05 Aug 2019
Top 6 things to do before starting Artificial Intelligence development projects


Top 6 things to do before starting Artificial Intelligence development projects

In 2019, AI (Artificial Intelligence) adoption has become mainstream with businesses across various industries, from banking, retails, automotive, and financial services etc. looking to explore AI by building proof of concept to putting AI into work at across their business operations. According to IDC, Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, a growth of 44.0% compared to the amount spent in the period 2018. And the figure is expected to become higher in the coming years, with a forecast of world-wide business spending on AI systems development (from chatbots to advanced data analytics and the infrastructure for development) could reach $79.2 billion in 2022. This result in a compound annual growth rate (CAGR) of 38.0% over the 2018-2022 forecast period.

Thus, we can safely say that there is steady growing trend in AI adoption in businesses. However, together with the advancement of AI, the related risks with Ai development have also increased too. In this article, we examine some of the common factors that business should consider before embarking on their AI development project.

#1 Define project scope and capacity

First of all, AI is an innovative but complex technology that requires extensive resources and time to implement. Thus, businesses should not expect to transform their entire operation with AI overnight.  It’s better that companies start small and gradually apply AI across business once their team starts gaining experience.

Serious consideration should be given for adopting a process to experiment and validating the results of such experiment before businesses start tackling the most important system.

Additionally, companies can introduce advanced AI applications into their businesses by using an MVP (minimum viable product) approach before opting for a full scale solution.

#2 Clearly identify the key problems to be solved by applying AI

 'Artificial Intelligence’ is an innovative technology that can help companies solve various business challenges that traditional software development was not capable of. However, AI is not a silver bullet that can make everything possible. The AI industry has been progressing strongly with many leaps forward in application such as: facial recognition, self-driving cars, predictive analytics, and fraud detections etc. in the past few years. However, companies still need to be aware of what AI can actually deliver and how they can integrate AI into their business operation process. Having a thorough understanding about application of AI in business will help companies to identify their key businesses problems that can be solved by AI. Because, clearly, it’s not a wise decision to adopt AI because it’s the trend to adopt AI now.


And, regardless of business industries, before turning to AI, companies need to involve their team in order to find out the key business problems that need to be solved, and can it be solved with the existing data available? Other than that, businesses also need to ask themselves if they can actually solve such problems without AI.

#3 Invest in the right IT infrastructure for AI development

As mentioned previously, AI is a new kind of advanced technology that is different compared to traditional software, and thus, would require companies to invest in a more advanced technologies infrastructure before embarking on their AI development project. Businesses that are already familiar with cloud computing, mobile and web application development, and big data analytics would usually find it easier to get started with AI development. Otherwise, if companies find themselves not ready to invest and leverage new digital technology such as cloud computing and advanced analytics, then they’re probably not ready for AI either.

#4 Establish a good data strategy

Many of the cutting-edge, innovative AI systems are built with Machine Learning and advanced analytics which require data and indeed, a lot of quality data. Without good data, AI applications wouldn’t be able to deliver result that is as accurate as one might expect. And in such cases, AI actually hurts more than helps. Furthermore, if businesses only have the same data as their competitors do, they’re going to only have the same insights as their competitors. Therefore, it’s important that businesses work on their own organizations data together with the external sources.

Read more about: Key factors to consider for a good data strategy

Equally important, before acquiring data from various sources, companies also need to identify their specific data needs to ensure those are the right kind of data. This is necessary because data scientists actually spend the majority of their time on AI project preparing and cleansing data. Otherwise, it would lead to a waste of time and resources working on the wrong data. Choosing the right kind of data also enables companies to select the suitable tools and process for data preparation. For starter, companies can begin with the data that is already in use for analytics and business which is among the key business processes that can be enhanced by AI.

#5 Define Metrics to measure project outcomes

As the goals of AI development is to improve various business operations process, such as decision making, sales, marketing and/or other various business activities, companies have to define key metrics to measure the outcomes of those AI development projects. This includes both adoption and outcome.

For example, in the traditional way of decision making, people rarely look at the bigger picture told by data but rather relying on intuition and experience. With AI technology applied, companies need to make sure that people will be making data-driven decisions from advanced analytics results instead of relying on guess-work. Ignoring data and not using AI will apparently lead to a waste of investment resources.

Furthermore, developing key metrics to measure results will provide business with an understanding of what success will look like. For instance, SaaS businesses might looking to apply AI in helping them to increase conversion rate. Or a large retails businesses might want to use Machine Learning to enhance their customer support by incorporating an AI powered chatbot to help answering FAQ (frequently asked question), ultimately reducing time to resolution. Each business in different industry will have different metrics according to their own AI development goals and specific industry requirement.

#6 Having the right team of data scientist and AI engineers with expertise

To effective implement AI, businesses need AI expertise and preferably in the form of dedicated data scientist team. In addition, those AI development team also need to work closely alongside other business units (e.g. sales, marketing etc.) whose problems they’re trying to solve. And in case businesses do not have a dedicated data scientist team, they can look to develop that expertise in-house from the IT team.

However, due to shortage of skilled data scientists, companies may find it difficult to hire a sufficient team data scientists in the local market for their AI development project. Such challenges can be solved by hiring offshore for collaboration and transferring knowledge from offshore experts to gradually build up the local data science team.


AI systems can help companies improve business performance, productivity and ultimately operation results. However, companies should not expect AI to replace humans work entirely. In reality, AI will provide the maximum benefits when humans and AI systems work together in collaboration. To achieve the best possible results from AI or Machine Learning development project, companies need to get the key staffs who are actually going to be users of the AI system to get involved, and evaluate what AI can actually help them perform their tasks better.  


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