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Ahh, the same story every day: New AI tools are getting released, and businesses are rapidly integrating them into their operations. However, we got to know via our research team that most businesses are facing certain challenges, or as they called it – AI challenges, to implement AI in their ventures. 

So maybe implementing AI is not as easy as it looks!

Now before we proceed, there’s an important question I need to ask you – Do you already fall in this category? If yes, you will reap the best out of this blog. And if you are yet to implement AI in your venture, let’s explore the opportunities together, shall we?

With that being said, let’s move on to address the AI challenges that you may encounter during the implementation and ways to overcome them.

The Significance of Implementing AI in Business

In a time ruled by progress, the integration of AI has surfaced as a transformative force in various sectors. Gartner says that by 2030, AI will take over 80% of tasks related to project management. Therefore, the first thing to do is to get to know what this technology ‘can’ and ‘can’t’ do! This alone, will give you an overall understanding of what AI challenges you can expect while implementing and ways to address them.

Like, AI is good at spotting trends and patterns, but it’s not so good at making moral choices.  For example, AI can create great images from text or commands. However, it would be difficult for it to customize that image to fit a particular brand culture.

Now, despite these challenges, it brings a host of benefits to businesses. I mean obviously!

For example, it is improving the speed of business operations with data-driven insights and monitoring capabilities. And on the other hand, it is opening up new horizons for businesses to expand their operating models across industries. 

Therefore, weighing the advantages and disadvantages is a good place to start. You can initiate by evaluating:

• What is the overall cost of implementing AI at each stage of your business?
• Which KPIs must be established to evaluate the ROI of AI implementation in your venture?

Now, to achieve scalable answers to these, it’s best to consult a digital solutions provider who has expertise in AI as well. 

AI Challenges In Business

Now that we’ve arrived at this point! It’s important to know about the problems that might obstruct a smooth implementation of AI. 

Here we go, 

Integrations with Legacy Systems

Legacy systems in a company are like its old and outdated, yet foundational pillars. They provide robust support, but when it comes to their modification, it presents quite a challenge.  As a result, introducing AI or any sort of modernization or digitalization into this ecosystem requires careful planning and execution. Hence, they often stand as a hindrance when it comes to digitalization. 

Talking of which, allow me to walk you through the AI challenges here:

• Compatibility Issues: Legacy systems use outdated technologies, architectures, & programming languages that don’t align with modern technology like AI or cloud storage. This mismatch leads to integration roadblocks, hampering the seamless flow of data and communication between systems. So, we recommend you conduct a comprehensive system audit to identify compatibility challenges before you think or try AI implementation. 

• Data Accessibility and Quality: The success of any AI application depends on ensuring that they have access to relevant, high-quality data. Often, data silos inside legacy systems obstruct productive AI-driven insights. Therefore, you must implement data cleansing processes to enhance the quality of existing data.

Additionally, we are aware that data cleansing processes might seem complex! We can get on a free consultation call to address that if needed. 

• Security Concerns: Data security is a global challenge everywhere. So how can you leave it out from the set of AI challenges anyway? Thus, you must implement robust cyber-security tools to detect and mitigate any potential anomalies. 

• Balancing Innovation with Operational Continuity: Unexpected modifications may result in lost productivity, downtime, and opposition from staff members. However, you may establish contingency plans, like identifying glitches & having backups to address any unforeseen challenges during the integration phase. 

Balancing Scalability 

Now with AI, scalability extends beyond the traditional notions of increasing production capacity or accommodating a growing customer base. Here, scalability refers to efficiently handling increased data, users, and complexity without compromising performance or significantly raising costs. 

However, there are certain things that you need to be aware of when implementing AI in your business, such as: 

• Technological Scalability: As businesses grow, the demand for AI systems increases. Larger datasets, more complex models, and a higher number of concurrent users can strain existing infrastructure. So, invest in AI solutions that are designed for parallel processing and efficient resource utilization.  Likewise, you can adopt cloud computing solutions for on-demand resources & elastic scalability. 

• Cost-Effective Scalability: Scalability shouldn’t come at the expense of financial sustainability. Rapidly expanding AI capabilities can lead to escalating costs, impacting the overall cost-effectiveness of the organization. Eventually, you must regularly review and adjust resource allocations based on your actual usage patterns.

• Data Scalability: As businesses scale, the volume and variety of data that they generate and process also grow. Ensuring that AI systems can effectively handle diverse data sources without compromising accuracy is a critical challenge. So, invest in robust data storage & retrieval systems for handling large datasets.

• Ethical and Regulatory Scalability: At times, scalability makes it more difficult to ensure that AI systems follow moral guidelines and changing legal requirements. Therefore, you must go for implementing explainable AI (XAI) techniques to enhance transparency in AI decision-making.

At times, achieving this level of scalability in AI indeed seems challenging. However, you are not alone in this & it shouldn’t be a holdback for you! You can efficiently achieve your scalability goals by making necessary adjustments based on the subtleties we discussed. Let us know if we can help you with it. 

Shortage of AI Professionals 

Job postings in the AI field have increased by over 300% in the past five years! This is outpacing the rate at which the workforce is acquiring the necessary skills. This shortage of skilled AI talent is multi-faceted, stemming from several key factors.

Here are some of them: 

• Rapid Advancements in AI Technology: AI is evolving at an unprecedented pace, introducing new frameworks, tools, and methodologies very frequently. However, this rapid advancement is often leaving businesses struggling to stay updated.  As a result, you can provide in-house training programs for existing employees to upskill in AI technologies.

• Interdisciplinary Nature of AI: Artificial Intelligence is not confined to a single discipline; it’s a fusion of computer science, mathematics, & data science. This interdisciplinary nature of AI makes it challenging for individuals to acquire a comprehensive skill set. Therefore, you must foster collaboration between AI professionals and experts from other domains (e.g., healthcare, finance, or marketing). 

• Intense Competition for Talent: The increasing demand for AI professionals has resulted in fierce competition among businesses to attract and retain top-tier talent. However, by promoting diversity in AI teams you can expand the talent pool & cultivate an environment of innovation and creativity.

The lack of AI experts is a major hurdle for companies that want to fully utilize artificial intelligence. But, don’t let this hold you back. We at TheCodeWork, understand the challenges and are equipped to help you navigate through them. 

Regularity & Legal Challenges

Just like any other new tool we are still learning how to use AI. The dynamic nature of AI makes it a bit difficult to regulate it. Along with that, some of its aspects like Machine learning and Deep learning add certain complexities. 

At present, as AI continues to advance,  regulatory bodies around the world are working to build rules for ethics, privacy, & responsibility.

Hence, let’s have a look at some of the regulatory and legal challenges that persists when implementing AI:

• Lack of Uniform Global Standards: The absence of consistent, standardized regulations across different countries and regions creates challenges for businesses. Also, divergent legal frameworks make it difficult for organizations to comply with a unified set of rules. Ultimately, It leads to potential legal ambiguities and compliance issues. Likewise, you can create regulatory sandboxes in your organization that allow you to experiment with AI technologies within controlled environments.

• Privacy and Data Protection: Regulations like the General Data Protection Regulation (GDPR) mandate strict data protection measures. However, you can implement anonymization and encryption techniques to protect user data while allowing for meaningful AI analysis.

• Liability and Accountability: Determining liability in the case of AI-related incidents or errors is a complex legal challenge. Questions about who is responsible for AI decision-making and the extent of accountability require clarification in legal frameworks. Therefore, you should clearly outline responsibilities in contractual agreements and work towards establishing industry-wide standards for liability attribution.

• Explainability and Interpretability: The opacity of some AI models poses challenges in explaining how decisions are made, especially in critical applications like Healthcare or Finance. To solve this, consider working with AI specialists to learn how to make AI systems explainable and understandable.

Case Study

Here are some relevant case studies that provide valuable insights into the AI challenges businesses face when implementing AI. 

Amazon’s Recruiting AI System

Challenge: Bias and Lack of Transparency

In 2018, Amazon abandoned an AI-powered recruiting tool designed to streamline the hiring 

process. The system, trained on resumes submitted over ten years, exhibited gender bias by favoring male candidates. Also, the model had learnt from historical hiring patterns, reflecting the male-dominated nature of the tech industry. Additionally, the AI system lacked transparency, making it difficult to understand how it arrived at specific decisions.

Lessons learnt: 

• The importance of regular audits and checks for biases in training data and algorithms.
• Transparent AI systems are crucial for understanding and addressing potential issues.

Microsoft’s Tay AI Chatbot

Challenge: Unintended Behavioral Responses

Microsoft’s Tay was an AI chatbot launched on Twitter in 2016. The AI was designed to learn from user interactions and engage in conversations. However, within hours of its release, Tay began producing offensive and inappropriate content. The chatbot had learned from online trolls and absorbed negative behaviors from users.

Lessons learned: The need for robust content moderation and filters to prevent the AI from learning and replicating harmful behaviors. Ongoing monitoring and intervention are crucial to maintaining the ethical use of AI.

IBM’s Watson for Oncology

Challenge: Misalignment with Clinical Practices

In 2017, IBM’s Watson for Oncology faced issues with its AI recommendations not aligning with real-world medical protocols. The system was trained on vast amounts of medical literature and data, but physicians found discrepancies. This raised questions about AI’s practicality in complex healthcare settings. 

Lessons learned: 

• The importance of collaboration with domain experts to ensure AI models align with established industry practices. 
• Continuous validation and refinement based on real-world feedback are also essential for the effectiveness of AI applications in critical domains.

Tesla’s Autopilot

Challenge: Balancing Autonomy and Safety

Tesla’s Autopilot, an AI feature for semi-autonomous driving, struggled to balance autonomy and safety. Incidents were reported where drivers overestimated the capabilities of the Autopilot system, leading to accidents. Tesla faced scrutiny over the system’s user interface, which some argued contributed to driver complacency.

Lessons learned: 

• Clear communication and setting realistic expectations regarding the capabilities of AI-powered systems are crucial. 
• Balancing innovation with safety considerations is an ongoing challenge, requiring continuous improvement in user interfaces and educational efforts.

In brief, these case studies underscore the intricate AI challenges that businesses often face when implementing AI initiatives. It shows how careful planning, help from experts, and constant updates of AI systems help you avoid such incidents. However, we are here to make your AI journey hassle-free! 

Bottom Line

To sum up,  AI is now as important as the Internet itself. To run a successful business, you need to take this shift seriously! Teaching and using AI are highly necessary for almost every business nowadays. 

As said earlier, putting AI to work in a business can be hard, but it can also bring big rewards! Businesses that handle the tough parts of using AI can get ahead of their competitors. With this, you can make better choices, work more quickly, & give your customers what they want in a better way. 

However, you will also need someone reliable to get through all the AI challenges by your side. At TheCodeWork, we offer expert AI development services guided by the best principles, work ethics & years of experience. Our development process takes care of everything from ideation to prototyping & deployment.