AI technology. How match solution to problem?

Combining the capabilities of AI technology with specific business needs is not always easy. But if properly prepared and planned, even a small company can significantly benefit from the implementation of artificial intelligence solutions. So how to approach this issue optimally? In this guide, we provide step-by-step guidance.

AI technology - table of contents

AI technology for your business – how to prepare for its implementation?

What is worth knowing to aptly modern technologies for the benefit of one’s business? First of all, the fact that not all companies need AI technology at its current stage of development. However, given the pace of development of artificial intelligence, it is worth thinking now about the opportunities it provides for businesses.

Most small businesses relying on a digital presence can already significantly improve business performance by using AI. Larger companies using customer data, planning logistics, or developing modern production lines will also benefit. In other words, almost all companies will soon not do without the help of AI technology if they want to remain competitive. However, where to start?

Define the business problem you want to solve with artificial intelligence

The first step to implementing AI technology in your company is to describe in detail the business problem you want to solve with it. We need to be clear about and understand its relation with our business goals.

Let’s look at the example of a small manufacturing company that is having trouble predicting demand for its products. AI technology can be used to:

Analysis of current market data,
Competitive research, and
Analysis of historical sales trends,

This will make forecasting more accurate for future demand.

A larger institution can do the same. For example, a bank that wants to optimize its lending procedures. It currently applies certain filters to loan applications that automatically reject the riskiest ones. However, the bank still approves too many applications that later face repayment problems.

In both cases, the goal is to create a predictive model that will facilitate planning – identifying potentially bad loans or forecasting seasonal fluctuations in demand. Regardless of the size of the company, in the first step of planning the implementation of AI technology, we need to verify that the customer data we have contains the information needed to solve this particular business problem.

Define goals and expectations for AI technology implementation

Next, it’s a good idea to define data analysis goals that will achieve the business objectives set. The goals should be specific, so use the SMART method, for example. Its name comes from the words specific, measurable, achievable, relevant and timely.

A SMART goal for a small accounting firm introducing AI technology could be as follows: “Automate data entry and analysis within 12 months to reduce customer service time by 50% and improve accuracy by 90%.”

Specific goals (SMART) are clear and well-defined. For example, instead of the stipulation “we will serve more customers,” a SMART goal specifies what specifically is to be done – automated data entry and analysis – and over what period, within 12 months,
Measurable goals help us to assess whether a goal has been achieved. For example, the goal “cut customer service time in half and improve accuracy by 90%” is measurable because we can see how performance has improved,
Achievable goals are realistic in light of the company’s past performance. The goal in the example is achievable if the accounting firm already has the knowledge and experience in data entry and analysis. AI technology can help the company achieve them.
Relevant goals concern to the company’s strategy outlined in the example and its business goals, as it has in improving productivity and customer service.
Timely goals have a specific completion date. This makes it easy to assess progress toward them and break them down into manageable sub-goals.

Here, AI technology can help analyze large amounts of data, detect anomalies and ensure accuracy.

With artificial intelligence, we should define measures of success for data analysis (e.g., 90% accuracy of a predictive model) and benchmarks for evaluating success (e.g., reduction in error rates). This will enable us to assess whether the implementation of AI has brought the intended business benefits.

Learn about the types of AI technologies and their applications

There are many AI techniques and tools that help in business. Among the most popular are:

Machine Learning (ML) – algorithms that learn and improve their performance based on data without the need for explicit programming, an example would be an algorithm that recommends products to customers that may interest them based on their purchase history and preferences,
Deep Learning (DL) – a more advanced variation of machine learning using artificial neural networks. It is used, among other things, to recognize customers’ faces in a store, allowing personalized service and recommendations.
Natural Language Processing (NLP) – understanding, interpreting and generating human language in textual or spoken form, used, for example, to create personalized emails to customers,
Virtual assistants and chatbots – automated systems that conduct conversations in natural language and provide, for example, a voicebot in the customer service department that automatically answers the phone and conducts conversations about the company’s offerings,
Predictive analysis – building models to predict future events based on historical data, which can be used, for example, to predict customer churn,
Robotic Process Automation (RPA) – automates repetitive tasks, such as data entry or invoicing,
Generative AI – to create text, images, voice or video, so you can significantly speed up the creation of marketing materials or generate automatically unique product descriptions for your online store based on images and main features,

A closer look at the capabilities of each of these technologies will ensure that you can select the right AI tools for your company’s specific business problem.

Prepare your data for AI technology use

Small companies often have limited data sets, so getting them right is key. However, even this limited set can be used to train simple AI models. For example, a small online store can use customer purchase data to make personalized product recommendations.

Once you make sure you have sufficient historical data, for example on customer behavior, it is often enough to combine the data you have with ready-to-use AI tools available in the cloud, such as:

Amazon SageMaker – a platform for building, training and deploying machine learning models,
Microsoft Azure Machine Learning – a tool for creating and using predictive models,
Vertex AI Platform – a set of AI and ML tools in Google’s cloud.

Source: Google Cloud (https://cloud.google.com/)

With automation, a company’s internal systems can be integrated with external AI solutions without involving developers to build models from scratch. This significantly reduces costs and speeds up AI implementation.

Explore AI implementation options and choose the right method

Various ways of implementing AI technology in business are possible:

Development of proprietary AI models and systems by an in-house team of developers and data analysts.
Outsource the building of dedicated AI solutions to an external company.
Using off-the-shelf AI models and tools available in the cloud in an “AI as a service” (AIaaS) model

Each of the above methods has its advantages and disadvantages in terms of cost, implementation time or flexibility. However, small businesses should first consider off-the-shelf AI solutions available on the market – such as the aforementioned AWS SageMaker or Vertex AI, which are often more cost-effective and easier to implement, offering ready-to-use predictive models that can be used to analyze customer behavior. And even more specialized tools, such as:

ClickUp, an AI tool for project management,
Jasper AI – AI-based assistance in writing marketing materials,
Microsoft Power BI – one of the best data visualization tools that features AI technology for image recognition and text analysis to discover hidden, valuable information in your data.

Source: Microsoft (https://learn.microsoft.com/)

Consider the costs and benefits of implementing AI

Implementing new technologies always comes at a cost. In the case of AI, the long-term benefits often outweigh the initial costs. However, one must evaluate:

the cost of developing and maintaining in-house AI systems or using an external AI platform,
potential savings through automated processes and better decision-making,
possible increase in revenue due to improved customer service, more relevant recommendations, etc.
other potential benefits, such as reduced turnaround times and reduced errors.

For example, a small logistics company investing in AI systems to optimize delivery routes can significantly reduce fuel costs and delivery times, which will directly translate into improved customer satisfaction and the ability to serve more trips in the same amount of time.

Prepare for change and monitor the results of AI technology implementation

Implementing new technology requires adaptation. Employees and business processes need to be prepared for it. For example, for a small hair salon, implementing AI technology to manage client scheduling and bookings may require staff training, but in the long run, it can lead to better organization and greater client satisfaction.

It is also worth monitoring the effects of the AI project on an ongoing basis and correcting the course if the results deviate from expectations. Measures such as:

accuracy of predictive models,
conversion rates or
customer satisfaction

They will provide information on whether AI is helping to achieve business goals. They will also allow continuous improvement of AI models to increase their relevance and value to the company.

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Author: Robert Whitney
JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.
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