What is artificial intelligence and how to use AI in business?

Artificial intelligence (AI) has ceased to be the domain of science fiction and has permanently entered the business world, becoming one of the most transformative technological forces of our time. It is no longer just a futuristic vision, but a practical tool that every day revolutionizes the way companies operate, compete and create value. From automating processes to personalizing offerings to analyzing massive data sets, AI is opening the door to possibilities that were unattainable a decade ago.

However, along with the enormous potential come new challenges. Data security, compliance with regulations such as RODO, ethical issues and the risk of costly implementation mistakes are real barriers that discourage many organizations. The key to success is a strategic and informed approach – understanding what AI is, where it can bring real benefits, and how to implement it in a secure and controlled manner. In this guide, we’ll take you through the world of artificial intelligence in business, explaining its basics, applications, and how to avoid pitfalls and measure the real return on this disruptive investment.

What is artificial intelligence (AI) and what are its major types?

Artificial intelligence (AI) is a broad field of computer science that aims to create machines and systems capable of performing tasks that normally require human intelligence. This includes abilities such as reasoning, learning, planning, problem solving, perception or natural language understanding. AI is not a single technology, but a collection of different techniques and approaches that allow machines to analyze the environment, interpret data and make autonomous decisions based on it to achieve specific goals.

In business practice, we most often encounter so-called “narrow” artificial intelligence (Narrow AI or Weak AI). These are systems designed and trained to perform one specific task, but do so with above-average efficiency. Examples of Narrow AI include recommendation systems in online stores, customer service chatbots, facial recognition software or algorithms that analyze financial markets. Although they do not have self-awareness or human-level general intelligence, their ability to process vast amounts of data and find patterns makes them extremely powerful business tools.

Within AI, there are key sub-fields, such as Machine Learning (ML), which is currently the most popular approach. It involves “teaching” a machine, instead of programming it step by step, to “teach” it from large data sets so that it can identify patterns and make decisions on its own. A particular type of ML is Deep Learning, based on complex neural networks, which drives the most advanced applications, such as speech recognition or image generation. Another important branch is Natural Language Processing (NLP), which allows machines to understand, interpret and generate human language.

Why ignoring AI’s potential could undermine your company’s market position.

In an era of digital transformation, ignoring the potential of artificial intelligence is no longer just giving up additional benefits – it is becoming a real threat to maintaining competitiveness and market position. Companies that proactively implement AI gain a huge advantage in key areas such as operational efficiency, innovation and customer understanding, leaving behind those who stick to traditional methods.

First, AI allows unprecedented levels of efficiency and cost optimization. AI-based process automation can drastically reduce task completion times, reduce errors and free employees from monotonous duties. Competitors who reduce their operating costs through AI can offer lower prices, faster service or invest the saved funds in further development. Companies that ignore AI are left with higher costs and slower processes, which directly undermines their competitiveness.

Second, artificial intelligence is a powerful driver of innovation and personalization. AI algorithms can analyze huge data sets about customers, their behavior and preferences, providing insights that are impossible to obtain through traditional methods. This allows the creation of deeply personalized offers, products and marketing campaigns, significantly increasing customer satisfaction and loyalty. Companies that do not use AI operate “blindly,” basing their decisions on intuition, while their competitors make decisions based on precise data, better hitting market needs. Ultimately, inactivity in the area of AI leads to a loss of market share to more agile and innovative players.

What data security risks are associated with the uncontrolled use of AI tools?

Enthusiasm about the capabilities of artificial intelligence, especially readily available tools such as public language models, carries serious and often underestimated risks to data security. Uncontrolled and uninformed use of AI by employees can lead to catastrophic information leaks, compliance breaches and financial losses, creating powerful new attack vectors.

One of the biggest risks is the leakage of confidential company data. Employees, in an attempt to improve their work, may paste snippets of source code, financial data, contract content, business strategies or personal customer data into public AI tools (such as free chatbots). Without realizing that this data can be used to train the model and potentially become available to other users or tool developers, they inadvertently cause a company’s most valuable assets to leak out. Such an action is a direct violation of company secrets and can lead to a loss of competitive advantage.

Another major risk is the generation and use of unsecured code. AI tools can generate code snippets from simple commands, which significantly speeds up developers’ work. However, this code often contains security vulnerabilities, outdated libraries or bugs, which can then be used by attackers to break through the application’s security. In addition, cybercriminals can deliberately “poison” the training data of AI models (so-called “data poisoning”) so that the code they generate contains hidden backdoors. Trusting AI-generated code indiscriminately is a simple way to create vulnerable systems.

Finally, legal and compliance risks should be kept in mind. Entering personal data into third-party AI systems without a proper legal basis and processing entrustment agreement is a serious violation of the RODO, with the threat of multimillion-dollar fines. To avoid these risks, companies need to put in place clear policies on the use of AI tools, provide training for employees, and, where possible, deploy dedicated in-house or hosted AI solutions in a trusted cloud over which they have full control.

“Shadow AI” threats

• What is “Shadow AI”? Uncontrolled and unauthorized use of external, public AI tools by employees without company knowledge or consent.
• Risk #1: Data leakage. Employees paste sensitive information (code, financial data, personal information) into public chatbots that can save and use it.
• Risk #2: Unsecured code. Indiscriminately copying AI-generated code that may contain security vulnerabilities or backdoors.
• Risk #3: Compliance violations. Processing personal data in AI tools without a legal basis is a violation of the RODO and risks heavy fines.
• Solution: Introduce policies, training, implement controlled, corporate AI solutions.

In which departments of a company – from marketing to finance – can AI benefit the most?

Artificial intelligence has the potential to transform virtually every department in a company, automating tasks, providing deeper analysis and enabling better decision-making. The benefits can be seen throughout the organization, from departments that deal directly with customers to internal operations.

In marketing and sales, AI is revolutionizing the way we reach customers. Machine learning algorithms analyze user behavior, allowing hyperpersonalization of offers and communications. AI systems can dynamically select products recommended to a specific customer, create personalized email content or predict which customers are most likely to churn (churn prediction). In sales, AI can automatically qualify leads, analyze sales conversations for best practices or forecast sales results with unprecedented accuracy.

In finance and accounting, AI brings automation and precision. Intelligent OCR systems can automatically read and process invoices, eliminating the need for manual data entry. AI algorithms are used to detect anomalies and financial fraud in real time, analyzing thousands of transactions and flagging those that deviate from the norm. They also help forecast cash flow, optimize budgets and manage risk. In HR, AI supports recruitment processes by automatically reviewing resumes and matching candidates to job profiles, and analyzing employee engagement data to improve satisfaction and reduce turnover.

In operations and customer service, the benefits are equally significant. In logistics, AI optimizes delivery routes and manages warehouse inventory. In manufacturing, AI-based vision systems control product quality on the production line with a precision unavailable to the human eye. Finally, in customer service, intelligent chatbots and voicebots are able to answer customer questions 24/7, solve simple problems and direct complex cases to the appropriate consultants, significantly reducing wait times and improving satisfaction.

How does the use of AI automate processes and significantly reduce operational costs?

Artificial intelligence is a natural extension and enhancement of traditional automation, allowing it to handle processes that were previously beyond the reach of rigid rules-based robots. Combining AI with technologies such as Robotic Process Automation (RPA) creates what is known as Intelligent Process Automation (IPA), which can automate entire, complex workflows, yielding significant savings.

Traditional automation requires structured data and clearly defined steps. AI is breaking down these barriers. With technologies such as optical character recognition (OCR) and natural language processing (NLP), automation systems can work with unstructured data, such as document scans, emails or chat messages. For example, the process of handling an invoice can be fully automated: an AI-based robot receives an email with an invoice, “reads” the supplier data, invoice number and amounts from a PDF file, and then enters the data into the accounting system, eliminating the need for any human intervention.

Another area is the automation of decision-making. Machine learning algorithms, trained on historical data, can replace humans in making repetitive, data-driven decisions. In customer service, an AI system can automatically analyze the content of a request, assess its priority and route it to the appropriate team. In the lending process, an algorithm can make an initial assessment of credit risk. Such automation not only drastically speeds up processes, but also ensures their consistency and objectivity. Ultimately, fewer manual tasks and automated decisions lead to a direct reduction in operating costs, as a smaller team is able to handle a much higher volume of operations.

How does a company’s AI implementation need to comply with RODO and ethics?

Implementing artificial intelligence, especially systems that process personal data or make decisions that affect people, requires a rigorous approach to legal and ethical issues. Compliance with the General Data Protection Regulation (GDPR) and adherence to AI ethics is not an option, but a necessity that protects a company from huge financial penalties and loss of trust.

From a RODO perspective, the use of AI to process personal data raises a number of obligations. First of all, the company must have a clear legal basis for such processing (e.g. consent, contract, legitimate interest). The principles of data minimization (processing only the data that is absolutely necessary for the operation of the algorithm) and transparency become crucial. The company must be able to explain to data subjects in a simple and understandable way how their data is used by the AI system. A particular challenge is the implementation of the right to be forgotten (deletion of data) in the context of trained models that can “remember” the data they have learned from.

Of particular relevance is Article 22 of the RODO, which deals with automated decision-making, including profiling. It gives individuals the right not to be subject to a decision that is based solely on automated processing and produces legal effects on them or similarly significantly affects them. This means that in many cases (e.g., in the recruitment or credit assessment process), a company must ensure that a human being can intervene and appeal a decision made by an algorithm.

Beyond the law, ethical issues such as fairness and avoiding discrimination (bias) are extremely important. Algorithms learn from historical data, and if that data reflects existing social biases, AI will replicate and reinforce them. For example, a recruitment algorithm trained on past data may learn to discriminate against candidates based on gender or ethnicity. Therefore, companies implementing AI must actively work to identify and eliminate biases in their models, ensure their explainability and hold them accountable.

Where to start with an artificial intelligence implementation project to avoid costly mistakes?

Starting an artificial intelligence deployment project requires strategic planning and a methodical approach to avoid the trap of investing in technology for technology’s sake, without a clearly defined business goal. The key is to start with the problem, not the solution.

The first and most important step is to define the specific business problem you want to solve with AI. Instead of asking the question “where can we use AI?”, ask “what is our biggest problem or biggest opportunity and can AI help address it?”. It could be, for example, low efficiency in the invoice handling process, a high customer churn rate or the need for better sales forecasting. A clearly defined business objective will be the compass for the entire project. Once the problem has been identified, a proof of concept (PoC) feasibility study should be conducted, which involves a small, experimental implementation to see if AI can actually solve the problem in the reality of our company.

The next fundamental step is to assess the availability and quality of data. Artificial intelligence, especially machine learning, is only as good as the data it learns from. Before starting a project, it is important to check whether the company has enough historical, clean and properly labeled data needed to train the model. Often it turns out that 80% of the work in an AI project is not building algorithms, but just the painstaking collection, cleaning and preparation of data. Lack of adequate data is one of the main reasons why AI projects fail.

After confirming the business objective and the availability of data, build an interdisciplinary team and start with a small pilot project. The team should include not only technical experts (data scientists, AI engineers), but also business representatives who understand the context and specifics of the problem, and legal and security specialists. Choosing a pilot project, which is relatively simple but yields quick and measurable benefits, allows you to gain experience, test processes and build support within the organization for further, larger investments in AI.

What is the difference between an off-the-shelf AI tool and a dedicated solution built to meet a company’s needs?

When deciding to implement artificial intelligence, companies face a strategic choice: to use a ready-made, off-the-shelf AI tool or to invest in building a dedicated, tailor-made solution. Both options have their advantages and disadvantages, and the choice depends on the specifics of the problem, budget, time and available competence.

Off-the-shelf AI tools are SaaS (Software as a Service) applications and platforms that offer predefined functionality based on artificial intelligence. Examples include off-the-shelf chatbots for customer service, CRM systems with built-in modules for sales forecasting, or platforms for automated analysis of social media sentiment. The main advantage of this approach is the speed of implementation and lower initial cost. A company can start using the solution almost immediately, without the need to build an in-house data science team. The disadvantage, however, is limited flexibility and customization. These tools are designed to solve standard problems and may not be able to fully address a company’s unique needs and specific processes.

Dedicated AI solutions are built from scratch or based on open frameworks (e.g., TensorFlow, PyTorch) specifically for the needs of a particular organization. This allows the creation of a model perfectly tailored to the company’s unique data, processes and business goals. This approach gives full control, maximum flexibility and potentially the greatest competitive advantage, as the solution is a unique asset of the company. The main disadvantages are significantly higher costs and longer implementation time. Building a dedicated solution requires hiring or hiring expensive specialists (data scientists, ML engineers), and the process of collecting data, training and implementing the model can take many months. The choice between the two approaches is a classic “buy or build” dilemma that must be considered in the context of the company’s strategic priorities.

What does the process of integrating AI systems into an organization’s existing IT infrastructure look like?

Integrating artificial intelligence systems with existing IT infrastructure is a critical implementation step that determines whether an AI solution will be able to function effectively and deliver real value. The process requires close collaboration between the data science team, AI developers and the IT (or DevOps) department, and involves several key steps.

The first step is to provide access to data. In order for an AI model to work (in a process known as “inference”), it must have constant and efficient access to the input data on which to make decisions. Data pipelines need to be designed and built to deliver data from various source systems (e.g., database, CRM system, data warehouse) to the AI system in real time or in batch mode. The key here is to ensure adequate performance, security and consistency of this data.

Then decide how to make the AI model available (deployment). The trained model must be “packaged” into some form of service so that other applications can use it. Most often, an API (Application Programming Interface) is created to send data to the model and receive its predictions in response. This API must be integrated with existing business applications. For example, an ERP system upon receiving a new invoice can send a scan of it to the OCR API, and upon receiving the processed data, continue the accounting process.

The entire process must be supported by an appropriate computing infrastructure. Training AI models, especially deep neural networks, requires enormous computing power (often specialized GPU graphics cards). The inference process is usually less demanding, but must be scalable and reliable. Companies often use flexible public cloud resources for this (e.g. AWS, Azure, GCP) or build their own internal infrastructure. It is also crucial to implement monitoring systems to track the model’s performance, its correctness and the so-called “model drift,” i.e. a decline in prediction quality over time, which signals the need to re-train it on new data.

How much does it cost to implement AI and what factors determine the final price of the project?

Determining “how much does it cost to implement AI” is as difficult as answering the question “how much does it cost to build a house?” – it all depends on the scale, complexity, materials used and expected standard. The cost of an AI project can range from a few thousand dollars for implementing a simple off-the-shelf tool, to millions of dollars for building a complex, dedicated system from scratch. The final price depends on several key factors.

The most important factor is the approach: “buy” or “build. “ Deploying an off-the-shelf AI-based SaaS solution will usually involve a monthly or annual subscription fee, the amount of which depends on the number of users or the volume of data processed. This is an option with a lower entry threshold. Building a dedicated solution generates much higher upfront costs, which consist primarily of personnel costs. Hiring or hiring a team of data scientists, data engineers and ML engineers is the largest part of the budget – the salaries of these specialists are among the highest in the IT industry.

Data and infrastructure costs are another major factor. If a company does not have ready-made, clean datasets, there are costs involved in acquiring, cleaning and labeling them, which is an extremely labor-intensive process. Training advanced AI models requires a lot of computing power, which generates costs associated with renting resources in the cloud (especially expensive GPU instances) or buying your own powerful server infrastructure. Maintenance and development costs must also be taken into account – the AI model is not perpetual, requiring constant monitoring, updating and periodic re-training, which generates ongoing operational costs.

How to effectively measure the return on investment (ROI) in artificial intelligence technologies?

Measuring the return on investment (ROI) of AI projects is crucial to assessing their success and justifying further spending, but at the same time it is a more complex process than for traditional IT projects. Effective ROI measurement must take into account not only direct financial savings, but also broader strategic and operational benefits that are often more difficult to quantify.

The first step is to define clear and measurable indicators of success (KPIs) even before the project begins, for a specific business problem. If the goal was to reduce operating costs in the customer service department, a KPI might be a percentage decrease in the number of requests handled by humans in favor of a chatbot and a reduction in the average time to handle a request. If the project was to optimize marketing campaigns, the KPI could be an increase in conversion rate or a reduction in customer acquisition cost ( CAC). These hard metrics allow for direct calculation of financial benefits.

In addition to financial metrics, operational and quality benefits should also be measured. This could be a reduction in the error rate in a process, an increase in customer satisfaction (CSAT, NPS) resulting from faster and more personalized service, or improved engagement and satisfaction of employees who have been freed from monotonous tasks. These “soft” benefits, although more difficult to express in monetary terms, have a huge impact on the long-term health of the company.

To calculate ROI, we need to add up all the benefits obtained (both financial and the estimated value of qualitative benefits) in a given period, and then subtract the total cost of the project (including implementation, licensing, infrastructure and maintenance costs). The resulting figure, divided by the project cost, gives us the ROI. It is important to track these metrics on a continuous basis, as the full benefits of AI implementation often materialize over the long term.

How can nFlo’s expertise in AI and cybersecurity help your company safely deploy artificial intelligence and realize real benefits?

Implementing artificial intelligence in business is a journey that promises great benefits, but is also fraught with pitfalls, especially in the areas of security and compliance. At nFlo, we have a unique dual expertise – deep knowledge of modern AI solutions and years of experience in cybersecurity. This combination allows us to support our clients in a holistic way, ensuring that their AI projects are not only innovative and effective, but above all secure.

Our support begins at the strategy and consulting stage. We help identify those areas in your company where AI can bring the most value, and then conduct a risk analysis, taking into account not only business aspects, but also risks to data security and RODO compliance. We help develop policies and procedures for the safe use of AI, protecting your company from the risks associated with “Shadow AI” and uncontrolled use of public tools.

We support our clients in the secure design and implementation of AI solutions. Whether you decide to go with an off-the-shelf tool or build a dedicated system, our team ensures that the solution architecture follows security best practices. We perform security audits of AI code and models, verify infrastructure configuration and help you securely integrate with existing systems. Our goal is to ensure that your AI investment does not become a source of new, uncontrollable risks. When you choose nFlo, you get a partner who understands that true innovation must go hand in hand with responsibility and security.