Here we will discuss on Micro Financing using Artificial Intelligence in Business.
Risks for Startups
Deep ROI Analysis
The primary contention point for many startups is determining whether an idea is really worth it or not. The primary way that startups figure out whether an idea is worth it or not is usually by running a return on investment analysis. The problem is that a return-on-investment analysis can take quite some time.
The reason why it takes time to do a return on investment analysis forecast is due to the primary issue of how long it takes to gather the necessary information to do the analysis. You might be thinking that the analysis would be based off of company information, which is correct and rather basic to acquire, but it also requires data from at least five years in the past. Data acquisition and running analysis on 5 years of data for x amount of companies is a mathematical nightmare. If you have 20 companies that would compete with your idea, you essentially have to look at and understand 100 years of collective worth in these other companies just to see if your idea is worth expanding on.
Artificial intelligence can be set up to not only acquire the necessary data but also to analyze the data so that it can be visualized. A startup company is usually interested in how much money can be generated within the first year, how much money can be generated in the years after that, and how much it’s going to cost to make that money. Artificial intelligence can not only gather the necessary data, analyze that data and visualize it but can show you outlier situations that you might have previously missed. Using clustering algorithms, one can associate what profitable practices were put into place during what times a company had been doing before and after the release of a product. This helps the start-up to not only gauge whether the ideas are worth it but also understand the past mistakes and successes of other companies.
Audience Analysis
Furthermore, startups can get key insight into what type of audience they should really be marketing to. It has been a long time since a brand-new company doing a brand-new thing came onto the market. There are new technologies that are coming out all the time, but the reality of it is that the different genres of companies have already existed for at least 30 years. Therefore, those that buy into technology really have the categories of only buying into conservative technologies, mixed technologies, and risky technologies. You might think that Facebook is different than the Oculus, which you would be correct in its utilization, but they would be categorized in one of these three categories. Oculus would be seen as a risky technology as it requires a lot of upfront investment on the consumer behalf to just use the technology whereas Facebook is a conservative technology because it barely requires anything to work beyond a working internet connection. The combination of the two make a mixed investment, so you may only want to use specific parts of a company.
The reason why this is important is because you can create these categories for the area that your startup is in and run an analysis of competitive companies to find out if more investment into a particular part of these categories made those companies more money. For instance, when you first set up an analysis on Facebook properties, you will likely set the Facebook company itself as a safe investment, the Instagram property as a mixed investment, and the Oculus as a risk investment. Then you find out how much money was invested into each program or how much money each program costs and what that program made in its return on investment at the time it was bought. You can then see whether a company is more invested in taking risk ventures, mixed ventures or safe ventures.
The importance of this comes down to the fact that you need to understand the path your company should take as a startup. If you are going up against your competitors, for the first year you want to (at least) follow the common practices of the successful companies you plan to compete with. You don’t exactly want to go into a market showing off a risky investment when all of your competitors made their successes primarily off of safe investments. Safe investments allow your audience to check out what you’re offering before they determine whether a risky investment is worth it or not.
Therefore, if a company invests more in the safe investments market then you understand that they are primarily about ensuring utilization amongst their tools. If a company is more interested in risky investments, then you understand that that company is more interested in risky investments and makes more money by creating new products to lure new customers in. If you are attracted to the prospect of new customers, then you might want to have your startup be a risky investment but if you want a slice of the pie of the audience that goes with the safe investment, you might want to make a product that adds functionality to an existing product to make it better than your competitor’s product.
Future Working Capital Analysis
Understanding the potential return on investment that you might get given a specific idea and matching it against competitors and then beginning to understand the audience of your competitors means that you can begin understanding the future working capital.
Big companies have a huge problem and that is that they change very slowly. The one benefits that a startup has over every other business in the industry that’s bigger than a startup is the ability to change quickly. This means that you are used to having better technology, a different game engine that has more features, and you generally have a lack of assets you are liable for. Understanding how much working capital that your competitors annually work with helps you to understand where improvements can be made to your business practices before your company starts. For instance, if your company runs a factory and that factory is brand new but has artificial intelligence in it, you can actually optimize how much your factory outputs versus your competitor who likely did not have the ability to incorporate artificial intelligence at every level of their business and continue operating. The difference between one and the other is that a fully optimized factory is capable of producing more and staying in production longer than the competitor.
Determining Future ROA
By being small, you have the ability to rapidly change without suffering a heavy affect most of the time. This means that you can drastically change your return on investments. For instance, let’s say that you want to get into the Farmers market because you think you have a product that would speed up the amount of food farmers produce. If that product is sold in stores, you are likely to be charged a nominal fee for having space in that store. This goes into your overall liabilities and makes sure profits suffer.
Let’s also say that the vast majority of small-time farmer product producing companies have either a barely functional website or no website at all. If you can sell these products online versus selling them in the store, you can create an online-only business where you are not charged that nominal fee that goes into your liabilities. If a large company tried this, they would have to determine if it was more beneficial to sell products online than it was to sell in the store because they have had success in the store, but the online world is far more competitive. They have to make sure that their change will be profitable beyond what they are currently making whereas you, as a startup company, are able to make that decision because you’re not making profits in stores but you do have a product to sell and you have access to an online platform that allows either companies or individuals to buy directly from you.
This example goes even further because most companies will prebake items based off of past orders. They will not do on-demand product making because they usually can’t afford to as it takes too long in the chain for stores to order new items and for that order to be delivered and processed by the factory. Remember, it was only recently that lot orders have been performed online via email and digital exchange. A lot of factory companies have rudimentary websites that are very difficult to navigate. You, as a start-up, do not have this problem and are able to process digital orders within a timely manner with an automatic system powered by artificial intelligence that can make products on demand. This means you can maximize savings and product sales whereas the bigger companies have to take a hit in production savings because they simply do not have a functional system that allows them to do the same thing as you do.
Risks for Small and Medium Sized Businesses
AI Shows Expansion Areas
When you are a medium-sized business, let’s say a consignment shop, there are at least 10 different variables that play a role in how much money you can make. This is the primary problem that companies like these have to contend with in order to understand whether they can expand their stores or if it is more profitable to stay as a single store.
The problem here is that most of the issue lies around population density verse is the active, ongoing sales count. Moving to an area or expanding to an area often represents a risk of investment without sales. A normal, small business is not going to have the research team capable of formulating and understanding the best areas to expand to. That isn’t to say that they don’t have the necessary equations to make a business decision, it’s just that there’s so much data that requires processing that there isn’t enough personal thinking power that allows the business to make the best, most optimized decision as to where that expansion should take place and whether there should be an expansion at all.
Artificial intelligence, on the other hand, can make this process smaller and easier. Artificial intelligence can take the numbers that humans provided and produce the visualized results so that it is more easily understandable on human standards. The possible variables of whether this consignment shop will do well is rooted in population density, competitor density, product variation, better alternatives, and current sales versus rent. If you are looking at a large city, you essentially really have to do this same equation for every mile in that City. A normal human could not do this in a week for a city that is 50 mi in diameter, but artificial intelligence can be set up to aggregate this information and do it within an hour or a few hours.
AI Allows for Rapid Improvement
Due to the effective ability of being able to understand how to improve products, how to expand audiences, how to choose specific expansion areas, and how to understand your products market better, artificial intelligence is fantastic for rapid advancement.
Companies like to either expand or increase product count. Essentially, companies like to expand on the land or expand in their product line. If artificial intelligence is set up correctly, it can do this on both fronts. Artificial intelligence can improve your products reach into a market and then also increase that products production rate on the land. After all, the goal of a business is to sell as much as possible as wide as possible. Therefore, artificial intelligence allows small to medium businesses to act and expand in a way that would normally have required large research teams that were worth hundreds of thousands of dollars to billions of dollars just to research where you could expand. To this day, large corporations invest massive sums of money into research that allows them to improve and add products to their lineup while also looking for areas to expand in. Artificial intelligence allows startups and medium businesses to make that research happen without the need for ridiculously large research teams.
AI Only Needs Data Not Money
The most important thing to understand is that artificial intelligence doesn’t need any money, it just needs access to data. We live in an era where there is more data available to companies than ever before. Unlike a research team, data is relatively cheap and is even sometimes free given the circumstances in which that data was retrieved. This means it is cheaper than ever to do the extensive and expansive market, product, and chain research into a business.
In addition to this, artificial intelligence usually only requires one very well knowledgeable person that works with neural networks and a relatively small investment in a computer with an impressive graphics card. The investment is so small that it is below 1% of what giant companies like Facebook and Google invest in terms of research but, built correctly, can provide just as much useful information as the research teams that Facebook and Google pay for. Startups and medium businesses excel at artificial intelligence utilization because the companies are not already liable to entire sections of the company that are outdated. That singular person can now serve as your research department.
Data Can Be Sold
Perhaps the best part about data is data can be sold to the right buyer on a consistent basis. You have research groups, companies like yourself, startups, and even big business willing to purchase data if it’s qualitative and quantitative enough. It’s just that you have access to the data and the people looking to buy either only have access to their data or don’t have the ability to collect that data.
Let’s say that your customer base has somewhere around 100,000 people in it. For most companies, that is a drop in the market bucket because it is so small in comparison to most countries’ populations and even some sovereign areas or states populations. However, 100,000 people can be expanded to a million people as a sample experiment. This is commonly done in scientific areas because usually, a big enough sample can represent the entire portion within a high accuracy, enough to offset the need to gather expansive data.
The benefit to be a company that can gather data and sell it is that you can charge maybe $6 per person on your platform. That’s over half a million dollars annually if you can sell it all and it is just information collected on your users, it doesn’t require any additional investment beyond what you would normally invest to benefit yourself. Companies like Google and Facebook do this on a regular basis because it’s their primary source of funding. They primarily sell this information to advertising companies and create barriers to accessing all of their data for privacy reasons, of course, but for the more common-sense reason is that they can then the charge more money by sectioning off the data. Knowing this data allows you to understand your user base for the market you are in, thus it is very valuable.
This is simply raw data that’s being collected by your server that is already at your fingertips that you can make money off of. This is fantastic for startup companies because it is a selling point for practically all startup companies. Startup companies collect information that is rarely obtainable, which is information that is associated with starting a company rather than a company halfway through its existence. For instance, Instagram was a company that barely had any user statistics in the very beginning but quickly adopted a platform that did so. As it grew, it was able to sell that information to advertisers and begin selling advertising space as they literally jumped leaps and bounds based off of that user data. User data is collected and analyzed with artificial intelligence and if you don’t have artificial intelligence steering the information that that data, you’re not going to be able to make sense of the sheer volume of data coming from the users of your platform. This may seem like it’s an Internet-only thing but if you’ve decided to make a new fridge or a new toaster or anything that can have a chip put into it, that device can send information back to the server and that means that products can be enhanced based on what you sell and how the user uses your product.
AI Result Data Can Be Sold
If you don’t like the idea of selling raw user data, there is now a market for selling the results of what you’re AI comes up with. You see, companies always try to find a way to lower the cost, an issue dated by certain programs. As I already told you, artificial intelligence usually requires quite a bit of basic investment in this structure of how it’s built in the beginning. Having said that, companies don’t normally understand how simplistic it is to start with artificial intelligence and so they usually hire separate companies to do the work for them or they just pay the company who are gathering the data to do it for them. This means that you don’t have to hand out raw data to buyers and you are able to actually have a better PR presence in saying that individual data is not handed out to third parties.
Facebook and Google have capitalized on this because you don’t receive the entire database of information, you just receive the most relevant information for your situation. The most common online tool that is used by most online companies is Google AdWords, which finds the correct search terms that are relative to the topic or product you are trying to get to the customer. This is an excellent keyword search engine that essentially only provides you with keywords associated with topics or products that you want to sell. This saves you on time in researching through the data yourself to find out the numbers for yourself.
They’ve essentially saved you, as a company, an extra step in the process but they also get to charge you for that additional step while also being able to sell other products in the same basket. These are companies that are specifically built on selling data collected through its products and they only sell the results of that. You, as a start-up, can sell this information to other companies that want to buy it from you without giving out personalized data. This allows them to make their own assumptions based on the AI results and you can avoid scandals such as sharing how much each individual purchase one of your items. You can essentially just do equations for the company that they will just go ahead and do themselves and then charge them for doing that equation. You also don’t need a massive user base for that data to be useful, you don’t need millions of users and in fact, the price only goes up with the number of users that you have.
Usually, once you pass 50,000 people you can begin selling data to companies interested in your type of startup. This is due to the fact that after 50,000 people, customers usually see it as a successful business and companies are able to put more trust into that startup.
Risks for Large Businesses
ROA Opportunities
Sometimes running a large business comes with quite a few different costs against your profits. The problem comes in the area of when you are attempting to figure out which areas of your company are costing you the most money.
Often, the easiest way to do this is to determine a return on assets and this allows you to assess each individual asset as individuals or as a group to determine the overall worth they have to your company. The sad thing is that sometimes you can generally do this with employees within the company thanks to performance charts, but business is business. Understanding how much an asset costs versus how much that asset makes you money means that you are able to differentiate between assets that actually cost you money, assets that break-even, and the assets that make you money. Needless to say, you sometimes want to get rid of the ones that cost you money or that just don’t make you any money, but how do you determine if that is going to harm your business?
For instance, how do you know that a particular asset is not something that a profitable asset relies on? This becomes a lot more complicated and it requires a full understanding of how your facility works.
Artificial intelligence can solve this problem because you just have to determine which components lead to which other components in your profit pipeline. You can then run a ROA and determine whether the asset negatively, positively or doesn’t impact the other assets in that line. If that negative asset is vital to other profitable components, which can be determined by your staff and engineers, then the artificial intelligence will calculate that into the return on an asset analysis.
Asset Acquisition Analysis
This set of calculations can be performed on whether you need to acquire new assets and just how beneficial acquiring those new assets will be to your profit pipeline.
Again, unlike other artificial intelligence forms, you essentially have to have your staff or employees that directly understand how that line of machinery or technology works to put in the necessary information to the artificial intelligence so that it can make a quantitative decision on how it will affect your profit pipeline.
Improve Lacking Sections
By having artificial intelligence consistently monitor and regulate machines in your profit pipeline, you can consistently get high-profit margins when you run a return on asset analysis. The maximum profit that is a specific asset is giving you, which means that you can easily pick out sections that are low in profits and replace them with new assets that increase profits.
This ultimately means that you are improving the overall system and thereby increasing your asset to profit value, you are actually increasing the quality and production rate of a particular profit line.
Customer Understanding
All of this boils down to maximizing profits and understanding your customer better. Your customer is ultimately what pays for everything in your company, which means that your customer will invest money in working solutions that they prefer.
By being able to monitor everything in your factory, facility, and technology you are able to fully understand every working mechanism that makes you money. By combining this with information gathered from your website, product sales, reviews, and other areas you are able to quickly adapt to changes in the market and apply new products to old product lines. This means that you are able to make money consistently, increasingly, and without much risk involved because it’s all based on accurate data.
