The New IT Skills Gap: How Different Departments Are Benefiting From AI
Where it was the most common use case several years ago, AI skills aren’t just for IT teams anymore. The technology has found its way into the software used by salespeople, marketers, developers, accountants, customer service representatives, and operations teams.
That creates a new kind of skills gap that employers are becoming ever so aware of. Employees may have access to AI, but that doesn’t necessarily mean they know where it can improve their work or how to use it effectively.
Taking into consideration knowledge from AI expert Marcus McGehee, owner of a St. Pete AI consulting agency, the full answer also looks very different depending on the department.
IT: Managing AI While Using It
IT teams have an unusual role when it comes to AI adoption. They’re both users of the technology and, increasingly, the people responsible for managing how everyone else uses it.
Where AI Is Helping
AI can assist IT teams with everyday technical work such as:
- Summarizing logs and error messages
- Searching technical documentation
- Drafting scripts
- Troubleshooting common issues
- Managing internal knowledge bases
- Responding to basic support requests
This can reduce the time technicians spend digging through documentation or repeatedly solving similar problems.
The New IT Skill Set
The bigger change may come from managing workplace AI itself.
AI capabilities are appearing in productivity suites, CRMs, development tools, browsers, meeting platforms, and standalone applications. IT teams need to understand permissions, integrations, security, data governance, and which tools employees should actually be allowed to use.
Software Development: Coding With an AI Assistant
Developers were among the first employees to see AI become part of their normal software stack.
AI coding tools can now generate boilerplate code, suggest fixes, explain unfamiliar functions, create documentation, and assist with testing.
Knowing What Not to Accept
The productivity gains come with a catch:
Generated code still needs someone who understands code.
AI can introduce inefficient logic, security vulnerabilities, outdated dependencies, and simple mistakes. Developers therefore need to become good reviewers of AI-generated work, not simply good users of coding assistants.
The technology can handle more of the repetitive work, leaving developers to concentrate on architecture, problem-solving, and the decisions that require technical judgment.
Marketing: Getting From Idea to Execution Faster
Marketing teams have plenty of obvious uses for generative AI, but the opportunity goes well beyond asking ChatGPT to write a blog post.
AI can help marketers:
- Summarize market and competitor research
- Analyze customer feedback
- Brainstorm campaign concepts
- Create variations of existing content
- Repurpose material for different channels
- Analyze campaign results
- Identify patterns within larger datasets
That changes the skill marketers need most. Generating 20 ideas is easy. Knowing which two are worth pursuing still requires experience.
Sales: Cutting Down the Work Around Selling
A significant portion of a salesperson’s day can disappear into work that happens before and after the actual conversation.
AI is increasingly useful for handling that surrounding workload.
Before the Call
AI can summarize a prospect’s company, industry, previous interactions, CRM notes, and other available information.
After the Call
It can also transcribe the conversation, summarize important points, extract action items, draft follow-up emails, and help update CRM records.
The salesperson still handles the part that matters most: understanding the prospect, building a relationship, answering difficult questions, and closing the deal.
Customer Service: Finding Answers Faster
Customer service employees often don’t lack information. They lack a fast way to find the right information while someone is waiting for an answer.
AI-powered knowledge systems can search company policies, manuals, previous cases, product documentation, and other internal resources using natural-language questions.
That can help representatives resolve problems faster without requiring them to manually search several systems.
Knowing When AI Has It Wrong
Customer service employees still need to recognize inaccurate answers and know when a situation requires escalation.
They also need to understand what customer information can safely be entered into an AI system. As AI becomes more involved in support, basic data and privacy knowledge becomes part of the job.
Finance: Less Processing, More Analysis
Finance departments deal with large amounts of structured information and repetitive processing, making them particularly interesting candidates for AI.
Potential applications include:
- Invoice and document processing
- Data extraction
- Reconciliation assistance
- Identifying unusual transactions
- Summarizing financial reports
- Finding trends in financial data
- Assisting with forecasts and scenarios
The goal isn’t to have AI make financial decisions. It’s to reduce the amount of time skilled employees spend gathering and organizing the information required to make them.
Verification remains essential when incorrect output can influence budgets, forecasts, reporting, or other major decisions.
Human Resources: Making Internal Information Easier to Use
HR’s AI opportunities go well beyond generating job descriptions.
Employees regularly ask HR the same questions about benefits, policies, onboarding, leave, and internal procedures. AI-powered internal knowledge systems can make that information easier to find without requiring an HR employee to answer every request manually.
AI can also help organize employee feedback, summarize documents, prepare internal communications, and support routine administrative work.
Some Decisions Still Need People
Hiring, compensation, performance reviews, and disciplinary decisions carry much greater consequences.
HR employees need to understand where AI can save time and where privacy, bias, accuracy, or fairness concerns require stronger human oversight.
Operations: Finding AI Opportunities in Everyday Processes
Some of the most useful AI applications worth looking into when considering corporate AI training are hiding inside boring, repetitive work.
Think about employees who regularly:
- Pull information from forms
- Categorize documents
- Prepare recurring reports
- Move information between systems
- Process internal requests
- Coordinate schedules
- Update records after completing another task
Individually, those tasks may only consume a few minutes. Repeated hundreds or thousands of times across an organization, they become significant.
Look at the Workflow, Not Just the Task
The larger opportunity comes from connecting these steps.
An AI-assisted workflow could receive a document, identify what it contains, extract specific information, update another system, prepare a response, and send the result to an employee for approval.
Marcus from The AI Consulting Lab describes part of its audit process as identifying “the true sources of wasted time and frustration” by talking with employees and mapping how work actually gets done.
That employee-level view matters because the biggest AI opportunity isn’t always obvious from management’s perspective.
The AI Skills Gap Is Different for Every Department
This creates a problem with generic company-wide AI training.
Teaching everyone how to write prompts might improve basic AI literacy, but an accountant doesn’t need the same AI skills as a developer. Neither needs exactly what a salesperson or system administrator needs.
The AI Consulting Lab puts it simply, stating, “People can’t use something if they haven’t been taught how it works, and the best way to use it.”
The harder question is figuring out what they actually need to be taught.
Start With an AI Audit
Before creating a training program, businesses can examine how AI is already being used across the organization.
An AI audit for your business can answer questions such as:
- Which AI tools are employees already using?
- Which repetitive tasks consume the most employee time?
- Where are employees creating their own AI workflows?
- Are employees putting company information into unapproved tools?
- Which departments have the biggest opportunities for automation?
- Where could incorrect AI output create serious problems?
- Which employees already have skills that could be shared internally?
The AI Consulting Lab goes on to describe an AI audit as a “tactical, no-hype assessment designed to find real, high-impact AI opportunities within your organization.”
The result gives businesses something much more useful than a list of popular AI tools: a picture of where the technology can actually make a difference.
Train Employees for the Work They Actually Do
Once those opportunities are identified, AI training for employees can become much more specific.
Developers can train around coding assistants and verification. Sales teams can work with CRM and prospecting workflows. Marketing teams can concentrate on research, analysis, and production. IT can focus on integrations, security, and governance.
Employees aren’t learning AI for the sake of learning AI. They’re learning how to apply it to work they’re already responsible for.
AI Is Becoming Another Workplace Technology Skill
AI won’t affect every department in the same way, which is exactly why the emerging skills gap is so important.
The businesses that get the most value from AI will need to understand where the technology fits into their existing workflows and where it doesn’t. Employees then need the skills to use those capabilities without sacrificing accuracy, security, or human judgment.
As AI becomes a standard feature of everyday business software, knowing how to work alongside it is quickly becoming part of being technologically proficient at work.
