Businesses can use Artificial Intelligence for improved operational efficiency, automating a wide range of processes and helping to make more informed decisions. Many businesses are unsuccessful at achieving their goals with their AI projects since they lack the necessary technical, organizational, and strategic support to be successful in implementing an AI solution. In order to successfully deploy AI solutions, businesses must establish what the major barriers are and then how to mitigate these barriers.
This article will provide insight into the common types of barriers businesses face when initiating an AI project and provide recommendations for overcoming those barriers.

1. A Lack of Clear Business Objectives

Challenge:
Many businesses jump into an AI project without having a clear understanding of what business problem they are attempting to address. This over-excitement often results in the inability to develop clear business objectives related to the company's investment in AI, resulting in no ability to achieve its objectives.

Solution:

  • Define measurable objectives for your business before starting any AI project.
  • Align your organization's AI objectives with your organization's key performance indicators (KPIs).
  • Identify specific proposed use cases that provide you with tangible value.

Use the exploratory phase of your AI project to address problems, not just experiment with AI technology.

2. Poor Data Quality and Availability

Challenge:
Creating accurate AI models is a major challenge due to the lack of access to high-quality and readily accessible data. In addition, there are many problems with low-quality data (inconsistent, biased, insufficient) that make it difficult to obtain reliable information and develop AI models that perform well.

Solution:

  • Invest in cleaning and preprocessing data,
  • Create a comprehensive data governance policy,
  • Establish a single source of truth (centralized repository of all organizational data) to facilitate the collection and storage of all organizational data.

High-quality data is a key component of developing effective AI applications.

3. Data Privacy and Security Concerns

Challenge:
The inability to store sensitive personal information presents a challenge to comply with applicable Data Privacy Laws and creates the potential for damaging the integrity of the data.

Solution:

  • Protect sensitive personal information with encryption and access control methods.
  • Anonymize any sensitive personal information,
  • Adhere to applicable Data Protection Regulations (the General Data Protection Regulation (GDPR) in Europe), and to follow all jurisdiction-specific Data Protection Laws.

Security should always be considered when developing and implementing AI Applications that have the potential to utilize sensitive personal information.

4. Expertise and Skills Deficiency

Challenge:
There is a lack of specialized AI experience in many organizations today.

Solution:

  • Enhance the skills of the current team through training
  • Strategically hire AI experts
  • Find vendors that specialize in AI development

Using a cross-functional team approach will increase the success rate of implementing AI.

5. Bias Risks and Ethical Concerns

Challenge:
AI models can inherit bias from training data, which can create unintentional unfair or unethical AI outcomes.

Solution:

  • Utilize diverse, balanced, and representative datasets
  • Continually audit AI models for bias
  • Implement ethical AI guidelines

Responsible AI builds trust and long-term adoption.

6. Integrating AI with Current Systems

Challenge :
AI systems can struggle to integrate into legacy systems and workflows easily.

Solution:

  • Create flexible architectures using APIs and modular architecture.
  • Involve your information teambefore the AI solution goes into production.
  • Build the AI solutions so that they will work well with other software solutions.

Seamless integration will provide a better experience for users and better results.

7. Scalability and Performance Issues

Challenge:
An AI model that works perfectly fine in pilot phases could fail once it is scaled up to support the entire organisation.

Solution:
Using a highly scalable cloud-based infrastructure. Optimize the performance of the models by monitoring system load and latency. Scalability planning is key for long-term success.

8. High Development and Maintenance Costs

Challenge:
Developing, deploying, and maintaining an AI can be expensive.

Solution:
Start small with pilot projects. Find pre-trained models or AI-as-a-service. Continuously evaluate ROI. Controlling costs ensures sustainable AI adoption.

9. Lack of Explainability and Transparency

Challenge:
Many AI models are complex and function as ‘black boxes’. Therefore, it can be hard for organisations to trust the results.

Solution:
Use AI techniques. Produce interpretable outputs. Capture decisions and decision processes. Increasing transparency will therefore create confidence among stakeholders and contribute to compliance.

10. Resistance to Change and Low User Adoption

Challenge:
The introduction of AI can cause resistance from employees due to fear of job loss or simply a lack of understanding.

Solution:
Emphasise AI being an ‘assistive’ technology. Provide training and onboarding. Involve users from the beginning of the development process. The human adoption of the technology is just as crucial to its success.

Summary

Developing AI-based solutions is not an easy task owing to many reasons, such as data acquisition difficulties, a lack of skilled professionals, and challenges associated with data and AI adoption. These issues could be overcome if companies set up effective and measurable objectives and acquire high-quality and useful data. They should also utilize expert resources to support their AI-related efforts and establish a comprehensive governance structure to facilitate effective AI development. By doing this, companies could easily overcome various AI-related complexities and tap into real business benefits.