Enterprise AI Readiness: How to Accelerate Transformation with Data Literacy

AI transformation is becoming the main focus for companies looking to stay competitive in a data-driven market. From enhancing customer experience to optimizing operations, AI can deliver significant value to enterprises across various industries.

Despite its potential to transform business operations, the potential of AI still remains relatively untapped. The struggle for most businesses involves one of their key assets: their workforce. A lack of data literacy hinders the adoption of AI for both generalists and specialists.

Data literacy is essential for making informed business decisions and leveraging AI technologies. In this white paper, we will explore the concept of data literacy and how it can accelerate the enterprise's AI readiness. We will also provide practical guidance on how to develop a data literacy roadmap to prepare the organization for AI-enabled transformation.

Impact of AI on the Enterprise

The rise of AI can be seen across all industries and boundaries. Global AI spending is predicted to exceed $310.B by 2026, according to data from IDC. Companies recognize the effect AI has on analyzing customer behavior, gathering large data sets, and eliminating repetitive employee tasks.

An MIT and Databricks survey found that 78% of executives said "scaling AI and machine learning use cases to create business value" is their "top priority" over the next three years.

All areas of operations are improved by AI technology. For example, imagine a shoe company that designs, builds, and distributes its own shoes without AI. Using the technology, that same shoe company can implement DTC shipping, A/B test shoe designs without production costs, streamline orders and inventory across all channels, and segment customer complaints for bot responses.

The potential of AI is high but it's only realized when companies dedicate a long-term strategy for implementation. Impressive use cases are possible, but the real value of an AI-enabled workforce is accrued over time through consistent and reliable execution.

Current State of Workforce AI Readiness

The rapid improvements and adoption of AI technology are leaving companies unprepared for the transition. An IBM survey shows 34% of companies believe "limited AI skills, expertise, or knowledge" is the largest obstacle to adopting AI in their business. Instead of the workforce using the innovative tool to immediately optimize operations and future predictions, employees struggle to understand how to integrate it into their daily tasks.

The lack of skills is a paradox to the potential employees and employers believe AI has to offer. Almost 97% of global workers believe companies need to prioritize AI skills when creating an employment development strategy.

Preparing the workforce for AI readiness requires more attention and action from employers. Software tutorials are not enough to teach employees how to use AI for cross-collaboration or how employees can use AI to develop strategies and insights.

Full preparation looks like comprehensive training programs dedicated to educating employees on data literacy. Both technical specialists who directly build and interact with AI, and generalist front-end users whose workflows are impacted by AI, benefit from data literacy.

Only 21% of employees are confident in their data literacy skills, compared to 75% of C-suite executives who believe their employees can already work with data proficiently.

AI readiness requires as much participation from employers as it does from the workforce. By adopting data literacy as part of an employment development program, employers empower employees to comprehend, analyze, and utilize data effectively, enabling them to make data-driven decisions, contribute to AI model development, ensure ethical AI usage, foster collaboration, and mitigate the costs and risks associated with inadequate data literacy. By prioritizing data literacy, organizations can optimize their AI readiness and unlock the full potential of AI technologies in achieving business objectives.

How Data Literacy Impacts AI Readiness:

The breadth of AI touches and impacts every department across an organization. From finance and HR to sales and marketing, every stakeholder in a enterprise needs a basic understanding of how AI can affect a project's scope, deployment, governance, and projected risk.

Data literacy provides the foundation of knowledge required to navigate the effects of AI in any role of a company. Companies that establish a baseline of data literacy will help generalists and specialist transition into AI without falling behind competitors or becoming outdated.

Data literacy impacts:

While data literacy is somewhat of an umbrella term, several key aspects stand out as central components of effective AI readiness. Companies that understand these key aspects create a more effective data literacy program for AI readiness.

Data Comprehension and Preparation

AI models rely on high-quality inputs to deliver exceptional outputs. Companies only receive accurate predictions and insights if employees are feeding the correct data into their AI tool. This skill requires data comprehension and preparation to prevent errors in AI models or skewed results.

Employees must understand the data's properties and characteristics, regardless of the department they work in. HR professionals need to comprehend the data behind salary trends or training programs, while marketing teams must understand the data behind customer behavior.

Just as important as comprehension is the preparation of the data for an AI tool. For example, including bots in customer behavior data will skew results and lead to ineffective campaigns. Finance teams must understand the impact of foreign exchange rates on their global AI models for accurate future predictions.

By understanding data properties and preparing them correctly, companies create more accurate AI predictions that turn into efficient strategies.

Data-Driven Decision Making

Companies that gather and use data to make future decisions have a competitive advantage in the market. A Mckinsey survey found that data-driven companies are not only 23 times more likely to acquire new customers, but they're also 19 times more likely to become profitable.

Only companies with data-literate employees can take advantage of these opportunities. Without data literacy, decisions made in an organization that affects operations are nothing more than guesswork. It takes tangible results and a team of educated employees to create effective strategies that drive profitability.

Data-driven decisions identify areas where AI can add value and improve business practices. They inform which problems are best suited for AI solutions.

AI Model Development and Evaluation

Employees won't understand how their actions influence AI development or its results without a basic understanding of data literacy. An AI model is only as good as the information it processes and an employee's knowledge of the data directly contributes to the effectiveness of the tool.

AI model development starts with gathering data and preparing it for the AI model. Data literacy helps employees identify which datasets are most relevant for their projects. Data-literate employees are able to communicate with data scientists or machine learning engineers to define relevant features, validate models, and evaluate their performance. 

With the wrong information entered, AI models become biased, skewing results and leading to inaccurate predictions. To prevent this, organizations must understand the importance of data literacy in AI model evaluation and maintenance.

Ethical AI Usage

Left unregulated, AI will continue to process data in a closed feedback loop. The consequences of which can be severe, leading to discrimination in hiring practices or gender and racial bias. Ethical AI usage is a key component of data literacy because informed employees can direct the tool toward inclusivity and diversity practices.

Implementing data literacy programs into an organization helps employees identify areas where bias may occur. It prevents the datasets that lead to AI bias from entering the model and creates a system of regulations for all future employees to follow.

Continually training the AI model not only directs the tool for current practices, it establishes a foundation from which the tool uses for all future decisions. The more inputs the AI model receives towards inclusivity and diversity, the more decisions it will make that include these qualities in strategic planning.

Effective Communication and Collaboration

Imagine a technology department is trained to learn data literacy while no other departments are included in the program. When future projects are worked on, no other department will have an understanding of what the AI does, how it works, or what effect it has on the company. Instead, they will be caught up in following traditional business practices.

When every department is trained on data literacy for an upcoming AI transition, they can all talk together to create a clear plan of action and design strategies to maximize the effectiveness of the tool. With everyone on the same page, departments are able to work together and ensure that their individual goals are all in line with those of the enterprise.

Data literacy gives everyone an even playing field and helps create a foundation of knowledge from which all departments can function, ultimately, leading to increased productivity, communication, and collaboration. A Forrester report found 94% of decision-makers claim data literacy leads to increased innovation in all departments.

Costs and Risks of Inadequate Data Literacy

The key aspects of data literacy expose why companies should integrate the skill into their current operations. However, companies that choose not to implement data literacy put their organization at unnecessary risk. The costs associated with forgoing data literacy programs should be enough to convince any manager or executive of its importance.

Employee Ambiguity

When employees lack the necessary skills and understanding to analyze and interpret data, they may struggle to make informed decisions. This can lead to confusion and uncertainty, which can be costly in terms of time, resources, and productivity.

Inadequate data literacy programs increase the risk of errors or misinterpretations in data analysis. Since AI tools rely on accurate data to perform, an error in data analysis has severe long-term ramifications for the organization as a whole. Future AI insights and predictions are compromised with inaccurate data.

Employees need to be properly trained and prepared for an AI transition to achieve the goals of the business. A Global Talent Trends survey showed 55% of employees trust their employers to reskill them if their job changes as a result of automation. It's unlikely employees will take the reskill task upon themselves, leaving employers responsible for employee ambiguity.

Inefficient Usage

Similar to employee ambiguity, organizations are at risk of inefficient operations if data literacy is not implemented into training and reskilling. Employees who are not educated in analyzing complicated data sets or can't interpret the results of an AI tool are not only unable to make informed decisions, but they also risk reducing an organization's ability to fully leverage AI and derive maximum value from its capabilities.

All departments rely on each other to run smoothly and get the information necessary to perform well. An inept department doesn't just slow down productivity for its team members, it slows down communication, collaboration, and the progress of all other teams.

Additionally, data illiteracy can lead to a lack of trust in AI models. Without proper understanding and skills, employees are likely to use AI tools as intended or to their highest potential.

Privacy and Compliance

Employees who lack the data literacy skills to interpret and organize AI data may inadvertently expose confidential information or violate privacy regulations. Several industries that are at risk include:

• Finance
• Law
• Healthcare
• Government
• Law enforcement

An employee who mishandles data exposes a company to security breaches in several ways. Incorrect data labeling, poor data classification, improper data storage, and overly permissive access can all lead to data privacy violations.

Improper data management in companies that handle sensitive material could result in significant fines, penalties, or lawsuits. Non-compliance can be prevented with thorough data literacy programs. Educating employees protects both the organization and its customers from potential harm.

AI Decision Making

Employees who are not properly trained in data literacy may not understand the limitations and biases inherent in AI decision-making. This can lead to blind reliance on AI-generated insights, potentially resulting in poor decision-making that could negatively impact the organization.

AI tools rely on human intervention to guide its results to align with business objectives. Employees that aren't skilled in data comprehension won't provide the guidance necessary to improve the process. While this may seem fairly innocent in the short term, companies will continue to struggle to implement AI in their workflow if employees aren't trained correctly.

By implementing effective data literacy programs, an organization can ensure that its employees are equipped with the knowledge and skills needed to properly interpret AI-generated insights and evaluate their accuracy, ultimately leading to better decision-making and improved outcomes.

3 Key Data Literacy Skills to Unlock AI Success:

While data literacy encompasses a wide range of competencies and skills, organizations must focus on a few key elements to fully capitalize on the potential benefits of AI. Data literacy training providers like Correlation One audit the current workforce to determine the exact skills needed to become AI ready.

Future program developments need to concentrate on the following three data literacy skills to thrive while adopting AI into business operations.

Acquisition and Evaluation

Acquisition is the process of collecting relevant data from various sources and ensuring its quality and accuracy. This skill is crucial for teams working with AI systems, as the accuracy and completeness of the data used to train AI models can directly impact the quality of their results.

Evaluation is the process of assessing the quality and relevance of collected data. This skill helps teams identify potential biases or anomalies in the data, which can help prevent errors and inaccuracies in AI-generated insights and decisions.

Together, these skills build and model an AI tool to produce reliable results. Without them, all outputs generated by the AI system will be unreliable and of limited use. Strengthening acquisition and evaluation involves specialized training that educates employees on what type of data to input, reliable sources for gathering data, and how to analyze data effectively.

Interpretation and Analysis

Interpretation involves the ability to identify patterns, trends, and relationships in data, and to draw conclusions based on this information. This skill is crucial for teams working with AI systems, especially those collaborating with other departments. Understanding specific marketing patterns in customer behaviors can inform sales questions, customer service scripts, and product development initiatives.

Analysis is the process of using data-driven insights to inform decisions and solve problems. This skill allows employees to evaluate potential outcomes more accurately, helping them make better decisions faster and with greater confidence. In the context of AI, teams must analyze the data gathered by the tool and make specific changes to improve the workflow.

For example, AI-generated insights can identify problems in an organization's supply chain. Employees must then analyze this information and take the necessary steps to address the issue and optimize distributors, manufacturers, or logistic teams.

Developing these skills requires training that helps employees understand how to interpret data, draw meaningful conclusions, and develop solutions based on their analysis.

Domain Knowledge and Contextual Understanding

Domain knowledge involves a deep understanding of the industry or field in which an organization operates. This knowledge allows employees to identify relevant data sources, interpret complex data sets, and recognize patterns and trends that may not be immediately apparent to those without specialized knowledge.

Contextual understanding involves a broader understanding of the business context in which data is being collected and analyzed. This skill allows employees to identify which insights and recommendations generated by AI systems may be most relevant to specific business objectives.

Companies that focus on domain knowledge and contextual understanding in their data literacy programs ensure employees have the necessary expertise to interpret and apply AI-generated data. With this knowledge, employees can unlock the full potential of AI systems, using them to drive informed decisions and actions that align with organizational goals.

AI Readiness: Specialists vs. Generalists vs. Executives

The power of AI transforms businesses at every level of the organization. Specialists, generalists, and executives all have different roles to play in creating an AI-ready organization.

For Specialists:

Data Comprehension and Preparation: Specialists in a company who have a strong foundation in data literacy are better equipped to comprehend and analyze data, which prepares them for AI readiness. AI relies on large amounts of data to train algorithms and make predictions, and specialists who are data literate can effectively prepare this data for use in AI applications.

Advanced Analysis and Model Development: Data literacy helps specialists comprehend and analyze data, which is essential for developing accurate and effective AI models. They are able to identify trends, patterns, and insights, which can inform the development of AI models.

Ethical AI Development: A data-literate specialist recognizes biases in training data and guides the AI development for inclusivity and diversity. They can use preventative measures against privacy breaches, regulation non-compliance, and discriminatory outcomes.

For Generalists:

Data-Driven Decision Making: Training generalists for data literacy enables them to make informed decisions when analyzing AI data. Their ability to interpret data sets and extract meaningful insights helps them communicate their data-related needs, ask relevant questions, and understand AI outputs.

Collaboration and Integration: With a base understanding and knowledge of data literacy, generalists communicate effectively with specialists to promote cross-team collaboration. This allows them to communicate their requirements, provide feedback on AI models, and contribute to their optimization.

AI Adoption and Change Management: Generalists are the bridge in an organization between technical and non-technical teams. With data literacy skills, generalists can communicate the potential of AI to other stakeholders and facilitate the adoption of AI technologies.

For Executives:

Strategic Decision Making: Executives can manage the direction of the company and its AI adoption by understanding it's benefits. With data literacy, executives can effectively evaluate AI use cases, identify opportunities for AI implementation, and make informed decisions about resource allocation, investments, and AI-driven business strategies.

Leadership and Vision: Data literacy provides a clear vision for executives to lead the AI transformation of their company. They can use data literacy to craft a detailed vision and strategy for AI adoption, create buy-in among stakeholders, and set up organizational structures that promote successful implementation.

Ethical and Responsible AI Governance: Data literacy skills expose not only the potential for AI but also the ramifications if left unchecked. With data literacy, executives can better understand the ethical implications of AI adoption and create responsible policies that ensure its use is safe and compliant with regulations.

The Digital Leader's AI Readiness Roadmap:

Before developing a training program that supports employees through their AI transformation, organizations should focus on a set of simple steps that makes the process more efficient. The process takes time and requires patience to reiterate changes while waiting for results. To expedite the process, working with a data literacy training provider like Correlation One gives companies all the resources they need while providing years of experience in guidance.

This roadmap is based on the advice of data literacy experts and will shorten the time to development and help ensure success.

1. Identify business use cases for AI across teams

The first step for an AI readiness roadmap is understanding what areas of the company need AI support. This could include improving customer experience, optimizing operations, or reducing costs. Once the business problems have been identified, businesses should conduct a data inventory to determine what data they have available and what data they may need to collect.

2. Assess workforce AI readiness

With specific business use cases identified, employers should next assess the current workforce and skills gap of the departments involved. This helps to identify what roles need data literacy development and ensures support from executive leadership. It also helps inform the curriculum for training programs and creates a workforce development plan.

3. Identify & empower the owner for the workforce AI readiness strategy

Each training program needs to identify a single owner of the project who will be responsible for the program’s success. The owner should have an understanding of data literacy and AI, as well as how to facilitate discussions between technical and non-technical teams.

4. Define and map AI competency requirements for generalists and specialists

To measure progress, an employer must first have a clear understanding of what skills teams should acquire. This includes defining the competency requirements for both generalists and specialists, mapping out the learning path, and providing training resources. Examples may include coding basics, data analytics concepts, and machine learning fundamentals.

5. Scope impact and projected efficiency gains across departments

A truly effective program considers the impact training has on workflow and operations. Programs cannot hinder operations but it must be given the proper time to be effective. To understand the trade-off, employers must also project efficiency gains across departments. This gives stakeholders a better understanding of the costs and benefits associated with data literacy.

6. Provide data literacy training for the entire workforce

A data literacy training program is only valuable if it translates across the entire workforce. Trained generalists cannot act as a bridge between non-technical and technical teams if specialists are not also trained. Executives won't drive data literacy if they don't have a basic understanding of how AI adoption affects the company as a whole.

What To Look For in a Data Literacy Training Provider:

Several factors make it difficult for companies to create and implement their own training programs. Time, money, and other resources must be spent on developing the curriculum, running the program, gathering feedback, making changes, monitoring progress, and ensuring success.

The burden is overwhelming and often leads to inefficient training programs or no program at all. To ensure that companies are able to maximize the value of their data literacy training, they should look for a provider with the key characteristics listed below.

Comprehensive Curriculum

A comprehensive curriculum covers all the necessary data literacy skills and knowledge that employees need to acquire to be proficient in data analysis and interpretation. When an AI tool is integrated into the current workflow, employees should be able to understand it and act on the data insights derived from it.

The only way to fully prepare them is through a comprehensive program. Most providers are familiar with a small set of data literacy skills and only apply those to their training programs. The results leave employees vulnerable to complex AI tools and unable to take advantage of the full potential of AI.

To effectively upskill their workforce in data literacy, companies should look for a provider that offers an extensive curriculum that covers all the necessary skills and knowledge. The program should also be tailored to the company’s specific needs so that it is relevant to their current workflow and objectives.

Flexibility and Scalability

Rigid training providers don't adapt their programs to reflect the needs of an organization. Flexibility allows companies to customize the training program to meet their unique needs, while scalability ensures that the program can be expanded as the organization grows and evolves.

Companies should ask whether or not a data literacy provider can customize the curriculum, delivery methods, or assessment tools to match the goals of the organization. Does the provider have previous experience with the specific industry of the enterprise? What previous success does the provider have in developing data literacy programs?

On top of aligning the curriculum with the goals of a company, a provider must achieve scale with their training programs. Companies are ultimately looking to grow their operations and they need a provider that will train a new employee just as well as an existing employee. Scalability also allows organizations to expand the program to new departments or functions, ensuring that all employees have access to the necessary data literacy skills and knowledge.

Combining flexibility with scalability keeps a training program relevant and effective over time. This can lead to better business outcomes, increased employee satisfaction and retention, and a competitive advantage in the marketplace.

Practical Learning Experiences

Learning from worksheets and lectures is not enough to acquire the necessary data literacy skills to be AI-ready. Employees need practical, hands-on experiences in order to gain a deeper understanding of the material and apply it to their work.

Employers have several options for applying practical learning experiences including:

• case studies
• simulations
• mentoring and coaching
• role-playing
• virtual reality scenarios

These experiences allow employees to work collaboratively on data-related projects, applying their skills and knowledge to solve real-world problems. By providing these types of experiences, employees are better able to retain and apply what they have learned.

Practical learning also reinforces the relevance and importance of a training program. When employees see the practical application of their newly acquired data literacy skills, they are more likely to be engaged and motivated to continue learning and improving.

Companies should seek out providers who incorporate real-world scenarios and practical learning experiences into their programs. By doing so, companies will employ workers who are armed with the necessary data literacy skills to capitalize on the potential benefits of AI.

Proven Track Record

A provider with a proven track record has demonstrated their ability to deliver effective data literacy training to other organizations, which increases the likelihood that they will be able to do the same for your organization. Providers that have worked with companies of different sizes and different industries are able to tailor their programs to meet the specific needs of each organization.

A more experienced provider should already have established processes and procedures in place to ensure that the training program is delivered on time, within budget, and to the expected quality standards. This keeps organizations from wasting money and other resources knowing that the training program delivered is from a reputable and reliable provider.

Companies should ask for references and case studies from other organizations that have successfully implemented the provider’s data literacy program. Testimonials provided on a website give further proof of the effectiveness of a provider. Sites like Glassdoor and the Better Business Bureau are great resources to get an honest and unbiased view of a provider’s services.

By choosing a data literacy provider with a proven track record, organizations can confidently invest in their data literacy training knowing that they are taking steps toward achieving their AI-readiness goals.

Conclusion & Key Takeaways:

The power of AI is no longer in question but the quick adoption of the technology is leaving the workforce behind. Enterprises are spending large amounts of their budgets including AI tools in operations but limited AI skills are hampering their ability to realize the full potential of AI. To bridge this gap, enterprises need to focus on data literacy as a key component in order to fully maximize the benefits of AI. A data-literate organization enables its generalists and specialists to improve data quality, increase efficiency in implementation, ensure ethical and accountable use of AI, drive collaboration and innovation, and improve the customer experience.

To develop data literacy within their organization, enterprises must focus on developing core role-based data literacy competencies in their workforce and partner with training providers that offer comprehensive and scalable training programs customized to their unique use cases.