With all the buzz around AI, it is very enticing for business owners to look into implementing a ‘smart’ system into their processes. However, not everyone understands which business problems can be solved by AI or what AI can do for them.
This guide will help you learn how to determine if your business problem is a good fit for an AI solution.
A common sentiment is that AI is the future and it will replace humans very soon. There is truth behind some of these buzzwords, especially when it comes to businesses who are looking to improve their operations. So why exactly would a business owner want to implement AI?
AI systems are as good as you develop them, but they stay as good for a long time. AI doesn’t get tired, doesn’t lose attention, the amount of mistakes it makes is far less than that of a regular worker.
What takes a couple of seconds to a minute to notice for the human eye and process for the human brain, takes less than a second for AI. In some instances, like live video analyzing, it takes 1 to 3 fps to detect an object, which is far faster than any human could.
Initial costs of developing and implementing an AI system are quite steep, and not every business can justify the money investment. For many businesses, ROI when it comes to AI is very low or nonexistent as AI systems are not a great fit for everyone. For many, it makes much more sense to hire and train additional employees rather than invest in a development of AI.
It is imperative one carefully calculates the investments vs savings and decides from the get go that AI is worth it. If you are not sure how to calculate the risks, we offer AI consultation and validation services which help to break down your AI idea into small increments, calculate the development cost, develop a prototype to evaluate its efficiency, and more.
Some work is just not possible to do without automation. And some work can only be done using AI due to the sheer amount of data needed to be processes.
The introduction of machine learning systems can not only transform existing business processes, but introduce entirely new ones that were not available before. Extraction of valuable data from a large array of files can only be done using AI, like in this case where AI analysed hundreds of Amazon reviews and extracted customer sentiment regarding particular products.
There are multiple ways you can identify if your business need can be solved by AI:
Even the name - artificial intelligence - implies that the problems AI can perform require some sort of intelligence to complete them. However, the definition of the word ‘intellect’ is different in an AI world. ‘Intellectual’ problems are those that require AI to come up with a new algorithm rather than follow an already established one.
For example, solving a system of linear equations requires intelligence in a broader sense, however when it comes to AI, problems like these are not ‘intellectual’ as they do not require AI to come up with a new algorithm. Problems like image or video recognition are ‘intellectual’ - while they may be easy to complete for a human, it is difficult to come up with an algorithm for them. Take a look at this example - we have developed a system which detects butterflies using a webcam and determines their species, a classic case of an intellectual problem.
Brand new problems that don’t have a solution yet - or may never have it - are not great for AI. Automation requires a base from which it can build a new, faster and better, way of solving the problem. When the problem in question doesn't have a ‘human’ solution yet, the attempts to introduce AI to solve it will be fruitless.
A good example is KYC analysis - the practice has been in place in banks and financial organizations for decades, but has been slow and inefficient before AI. Professionals know what to look for when determining the reliability of a new client, and AI can significantly speed up this process.
Every good AI project starts with the ‘Do you have a dataset?’ question. Data is crucial when developing an AI system since modern machine learning algorithms revolve around using a large dataset to learn. The more data you ‘feed’ to an AI algorithm, the more accurately it will perform, so if your business problem has a lot of photo, video, text or other data associated with it, it most likely can be solved using AI.
Surveillance, in particular, is one of the areas where AI shines best since footage from surveillance cameras is often stored for a period of time, and new footage comes in every day. For example, AI can help detect how many people are in a queue currently and help with faster customer service.
It is important to understand if the data you have is usable for AI development. Remember - not all data is created equal, there are certain requirements when it comes to its quality and balance. Data used to train a machine learning algorithm should be of reasonably high quality and be balanced, i.e. each data class should have approximately the same amount of information in it. For example, if you need to recognise different objects in a video stream, each type of object should have the same (or close to the same) amount of data.
There are the types of data which can be used to develop an AI system:
This is the most common data type when it comes to computer vision systems, it is required to train visual analysis systems. Photos and videos can both be new, like coming from a live video stream, and historical, like photos from a surveillance camera from the past month.
Depending on a system, there can be requirements for minimal image quality and resolution. The more objects need to be detected within one frame, the bigger the resolution is required. For example, queue recognition systems for a small office require a smaller resolution as less people are present in the frame at any given time, whereas a system dedicated to crowd recognition requires a larger image resolution.
The visual data present in the images and videos can be of any nature, including computer graphics, hand-drawn images, etc. We have developed a machine learning system which analyses video game recordings, which serves as a great example for alternative image and video data used to create an AI system.
Audio data cannot be analysed as is, it is converted into visual data, like a spectrogram, and then analysed visually, which means any audio data, as long as its converted, can be used to train AI.
Sensor data is similar to audio data in that it is also converted into visual data for the purposes of training an algorithm.
Probability and prediction models, however, often utilise either raw sensor data or altered data as input, eliminating the data conversion step altogether.
Documents, scanned or digital, are analysed as images. AI is often used to detect text fields, determine their content and extract it. This is particularly useful for automatic document scanning, checkpoint systems, and KYC systems. AI systems can detect both typed and hand-written text.
Beyond text data, AI is used to analyze complex documents, like floor plans, to find and calculate various special symbols, extract relevant data, and more.
NLP systems can take text as input and perform various types of analysis, like extraction of customer sentiment and main topics. This is especially useful for marketing research, as marketing agencies can in a matter of seconds analyse thousands of reviews, webpages, etc. to receive relevant data. Read all about how we have implemented a marketing automation system here.
If you have read all of the above and have determined your business is indeed a good fit for AI, there's still an important step to complete. Not every AI is feasible - AI has its limitations which are not always obvious to someone just introducing themselves to the field. Before you start dedicating time and money to AI implementation, you need to calculate the risks - see if its possible to implement your idea given the current state of AI, check if the cost will be worth it, see what data you need to prepare.
The best way to go about AI idea validation is consult with seasoned AI developers to get their professional point of view, as well as get an accurate assessment of your idea and how AI can help you.
Here are a few crucial steps that need to be performed before diving head first into AI:
1. Analyse Dataset Quality
If you already have a dataset, you need to check its quality (from an AI point of view) and balance. It’s better to start with a high quality dataset rather than frantically look for ways to gather more data in the middle of development.
2. Build Prediction Models And perform Accuracy Assessment
Prediction models will help you understand how efficient you can expect your AI system to be. In some cases, there's a cap to the performance accuracy depending on the nature of the problem, the dataset, technologies used, etc.
3. Build an Initial Prototype
Proof-of-concept is an important step before going all in and starting full on development. A prototype shows you a glimpse into what the future system will look like, making it a perfect step to either continue on with the development or stop due to poor prototype performance.
4. Generate Assessment Documentation
This step includes a thorough overview of all of the testing activities performed, as well as insights and advice on where to take your project next.
We at Businessware Technologies have a dedicated AI and machine learning team who are ready to consult you on your AI idea.
AI is an enticing new shiny thing often praised without mentioning its limitations, which leads to inappropriate money investments into poor designed systems bringing no value for your business. It's important to understand which business problems can be solved using AI and learn how to separate them from the rest.
A thorough understanding of what exactly AI can do for your business will not only help you save money, but be a more conscious consumer of AI-related content.