83% of organizations believe that artificial intelligence holds power to propel their business operations to the next level [1]. As a matter of fact, there is hardly any industry out there oblivious to the revenue-generating potential AI has to offer. While the quick adoption of AI has presented numerous benefits to organizations and end-users alike, it comes with a few challenges of its own. This has prompted many business executives to seek more effective ways of managing artificial intelligence project approach.

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In this article, we’ll discuss the artificial intelligence project approach in its entirety, from how to prepare for an AI project to how to manage it effectively. Read on for more insight.

How to prepare for an AI project

In a recent survey from Databricks, researchers found that 90% of organizations work on AI projects, but only one in three of those projects is successful [2]. We believe the major contributing factor behind their failure is the fact that many organizations are neglecting some fundamental steps in AI implementation.

Here are four steps that you should take before embarking on an AI project:

Focus on the intended purpose of the project

What do you want to achieve through the AI project? It might sound straightforward, but many companies are starting AI projects without clearly defining the problems they’re trying to solve prior to the project.

The other problem is having unrealistic expectations of the power of AI. While it does work for numerous applications, AI is not magic; some problems are just way too complex for any AI solution to fix. The key to a successful AI project is to target applications with achievable targets that are commensurate with the current state of technology.

It might be interested for you: Estimating the Delivery Time and the Cost of an AI Project

How to prepare for an AI project

Build a foundation that will last

We often see companies experimenting with AI in one-off analyses using outdated data extracts that never make it to production. In other cases, data scientists spend months creating an AI model to get a good prediction, only to have to redo the process every time they need a new prediction.

The most effective way to solve this is to model nature over time. This means that organizations should automatically feed accurate, up-to-date data to their AI models as the data changes and evolves. They should also validate the quality of data sets in the models to avoid biases.

Most importantly, organizations should approach AI projects as something that will scale up and last. A recent KPMG report found that although 60% of the organizations surveyed use intelligent automation, only 11% leverage a scalable integrated solution approach [3].

Bridge the gap between data science and business goals

Machine learning shouldn’t be solely used in the production environment. It should also be leveraged to help achieve business goals. Therefore, before you start an AI project, make sure your data scientists understand your business needs.

This way, your data science teams can move beyond experiments and create accurate and repeatable predictions that will ultimately help you achieve your business goals.

Split AI into two distinct categories

What category does your AI project belong to? You can either incorporate your project into an existing AI or create a custom AI project to solve specific needs.

• Existing AI solutions: The inclusion of AI in pre-existing models has become increasingly common. There are numerous ready-made tools that can allow you to integrate solutions in a way that works best for your business. These include platforms like Amazon Machine Learning, Google AI Platform, and Microsoft Azure AI.

• Custom AI Solutions: If you have a complex project in hand, integrating your project into a pre-existing model is out of the question. In this case, you have to resort to custom AI solution development.

How to manage Artificial Intelligence projects

Identify the problem

The first step to managing a successful AI project is identifying the problem you’re trying to solve and your desired outcome. While you’re at it, it is important to note that AI in itself is not a solution but a tool to meet the need.

Read more about AI Development: In-house vs. Outsourcing

How to manage AI project

Test the problem-solution fit

Before you get your AI project underway, it is important to test whether it will solve the problem in the first place. You can test the problem-solution fit through various techniques like the product design sprint [4] and the traditional learning approach.

Prepare and mange your data

Having reached a point where you know that the AI project can solve a predetermined problem, you can now initiate your artificial intelligence project by gathering data and managing your data. You can start by dividing available data into structured and unstructured datasets and then cleaning up the data where necessary. If you’re working with a huge proprietary database, you may need to adopt new data management solutions [5] to make it more accessible.

Choose the right algorithm

There are different types of algorithms to choose from. Your choice will depend entirely on your project. For example, if you’re working on a machine learning project, you can choose between supervised learning and unsupervised learning[6]. There are two types of algorithms for supervised learning; classification and regression. Classification predicts a label, while regression predicts the quantity [7]. However, if you’re dealing with unstructured data, you may need to use unsupervised learning by employing clustering algorithms to find links between different objects and data sets.

Train the algorithms

Once you’ve selected an algorithm, you can now move on to training the model, whereby you feed data into the model while maintaining data accuracy. At this stage, you may need to employ tech experts who are well versed in java, C++, and python, depending on your project’s specific needs.

Deploy the project

For the best results, you should go with ready-made platforms as a service for your product launch and deployment needs. Ready-made platforms are specially designed to simplify and facilitate AI and aid in the deployment phase of an AI project. Some platforms also provide cloud-based advanced analytics that you can use to add additional languages and algorithms to your project.

Final thoughts on the artificial intelligence project approach

The key to a successful artificial intelligence project approach lies in proper planning and using the right data.

For example, projects involving predictive AI require a substantial amount of structured data stored in a time-phased manner so they can ‘learn’ and make predictions.

Organizations that understand and embrace the principles of proper planning and using high-quality data can fully realize the value of their AI projects without having to lurch from experiment to experiment. If you’re looking for a partner to help you understand the complexities of AI consulting, we would be pleased to talk with you and give you our expert advice.

 

[1] Forbes.com. How AI Is Revolutionizing Business in 2017. URL: https://bit.ly/3m9KIcD. Accessed May 26, 2022
[2] Techrepublic.com. 90% Of Companies are Working on AI Projects But They’re Making One Big Mistake. URL: https://tek.io/3GKUdZ4. Accessed May 26,2022
[3] Assets.kpmg. Easing Pressure Points: The State of Intelligent Automation. URL: https://assets.kpmg/content/dam/kpmg/xx/pdf/2019/03/easing-pressure-points-the-state-of-intelligent-automation.pdf . Accessed May 26,2022
[4] Medium.com. A Beginner’s Guide to Product Design Sprint. URL: https://medium.com/swlh/a-beginners-guide-to-product-design-sprint-afb99fa218ee. Accessed May 25,2022
[5] Northwestern.edu. Data Management. URL: https://libguides.northwestern.edu/datamanagement. Accessed May 27, 2022
[6] Ibm.com. Supervised vs Unsupervised Learning. URL: https://ibm.co/3mibtvf. Accessed May 27, 2022
[7] Javapoint.com. Regression vs Classification in Machine Learning. URL: https://bit.ly/38LDZCD. Accessed May 27,2022

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