How Artificial Intelligence Can Solve Business Problems

The article was first published here: https://medium.com/authority-magazine/ilya-smirnov-of-usetech-on-how-artificial-intelligence-can-solve-business-problems-e5b62b0703c0

An Interview With Chad Silverstein

Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?

Thank you for this opportunity! Of course, it is my pleasure to tell you about my career path. After graduating from university, I started doing research work in the field of vibration, theoretical — I got a PhD, and the practical application of my developments was implemented in a geophysical company. We did research on microseismic signals: we detected the presence of a signal pattern in the general seismic background with a signal-to-noise ratio of less than 10%. In those years, this was called statistical mathematical modeling. And the term Artificial Intelligence was not yet as well-known as it is now.

Can you share the most interesting story that happened to you since you started working with artificial intelligence?

Usetech works with many interesting projects to develop AI-based solutions, implement computer vision systems for industrial production, security breach detection and object recognition. We also develop support and decision-making systems to optimize resource consumption and implementation of advanced technologies. For example, we have our own digital products such as Octopus and UseBus. Is a hybrid integration platform (HIP) for automating interactions between applications as well as data integration, with a focus on protecting processes from unauthorized access. Octopus is an automatic data center resource optimizer/balancer that:

• Continuously keeps your server infrastructure in an optimal state;
• Can manage different types of hypervisors and orchestrators;
• Allows for semi-automatic balancing (with operator confirmation) if needed.

The solution brings the operation of western server hypervisors, which are the de facto standard (VMWARE, IBM, Oracle), as well as domestic ones, to the optimal mode of consumption of system resource of the data center, thus freeing up the system resource reserved in a non-purposeful way as a reserve for business-critical operation of the production resource of the data center.

In addition, there are purely mathematical modeling and optimization tasks. If you are interested in such opportunities, please contact us. We will conduct a consultation, ask you about your problems and needs, and together we will propose an effective solution that will optimize your work processes.

You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

I think it’s the interest and desire to learn something new, and help companies not be afraid of AI and implement its solutions to improve efficiency.

Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?

Of course, one of the cases I want to share is the use of AI-based technologies in recruitment. In today’s world, where competition in the labor market is reaching unprecedented levels, large companies and recruitment agencies receive hundreds, and sometimes thousands, of resumes for every open position. This process requires significant effort and resources as companies strive to find that right candidate. In this race for talent, HR professionals play an important role in the selection and initial processing of resumes. One of the significant challenges in the recruitment process is the manual processing of resumes. This routine and time-consuming process requires a lot of time, effort, and concentration.

Machine Learning technologies and large language models (LLM), unlike traditional systems, allow you to significantly increase the accuracy of search and find highly qualified, relevant to the request, specialists. Intelligent search can help you find candidates who perfectly match the job requirements much faster than using manual methods or conventional search systems with filters.

Imagine that an HR specialist can ask a free-form query, such as “experienced Python developer”, or simply specify a set of keywords such as “data science, machine learning, TensorFlow”. A natural language processing (NLP) module will analyze the query, highlighting key skills and competencies. Moreover, AI-based technologies offer unique functionality to search for similar candidates. After uploading a resume file, the service analyzes its content, extracting key skills, work experience, education, and other important parameters. Based on this analysis, the system searches its database for candidates with as similar a profile as possible, offering the HR specialist a relevant list of alternatives. AI-based tools can significantly reduce search time and increase the efficiency of recruiters’ work. And that future has already arrived. As a result, the use of intelligent technologies in recruiting opens up new opportunities for effective search and selection of highly qualified specialists, optimizing processes and reducing time costs.

Many companies are now beginning to implement automated systems to process resumes. Technologies such as machine learning and artificial intelligence help speed up the initial selection process by analyzing resumes according to specified criteria and identifying the most suitable candidates. This not only saves HR professionals time, allowing them to focus on analyzing the top 3–5 resumes that perfectly fit the job, but also on more strategic tasks, such as creating and maintaining corporate culture. Moreover, machine learning-based services can be delegated organizational tasks, such as specifying meeting times and scheduling interviews. Thus, the combination of human expertise and modern technology can significantly improve the recruitment process, making it more efficient, focused and faster.

What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?

It is now more common to encounter people’s fear of using AI, as well as companies’ fear of changing their processes or implementing an AI-powered assistant. But to overcome this fear, it is necessary to show figures and success rates of such companies, as well as to start with the gradual introduction of AI-based tools and solutions.

In your opinion, what is the most significant way AI can make a positive impact on businesses today?

AI is having a huge impact on business. According to analytics companies and statistics, AI market will grow exponentially from 2025 to 2030. Just imagine: From $136.55 billion in 2022 to $1,597.1 billion in 2030, at a compound annual growth rate (CAGR) of 37.3%.

What is driving this growth? Of course, the development of AI itself and its technologies, significant investments by leading companies, increasing knowledge about AI and improving the skills of specialists. In addition, it is impossible not to notice the active implementation of AI in various industries, which we could not have imagined a few years ago!

Any AI technology — Machine Learning, robotics, NLP, Computer Vision — can be successfully applied in business in almost any industry. AI makes it possible to automate routine business processes and make them more efficient, improve forecasting, increase marketing efficiency, and reduce business costs.

Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.

That’s a great question! I’ll give some examples based on cases developed by Usetech for different industries. I want to emphasize that technologies such as AI, Machine Learning, Data Science, Computer Vision can be in demand in many industries — from industry and energy to agriculture and oil and gas.

As an example of our experience, we can state that today AI is actively used for solving important practical tasks, but so far this implementation and use is fragmentary. That is, locally, within the framework of optimizing a business process in some area. For example, we have been developing various AI-based models of energy consumption for oil fractionation units, but not as part of the entire technological process, but only a small piece of it. Or we realized projects on hydrocarbon and ore deposits prospecting, but without taking into account their efficient extraction.

In terms of Computer Vision, in recent years, we have solved many problems. For example, tasks on recognizing pellets on a conveyor belt to reduce the downtime of mills and remote monitoring of power lines. Other practical aspects of AI implementation were related to the automatic selection and design of a contract template depending on the type of contractor for a client with more than 1,000 contractors, or the development of algorithms for building a dynamic evacuation plan in case of smoke from fires or gas leaks in a building and modeling the spread of a cloud of gas contamination from moving objects.

How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?

You should start by analyzing your company and understanding your goals: Why do you need AI? For what purposes? What tasks will it cover? What are the indicators now, before AI implementation, and how will they change after AI implementation?

In my experience, I have encountered fragmented AI implementation. A fragmented approach to developing appropriate AI models results in solution architects designing only what is needed for the individual AI projects their teams are developing, rather than considering the big picture of the enterprise IT landscape. As a result, siloed systems make it difficult for companies to adopt AI best practices and limit the technology’s effectiveness. These structural barriers make the technology changes being implemented poorly effective.

This approach does not guarantee that the AI solution created will actually be adaptive to potential business process changes. And companies will have to invest in new AI models that take into account all business data, rather than supporting multiple models that work in isolation.

A reference AI architecture that enables a holistic and agile AI implementation involves combining a layered approach and modularity of AI development to level out any dependencies on underlying technologies and ensure that all AI stakeholders are able to participate in the development process. I talked more about this in an article for Top AI Tools (https://topaitools.com/articles/how-can-businesses-create-a-benchmark-ai-framework-).

What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?

I think it would be more correct to clearly define the spheres of AI implementation. The media and school education should explain more to people what this technology is, how it works, what it can do and what it cannot do. Then people will not be afraid of the development of AI. You can take the example of the recent epidemic, because until 2020, humanity wasn’t seriously frightened by the story of viruses spreading and random mutations. I think this is a much more frightening shock to the whole humanity than a talking and thinking robot.

In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?

The role of AI in business is growing due to AI’s ability to reduce costs and improve operational efficiency. In the era of digital transformation, using the best available technology is no longer a matter of competitive advantage, but of survival and keeping a business up to date. Artificial Intelligence is not only capable of increasing human productivity, but also of fully automating many business processes. And as for trends, I can highlight the following:

How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?

AI can improve customer relationships through chatbots, for example. I have commented on this topic many times. Using a chatbot can improve business efficiency and reduce costs. Such immediate help (we remember that chatbots can be available 24/7 unlike humans) helps to improve customer satisfaction through speed of operation.

As for employees, the company may have an AI-based loyalty and incentive program. Such platforms can also be used to improve communication between managers and employees, which improves work efficiency.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. 🙂

Perhaps it would be a movement talking about AI so that people would be less afraid of it.

How can our readers further follow you online?

I think it would be best to follow our Usetech page on LinkedIn, where we often share our cases and articles: https://www.linkedin.com/company/usetech-integration/
Well, and follow our company blog on the official website, where articles about AI or other technologies often appear: https://usetech.com/

This was great. Thank you so much for the time you spent sharing with us.

About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.