Artificial intelligence (AI) is so present in everyday life, in voice assistants, phone facial recognition, chatbots, suggesting new things to watch on your streaming account, and more. But what we really want to know is how this magic happens!How to implement AI to your business products and services? What are the available toolkits? What are the pros and cons of this technology? Before getting there, if you are just starting out in this, check our article How to use AI in your startup for some basic concepts.
Artificial intelligence has proved to be very useful for several tasks. Many companies have adopted AI to identify patterns in huge amounts of data, update customers’ files, handle customer communications, to understand human language, called Natural Language Processing (NLP).However, AI is still somewhat unpredictable. You never know the quality of the deliverables you might get. Think of Siri, Cortana, or Google Assistant, they can be incredibly helpful but still get things wrong sometimes. Since we cannot anticipate what the results will be, you can’t trust the machine 100%.It requires human-guided machine learning. You will usually need someone who knows Python—and you need to specify everything—from the data that you have, to the results you want to get.Some advantages of implementing AI in your business are cutting down on outsourcing costs and automating several processes. This way, your team can develop more strategic and complex tasks.But maybe the most common usage for this technology is related to data. As machines are far more capable of analyzing and turning it into valuable insights, much faster than humans, that’s where most companies reach for. If your employees spend a long time inserting data manually, that’s something you should consider automating, for example. Avoid keeping your customer support team busy with simple and easy questions, instead, consider having a bot to deal with these queries.The idea behind cognitive projects is integrating machines to an existing system, to improve workflow. Determining which areas will benefit from this technology, both in the short and long term, and the project implementation time is key for success.
Evaluating the business needs and understanding the limitations of AI technology are the very first steps to implement it.
Using existing cases of how AI has provided value in solving other companies’ problems. Brainstorm and get creative on how it can be added to your products and services, or even improve processes within your own company.Now, you must take into consideration the three kinds of artificial intelligence: process automation, cognitive insights, and cognitive engagement. Each of these can be useful for different business areas.Gather information from your different departments and transfer it to a system of records, for example, it’s something simple for Robotic Process Automation (RPA). It’s among the most basic usages for AI, as it doesn't require learning and improving.Projects related to speech and image recognition, interpret data meaning and automate digital ads, demand technology capable of cognitive insights, like deep learning. This can be more costly and you will need developers dedicated to training it.But if your focus is on IT support for your customers or even for your business team, then the solution should be cognitive engagement. The chatbots, for example, use NLP and have some autonomy to reply to frequently asked questions, but complex cases are transferred to human representatives.Building AI from scratch is not something a startup can do from day one. Instead of running before crawling, you should rely on pre-existing solutions. Even though they are not as customizable, it’s much less expensive and can still provide a taste of what you are setting out to do—attracting clients or investors, and hopefully leaving them wanting more.
The very first thing you’re going to need is data—and lots of it! Create the biggest database possible. This will come in hand when using machine learning techniques, finding patterns to ultimately get an algorithm you can profit from.To create your own database structure service, we suggest using Compose.io which provides Database-as-a-Service (DBaaS). In order to collect data, there are ETL tools (Extract, Transform, and Load).Talend is a great ETL to synchronize various formats or connectors, like API, FTP, JSON files, CSV files, or external databases. You’ll probably need a data scientist further down the road, but for now, you can get more info about these tools on the web, and play around with them.Keep in mind that Business Intelligence (BI) is all about making data speak. You need to create visualizations to better understand and give a voice to all of this data. Tools like Tableau helps to create a dashboard and improve it with customer use cases, and present your data more efficiently.This will be the beginning of your AI journey. Done right, it will definitely be enough to jump-start your investments.
Ideally, to implement artificial intelligence in your project, you should consider the following steps:
Check all the possible applications for the technology and how it can enhance your product, service, and internal processes.
Determine the possible software models, how much it will cost, and how long it will take. It’s the Roadmap for your project, which will help your plan the implementation process.
Identifying the possible flaws and estimate their impact on service quality and project costs, before moving on to the next steps of the project.
Check the given data validity and stratification against the viability study. It’s a very important step to determine if data has been compromised the accuracy of the AI analysis.
The goal is to create a complete flawless data set for analysis, through categorization of how to deal with missing information, scaling, and defining important features.
The POC will describe your full model and prove its efficiency, helping developers to better understand the project foundations and how it should be implemented.This stage requires specialists like Data Scientists and AI experts, working together with the Product Manager, who is responsible for the project vision.It’s hard to estimate how long it might take to finish the implementation, as it depends on the amount of data and how ambitious the project is.
When it comes to ML, there are several solutions available in the market, provided by tech giants like Microsoft, Amazon, Google, and IBM. The toolkit isn’t a definitive solution, but it will help to create, validate, manage, and operationalize ML models through a guided user interface (UI).
Microsoft’s Azure Cognitive Services niche is to enhance user experiences (UX) in apps, using an API. It doesn’t require previous ML knowledge.
AWS offers courses for executives, developers, and data scientists as a plus, to the actual cloud solution. The automation solutions include fraud detector, video and image analysis, and data extraction from unstructured text.
Built by Google, TensorFlow is available in Python, C++, JavaScript, and other languages. It constructs deep neural networks to conduct tasks like NLP, image recognition, and translation.
Watson is focused on corporate customers, providing solutions for IT operations, customer services, risk and compliance, and financial operations.
Building a startup can be challenging. But to implement artificial intelligence to that endeavor makes it a daunting experience—especially at the beginning since startups usually lack in-house developers and the capital necessary to tackle such complicated projects. You need the right people, tools, and business models beforehand, to implement artificial intelligence. Show that you have control over your market, that you know what’s needed, and have the data to support it.Book a GPS Assessment to better identify when to integrate AI into your project, how to plan out your development, and show investors, accelerators, and incubators what you can bring to the table!
