According to BCG research, 74% of businesses have integrated AI in near-daily operations, but only 36% of respondents feel they’ve provided sufficient training.  This leads to a skills gap, which is especially impactful in operations- and it’s the COO’s responsibility to address it. If unaddressed, it can stall initiatives, reduce productivity, increase errors, and lead to overall waste.

The gap may grow as AI adoption increases, but there are ways for COOs to prevent it from expanding. This article explains what they can do about it.

The Nature of Today’s Skills Gap

AI training doesn’t require turning ops managers into data scientists or prompt engineers. Most issues are basic and can be solved when workers understand when data is accurate, when to escalate to a human, and the best ways to apply AI. Though simple in application, many companies are overwhelmed, stalling initiatives, investing in tools that go underused, and creating issues when teams use AI tools differently across departments.

Why the Gap Is Widening

While the skills gap should be closing with increased adoption, it’s widening instead. Here’s why.

• The Increasing Pace of AI Releases: New tools are being updated and released at a lightning pace, and training programs can’t keep up. By the time employees are sufficiently trained, there is often a new program or update to take its place.
• It Falls on the Shoulders of Middle Management: Middle management is often responsible for implementing training, but rarely has the resources to do so effectively.
• No Single Function Owns Operational AI Capability: As a tool, AI’s operational capability is often split among IT, which owns the tools, HR, which owns the training, and IoT, which owns the workflow. As a result, departments work in silos, and training is fragmented at best and unavailable at worst.

The Cost of the Gap

Without proper training, here’s what’s at stake:

• Missed ROI: According to McKinsey’s State of Organizations Survey of 100,000 senior executives, 81% experimenting with AI have not experienced meaningful improvements in their bottom line.
• Increased Risk of Lower Quality and Misinformation: With limited training, AI-generated information is more likely to go unchecked, causing reputational damage and errors.
• Inability to Compete: Businesses that are not implementing AI to its fullest capacity risk being overtaken by those that do.

Core Competencies to Be Prioritized

As mentioned earlier, AI training doesn’t mean teaching employees advanced skills. Here are a few basic core competencies to focus on:

• AI Literacy: Understanding the tool’s strengths and weaknesses and interpreting its output.
• Process Re-Engineering: Integrating AI to redesign workflows based on the tool’s advantages rather than simply sticking AI into an existing process
• Data Governance and Compliance Strategies: Recognizing that AI quality is contingent on the data it’s fed and developing habits to protect integrity
• Change Management and Cross-Functional Communication: Adopting new ways of working without an erosion of trust or morale
• Critical Evaluation of AI Output: Maintaining human judgment before implementing AI into decision-making, tools, or reports

Strategies for Closing the Gap

The skills gap exists, but COOs can close it with the following strategies:

• Run a skills audit to determine what employees know, what they need to be taught, and the best training tools and processes
• Build Tiered Training- Employees require different training information based on their department and position; a one-size-fits-all approach won’t do
• Embed AI Advocates Into Operational Teams: These are people who can help boost morale when AI feels frustrating, and may even help when difficult situations arise
• Partner Across L&D, IT, and Vendors: When all departments come together, they can pool their resources and paint a unified picture for the company
• Update Job Descriptions and Hiring Criteria: Reevaluate your recruiting process, ensuring you hire individuals with the skills your company requires

Leading Cultural Change

AI adoption goes beyond training; it requires a cultural change that COOs can implement, as follows:

• Model AI Usage: Lead the change by implementing AI into your workflows and decision-making processes
• Acknowledge a Learning Curve: Create a system where employees feel okay asking questions and experimenting
• Balance Efficiency Against Realistic Timelines: It may take time for your team to become efficient using AI tools. Plan timelines accordingly. At the same time, it is wise to set goals, ensuring employees have achieved efficiency within a reasonable timeframe.

Want to learn more about keeping operations updated with the latest technology? Sign up for our newsletter today.