Over the past few years, generative AI has moved from being a buzzword to a practical tool, impacting various business operations.
According to a recent McKinsey Global Survey, 65% of respondents said their organizations use generative AI — nearly double the number from ten months earlier.
Generative AI offers great opportunities for improving products, services, and operations. However, with opportunity comes a great deal of generative AI challenges. Many startups and small-to-medium businesses face hurdles such as a lack of knowledge about how to build a GenAI solution, the need for more input data to train on, or ethical issues.
In this article, Oleh Komenchuk, the ML Department Lead at Uptech, and Andrii Bas, the co-founder and R&D Lead at Uptech, will discuss the most common challenges of generative AI and provide practical insights for businesses looking to adopt this technology.
Key Generative AI Challenges in Adoption and How to Overcome Them
If you fail to identify the everyday challenges of generative AI, you could end up wasting your resources and taking too long to adopt this technology for your business.
Ignoring these hurdles could leave you lagging behind competitors who are already making AI work for them.
In this section, we’ll highlight the most critical challenges to be aware of and show you how to tackle them so your business can stay ahead.
1. Technical expertise
PwC's 2024 AI Jobs Barometer shows that 69% of global CEOs predict that AI will require most of their workforce to develop new skills within the next three years, indicating an AI skills gap. The lack of in-house GenAI expertise is one of the biggest generative AI challenges at present.
That’s because successful integration of generative AI requires:
- Programming skills: For example, Python for model development.
- Framework proficiency: Langchain, LlamaIndex, and Diffusers for working with LLMs and generative models.
- Cloud platform expertise: Amazon SageMaker, Azure ML for scalable deployments.
- Data engineering knowledge for preprocessing, feature engineering, and validation.
- Optimization skills like hyperparameter tuning and balancing performance vs. accuracy.
Why it’s a problem:
- Many teams struggle to decide on the right tools (GPT-based models vs custom-made solutions).
- Integrating generative AI technology into legacy systems often can be time-consuming and expensive, especially for older systems.
- Hiring skilled AI professionals is costly and highly competitive for most organizations.
Solutions:
- Partner with experts. Engage with IT outsourcing companies, AI consultants, or freelancers to assess your generative AI problems and needs.
- Use pre-trained tools. Explore low-code, ready-to-use solutions like OpenAI’s API, Hugging Face, and Google’s Vertex AI to reduce technical barriers.
- Upskill your team. Invest in training programs like AWS certifications for your employees.
- Start small and scale. Use simple projects to refine processes before moving to complex generative AI use cases.
- Document and share knowledge. Create an internal knowledge base of successful projects, challenges of AI, and solutions. Promote collaboration among key stakeholders to successfully implement AI with ethical guidelines in mind.
2. Cost considerations
A new report from IBM’s Institute for Business Value (IBV) reveals that the average computational costs are expected to climb 89% between 2023 and 2025. 70% of surveyed executives attribute this growth to generative AI.
Generative AI adoption comes at a cost, which can be quite steep for startups and SMBs with tight budgets. GenAI implementation includes a few costly processes.
Specifically:
- Initial setup expenses: Hardware, software licenses, and developer fees.
- Recurring costs: Cloud storage, API usage, and model maintenance.
- Hidden costs: Data acquisition, compliance, and infrastructure scaling.
Why it’s a problem:
The uncertainty surrounding AI ROI makes it difficult to justify investments and distribute money effectively. The challenge isn’t just how much AI costs — it’s understanding what drives those costs and how to optimize them.
Without a structured breakdown of expenses (software, infrastructure, talent, cloud usage, ongoing maintenance), you risk:
- Underestimating hidden costs, like API usage, data storage, and model fine-tuning.
- Overspending on unnecessary AI capabilities without getting measurable returns.
- Struggling to track and control expenses, leading to budget overruns.
The underlying issue is not the cost but cost visibility. If you aren’t keeping an eye on AI spending, expenses can quickly get out of hand with you barely aware of where your budget is going.
Solutions:
- Use the OpenAI API to access advanced language models. This way, you can avoid additional development costs.
- Choose cloud solutions. Look for pay-as-you-go platforms like AWS, Azure, or Google Cloud to reduce initial investments and drive business growth.
- Outsourcing. Outsourcing or outstaffing AI developers from other regions can provide the needed expertise in a cost-effective way.
- Focus on quick wins. Focus on applications that can deliver short-term ROI.
While working on Dyvo.AI, the AI image generator app, our team faced a similar challenge with optimizing GPU (graphics processing unit) costs for image generation. At first, we considered purchasing GPUs. However, at $300-400 per month for each GPU, the cost was too high.
Instead, we decided to try a service that lets you rent GPUs at an hourly rate. Using this approach, we only paid for what we used, keeping costs down without compromising on performance.
Read our case study for more details.
Learn about AI cost in our blog post.
3. Talent acquisition and retention
A recent Deloitte survey showed that 68% of executives acknowledged a moderate to extreme AI skills gap in their organizations.
Even well-established companies have a hard time finding and retaining qualified AI talent.
Such specialists require a diverse skill set, including expertise in machine learning, networks, natural language processing, and programming languages such as Python and Java.
Why it’s a problem:
- Difficulty in sourcing skilled generative AI engineers and data scientists.
- High salary demands in a competitive talent market.
- Limited ability to assess technical expertise during hiring.
Solutions:
- Work with outsourcing companies. Partner with companies like Uptech to access a pool of specialized AI talent.
- Upskill internal teams. Train your internal employees using educational platforms like Codefinity or Coursera.
- Hire freelancers. Use platforms like Fiverr to find specialists for short-term generative AI projects.
4. Resistance to AI adoption
Having AI that operates well is one thing — getting employees and customers to actually use it is another. A lot of people are resistant to AI out of fear of job loss, lack of understanding, or disbelief in its capabilities.
The 2024 Edelman Trust Barometer Report captures this hesitation: globally, just 31% of respondents embrace AI, and 25% reject it outright.
Why it’s a problem:
- People are afraid of AI, thinking it will take away their jobs and as such, are resistant to it.
- Employees don’t fully understand what AI can do due to lack of AI literacy.
- Customers might be doubtful about AI-generated content or AI assistance.
Solutions:
- Onboard employees about AI’s role. Host workshops to educate employees about AI, and the fact that it complements human work, not replaces it.
- Gradual adoption of AI. Start with low-impact AI tools before attaching them to mission-critical operations.
- Build customer trust in AI. Have human oversight for AI outputs through a human-in-the-loop approach, where experts review generated information before presenting it to users. Additionally, use UI cues like “Expert Verified” labels to build more trust.
5. Scalability
Most organizations start small with AI, adding it to a single workflow or product feature. But when it’s time to scale across multiple departments or operations, things get complicated.
The reason behind that?
Generative AI models require computing power, data pipelines, and infrastructure upgrades to handle large-scale implementations. According to a recent McKinsey survey, 70% of top-performing companies have faced challenges integrating data into AI models — whether due to poor data quality, unclear governance, or insufficient training data.
Without proper planning, scaling AI can result in performance bottlenecks, vastly increased costs, and system inefficiency.
Why it’s a problem:
- Data infrastructure may not support enterprise-wide AI adoption.
- AI models trained on small datasets may fail when applied to broader or more complex tasks.
- Scaling AI often demands a lot of GPU/TPU resources, cloud storage, and continuous retraining, leading to high operational costs.
Solutions:
- Invest in scalable cloud solutions. Use AWS SageMaker, Google Vertex AI, or Microsoft Azure AI to scale computing resources on demand without massive upfront infrastructure costs.
- Optimize AI model efficiency. Implement techniques like model distillation (creating smaller, efficient versions of large models) and quantization (reducing precision to lower computational needs).
- Improve data infrastructure. Consolidate and clean data across departments data lakes (large storage systems that keep raw data from different sources in one place), ETL pipelines (tools that Extract, Transform, and Load data into a usable format), and AI-ready storage solutions (high-performance storage designed to handle large datasets quickly) to ensure smooth AI model training.
6. Data challenges in generative AI
Data is the lifeblood of any generative AI system, yet companies regularly struggle with data security, quality, and compliance issues. According to projections from IDC, 80% of worldwide data will be unstructured by 2025.
Why it’s a problem:
- Insufficient training data can lead to false language model predictions, underperformance, and other similar problems of generative AI solutions.
- Raw datasets require extensive preprocessing, including cleaning (removing errors and duplicates), normalization (scaling and standardizing), and augmentation (generating synthetic data), which can be time-consuming and resource-intensive.
- The use of regulated data brings legal consequences, particularly with frameworks such as GDPR or HIPAA.
- The process of training AI models becomes time-consuming and expensive if the dataset is unstructured or not labeled. To make these models more accurate, human annotators label datasets so AI can learn from the right data in a structured manner. This manual effort, however, adds a whole new complexity and cost to AI development.
Solutions:
- Take advantage of data preprocessing tools. Tools like NumPy, Google Cloud Dataflow, AWS Glue are designed to automate and streamline generative AI problems like data cleaning, transformation, and organization.
- Augment datasets. Fill gaps in real-world datasets by creating synthetic data on platforms like Gretel.ai to improve model complexity.
- Prioritize compliance. Work with legal experts or use compliance management tools to ensure compliance with regulations like GDPR or HIPAA.
- Use labeling platforms. Use solutions like Amazon SageMaker Ground Truth to label and structure data efficiently and reduce manual workload.
7. Ethical and regulatory concerns
Among other problems in generative AI domains, we can’t overlook the critical importance of addressing the responsible use and ethical/legal implications of generative AI integration.
Why is it so important?
Consider the recent case of Clearview AI, an American facial recognition company. It has been fined $33.7 million by the Dutch Data Protection Authority for violating GDPR. The company, which created a database of images scraped from social media, has faced global regulatory scrutiny over privacy concerns.
It’s important to highlight that no company or AI technology is exempt from these challenges in artificial intelligence. Steps must be taken to comply with data privacy laws and ethical use of generative AI.
Why it’s a problem:
- AI-generated outputs can amplify existing biases, leading to unfair or harmful outcomes.
- Adjusting to frameworks such as GDPR, HIPAA or new AI-specific regulations on ethical use can be complicated and resource-consuming.
- Issues related to misinformation, AI misuse, and ethical implications can create reputational risks.
Solutions:
- Adopt ethical AI practices. Regularly audit generative AI systems using open-source tools such as AI Fairness 360 or the Microsoft Responsible AI Toolbox.
- Stay informed. Stay informed about new regulations via AI-focused legal blogs, compliance dashboards, or legal advisors.
- Build transparency. Develop documentation to explain how your generative AI models are trained and deployed. Communicate clearly with users about AI systems' capabilities and limitations.
- Partner with experts. Partner with IT outsourcing companies that focus on generative AI to maintain ethical and legal standards. They can ensure the successful implementation of AI solutions, mitigating any risks associated with bias, fairness, and regulatory non-compliance.
8. Maintenance and updates
Generative AI isn’t a "set it and forget it" solution—it needs regular maintenance to stay effective. Without updates, models can drift, producing outdated or biased results that hurt the accuracy of generated outputs.
To prevent this, businesses should fine-tune AI models periodically, refresh training data, and monitor outputs for quality. How often? It depends on usage, but for high-impact systems, monthly reviews and quarterly updates are a good starting point.
Why it’s a problem:
- There is a constant need to fine-tune existing models. If AI models aren’t fine-tuned, their accuracy declines over time, a phenomenon known as model drift.
Solutions:
- Implement monitoring tools. Use platforms like MLflow to track model performance, identify drifts, and detect issues early.
- Plan for updates. Budget and deploy resources for routine retraining and infrastructure upgrades. Create a model refresh timetable to keep systems relevant and effective.
- Consider generative AI development services. Work with IT outsourcing companies specializing in generative AI maintenance to handle regular updates, fine-tuning, and infrastructure improvements.
- Reduce dependency. Build in-house expertise to minimize reliance on external providers. Where feasible, migrate critical components to internally managed systems for better control and cost efficiency.
Uptech Use Case: Turning AI Challenges into Scalable Solutions
At Uptech, we have worked with a number of businesses to help them overcome key generative AI challenges. One of our standout projects, TiredBanker, exemplifies how we approach cooperation and deal with AI obstacles.
TiredBanker Case Study – How We Made Investment Data Simple
Investment reports are hard to digest. TiredBanker set out to change that, using AI to break down complex financial data into clear, actionable insights. And our team helped them to bring these ideas to life.
Challenge: To train the model on complex, unstructured text data to transform 10 years of earnings reports from S&P 500 companies into a clear, insightful format that helps bankers make smart investment decisions.
Solution: We built an AI-driven platform on Webflow that processes and visualizes data, using GPT-4 to simplify complex reports and turn them into clear insights.
Result: An AI platform that helps users explore investment opportunities and insights without sifting through difficult reports.
Read our case study for more details.
Dyvo.AI Case Study — How We Created a Platform That Generates Personalized, High-Quality Avatars
Besides cost optimization challenges, it's worth mentioning the other key aspects of work on Dyvo.AI functionality. In this case, we specifically worked with visual data and tried to achieve the best quality possible.
Challenge: We aimed to get the most out of Stable Diffusion technology and set three main goals:
- To make the images look as close as possible to the people in the original photos.
- To avoid common issues, like artifacts, that often pop up with AI-generated images.
- To create images that users would actually enjoy (the most important challenge).
Solution: Our team optimized the picture generation process through extensive experimentation, fine-tuning elements like prompts, sampling methods, CFG scale, steps, seeds, and X/Y plotting.
Result: An app that creates personalized, high-quality avatars people love — quick, easy, and great for both professional and creative needs.
Want to know more? Read the full case study here.
Conclusion
While generative AI holds great potential, businesses must overcome considerable challenges for generative AI adoption.
A winning recipe is to start small, focus on high-impact applications, and foster employee collaboration.
If you’re looking for expert assistance to tackle the challenges of artificial intelligence and make the most of it, our team is here to help. With experience delivering custom AI solutions, Uptech can help you implement GenAI in your system or operations. Contact us to learn how we can support your GenAI journey.
FAQ
What are some of the challenges of generative AI?
Generative AI is powerful but comes with key challenges:
- Ethical concerns (deepfakes & misinformation). AI can create convincing but false content.
- Bias in outputs. AI reflects biases in training data, which can reinforce stereotypes.
- Privacy issues. AI often trains on massive datasets, so it may raise concerns about personal data use and compliance with laws like GDPR.
- Intellectual property risks. Unclear ownership of AI-generated content creates legal uncertainty, especially when trained on copyrighted material.
- AI "hallucinations". AI can generate incorrect or misleading content, so human oversight is essential in its key applications.
What are the limitations of generative AI?
Generative AI has limitations such as bias, inaccuracies, and dependency on large datasets. It can be expensive, have difficulty with context, and sometimes produce inaccurate or misleading information. The challenges of security, privacy, and regulatory compliance also remain, making human oversight necessary.
What is the biggest risk of AI?
It's possible that AI won't align with your business goals. Badly integrated or poorly designed AI systems may waste time and resources, as well as bring unexpected results that may compromise your operations or frustrate customers.
That’s why it’s so important to make sure the AI is built around your specific goals and is regularly checked to make sure it's working as it should.
Other key risks of AI include:
- Data privacy and security. AI systems use a lot of data, and if it's not protected, it could lead to privacy issues or security breaches.
- Bias in AI models. If the data used to train AI is biased, the system could generate unfair or discriminatory statements, which could harm your brand and customer trust.
- Over-reliance on AI. AI has its limits, so relying on it too much without human input can lead to mistakes.
- Regulatory and compliance risks. As AI technology grows, so do the rules around it. It's important for companies to stay on top of the laws to avoid legal trouble.
What are the good uses of generative AI in business?
Decision makers apply AI in many business areas, like software development, marketing, and customer service. Here are a couple of generative AI use cases:
- Code refactoring and debugging. AI can find inefficiencies, suggest ways to improve code, and even automate bug fixes to make software work better.
- A/B testing. AI can analyze user behavior to find out which design variations work best. This helps improve user engagement.
- Personalization for SaaS products. Using AI to give personalized recommendations can improve user experiences and keep customers coming back.
- AI-powered threat detection. AI can analyze network activity to identify and respond to potential cyber threats in real time.
- Predictive modeling and forecasting. By analyzing historical data, AI can predict market trends, customer behavior, and financial results.
- Automated reports. AI can help summarize business reports in simple language and make complex information easy to process and understand.
- AI-powered virtual assistants. AI chatbots can address common customer queries while passing on complex problems to humans..
- Sentiment analysis. Businesses can use AI to analyze customer feedback on social media platforms and improve their services based on reviews.
What is a key challenge in developing generative AI models?
The key challenge in building generative AI models is to make sure that outputs are high quality and reliable.
To ensure this, the development team must focus on fine-tuning, filtering high-quality data, using human feedback reinforcement learning (RLHF), doing automatic bias checks, and applying strong human oversight.
How to detect the generative AI challenges?
Each challenge has its own way to detect it.
Ethical and compliance concerns: Establish and follow ethical guidelines for AI development, such as AI compliance checklists aligned with GDPR, CCPA, or the AI Act (EU). Involve third-party audits to validate responsible AI use.
Bias: Use fairness evaluation tools like IBM AI Fairness 360 to identify biases in training data and outputs. Analyze AI training data and outputs.
Scalability issues: Test AI in larger use cases.
Cost considerations: Track budget overruns, ROI and AI-related expenses.
Data challenges: Review your data pipeline. If AI outputs are inconsistent or hard to integrate, improve data quality.
How do challenges with generative AI affect the output?
Challenges directly impact the quality of generated AI outputs.
For example, poor quality training data results in biased or incorrect results. A lack of tech expertise within the team leads to unsuccessful implementation of AI in processes. High costs and scalability issues limit AI’s reach and reduce its effectiveness in real-world applications. Ethical and compliance missteps risk creating reputational damage and legal issues.
Any challenge has its consequence, so it's essential to be attentive, identify them early, and address them quickly.
Can I avoid the generative AI challenges?
Challenges are inevitable, but with the right approach, you can limit their impact.
Hire skilled professionals, use pre-trained models whenever possible, and focus on high-quality input data. Establish strong governance policies for ethics and compliance risk management. Start small with AI adoption and scale as you refine processes. Regularly monitor and update models to maintain performance. And finally, build AI literacy within your company so that you can integrate and use AI responsibly.