The 6 Essential Building Blocks of AI Readiness

Artificial intelligence (AI) is steadily emerging as the lynchpin of technological innovation. What was once the stuff of science fiction now has the potential to become a critical component of business infrastructure, playing a pivotal role in decision-making, operational efficiency, and strategic planning. However, harnessing the full potential of AI is no simple task; it requires a comprehensive and multi-faceted approach to prepare and equip your organization for a successful transition. 

In this blog post, we will delve into the six crucial building blocks that form the backbone of an AI readiness framework: data strategy and governance, infrastructure and computing power, talent and skills development, ethical and responsible AI practices, collaborative partnerships and ecosystems, and change management and cultural readiness. Each element is integral in its own right and interconnected in its function, building a robust foundation that enables organizations to fully capitalize on the transformative power of AI.

1. Data Strategy and Governance

To prepare the workforce for AI integration, enterprises rely on high-quality data to create AI models and algorithms. Inaccurate data or data that hasn't been properly cleaned and processed has long-term effects on AI tools. It gives AI models a poor foundation on which they will continue to build inaccurate future predictions and strategies.

This is where data strategy, data collection, and data governance practices come into play. Enterprises must ensure that the data used to drive AI models is accurate, up-to-date, and aligned with their objectives. Data strategy and governance serve as key methods for guiding and helping AI reflect the values and goals of an organization.

To build a healthy and sustainable data strategy for AI adoption, enterprises need to consider how they implement data security and privacy. Protecting all stakeholders within an organization should be a top priority when adopting AI in workflows. Companies also need to monitor AI compliance with federal regulations on customer privacy, data security, and anti-discrimination.

2. Infrastructure and Computing Power

The power of AI lies in its ability to process large amounts of data quickly and accurately. To do so, AI requires powerful computing resources that can handle the sheer amount of data it needs to analyze. Companies preparing for AI readiness need to consider the infrastructure requirements for AI along with computing resources. AI infrastructure also encompasses server capabilities such as storage, network connectivity, and backup requirements. The total AI infrastructure market is expected to grow from $28.7 billion in 2022 to 96.6 billion by 2027. 

Cloud computing is an effective method to handle the storage and processing capability necessary for AI workloads. It allows off-site data storage to prevent in-house overload and it also increases an organization's ability to scale. As more data is required when a company grows, cloud computing allows more storage space without a company needing to invest in expensive in-house infrastructure.

When adopting AI tools, companies need to optimize their infrastructure for AI model training as well as AI deployment. Companies must be able to monitor and change the AI model to affect future AI predictions but they must also make sure AI can easily be integrated into current workflows.

3. Workforce Readiness and Skills Development

Investing in high-powered AI tools with a data-illiterate workforce is like buying a Lamborgini for a 16-year-old. Even though the tool itself has untapped potential, the lack of skills and knowledge will hinder its ability to drive and reach an organization's goals. Organizations need to invest in data literacy programs to ensure successful AI adoption. Employees crave education. A Forrester study found 71% of employees wished their employers offered more training then they currently do.

Employees must be trained on the technical, ethical, and regulatory aspects of AI tools. Companies should also train their employees on how to interpret, analyze, and use data effectively for making informed decisions. By doing so, companies prepare their workforce for operating modern AI tools.

Not only should companies invest in data literacy programs to onboard the adoption of AI, but they should also offer continued upskilling and reskilling programs to foster a culture of AI expertise. Only through consistent investment in talent and skills development can organizations unlock the full potential of AI.

4. Ethical and Responsible AI Practices

Artificial intelligence relies on guidance to function properly. Without human intervention, AI can make decisions that may not be in the best interest of an organization or its customers. Inherent bias, hiring discrimination, and privacy violations are some of the risks associated with AI adoption. For example, machine translation systems like Google Translate are trained in 99% English. As a result, AI tools inaccurately translated gender male and female 40%-65% of the time. Ethical frameworks need to be established that reflect the values and goals of an organization.

Using these frameworks, enterprises can influence AI decision-making processes to reduce bias, respect customer privacy, and ensure fair hiring practices. Companies should also focus on developing trust between their AI tools and customers with clear policies that outline how data is collected, stored, and used in AI models. This includes implementing appropriate measures for data security, compliance with government regulations, as well as monitoring and mitigating potential bias in the decisions made by the AI system.

A system of accountability must be created through external feedback and within specific departments. Since the entire workforce will be affected by AI implementation, companies must mitigate the risks of unethical AI usage through continuous monitoring.

5. Collaborative Partnerships and Ecosystems

It will take a community to properly prepare for AI adoption. The breadth and scope of AI initiatives mean organizations need to collaborate with external partners, research institutions, and technology providers. By leveraging the expertise of these partners, organizations can increase their AI knowledge base and gain access to AI technologies that they would not be able to develop in-house.

In addition to forming collaborative partnerships, companies should also focus on creating an ecosystem of data producers and consumers. Data sharing can provide valuable insights and help inform future strategies. It shortens the time to implementation and reduces the learning curve by borrowing ideas and tips from providers and institutions who already have the experience.

Collaborative partnerships also foster innovation and can help keep organizations up-to-date on the latest trends and strategies in AI. Strategic collaborations bypass trial and error and help companies reserve capital and other resources while simultaneously creating new opportunities for their AI initiatives.

6. Change Management and Cultural Readiness

Each company has a built-in culture, whether it has been implemented consciously or unconsciously. Integrating AI tools into an existing company culture causes disruption, which is why organizations must ensure their culture and processes are ready for change before they begin the implementation process. Change management practices are important building blocks to create an organizational structure for AI readiness. When integrating AI, 37% of executives underestimate the importance of operating model changes, which leads to transformational failures within an organization.

AI adoption requires a significant shift in mindset and behavior. Companies need to be willing to adapt to new ways of working in order to leverage AI technologies that drive innovation. They also need to foster continual innovation and experimentation since the nature of AI is always changing and progressing. To stay competitive, an organization needs employees constantly challenging themselves to learn new methods and ways to integrate AI.

The process of change management starts at the top. Leadership support is mandatory for successful AI implementation. An organization's goals and values must be aligned with AI principles and must be promoted throughout the entire organization. Leadership has the biggest role to play since the workforce will follow their direction on AI adoption.

Building AI Readiness Requires Investment in the Workforce

AI readiness is not a destination but a journey. It requires continuous effort, strategic planning, and a deep understanding of how AI can bring value to an organization. Adopting AI involves more than just integrating new technology; it requires a shift in culture, increased data literacy, ethical responsibility, and a robust and secure infrastructure. To fully harness the potential of AI, organizations must ensure their teams are equipped with the necessary knowledge and skills.

At Correlation One, we're committed to helping business leaders forge a path to a data-driven, AI-enabled future by investing in their most valuable asset: their workforce. Through our enterprise-scale data literacy upskilling and reskilling programs, we've helped thousands of workers prepare for the workplace of tomorrow. Curious how Correlation One can help you invest in the future of your workforce and get ready for AI? Reach out to learn more.