also a significant step in overcoming a common obstacle in the process of AI application development. Experienced domain experts, skilled engineers, data scientists, all can help you in meeting higher standard goals and can give you a real-world performance.

 4. Timeline Framing

You know that any AI application development process takes a lot of time, days, months. It is not an overnight process. However, you should keep an eye on this challenge and make it smoother by adopting an accelerated development-specific timeline. Either you can partner with a credible AI application development organization or hire some efficient professionals who can manage tasks on time, or train your current ones for enhancing efficiency and speed. A severe bottleneck or obstacle happens to be those of in-house data collection(you can think of partnering here)and annotation, which may stall or hold the development process. The solution step is to create your in-house library having vast ready-to-use data, and hire people with deep industry knowledge, and a strong global network. If there is no burden of sourcing, annotation, your team can easily manage actual development more smoothly!

 EndNote

 Artificial Intelligence (AI) is the most happening digital technology around the world. It takes time to develop projects based on requirements specific to industries. Moreover, the entire development cycle needs a lot of research, basic work, data collection, implementation, debugging, testing, etc. And only an expert data scientist and AI engineer can spot the gap if existing anywhere in the loop. Steps are needed to implement and overcome the obstacles that surface during an AI application development process. If you want recommendations on this, you can contact Fusion Informatics, a reputed web & mobile application development company in India, the US, and the UAE.