5 Steps To Implement AI in Your Business Without Breaking The Bank

Artificial intelligence keeps booming, and if it continues permeating into every industry, it will completely transform the way we live.

As a result of this, integrating AI into their companies has become an utmost priority for many founders. Even individuals are looking for ways to leverage AI to improve their personal lives.

The hype is such that Collins Dictionary, a landmark language authority, has named AI as the term of the year, because of its surge in popularity.

Having said this, for most organizations, there is a huge gap between idea and reality when attempting to incorporate AI into their processes, because the path is not as straightforward as it seems, and it can be very expensive, both in terms of capital expenditures needed and in wasted time, because the developments will not bring the expected results. This has landed several businesses in trouble. For example, CNET experimented with AI-written articles, and they turned out to be full of flaws. Other companies, like iTutor Group, have faced hefty fines in addition to public ridicule because of their poor AI implementations.

As these cases show, businesses can make a lot of mistakes with AI, and unless a venture has the financial cushion of Amazon, Google, Microsoft, or Meta, these failed experiments can effectively bankrupt a company.

If you are a founder or business owner, here is a guide with five steps to help you implement AI in your business, all while making prudent use of your resources–money and time, which ultimately is money–and while reducing the possibility of fatal errors.

1. Be clear on the problem that you are trying to solve

No company is immune to AI failures. And as Amazon painfully found–through its floundering cashierless stores Amazon Go–not every business case needs AI.

Therefore, it is critical that you define the problem that you are aiming to solve with AI. This needs to be outlined as clearly as possible.

For example, a common application of AI is customer support. Implementing AI in such a case is possible in a way that has specific outcomes, for example, reducing call center costs by X amount of money per month or speeding up the average time it takes to solve customer inquiries by X minutes. With this approach, we have a measurable indicator in the form of money or time, which we will try to attain by implementing AI and see whether this has any impact.

There are various ways in which this could happen. For example, instead of a chatbot, we can develop or buy a service that will determine if a customer’s query can be answered with a FAQ page. It will work like this. When a customer writes a message, we run this model and it either tells us we need to transfer this conversation to an agent, or shows them a relevant page with an answer to their question. Developing this model is faster and cheaper than building a complex chatbot from scratch. If this implementation succeeds, we will accomplish our goal of reducing costs while optimizing our AI-related capital expenditures, in comparison to the expense of developing a chatbot.

A pioneer in this approach was Matten Law, a California-based law firm that integrated an AI-powered assistant to automate many tasks, enabling lawyers to spend more time listening to customers and studying those aspects of a case that were the most relevant. This illustrates that even the most rigid of sectors can be disrupted through AI in a way that bolsters the user experience, by amplifying the human touch where it is needed the most.

Additional common problems that could be addressed with AI’s help include data analysis and the creation of customized offerings. Spotify is an extraordinary example of a company that successfully leverages AI to develop an intelligent system for music recommendations, which goes as far as taking into account the time of day in which someone listens to a specific genre.

In both of the aforementioned scenarios, AI is helping to provide a better experience for the customer. However, the reason why these companies used AI successfully was because they were very clear on the aspects that needed to be delegated to AI.

2. Decide on the data that you will need to analyze

Once the main problem is well-defined, we need to take into account the data that we need to feed the system with. It is key to remember that AI is an algorithm, which analyzes and adjusts to the data we provide. The basic scenario for data collection is as follows:

  1. Understand what data we might need to implement AI.

  2. See if our business has that data.

    1. If it does — great.

    2. If not, we need to sit down and figure out if we can start the right data collection process in-house. As another possibility, we can ask developers to save the data we need if we’re not doing so yet.

Here’s an example. We own a coffee shop, and we need data on how many patrons visit it. We can do this by implementing personalized loyalty cards that users will present when making a purchase. This way, we will have the data we need, like which customers came, when they came, what they bought, and in what quantity. Once we have that, we can use this data to implement AI. However, there are times when collecting this data can be very costly. And that’s when AI can come to our rescue. For example, if we have a camera installed in our coffee shop–which we might at least for security purposes–we could leverage it to collect data from our visiting patrons. I must say that prior to implementing this, it is important to consult on personal data laws, such as GDPR, as this approach could not work in every country. But in those jurisdictions in which it is allowed, this can be a seamless way to gather the information you need, and enlist AI’s help to analyze it and process it.

If you are wondering, this personalized loyalty program is what Starbucks did, with great success. Starbucks’ rewards scheme went as far as providing personalized incentives whenever a customer visited their preferred location or ordered their favorite beverage.

3. Define a hypothesis

There might be situations in which you feel uncertain as to which processes can or need to be optimized by AI.

If this is your case, then, you can start by breaking down your entire process into stages, and identify those phases in which you feel your business is underperforming. What are those areas that you are spending too much money on? What is taking longer than usual? By answering these questions, you can pinpoint the critical areas for improvement, and decide whether AI can be of help.

As you will find, there are instances in which conventional solutions might be more effective. If you are struggling with which product offerings to highlight to your customers, suggestions based on the most popular products are frequently far more effective in marketplace recommendation systems than attempts to forecast user behavior. Therefore, try that first. Once you have a result–whether it is positive or negative–then you can have a hypothesis for AI testing. Otherwise, the field of action will be too vague, and you might end up wasting time and money.

4. Leverage the solutions that already exist

Many companies aim to, right away, design their own machine learning algorithms. However, if you do not plan on training them with sizable data sets over an extended period of time, don’t do that. It will be very expensive and time-consuming.

Instead, I suggest that you focus on solutions that are already available. Companies like Amazon, Google, Microsoft, and many others have AI-powered tools that can help you accomplish many goals. Then, gradually, you could sign a contract with one of them, and hire an internal developer to skillfully configure the necessary API requests.

The basic idea is that these tools can be integrated by business developers (not ML specialists), which will allow us to quickly test the hypothesis of whether AI brings the expected effect or not. If it fails to do so, we can simply disable these tools, and our cost of testing our hypothesis would only be the developer time we spent integrating with that service and the amount we paid to use the tool. If we were developing a model, we would spend the salary of the ML specialist times the amount of time they spend developing the model in addition to any infrastructure costs. And then it’s not clear what to do with the developer and the model if, in the end, the expected effect is not there.

If our hypothesis is proven, and the AI-powered tool brings the expected effect, we rejoice and come up with a new hypothesis. In the future, if we foresee that the costs of the tool grow significantly, we can think about developing this model ourselves, and thus reduce the costs even more. But we need to first evaluate whether the cost of development is in fact less than what we would pay to use a tool from another company that specializes in developing these tools.

My advice is that you consider developing your own machine learning product only after you have obtained good results from using AI with the tools mentioned above, and once you’re certain that AI is the right way to solve your problem in the long run. Otherwise, your ML project will not deliver the value that you’re looking for, and as a brilliant recent piece by the Harvard Business Review said, the AI hype will only distract you from your mission, which doesn’t need AI.

5. Consult with AI specialists

In the same vein, another very common mistake that founders and business owners make is that they try to do everything in-house. They hire an AI chief engineer or researcher, and then more people to form a team that can create a cutting-edge product. However, that technology will be worthless to your company’s purpose if you do not have a properly defined AI implementation strategy. There is also a case when they hire a Junior ML Engineer, to save money compared to hiring a more experienced specialist. This is also dangerous, because a person without experience may not know the subtleties of ML system development and design and make “rookie mistakes”, for which the company will have to pay too high a price, almost always exceeding the price of hiring one experienced ML specialist.

Hence, my recommendation is that you first hire one AI expert, like a consultant, who will guide you along the way and evaluate your AI adoption process. Leverage their expertise to ensure that the problem that you are working on requires AI, and that the technology can be scaled effectively to prove your hypothesis.

If you’re an early-stage startup, and are worried about funding, a hack for this is contacting AI engineers on LinkedIn with specific questions. Believe it or not, many ML and AI experts love to help, both because they are really into the topic, and because if they succeed at helping you out, they can use it as a positive case study for their consulting portfolio.

Final Thoughts

With all the hype that is surrounding AI, it is normal that you might be eager to incorporate it into your business and develop an AI-powered solution that takes you to the next level. However, you need to keep in mind that the fact that everyone is talking about AI means that your business needs AI. Many businesses, unfortunately, rush to integrate AI without a clear aim in mind, and end up wasting enormous amounts of money and time. In some cases, especially for early-stage companies, this can mean their demise. By clearly articulating a problem, gathering relevant data, testing a hypothesis, and using the tools that are already available with the help of an expert, you can integrate AI without draining your firm’s financial resources. Then, if the solution works, you can gradually scale up and incorporate AI in those areas in which it increases the efficiency or profitability of your company.

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