Businesses are now overwhelmed with AI sales pitches promoting the technology\u2019s potential to automate tasks, cut costs and improve performance.<\/p>\n
Some of you are probably thinking about integrating an AI-based solution into your organization. Well, the good news is that you do not need to be an expert on AI, but you do need to understand the basics such as the importance of data(many articles are available for non-tech people on Medium). Once this is done, you can proceed to automate key tasks and use data to detect patterns and outcomes.<\/p>\n
Before jumping into the AI bandwagon, please ask yourself these three questions:<\/p>\n
It might be tempting to start your AI transformation by trying to completely reinvent your business process. However,\u00a0this is the best way to fail.<\/strong>\u00a0It is wiser to gain experience from smaller projects that will create a solid foundation to move onto bigger, more complex human interactions and AI challenges. Moreover, I recommend focussing on projects that have mature user cases with a proven ROI.<\/p>\n Try to avoid tasks with too many variables and potential outcomes.<\/p><\/blockquote>\n In AI, it is better to focus on small parts of a process rather than the entire one. Given today\u2019s capabilities., it is very difficult to replace a complete process with a single AI solution, designing such a system can be very hard. However, that same process can be broken down into smaller parts and some of those can be very easily automated.<\/p>\n Organizations should question whether the task they want to \u201cautomate\u201d allows for repetition, and low cost of mistakes.\u00a0The tasks that satisfy these specific criteria are excellent starting points for AI implementation.<\/p>\n In many projects, I noticed that decision-makers do not have a clear vision of what their goal is with AI. Individuals can have different perspectives on the same problem and how to solve it. In the end, the lack of a clear vision can prevent full realization of benefits.\u00a0My recommendation is to always spend time defining the goal. What are you trying to achieve and why?<\/strong><\/p>\n You also need to have the right people part of the definition process. Indeed, it has to be a conversation between business decision-makers and data scientists that truly understand the business case and that can identify the right methodology and work with it best.<\/p>\n Indeed, your developers aren\u2019t machine learning specialists. There is a difference between developers and data scientists. At the same time, you need to make sure that the AI will be easy-to-use and represent a long-term solution.<\/p>\n There are a lot of different ways to do machine learning, and the right solution is going to depend on a good understanding of the problem. If you lack the necessary human resources, I strongly recommend you to sollicitate the services of an AI specialized development agency. Indeed, working out what kind of AI is needed for different processes and whether these will be carried out in-house, outsourced or in partnership is an important step to developing a strategy.<\/p>\n If you decide to develop an AI solution within your organization, don\u2019t forget that the learning curve is steep and the required resources to build your own can be very high. For example, a simple recognition model solution can be worth over 100K. It is worth it to have a look at the market for plug-and-play variants that piggyback on existing software services or platforms that are already familiar to companies. Most of the time, these solutions either require a few weeks of integration before they\u2019re up and running.<\/p>\n After having established a clear definition of what the AI must achieve, you need to create a roadmap with deliverables. If you decide to outsource the development of an AI solution, it would be wise to have both a technical and business specialist (potentially an end-user) involved in the development process. Moreover, data scientists, data engineers, and operational systems engineers need to come together as a team to deliver a solution that can perfectly be used within your technical environment.<\/p>\n Furthermore, it is mandatory that your \u201cAI solution has built-in feedback loops so that predictions and outputs from the AI can be corrected by those employees overseeing operations.\u201d (source<\/a>)<\/p>\n Finally, you need to build an interface to possibly interact with your new AI tool. In that context,\u00a0it has to be easy-to-use, scalable and safe.<\/strong><\/p>\n I recently worked for a company that wanted to develop an image recognition model to identify products in videos for marketing purposes. The AI did exactly what it was created to do. However, the company had issues with the solution because of the complex interface. It was basically made for technical users\u2026<\/p>\n Based on my experience, most AI projects became issues because of the following elements:<\/p>\nKnow exactly what you\u00a0want<\/strong><\/h3>\n
Best ways to fail an AI\u00a0project<\/strong><\/h3>\n