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Massively Scaling AI Projects for Long Term Success in…
Many organizations realize that they need to integrate AI into their entire workflow to be successful. This enterprise AI scaling will have a massive effect on whether they are successful or not. However, there is a right way to scale AI projects, and these companies will have to identify and do those things. AI has taken off in recent years, which has led to many issues for many people in the industry. There is a lot of fragmentation caused by companies not understanding how crucial AI is to their success. Many organizations think that it is enough for them to slap AI code onto a single project and have that be the major growth factor they need to succeed in this industry. That could be further from the truth, and it is something they need to work on for the future.
Scaling AI Needs to Make Sense
The major issue with scaling AI is that many organizations see it as a fad they want to jump on. They don’t do the cost-benefit analysis that many companies need to make smart decisions. They see it as something that they should do because everyone else is doing it. They want to be able to say that they have this sweet feature integrated into their products. However, before you get started with AI in business, you need to decide whether it is worth your time and effort. Enterprise AI must make sense for your specific business needs, or you will be in a world of hurt when implementing it. You will spend a lot of time and money doing something that brings you no benefits. What value are you getting from your AI integration? That is the question that should drive you for machine learning in business to make sense.
Testing to Make Sure You Get Great Results
Did your AI and machine learning tests deliver in the way you expected? It is also a big question that businesses need to start asking when working with AI projects. You need to understand that it is something that should aid your bottom line. It is not enough for your project to be fancy or something else. It will have to make sense for your specific business needs, or you will not profit from using AI in this way. Can you trust the people in your organization to grow your business quickly using the power of these algorithms? It is also something you need to ask, and the answer can have a massive impact on where your business goes in this industry.
Get Workers to Work Together
The final piece of the puzzle when scaling AI is how well your teams can collaborate. Collaboration is key, as you cannot have one team building massively scalable AI systems inside an organization. If you are to integrate AI into all aspects of your business, you will need everyone on board with that decision. They will have to understand how collaborative AI works, and they will need to understand the value of machine learning in business. These things must come together to form a cohesive unit that can get you the results you want. However, these things are just a start, and you will have to do many more things to be successful in the long run.
Blogs
Things to Consider Before Starting AI Projects
Many organizations are moving towards adopting artificial intelligence in business. However, there are a few key things you need to do to make that transition for yourself. AI projects can bring many benefits to your business, but the AI journey is not always an easy and straightforward one. Getting started with AI can be difficult, and there are a few areas where it is not needed. You must analyze the various artificial intelligence projects you can undertake and figure out if they will work for you. The first thing you want is to ensure that you are getting a good return on your investment when starting AI projects. Unfortunately, this is what most businesses get wrong when taking the plunge. They see AI as a new fad, and they want to get involved, despite not having any reason to do so.
Does It Make Sense for Your Organization?
Before embarking on an AI project inside your organization, you need to be honest and ask yourself whether it makes sense or not. AI projects work best when they are integrated into wider ecosystems that were designed to accommodate them. If you build a project that works well without artificial intelligence, there is no need for you to try to glue it on right now. Your manpower could be spent doing something more valuable for your organization. For example, there are many computer algorithms that you can use that works similarly to artificial intelligence. They might be easier and faster to implement, and they will not cost you too much as well. Artificial intelligence and data science also requires extra skillsets that many organizations do not have. Artificial intelligence in business is also something that needs to be considered.
Do You Have the People to Start AI Projects?
Once you have decided that an AI project makes sense for your organization, it is time for you to start marshaling resources to ensure that it can be done in an adequate amount of time. To do things faster, you should spend your time and effort acquiring the necessary pieces to make a cohesive unit. Despite what you have heard, getting started with AI takes a long time. You will not just be able to bake a simple solution into whatever you are already working on. The best outcome for you will come when you build AI into the foundation of your next project. It means having the right AI platform at your fingertips, so you can get your employees up to speed with how everything works. Artificial intelligence and data science are also heating up, which you need to consider when working in one of those two fields.
Make Decisions with Everyone at the Table
One mistake that business executives make is to embark on an AI project without consulting their engineers. When it comes to integrating AI into your project, you need to have everyone at the table making the decisions. Your engineers need to get feedback from you and vice versa. Your engineers will understand the technical details a lot better than you, and they will be able to guide you in the right direction. If you don’t follow this step, you are more likely to waste money going in directions that don’t benefit your company.
Healthcare
Underwriting Process Augmentation
Accurate claim records can positively impact reserve requirements/loss ratio. This leads to premiums that are more reflective of a payer’s actual experience, rather than a conservative estimate. Knowledge of claims history is powerful information when it comes to negotiating/pricing fair and competitive insurance premiums.
Retail
Retail Sales Prediction
Our client, a Florida-based retailer, was using qualitative techniques to forecast sales using limited available information: expert opinions and information about special events. Store sales are influenced by many more factors such as promotions, competition, school and state holidays, seasonality, locality, etc. They wanted to analyze historical data, to find patterns in the dynamics of the data like cyclical patterns, trends and growth rates. They also wanted to explore social factors influencing sales.
Retail
MLOps for Auto Attribution of Images Using Computer Vision
This is a major American designer and marketer of children’s apparel. The use of a line assortment application for mapping product attributes to facilitate planning. This process is manual-intensive and takes a lot of time. On average there are 130 attributes per product and around 4 million attributes are being managed per year. The sales and marketing teams needed a new, better way to describe the product. They wanted to leverage artificial intelligence for recognizing images of children’s clothing and predict labels with respect to certain attributes such as sleeve length, leg length, and patterns to reduce manual intervention. The situation demanded the need for an integrated platform for data scientists equipped with accelerators and tools to manage the entire AI application development life-cycle.
Healthcare
Sentiment Analytics
Press Ganey is a leading provider of patient experience measurement & performance analytics. They conduct a variety of patient experience surveys in hospitals and collect feedback to analyze every dimension of the patient experience. Healthcare providers are then provided a rating (on a five-point scale), based on the feedback. While it may be relatively easy to analyze the structured ratings provided in the feedback, additional effort is required for the analysis of unstructured comments. Reading all the comments in every section of each feedback form is humanly impossible.