Organizations tend to allocate their budgets and invest heavily in recruiting, hiring, and promoting employees. However, one critical area that often gets overlooked is ensuring employees have the proper data and AI skills to succeed in their roles.
As businesses become increasingly reliant on data-driven decision-making, employees across all levels—whether in leadership, management, or frontline roles—must understand how to use and leverage data effectively. Yet, many organizations fail to provide structured, role-specific training, leaving employees to struggle, remain hesitant, and ultimately underperform in critical areas.
This knowledge gap comes at a cost: lower productivity, poor decision-making, and a larger degree of frustration experienced among employees. According to Harvard Business Review, studies show that employees who lack confidence in their ability to work with data are 40% more likely to make errors in analysis, leading to costly business mistakes.
In this article, we’ll explore:
- The challenges employees face when learning data and AI on the job.
- The reason why self-paced training often falls short compared to live training.
- Why organizations should invest in hands-on, job-specific training.
The Challenges of Learning Data and AI on the Job
Many companies assume that employees will naturally hone their data and AI skills through self-guided learning or engaging with AI tools at their convenience. Unfortunately, this assumption leads to skill gaps, decreased productivity, and a reduced confidence in decision-making.
This includes:
1. New Hires and Promoted Employees Feel Overwhelmed by Data Tools
A recent study by Gartner found that 60% of employees feel unprepared to receive training that supports their core jobs skills, despite the growing emphasis on data-driven decision-making.
This knowledge gap can be overwhelming for employees stepping into these new roles:
- Employees hesitate to use data-driven tools due to a lack of confidence.
- Workers rely on gut instinct rather than evidence-based decision-making.
- Employees miss opportunities to optimize business processes through automation and AI.
2. Without Guidance, Employees Rely on Guesswork Instead of Data
Without structured training, employees often resort to outdated methods or develop inefficient workarounds, which can lead to:
- Data misinterpretation, increasing the risk of flawed business strategies.
- Slow decision-making, as employees lack the skills to analyze data efficiently.
- Inconsistent data use, leading to fragmented reporting across teams.
A study published by McKinsey found: “low data quality was the factor most often cited as the biggest impediment to getting employees to use data consistently for decision making.”
3. Self-Paced Learning Leads to Low Completion Rates and Frustration
Many companies provide self-paced online courses for employees to learn data and AI concepts. However, research shows that less than 10% of employees complete these courses due to:
- Lack of accountability—no deadlines, check-ins, or team discussions.
- Minimal real-world application—employees struggle to connect what they learn with their daily responsibilities.
- No mentorship or feedback, making it difficult to troubleshoot challenges.
In contrast, according to Harvard Business Review, hands-on, mentor-led training has been shown to improve retention rates by 40%.
The Solution: Hands-On, Job-Specific Training
To close the data literacy gap, organizations must invest in structured, real-world AI and data training that employees can immediately apply to their jobs.
At Data Society, we help organizations train their workforce in data and AI skills through hands-on, expert-led programs. Unlike generic online courses, our training is:
- Industry-Specific: Aligned with real-world use cases in healthcare, finance, logistics, and more.
- Designed for Immediate Application: Employees work on projects related to their daily tasks.
- Led by Experts: Live instruction and mentorship ensure employees get answers to their real questions.
The Business Case for Investing in AI and Data Training
Companies that invest in structured, hands-on training see measurable improvements in workforce performance:
- Higher Retention: Employees who receive professional development opportunities are 50% more likely to stay with their company (LinkedIn Learning Report).
- Increased Productivity: Employees are more equipped to handle data and make informed decisions. Organizations that do not invest in training lose an average of 43 hours per employee each year due to data-induced stress and procrastination (Accenture).
Faster Decision-Making: AI-trained employees cut down decision-making time by up to 30%, leading to faster innovation and competitive advantages (Deloitte AI Report).
Preparing for future uncertainty
Instead of expecting employees to “figure it out” on their own, give them the tools and knowledge to use AI and data effectively from day one.
- Assess your team’s current skill level
- Identify industry-specific training needs
- Implement hands-on, expert-led learning
- Provide mentorship and continuous support
At Data Society, we work with organizations to create tailored training programs that equip and prepare employees with the confidence and skills to use AI and data effectively.
Want to learn more? Schedule a free consultation today.
