6 Key Challenges in AI Engineering and How to Overcome Them

Key Takeaways:

• The fast pace of AI development makes deployment challenging
• AI use must be innovative and support human endeavor ethically
• High-quality data collection at scale is vital for success
• Working with an expert AI partner can help navigate issues

6 Key Challenges in AI Engineering and How to Overcome Them

New AI developments are coming on stream all the time, as the world continues to appreciate just how much of a difference the technology can make. It can enhance research and development, analyze data at greater speed and scale, augment human endeavor and automate routine tasks, to name just a few of its typical everyday use cases.

However, as with any emerging technology, there are practical barriers to overcome in order to maximize the potential of the innovation. This blog will explore the six biggest challenges to overcome in AI engineering, and how this can be achieved.

What Are the Biggest Challenges in AI Development Today?

1: Data-Related Challenges

The output of AI is only as good as the input: that is to say that the quality and quantity of data fed into the AI tool need to be as high as possible to deliver the best possible results. To enable this, it’s important to establish data augmentation techniques and robust data pipelines, so that datasets can generate the most relevant, accurate results possible.

These solutions can also extend into areas such as transfer learning (where machine learning models trained on one task are fine-tuned to be used on another), and synthetic data generation (artificial data that mimics real-life equivalents to simulate patterns and AI algorithms).

2: Legacy System Integration

According to Forbes, as many as two-thirds of businesses are still using mainframe or legacy applications for their core business operations. This use of increasingly outdated technology means their ability to integrate AI is severely impaired, particularly when it comes to solution compatibility, data silos and future scalability.

The most practical way to navigate this issue is to use middleware as a bridge between old and new. These robust connectors enable legacy systems to integrate with AI tools and enable AI insights and efficiencies to be enjoyed across a network - without the cost and disruption of a large-scale system overhaul.

3: Ethical Considerations and Privacy Concerns

As AI adoption grows, businesses face pressure to use it ethically. With only 29% of business leaders confident in ethical AI application, responsible frameworks and transparency must be integrated from the start.

Advanced solutions to address these challenges include:

• Differential Privacy: adding calibrated noise to datasets, protecting individual identities while maintaining statistical accuracy
• Federated Learning: training AI models on distributed datasets, sharing only model updates to reduce privacy risks
• Explainable AI (XAI): making AI decision-making processes more transparent and interpretable, enhancing trust and accountability

These solutions help protect sensitive data, prevent biases, and build trust, leading to more robust and widely accepted AI systems across industries.

4: Scalability and Performance

It can often be difficult to scale up the use of AI systems without compromising performance and quality levels. This is because larger datasets often get held back by processing bottlenecks, as well as by the workload being placed on algorithms across distributed systems

This can be resolved by using scalable cloud-based architectures to optimize computational resource to AI needs. This means having varying compute capabilities within virtual machines, allied to cloud storage space, enabling scalable and cost-effective analytics. Running these architectures in the cloud also allows these capabilities to be scaled up or down quickly as new business demands require.

5: Keeping Up with Rapid Changes

With the AI market expected to grow by as much as 120% year-on-year, keeping pace with new innovations will be a challenge, even for the most agile businesses with the deepest resources. 

The best way of staying on the front foot with all things AI is to adopt an approach of continuous learning, ideally in collaboration with AI research communities around the world. Ideally, all AI stakeholders will be proactive in taking courses, reading up on new AI developments, and sharing what they’ve learned with their co-workers. This can help build up engagement and enthusiasm for staying at the cutting edge of AI developments.

6: Talent Gap and Skills Shortage

One of the major inhibitors to AI innovation is leveraging human AI expertise. For example, according to Salesforce, 60% of public-sector professionals say a shortage of AI skills is their biggest implementation challenge. Of course, there is a skills shortage in all areas of technology, but with AI emerging and evolving so quickly, the scarcity of experts is even more acute in this area.

With it proving so difficult to recruit the right skills externally, many organizations have turned to internal training and upskilling, that embeds AI and machine learning practices into the everyday skill sets of employees. This can help further ingrain an AI culture across the workforce, and better equip employees to deal with issues such as hallucinations, regulatory compliance and ethical AI use as they arise.

How to Overcome These AI Development Challenges

While some of these challenges might seem difficult to overcome, the good news is that none of them are insurmountable. What is required is a carefully planned approach to AI implementation, one that takes into account these challenges and addresses them along the way.

From our extensive experience of working with AI technologies, and supporting businesses with their AI deployments, we recommend the following for overcoming AI obstacles:

1. Defining objectives and requirements clearly, as early as possible
   
2. Evaluating suitable data sources for AI data quality 
   
3. Training algorithms to process data and generate insights
   
4. Assess ways to integrate AI deployments with existing systems
   
5. Ensure ethical and responsible use of AI is adhered to throughout
   
6. Work proactively to engage users with the new technology and maximize its adoption
   

In Summary: A Partnership That Pays Dividends

Making that process work as smoothly as possible, and ensuring that it’s perfectly aligned to the specifics of your organization, can be difficult to achieve in-house. That’s why the best way forward is to work with an expert partner, who has the solutions, expertise and experience to guide you down the right AI path from start to finish.

Ciklum’s global team works with businesses like yours every day to drive digital transformation and smart decision-making with the help of AI. Find out more on our AI services and support here, then get in touch with us to discuss your requirements.