5 Barriers to AI Readiness and How to Overcome Them

The promise of artificial intelligence (AI) as a transformative force across numerous industries is no longer just a theoretical concept, but a tangible reality. From healthcare to financial services, AI holds the potential to revolutionize business operations, driving improved efficiency, and yielding unprecedented insights. Yet, the road to AI integration is filled with challenges that businesses need to acknowledge and overcome. The journey to becoming 'AI-ready' requires proactive preparation, strategic planning, and a comprehensive understanding of these hurdles.

In this article, we'll examine five barriers that stand in the way of AI readiness: the issue of data accessibility and quality, the need for adequate infrastructure and computing resources, the imperative of data literacy, the ever-present ethical and legal considerations, and the all-too-human resistance to change and cultural barriers. Then, we will provide actionable strategies to navigate through them, enabling businesses to confidently forge ahead on their AI journey.

Barrier 1: Lack of Data Accessibility and Quality

Data accessibility and quality play a critical role in AI readiness and operations because they directly influence the accuracy, reliability, and effectiveness of AI systems. AI algorithms learn and evolve through a process called machine learning, where they're trained on large datasets to recognize patterns, make predictions, or perform tasks. The larger and more diverse the dataset, the better the AI model can learn and generalize its knowledge to new, unseen data. If an AI system doesn't have access to sufficient or diverse data, its learning process can be severely limited, leading to inaccurate predictions or biased results.

For an organization to be truly 'AI-ready,' it must ensure that it has access to a large, diverse, and high-quality dataset, and that it can handle this data in a way that is secure and respects privacy regulations. Without this, the organization's AI systems may fall short of their potential, and may even create new risks and problems.

Here are three strategies that companies can implement to overcome poor data accessibility and quality: 

  • Establish a Data Governance Program: A data governance program provides a framework for managing and optimizing the use of data within the organization. It includes setting rules for data collection, storage, processing, and protection, ensuring data quality, accessibility, and security. A robust data governance program also includes the enforcement of data standards, roles, responsibilities, and procedures to maintain the quality and integrity of data over time.
  • Implement Data Cleaning and Preprocessing Techniques: Before data can be used in AI systems, it needs to be cleaned and preprocessed. This involves handling missing values, removing duplicates, correcting inconsistencies, and resolving discrepancies. Preprocessing can also involve transforming the data into a format that can be more easily and effectively processed by AI systems, such as normalizing numerical data or encoding categorical data.
  • Collaborate with Data Providers: Partnering with external data providers can also help improve data accessibility and quality. These providers specialize in collecting, curating, and providing high-quality data, and can offer datasets that a company might not be able to generate on its own. However, it's essential to ensure that the use of external data aligns with privacy regulations and ethical guidelines.

Barrier 2: Insufficient Infrastructure and Computing Resources

AI and Machine Learning (ML) applications often require extensive computational resources as they involve processing large volumes of data and complex algorithms. Insufficient computational resources can drastically slow down the speed of data processing, making it impractical to train models or run applications within a reasonable time frame.

As AI initiatives grow and mature, they typically require an increase in computing resources to handle larger datasets and more complex models. Organizations with insufficient infrastructure may find it difficult to scale their AI efforts effectively, limiting their ability to innovate and stay competitive.

Companies of any size have several options for building a comprehensive infrastructure and gaining computing resources: 

  • Leverage Cloud Computing Services: Cloud platforms provide businesses with flexible, scalable, and often cost-effective solutions for their infrastructure needs. With cloud services, businesses can access high-level computing resources on-demand, without the need for large up-front capital investments. Many cloud providers also offer AI-specific tools and services, which can help accelerate AI development and deployment.
  • Invest in High-Performance Computing (HPC): For AI workloads that require intensive computational power, especially for training complex machine learning models, high-performance computing can be a necessary investment. HPC systems are designed to handle and process vast amounts of data quickly and efficiently. They can significantly reduce the time it takes to train AI models, accelerating the development process.
  • Perform a Technology Audit: It's important to assess your current technological capabilities and identify gaps that need to be addressed. This involves evaluating your current hardware, software, and network capabilities, and comparing them with what's needed for your planned AI initiatives. The audit should cover areas like data storage capacity, processing power, and network speed. Based on this assessment, you can then create a strategic plan for upgrading and expanding your infrastructure.

Blocker 3: Insufficient Data Literacy 

To leverage AI effectively, individuals across the organization must understand how to interpret and use data. This includes knowing how to read, work with, analyze, and argue with data. Without data literacy, it becomes challenging to extract meaningful insights from AI systems, limiting their value.

Unfortunately, insufficient data literacy continues to be a barrier for many organizations. According to data from Accenture, only 21% of employees are confident in their data literacy skills. If only a small proportion of employees are confident in their data literacy skills, it means that most of the workforce might struggle to effectively interpret and utilize the outputs generated by AI systems. This could result in the underutilization of AI technology, limiting its potential benefits.

Here are a few ways employers should address data literacy in their own companies:

  • Assess Skill Gaps. Companies will first assess their current workforce data literacy skills. Assessments should be sent to every employee that evaluate their current understanding and knowledge of data, AI tools, and other skills necessary to work effectively with data. The results will be different for each department so employers must specialize their solutions to fit the skills gap of each team in the company. A one-size-fits-all approach won't be effective to train employees across the organization, as their skill levels and needs will vary.

  • Invest in Data Literacy Training Programs. Enterprises that utilize the skills gap assessment to develop comprehensive training programs for their employees will be able to most effectively utilize AI. Companies should invest in data literacy training programs that provide tangible, real-world scenarios and examples so employees can better understand how to use the tools available. By investing in high-quality data literacy programs, companies can ensure that their employees are fully educated on the latest technologies and have the necessary skills to use them. This will enhance the team's ability to extract value from AI data and make informed decisions.

  • Work With Data Training Providers. Building an effective program from scratch with specialized training curated for each department is time-consuming and resource-draining. Companies need support from data training providers to quickly implement AI to stay competitive in their respective markets. Look for data training providers that specialize in data literacy and have the experience to build comprehensive training programs tailored to each department’s specific needs. Training must cover the latest technologies, best practices, and how to generate insights from AI tools.

Need help assessing the data literacy of your team? Correlation One can help

Blocker 4: Ethical and Legal Concerns

The adoption of AI has brought about many benefits in various industries, from increased efficiency and productivity to improved decision-making processes. However, it has also raised numerous ethical and legal concerns, particularly around data privacy, bias, accountability, and transparency. Academic papers covering ethics in AI have more than doubled in recent years as the topic is becoming increasingly prevalent with the global adoption of AI. 

  • Develop Ethical Frameworks. Companies must bring awareness to their own ethical frameworks before establishing one in an AI tool. Executives, along with employees, should define clear ethical principles that will guide the development and use of AI systems. The frameworks should reflect the values and goals of the organization as well as societal standards.
  • Establish AI Algorithm Transparency. Transparency is the method by which companies build trust with their customers as well as other stakeholders. Business leaders must be able to demonstrate the ethical principles of AI systems they’ve developed and explain how customer data is being used. Companies need clear documentation of the algorithms used in their AI systems as well as regular audits of these systems. Setting up a monthly newsletter will help inform employees of how the AI tool works and what it is being used for.
  • Adhere to Compliance Measures. While there are no clear laws and regulations around AI usage, organizations must still remain compliant with federal and local requirements. This could involve data privacy, anti-discrimination laws, and consumer protection laws. Regular risk assessments will help organizations identify potential liabilities and take appropriate action.

Blocker 5: Resistance to Change and Cultural Barriers

Resistance to change and cultural barriers are common challenges that organizations face when introducing AI technology. Only 24% of companies in a NewVantage survey created a data-driven company. Some employees may feel apprehensive about the use of AI, fearing that it will take their jobs or reduce their opportunities for career growth. In some cases, there may be a lack of understanding of how AI works and how it will benefit the organization.

To overcome this obstacle, companies must build a culture of innovation and AI literacy, educate stakeholders about the benefits of AI adoption, and encourage collaboration and cross-functional teams. These strategies will take time and must be implemented with caution. Changing company culture and breaking down cultural barriers may cause disruption in an organization and not all employees will respond positively.

Overcoming the Blockers of AI Readiness

The journey to AI readiness is not without challenges. From data accessibility and quality issues, insufficient infrastructure and computing resources, data literacy gaps, ethical and legal concerns, to cultural barriers and resistance to change, these blockers can seem formidable. However, with the right strategies and tools, these challenges can be overcome, paving the way for successful AI adoption.

A foundational step towards AI readiness is boosting data literacy within your organization. Without a fundamental understanding of data and how to interpret it, other initiatives will struggle to take root. To improve data literacy across all levels of your organization, you need comprehensive, tailored training that caters to varying levels of data literacy.

Correlation One offers top-tier data literacy training programs, crafted to bridge the knowledge gap and instill the skills your team needs to harness the power of AI. With a strategic and hands-on approach to data literacy, Correlation One can equip your organization with the necessary skills and knowledge to overcome these barriers, ensuring a smooth and successful journey to AI readiness.

To learn more about how Correlation One can help boost the data literacy of your team, reach out today.