Generative AI now reaches across every industry — yet its move into real business processes is shadowed by data failures, cyber threats, algorithmic bias, and a fast-shifting regulatory map. This study identifies and classifies those problems.
Keywords: artificial intelligence, big data, disinformation, risks, algorithmic bias · JEL classification: M15, K24, O33
The intensive development of artificial intelligence (AI) technologies and their implementation in the business environment pose serious challenges and uncertainty regarding the further application of innovations in many areas of life. The highest level of generative AI adoption in marketing, software engineering, and analytics has been achieved by the technology sector, thanks to the possibilities of personalization, automation, and optimization of operational processes. However, implementation has become more complicated due to the growth of cyber threats and ethical and legal restrictions, particularly in traditional sectors such as manufacturing and supply chain management.
The article examines key problems of AI implementation in business and marketing, identifying negative consequences and potential threats associated with the incorrect use of innovative technologies. The use of generative AI to create differentiated content is related to many challenges — deep fakes, disinformation, manipulation — and there is a serious need to regulate regulatory acts at the national and global levels. The analysis shows the evolution of tools and policies for regulating AI in several countries, including the EU, the USA, and China.
The main problems of implementing AI in the business environment are classified by the following criteria: data problems, integration of AI with existing information systems, insufficient computing power and infrastructure, an acute shortage of qualified AI specialists, and security and data-privacy problems. Priority strategic vectors are identified — investing in data-processing infrastructure, implementing transfer and federated learning, developing tamper-resistant models, strengthening cybersecurity, and cooperating with regulators to comply with norms such as the GDPR and the AI Act.
The rapid development of AI opens new opportunities for business, advertising, and marketing, while raising risks for human rights, personal-data protection, and democratic processes.
The use of AI is associated with a number of serious risks concerning human rights, the protection of personal data, and democratic processes — among the main topics discussed at global forums with regulators, the public, media professionals, and platforms, as well as in numerous scientific publications. AI is also actively used in Ukraine, which makes it necessary to balance the benefits and risks this technology brings.
The integration of AI into business processes and the potential risks of its implementation are covered by a range of scholars — Mahbub & Ayman; Forradellas & Gallastegui; Soni et al.; Kar, Kar & Gupta; Bharadiya; Abisoye & Akerele; Richey et al.; Wach et al.; El Hajj & Hammoud, and others. Their work confirms significant achievements, but the emergence of new threats in the use of AI algorithms requires identifying the latest of them through a proactive strategy for transforming business processes and interacting with partners and clients.
Priority strategic vectors for AI companies follow from this analysis: investing in data-processing infrastructure, implementing transfer and federated learning, developing tamper-resistant models, strengthening cybersecurity, and cooperating with regulators to comply with norms such as the GDPR and the EU AI Act. The choice of strategy is justified by company size, time horizon, and market opportunities.
To determine new opportunities for using AI in business and marketing environments, to identify technical barriers to applying high-performance big-data algorithms, and to analyse their regulation at the global level.
Implementation is observed across all industries; leadership belonged to the technology sector with an impressive 88% adoption across all functions. The main value in 2024 was the personalization of communications with clients and consumers.
The technology industry's lead (88%) reflects the creation of innovative tools used to optimize processes — primarily in marketing and software engineering. Generative AI is applied for analytics and consulting; in media and telecom it plays a key role, ensuring fast feedback, content management, and creative work.
Professional services (80%) and media/telecom (79%) also adopt actively, while financial services (65%) and retail (68%) lag somewhat despite significant potential. Finance shows selective integration toward services and compliance — its risk-management and legal focus is tied to rising cyber risk, prompting AI use for fraud combat, AML procedures, and regulatory compliance.
Retail's high share in service operations (37%) reflects automation of customer support, chatbots, virtual assistants, and personalized recommendations. The complexity and high cost of change leave traditional sectors behind: manufacturing (5%) and supply-chain management (7%).
| Function | Technology | Prof. services | Media & telecom | Retail | Financial services | Total |
|---|---|---|---|---|---|---|
| Marketing & sales | 55 | 49 | 45 | 46 | 40 | 42 |
| Product / service development | 39 | 41 | 26 | 21 | 25 | 28 |
| IT | 31 | 16 | 22 | 20 | 24 | 23 |
| Service operations | 30 | 23 | 37 | 13 | 26 | 22 |
| Knowledge management | 26 | 34 | 26 | 12 | 16 | 21 |
| Software engineering | 36 | 9 | 30 | 8 | 20 | 18 |
| Human resources | 16 | 17 | 22 | 8 | 11 | 13 |
| Risk, legal & compliance | 12 | 9 | 6 | 11 | 21 | 11 |
| Strategy & corporate finance | 14 | 14 | 10 | 7 | 7 | 11 |
| Supply chain / inventory | 10 | 4 | 3 | 14 | 4 | 7 |
| Manufacturing | 5 | 3 | 3 | 8 | 0 | 5 |
| Gen AI in ≥ 1 function | 88 | 80 | 79 | 68 | 65 | 71 |
Using AI helps creators generate more content in less time — but deepfakes, disinformation, unvalidated information and manipulation (including during electoral processes) have pushed regulators toward new rules. Over the assessment period, two risks dominate.
The dominant risks are predicted to be errors of algorithms with real consequences (35%) and failure to achieve the expected value (34%). This creates a need for constant interaction with the business to prove the value of investment in innovative projects and to minimize reputational losses.
30% of respondents see the availability of sufficient high-quality data as a problem, raising questions about developing information infrastructure in line with the specifics of training and integrating AI models. Using generative AI to create texts and optimize client communications requires addressing the loss of trust from bias and hallucinations (29%).
Legal and intellectual barriers in the global digital environment also demand attention (25%), since the internet is marked by constant borrowing of others' ideas in creating content and digital products without the consent of rights holders.
Norms for AI regulation have already been created at the highest state level. The analysis shows a clear evolution of tools and policies across the three jurisdictions.
The Cyberspace Administration of China, with related state institutions, first introduced the Interim Measures for the Management of Generative Artificial Intelligence Services. Legislation regulates mandatory safety, transparency, truthfulness, and content compliance with core values, plus obligations on model licensing, ethical responsibility and control.
The Act provides a diversified classification of risks and lists systems to be banned in the EU — for example, manipulation (algorithms that influence a user's final choice or political decision they would not otherwise make), exploitation of vulnerable groups, crime prediction, facial recognition, emotion analysis and biometric categorization. Its first provisions began to apply from February 2025; implementation runs over three years.
LLM developers were required to sign voluntary codes of conduct and declarations on responsible AI development. California, Colorado and other states introduced AI legislation; at the federal level the so-called No AI Fraud Act — the "No Artificial Intelligence Fake Replicas and Unauthorized Duplications Act of 2024" — is being discussed.
"Labeling Rules" require marking of AI use in content creation. Implicit marks are added to file metadata; explicit marks are placed in text, images, audio, video and virtual content, so users can easily learn that content was created with AI.
Implementation of AI in the business environment is accompanied by technical, ethical, organizational and economic problems. Neglecting the technical ones can cause significant delays, budget overruns, low effectiveness, or full project failure. The key challenges classify into five directions.
AI models learn from data, and their effectiveness is directly determined by data quality. Incomplete, inaccurate, outdated, inconsistent, or biased data can lead to wrong conclusions, erroneous forecasts, and discriminatory decisions. A common vulnerability is fragmented data stored across different systems without unified standards.
Most companies use outdated IT infrastructure incompatible with modern AI algorithms. Modernization requires significant investment, time, and skilled specialists who are scarce on the labour market.
Deploying complex AI models — especially deep learning — demands significant compute resources such as GPUs or TPUs. Acquiring and maintaining such equipment is expensive and may be unjustified for many companies.
There is a sharp deficit of machine-learning engineers, data scientists, AI/ML architects and MLOps engineers. Team integration is also difficult: successful AI implementation depends on close cooperation between the IT department, data specialists and other business units, which imperfect communication and organizational barriers can complicate.
Insufficient cybersecurity can cause data leaks, privacy breaches, and serious reputational and financial losses. Compliance with personal-data rules such as the EU GDPR is essential — AI systems should be built on a privacy-by-design basis. The GDPR entered into force on 25 May 2018 to give individuals greater control over their data and set clear rules for institutions that collect, process or store it.
AI acts as a tool for implementing cyberattacks — highly effective against modern information-protection systems. At the same time, high-performance machine-learning algorithms are used to build cybersecurity systems.
Demand for these data-protection technologies grows substantially: the market expands by more than 5.5× over the period, with an average annual growth rate above 28%. This is driven by significant growth of cyber threats worldwide, correlating with the active development of digital platforms, cloud services and IoT.
Until 2025, gradual growth reflected the initial deployment of AI in cyber defence. For 2028–2030, AI is forecast to become a key component of cybersecurity, playing a special role in finance, e-commerce and the public sector.
Ethical and legal challenges — primarily algorithmic bias — are central. If training data contains systematic errors, AI is highly likely to reproduce discriminatory decisions. Two cases illustrate the stakes.
Amazon cancelled its AI recruiting tool when it turned out the system favoured men due to bias in training data. Built to automate résumé evaluation, it lowered the ratings of female candidates — penalizing résumés containing the word "women's" and downgrading graduates of women's colleges.
The cause: the algorithm was trained on the previous 10 years of data, dominated by men, reflecting gender imbalance in the tech industry. Despite attempts to correct it, Amazon could not guarantee the absence of other forms of discrimination and abandoned the tool entirely.
Tay was an experimental chatbot built by Microsoft Technology & Research and Microsoft's Bing to interact with users on Twitter, Kik and GroupMe, learning from online communication. The core problem was uncontrolled training on low-quality, biased external data and insufficient resistance to manipulation.
A group of internet trolls deliberately "fed" the bot racist, sexist and xenophobic statements. Because Tay formed responses from programmed input, it echoed offensive phrases, turning into a "hateful" bot. With no real-time filtering, no tamper-resistance and no human-in-the-loop, its behaviour degraded fast — and Tay was shut down less than 24 hours after launch, a major reputational blow.
Careful selection & cleaning. Simple use of "big data" is grossly insufficient — relevance, accuracy and absence of bias must be ensured.
Robust moderation & security. Especially for systems interacting with public sources: effective filters and monitoring prevent manipulation or the spread of harmful content.
Tamper-resistant models. Designed to resist targeted attempts to distort their training or outputs.
Human-in-the-loop monitoring. Constant monitoring of AI behaviour with prompt human intervention, especially in early stages of deployment.
AI often needs access to clients' personal data, which can conflict with rules such as the GDPR — violations bring significant fines and reputational loss. In many countries, liability for decisions made by AI still lacks clear legislative regulation, creating uncertainty.
Much of the staff perceives AI as a threat of job loss, prompting resistance to change — McKinsey research confirmed such fears in 30% of workers. Organizational inertia compounds this, as AI often requires restructuring entire departments and can trigger internal conflicts.
Building an in-house AI system can cost from hundreds of thousands to millions of dollars. Per Gartner, only 15% of companies achieve significant results in the first two years, and about 30% of GenAI projects were set to be scrapped by 2025 due to weak returns and unclear business value.
To solve the problems of AI implementation — particularly overcoming data shortages and ensuring data validity and transparency — several approaches are recommended, useful across industries such as fintech and medicine.
Use pre-trained models with a valid dataset, then optimize on limited specialized data — efficient where domain data is scarce.
Generate synthetic data or apply techniques such as image rotation or text paraphrasing to expand limited training sets.
Train across data from different sources without centralized collection — preserving privacy while widening the data base.
Adopt models with less data dependence — few-shot or zero-shot learning — and design them to resist targeted manipulation.
Implementing AI in the business environment opens broad opportunities, but is accompanied by significant challenges. Technical and functional problems — data quality and system compatibility — together with ethical dilemmas, organizational barriers, economic constraints and reputational risks create a set of complex obstacles.
For successful integration, companies and brands must carefully plan their strategies, invest in staff training, cooperate with regulators, and account for societal expectations. Only a comprehensive and proactive approach makes it possible to maximize AI's potential while minimizing the associated risks.