How to Overcome Common Generative AI Challenges [A Straightforward Guide]

Common Generative AI Challenges for Businesses

Let's talk about some of the common generative challenges that companies face when implementing AI solutions.

Challenge 1: Data Quality and Quantity

AI programs rest on immense data amassed from diverse sources, and they generate different forms of data, such as text, audio, graphics, etc.

With limited and irrelevant model outputs, they become suboptimal under limited data, non-diverse, or poor quality (i.e., incomplete, noisy, or biased).

In such situations, the outputs can be inaccurate, less innovative, or cannot generalize across contexts.

In a study, output from two models was compared, and the model trained on a high-resolution, diverse set of datasets produced better images. Meanwhile, the models trained on low-resolution images produced blurry, unrealistic images. In summary, if you feed them second-rate data, they will throw out garbage.

Solution

Build a multi-pronged and proactive strategy to overcome this challenge. You need to ensure that the data you have on hand is accurate, consistent, and relevant. Today, businesses at the forefront of innovation have invested in building a powerful mechanism to clean and organize data. You can source from trustworthy sources and remove defects. 

Data augmentation is a method that major AI/ML development companies use for data refinement. You can achieve dataset expansion by creating variations of available data. You can rotate images, paraphrase sentences, or create new samples. 

Transfer learning is another popular method used for performance enhancement when you have limited data. Data updating is another popular practice used by AI development companies to make AI systems current and effective. These are the methods an AI development firm can use to unlock the optimal potential of an AI model. 

Challenge #2: Model Training Complexity

It is resource-intensive to train a generative AI model. For smaller businesses, there could be infrastructural barriers. You need professionals well versed in building advanced machine learning programs, an infrastructure containing high computing and processing power, specialized GPUs, and TPUs. Training a large language model from scratch, for instance, can take several weeks or months, even on a high-performance computer cluster. The long time, combined with high energy consumption and the need for expert personnel, makes model training extremely pricey and out of the reach of most organizations, especially small ones or those just beginning with AI.

Besides time and cost, the process involves sophisticated procedures—such as tuning hyperparameters, avoiding overfitting, and stabilizing the model—that further complicate the process. All these pose a high entry barrier, limiting those that can effectively leverage generative AI.

Solution

It may not be possible for many companies to train and build a model from the ground up. You can get pre-trained models trained on massive datasets at a fraction of the cost it would take to train AI models from scratch. Several models are available, such as from OpenAI, Hugging Face, etc. These companies provide pre-trained models, already training large and diverse datasets.

They are trained by expert data scientists, offering a solid foundation for building sophisticated models. All you need to do is fine-tune them to make them ready for specific tasks. With this, you can save considerable time and cost. 

In addition, you can outsource an AI development company for this task without hiring experts and setting up a team. These companies have worked thoroughly in real-life projects, guiding companies through critical steps.

Challenge #3: Ethical and Bias Issues

Generative AI models carry serious ethical risks, most particularly around bias, says AI pioneer Geoffrey Hinton. In a New York Times interview, Hinton warned, "The danger is that you'll get a system that's incredibly good at something, but it's biased in ways that we don't fully understand or control. And then it becomes very difficult to fix those biases."

When you make use of human-created real-world data, human biases may be inherited by AI systems. They learn based on human-generated real-world data. You can understand it this way: a language model trained with a dataset consisting of discriminatory data can generate objectionable outputs. Likewise, an image-generation model trained on unrepresentative data can perpetuate visual biases like the underrepresentation of a specific demography. 

Solution

You can take a multi-pronged approach to ethical AI development to identify biased behavior and decision-making. Regular monitoring is important. For this, you can develop a fairness metrics system. Many tools, like IBM’s Fairness 360 and Google’s What-If Tool, can be used to detect biases in AI programs. 

Let’s understand this with an example: Suppose you are building an AI-powered hiring tool that favors male candidates over female candidates. You will need to use a fairness metric to detect biases in decision-making.

Set clear rules defining what is acceptable. Regular check-ups are crucial. The best way to make this more effective is by working with diverse groups of people—individuals from different backgrounds, geographies, and cultures can spot biases that might otherwise go unnoticed. You can run a survey or hire an agency to have people from different backgrounds evaluate the models.

How would you ensure transparency? It’s a major challenge with today’s ML models.

To address this, outline how models are developed, what kind of data has been used, and the path they take to reach conclusions. You can identify a model’s strengths, weaknesses, and potential risks with impact assessments.

Successful companies collaborate with organizations, universities, and diverse groups of people, incentivizing them to evaluate their models. Without this collaborative approach, it could be hard to develop a transparent AI system. When progress is shared, you can solve complex problems with less effort and craft high-standard programs with less investment.

Challenge #4: Integration with Existing Systems

You may be running your business on an infrastructure comprising a mix of legacy, modern, and cloud services. With this kind of infrastructure, you may encounter unique challenges. You have a mix of systems, and integrating them is challenging—one of the key factors slowing down AI adoption across industries.

Some common challenges include incompatible programming, outdated legacy systems, mismatched data pipelines, and systems with limited processing speed. Beyond technical barriers, AI integration shouldn’t disrupt your existing workflow. Businesses often face multiple challenges during AI integration and deployment.

Let’s understand this with an example. Suppose you plan to enhance your CX platform by infusing AI capabilities to ameliorate customer service experience. With technical difficulties, integration presents challenges like security concerns, data privacy, and ethical concerns, demanding careful thought on how to regulate and manage these risks.

Solution

Let’s examine how a company can solve these challenges in a customer service platform. You’ll need experts to handle technical hurdles and a team to assess outdated systems—hardware, software, and programming languages—while redesigning the infrastructure without a complete overhaul. This ensures minimal changes while keeping the system efficient.

The legacy systems often struggle to keep up with modern requirements. APIs and middleware act as connectors, enabling legacy software to communicate with modern systems. With APs, you can smooth out AI adoption without restructuring your entire infrastructure. For example, APIs can connect AI tools to a CRM, and secure third-party services can further simplify the process.

A common mistake businesses make is skipping a phased rollout. Deploying AI solutions step by step makes debugging easier by breaking problems into manageable parts.

Phased deployment is the best way to start. Run a pilot test in a controlled environment. It helps you identify latency spikes, data mismatches, and several other issues. Both technical and non-technical teams and end-users collaborate in phased deployment for smooth integration. And with an iterative approach, you can reduce the complexity level requiring significant effort in one go.

Challenge #5: Scalability

Generative AI spending will be more than $600 billion in 2025 - a 76.4% jump from 2024, Gartner reports.

This shows that generative AI is going to be the most-demanded technology this year. Industry leaders are optimistic, too. And they don't want to be left behind in this AI adoption. However, running AI programs in-house is out of reach for many companies. Custom AI solutions, running on massive datasets and high-end GPUs and TPUs, need significant energy consumption. This becomes a major bottleneck for small businesses with limited infrastructure and budgets.

Solution

While new hardware and services can contribute to scalability, it is not the sole solution. Appending new hardware and services is not the only way to achieve hyper-scalability. Go for the time-tested services.

Ask a simple question to yourself: Is the tech stack you’ve chosen battle-tested, or are you just jumping on the bandwagon? IT infrastructure is like building a house—its foundation and layout are crucial when it’s time to expand. The architecture you develop matters. From the very beginning, the focus should be on building an infrastructure that is scalable, flexible, and future-proof.

For most businesses, cloud technology helps build scalable systems. AWS, Google Cloud, and Microsoft Azure are popular cloud platforms that provide both infrastructure as a service and platform as a service to run complex operations on the cloud. On-demand services are a great way to offload specific operations to third-party vendors.

This way, you don’t have to worry about choosing hardware and technologies for an on-premise setup. Instead, you can leverage powerful GPUs, computing systems, and cutting-edge AI services by paying only a fraction of what an in-house setup would cost.

Cloud is a crucial component of scalable infrastructure. Besides that, modular infrastructure is more scalable because each layer in this architecture can be scaled independently.

Moreover, you can distribute your workload across multiple machines. TensorFlow or PyTorch are the favored libraries used in distributed computing environments. Real-time monitoring tools offer a proactive way to track system performance and identify unstable resources.

By these means, you can improve system performance. This list isn’t exhaustive, but these are some of the most popular ways to make infrastructure scalable.

Challenge #6: Cost Management

Small and medium-sized businesses (SMBs) with a tight budget may not be able to afford large AI model training, as it requires a huge investment.

You need high-end computers, specialized hardware, and a cluster of GPUs or TPUs. The cost of training AI models is one of the biggest barriers for small businesses. Developing an AI model alone can cost tens of thousands of dollars or more, and keeping it accurate and relevant adds to the expense.

For SMBs, setting up the right environment and hiring AI experts to manage the process can be a financial strain. Until AI becomes more accessible, AI development remains out of reach for many SMBs with limited budgets.

Solution

The most effective method of minimizing AI development expenses is an incremental strategy. Begin with small projects that can be accomplished without a huge initial investment.

We can perceive this with an example: You are creating a customer support AI tool. To create the basic features is best. You may use a light model for it. Basic features would include automated responses and welcome screen creation, which can be done using light models.

This is your initial move to create a revolutionary tool. Launch it and observe how users react. Collect feedback and begin to iterate your product. It will give you an idea of what is going right. Thus, you will reduce risks without undertaking a huge upfront investment. In a technical sense, you would only be constructing what is impactful.

Creating the whole product at one time will take a lot of effort, and debugging will not be simple. It will also impose a financial burden on your company. On-demand cloud services are a savior for companies with limited funds, offering a choice to pay only for what they consume. Selecting pay-as-you-go services can save costs. The greatest advantage of these services is that they prevent you from overpaying, saving you from the cost of keeping and buying hardware. Nearly every cloud platform now provides an environment to develop and execute resource-intensive AI applications.

By using pre-trained models in the cloud, you can economize on thousands of dollars, which would otherwise be required to build an AI model from the ground up. Using pre-trained software saves you both effort and money.

Before initiating any project, establish well-defined objectives. Do you wish to save money with AI solutions, enhance service, or increase sales? What will it cost to create the solution? And how much of an impact will it have? Establish an estimate for the cost against the hoped-for benefit.

Rapid application development is another popular approach for quickly building products. Begin small, utilize cloud and pay-as-you-go services, and implement pre-trained models first to construct something huge with minimal investment.

Challenge #7: Skill Gaps

The shortage of qualified AI professionals continues to be one of the biggest roadblocks to implementing AI initiatives at companies globally. The demand for AI professionals has been increasing yearly, leaving many organizations with a talent gap. Most companies can't implement their AI plans because they are unable to close this gap and bring in professionals with the correct set of AI skills. Even though over 90% of businesses plan to use AI, more than half admit they are having trouble finding the right talent.

SnapLogic surveyed around 300 IT leaders from companies with over 1,000 employees in the US and UK. Interestingly, they found that the shortage of skilled talent was one of their biggest concerns.

Solution

Companies are still struggling to hire best AI talent. They are taking a multi-pronged approach to bridge this talent gap, like upskilling talent by enrolling them in foundational machine learning courses and providing hands-on training with frameworks and real-world concepts.

Additionally, companies are partnering with educational institutions and enrolling employees in online platforms like Coursera and Udacity to help them grasp the nuances of AI development.

This approach enables organizations to upskill their workforce and move toward the implementation stage.

Another option is outsourcing specific AI tasks to expert development firms. Companies can hire full-time specialists or consultants to bring high-level expertise to their teams, accelerating project timelines.

Outsourcing is often cost-effective, allowing businesses to access skilled talent without setting up an in-house team.

Tips for Fair AI Development 

Here are some key strategies for building a successful AI program.

1. Implement AI ethics guidelines.

There is no denying that AI is changing our lives, but there is also a negative side to it. Many AI systems have been found to produce inappropriate and unfair results. To address this, industry experts have outlined ethical guidelines for building responsible AI programs.

Top tech companies are actively working to follow these guidelines, safeguarding their AI models from legal and regulatory issues. However, since AI is evolving faster than regulations, AI development companies can expect more rules and standards in the future to ensure safer AI development.

2. Audit AI models regularly

Without proper auditing, organizations risk serious consequences. Waiting until an AI system is deployed is a mistake. AI’s "black-box" nature requires continuous monitoring throughout its lifecycle. Several frameworks, such as COBIT, COSO ERM, and IIA AI, can help organizations manage AI risks. 

3. Use Explainable AI techniques

AI systems have been developed to emulate human brains in some way. They are fed with trillions of parameters, and because of this ability, they can make decisions like humans. That's why companies are focusing more on understanding how these AI models reach conclusions. Today's AI models can answer our queries and solve mathematical equations, but they cannot explain how they arrived at them. This is why explainable AI is an emerging trend in 2025.

How to get started with AI development

It's a big endeavor. It requires careful planning, resources, and expertise. Here’s a roadmap to move forward with AI integration at your business.

1. Do Your Research and Identify Priorities

Identify the best opportunities. Determine which processes will benefit most from AI. You can rank them in order of urgency. For example, if you have a big backlog of service tickets, you could consider AI to help you prioritize and categorize them.

2. Figure Out How to Fund Your AI

You can use an AI cost calculator to help you with this. Make sure to include all your costs, including labor, training, and any external consultants you may need. Once you clearly understand how much the entire project will cost, you can figure out where the money is coming from. You can use a combination of budgeting cuts, savings, and external funding sources.

3. Training

It’s not enough to just set up a few AI systems. To succeed, you need your team to embrace AI. The best way to ensure this is to educate them on effectively integrating AI into existing workflows and practices. There should be a training curriculum specifically designed to integrate AI into business functions in your industry. You should also work with experts and consultants to figure out how to develop your own AI training program.

4. Choose Your Platforms

To select an AI platform to work with, you need to know how you’ll be funding it and where you’ll be using it. You’ll want to ensure all your chosen platforms are compatible with your existing systems.

5. Build a Strategy

Before you begin using any AI, you should have a solid plan. Having this plan in place will help you avoid some common errors and obstacles. The plan should be clear on the specific AI you’ll integrate. It also needs to address how the AI will be integrated and how to modify it to work with your team and current processes.

6. Implement and Integrate

Once you've chosen your AI tools and built a plan, it's time to make them work and fit in. The main thing here is adding AI to your business processes without interrupting the flow.

7. Monitor and Measure

Monitor its effectiveness by looking at the impact of the AI on your business. Identify what has improved. Find the areas that could still be improved. By carefully monitoring the AI implementation, you can spot any errors, make timely fixes, and get the most out of the systems.

8. Adapt

Over time, AI will continue to be developed, and new ways to utilize it will emerge. You’ll need to adapt to these new technologies and techniques to remain at the top of your game. You can continuously improve your business processes by actively evaluating new AI tools and methods and figuring out how to integrate them.

In summary 

In this article, we have explored some key integration challenges in AI development and discussed ways to overcome them.

Brilworks is an expert AI development firm. We strive to solve businesses' unique challenges with tech-driven solutions, including the development of AI solutions.

AI development requires significant effort, investment, and highly skilled talent, which may be out of reach for many small and medium-sized businesses. Scaling an AI program with an in-house setup can be challenging for many companies; however, with the right strategy and by partnering with a generative AI development company, you can build AI solutions at a surprisingly affordable cost. Brilworks offers cost-effective AI development services, helping businesses solve complex challenges and drive transformation through innovative solutions. Need guidance on getting started with AI development?