Adopting AI: What are Technical Roadblocks?

Where on one hand, there are many opportunities for business owners, on the other hand, there are also several barriers. After understanding these barriers and technological roadblocks, you can create an effective AI development plan and minimize upcoming challenges. So, let's talk about some common roadblocks that can help you make your AI projects successful and ensure proper utilization of resources.

According to a report by Markets and Markets, the AI market is expected to reach $51.8 billion, growing at an average CAGR of over 35%. With such growth, there's quite a buzz in the software development industry about adopting this rising technology and developing solutions through it.

While the world is witnessing its revolutionary aspects and its ability to transform the tech space, many barriers have come up over the past few years in adopting this technology. We'll go over some of those barriers in this article.

How AI is Transforming Business Sector

Today, AI is transforming businesses everywhere, from marketing to customer service. It's also becoming a crucial technology for product and service deployment.

While AI presents numerous opportunities for business owners, there are also significant roadblocks along the way. To understand how AI can benefit businesses, let's explore some of the common challenges that businesses face when adopting AI.

Technical Roadblocks in Adopting AI

1. Data Acquisition and Compliance

When it comes to AI, talking about data becomes crucial because the better the data is used, the more effective the model will be. In this context, it's important to have a mechanism in place to ensure data quality, and you also need a significant amount of data.

At the same time, evolving regulations and privacy concerns make data acquisition challenging and more costly. For instance, complying with regulations like the General Data Protection Regulation (GDPR) is necessary to collect, store, and use data to avoid legal issues.

Additionally, the cost of data acquisition can also be a barrier. Developing a large language model requires enormous amounts of data, which can be quite expensive. However, the acquisition cost depends on the type of data.

In this scenario, utilizing publicly available data has become popular, but it comes with other regulatory and ethical AI challenges.

2. High Development Costs

Right now, this is a new technology that can weigh heavily on the pockets of small and mid-sized companies. Many companies are working on building models by turning to open-source models.

However, to develop a powerful and effective model, you need strong hardware, like GPUs and TPUs, to pull off complex AI-related operations. But the hardware used in AI development doesn't come cheap, so high development costs could hold back those wanting to dive into AI development.

When we look at the development cost of today's large language models, it is found that more than several millions were spent to develop these models. To develop such models, a company may need for anywhere between a few thousand dollars to several million dollars. On average, several thousand dollars have to be spent to create a basic model.

On top of that, hiring experts with specialized skills in machine learning, deep learning, and data science can get pricey, especially with the shortage of skilled AI developers. 

While the field has caught everyone's eye, competition is expected to heat up in the near future. This means staying competitive through research and development will also drive up costs, which could take a toll on your return on investment. So, before jumping into AI, having a solid strategy in place is essential.

If we talk about the cost of training large language models, where training a transformer model used to cost just a few hundred dollars, the cost of training models like GPT and Gemini has now climbed close to $100 million. Developing large language models isn't easy, and that's why the development of smaller LLMs is starting to pick up.

One of the biggest roadblocks here is the high cost of developing AI technology, especially for major companies still speculating about the ROI. Part of the reason for this is that customer adoption of these solutions is still somewhat limited, though it's expected to grow in the near future.

3. Bias and Ethical Concerns

The world’s best models have always run into issues of being biased, and these problems usually pop up because of the data. When you're feeding data into your model, the chances of bias and other issues only go up.  

Because of bias and unethical output, your reputation can take a hit, and you might even run into legal obligations.  

For AI bias mitigation, it’s crucial to take steps like using high-quality data for training, auditing from time to time, and putting together a solid strategy to measure transparency.

4. AI Scalability

AI is a game changer, but making it efficient involves effort, expertise, and a bunch of challenges. Scaling it up isn’t a walk in the park; even a tiny mistake can spark issues. AI algorithms typically require heavy computing power for tasks like matrix manipulation, linear algebra, and complex recalculations happening continuously.

In this context, handling thousands to millions of data points is no small feat. Additionally, keeping a large volume of data in one place brings challenges like maintaining data security and privacy.

Plus, there's always the technical hurdle of data cleansing and preparation. So, scaling AI systems isn’t exactly a piece of cake. AI scalability is another major concern for many business owners who don't have the right expertise and experience for AI development.

 5. Talent Shortage

The Deloitte report shows there’s a big shortage of talent in AI development. Many companies are scrambling to find skilled workers because the need for AI experts keeps growing. However, there just aren’t enough people with the right skills to meet this demand. 

Training and education aren’t keeping up with the fast pace of technology changes. Companies are trying to solve this by upskilling their current staff and teaming up with schools to create better training programs. With global competition for AI talent heating up, businesses are finding it even harder to attract and keep top experts.

Many are also offering higher salaries and better benefits to lure in the few available experts. As a result, the market for AI talent is becoming very competitive. To stay ahead, businesses need to focus on developing their own talent pipelines and investing in employee training. In the long run, addressing this shortage is crucial for companies to harness the full potential of AI technologies.

6. Model Deployment and Integration

Training a model and then deploying it on an existing platform is proving to be another challenge for companies. The integration process is another technically complex task, and many systems are facing compatibility issues.

Maintaining performance in a production environment is also becoming a tricky task, with several factors contributing to this, like data drift. Data drift happens when the data used for training is different from the data you’re using in production, leading to performance and quality issues.

In such cases, companies should have regulations or internal policies in place for using data during training to ensure consistency and mitigate these problems.

 7. Legal and Intellectual Property Risks

Legal issues and intellectual concerns around AI are really starting to heat up. For example, if a user generates an image, who owns it—the user or the service provider? So far, there haven't been any solid steps or worldwide rules established to address this accountability.

Copyright laws have been around to protect original works, but with AI, it's still unclear who holds the ownership rights—the AI model or the user. This is a growing concern for companies, especially if their models generate images or text. They might face legal and intellectual property issues because of this. At this point, nothing has been firmly decided. 

 8. Security and Risk Management

Security issues have always been a concern in the digital world. With the rise of AI, business owners need to keep security at the forefront while developing their existing and new AI software.

 Large language models, in particular, have captured global attention with their impressive abilities to produce different outputs in various situations.

However, these models still have several flaws. For example, they can generate incorrect statements that seem like facts—a phenomenon known as AI hallucination.

When it comes to security, prompt injection attacks have been the most reported weakness in these models. Another concern is data poisoning, where data can be tampered with, leading these models to produce undesirable results.

 9. Market Differentiation

In 2024, according to McKinsey, more than 65% of companies have jumped on generative AI, which is double the number from the past ten months. Generative AI adoption has also shot up significantly compared to 2023.

Because of this, software development companies are scrambling to bring AI development experts on board to keep up with the rising demand for AI-related requirements. Today, for companies building major digital solutions, AI development has become one of the top offerings. Standing out in a crowd of millions can be quite a challenge.

To tackle this, you might focus on targeting specific industries and developing and selling your product for particular use cases. The demand for tailored solutions is set to grow, and according to a report, by 2027, more than half of large organizations will turn to custom AI solutions for specific use cases, signaling that the demand for tailored solutions will stick around in the coming years.

 10. Return on Investment (ROI) Concerns

PWC put out a report that found businesses aren't really seeing a return on their AI investments. According to a report by Gartner, figuring out the ROI or showing the value of AI technologies has been one of the biggest barriers for companies looking to adopt AI.

Many businesses struggle to measure the long-term benefits, and without clear results, they hesitate to push forward with larger AI initiatives. This concern often holds back innovation and slows down AI integration into their operations.

Best Practices for Overcoming Roadblocks

The most perfectly planned projects can sometimes not go according to the plan. Facing technical difficulties related to generative AI can make your project go south. But you can make some tweaks to make your project bulletproof. Obstacles can be turned into building blocks with the correct tactics. Let’s explore tried-and-true methods that will help you manage complexity, lower risks, and hasten your AI journey.

1. Start Small: Pilot Projects Are Your Safety Net

Before going all out, it wouldn’t hurt to test. “Great things often have small beginnings” is a famous quote that reminds us that success starts with a small, manageable step. Instead of diving headfirst into a full-scale AI rollout, it's wise to test the waters with pilot projects. How much to test and to what degree is for you to decide, but let’s give you a general idea.

Example:

A logistics company launches a pilot in one delivery zone to optimize routes using generative AI solutions. This small test helps them track improvements in delivery times and cost savings, revealing any issues before scaling up.

For a step-by-step guide, check out our [Roadmap to Successful AI Implementation].

2. Data Governance: Your AI’s Foundation

Reliable data is the lifeblood of any successful AI system. Without it, even the most advanced models can falter. Build a robust data governance strategy by setting clear standards for data collection, cleaning, and compliance.

Key Steps:

1. Regularly audit your datasets to eliminate bias and ensure accuracy.

2. Align your practices with GDPR, CCPA, and any relevant industry regulations.

3. Consider using synthetic data when real data is limited or sensitive.

3. Embrace Open-Source Ecosystems

Why reinvent the wheel when the AI community has developed powerful open-source tools? Leveraging established frameworks not only accelerates development but also minimizes costs.

Highlights:

1. Pre-Trained Models: Use platforms like Hugging Face’s Transformers or PyTorch Lightning to jump-start your projects with robust, community-vetted models.

2. Cost Efficiency: Reduce your reliance on expensive proprietary software by tapping into free, open-source alternatives.

3. Community Collaboration: Engage with vibrant developer communities on GitHub and forums, where you can find support, share insights, and continuously improve your implementations.

4. Upskill Teams: Turn Employees into AI Champions

In the rapidly evolving landscape of AI, your team’s expertise is one of your most valuable assets. Investing in continuous learning and development transforms your employees into AI champions, ready to tackle emerging challenges.

Suggestions:

1. Structured Training: Organize regular training sessions, workshops, or online courses on topics like deep learning, model interpretability, and AI model optimization.

2. Practical Experience: Encourage internal hackathons and project-based learning to give your team hands-on experience with real-world AI challenges.

3. Mentorship: Create a mentorship program pairing seasoned AI professionals with newer team members, fostering knowledge transfer and innovation.

5. Collaborate with AI Experts

Not every organization has all the specialized skills required for advanced AI projects. Strategic partnerships with external experts can fill those gaps and accelerate your development timeline.

Considerations for Collaboration:

1. Access to Advanced Tools: Partner with vendors or research labs that have state-of-the-art hardware like GPUs and TPUs, essential for complex model training.

2. Specialized Knowledge: Outsource specific components—such as large language model training or natural language processing tasks—to organizations with proven expertise.

3. Risk Sharing: Collaborations can also help distribute the risks associated with high-investment projects, allowing you to innovate without bearing all the financial burden alone.

6. Monitor Relentlessly: AI Is a Living System

Deploying an AI solution is not a “set and forget” endeavor. Continuous monitoring is crucial to ensure your system remains effective, accurate, and secure over time.

Best Practices for Ongoing Maintenance:

1. Automated Monitoring: Implement automated alerts to keep an eye on key performance metrics such as latency, error rates, and potential data drift.

2. Regular Retraining: Schedule periodic retraining sessions—quarterly or sooner if significant changes in data patterns occur—to maintain model accuracy and relevance.

3. A/B Testing: Before a full-scale update, use A/B testing to compare new model iterations against the current version, ensuring improvements are truly beneficial without introducing unforeseen issues.

Dive deeper into troubleshooting with Overcoming Common Generative AI Challenges.

The Future of Generative AI: What’s Next?

Generative AI is evolving at an unprecedented pace, and the next few years promise even more transformative advancements. As businesses and researchers continue to push the envelope, here are some key trends and developments to look out for:

1. Multimodal Capabilities

The peak we are seeing in AI is truly remarkable. However, we are seeing a peak of AI in one type: Text. But the true potential of generative AI is just growing. Currently, at the pace of AI scalability, soon it will start taking in not only text but photos, videos, and different kinds of formats like Excel sheets and PDFs as well. Soon, AI will not only write but will speak, hear, read, and create as well. Imagine AI creating your article. Now, let’s take it a step further. Imagine AI that also writes and creates the necessary images for the article as well. That’s the potential of AI we are currently looking at.

2. Increased Model Efficiency and Customization

Soon, AI models will become smarter and more efficient. Thanks to improvements in techniques like pruning and transfer learning, these models will run faster and use fewer resources. Plus, we’ll see AI that’s tailor-made for specific industries or even unique business needs. In short, your AI tool will work just the way you need it to, without the extra fluff.

3. Enhanced Ethical Frameworks

As AI takes over the routine tasks of our daily chores, it’s a concerning matter of how people will use it. The bias depends on the data that the AI is learning from. Therefore, even though there have been some huge leaps of advancement, artificial intelligence will act as humans have designed it to. Future developments will focus on reducing this bias in AI outputs. We have already seen a current change in AI, which is the “Reason” feature. Be it ChatGPT, DeepSeek, or Grok, there is an option for reasoning. This means you can see what the AI is thinking before answering any question. This shows that we are on our way towards the reduction of bias. AI systems are becoming more transparent; they not only work well but also build trust by aligning with global ethical and privacy standards.

4. Real-Time Learning

Imagine an AI that doesn’t just learn from past data but can adapt on the fly. With reinforcement learning, future AI systems will continuously tweak and improve their performance based on real-time feedback. This ability to self-correct means your AI tool will stay accurate and relevant as conditions change—kind of like having a system that’s always learning from experience.

5. AI Applications Across Industries

The potential for generative AI is only growing. In healthcare, it could help create personalized treatment plans or speed up drug discovery. In finance, it might provide sharper risk assessments and smarter investment advice. And in creative fields like entertainment, AI could mix storytelling, music, and visuals into entirely new experiences. For businesses, generative AI is already transforming workflows—from automating customer service to optimizing supply chains. The applications are broadening, and each industry stands to gain a lot from these advancements.

6. Collaborative and Open Innovation

The future of AI isn’t something one company can do alone. Open-source communities and industry partnerships will continue to drive innovation, making cutting-edge AI tools available to everyone. This collaborative spirit means breakthroughs will spread faster, allowing even smaller organizations to benefit from the latest technology.

Conclusion

In this article, we've talked about some important roadblocks that business leaders can work through to make better use of their resources.

All these factors can affect AI adoption and development in the future, like the need for large amounts of high-quality data, the challenge of integrating with existing software, and the requirement of high-end infrastructure to develop these models.

By tackling these challenges effectively, you can make your AI development journey smoother and reduce potential technical AI roadblocks significantly.

If you’ve got some ideas you want to bring to life digitally, we can develop scalable, cutting-edge AI solutions for you. Our experts will support you every step of the way—from proper planning to complex integration, deployment, and post-deployment—so you can successfully build your digital products. So, what are you waiting for? Contact us to build a successful future together.