May 26, 2026 AI Is Ready – Is Your Workforce?

AI is transforming business, but without employee fluency and governance, organizations risk falling behind.

By Dave Hickey

Key Insights

• From top leadership down, AI fluency is important for all employees.
• AI Governance helps prevent individual, organizational, and strategic risk.
• Many companies spend money on AI initiatives but miss the opportunity to train employees on how to use AI.
• There’s an important difference between bots, agents, LLMs, and ML that employees need to understand.
• Agentic Orchestration is key to successful integration of different AI capabilities.
• One of AI’s limiting factors is humans’ limited ability to adapt to a rapidly changing AI landscape

Companies are racing to adopt AI, but who’s training the employees, setting the rules, or defining what success looks like?

A recent survey found 88 percent of U.S. companies are using AI for at least one business function, but 57 percent of employees are using their own personal generative AI accounts (i.e., Claude, ChatGPT or Gemini) for work projects. Even more startling, a third of employees admit to uploading sensitive information into Large Language Models (LLMs) or unapproved AI tools. When combined with a lack of AI fluency, these independent users create significant risk for themselves and the companies they work for.

The Importance of AI Fluency Among Employees

AI fluency is the ability to understand, communicate, and strategically apply AI to real world contexts. To be AI fluent, employees don’t need to be experts in coding or data science, but they do need to be able to work with AI intelligently and ethically. This includes the use of enterprise AI systems, where companies deliberately create and deploy one closed AI system across their organization, and individual accounts, where employees use independent standalone AI tools.

Organizations who are interested in increasing their AI fluency first need to set up a credible AI governance framework, including guidance, programs and training for employees on how to use AI effectively. Employees need to clearly understand what data can and cannot be shared externally, the boundaries of AI agents, and even the risks associated with improper use of AI.

Knowing the Difference: Bots, RPA, ML, IDP, Gen AI, LLM, Agents, and IPA

An explosion of media coverage and dilution of precision in the use of industry vernacular adds to the confusion surrounding AI capabilities. As AI has rapidly matured over the last four years, distinct definitions have simultaneously emerged.

In simple terms, AI is an umbrella term for the field that refers to computer systems that perform tasks that require skills like reason, decision-making, and pattern learning.

Enterprise AI can be viewed across three major layers. The first is the knowledge layer, where organizations use LLMs across structured and unstructured data to extract insights, summarize information, and unlock institutional knowledge. The second is the process layer, where intelligent process automation (IPA) orchestrates bots, AI models, agents, systems, and people across end-to-end workflows. The third is custom AI builds, where organizations invest more deeply in data science and AI engineering to create differentiated, domain-specific solutions.

• Bots automate repetitive, rules-based work that people would otherwise complete manually. Also known as Robotic Process Automation (RPA), bots follow predefined steps and logic to execute tasks consistently (1+1 = 2) and at scale. They do not learn, reason, or adapt on their own; if they encounter an exception or a scenario outside their programming, they require human intervention or updated rules.
• Machine Learning (ML) is a subset of AI that learns from historical data to identify patterns, improve predictions, and make decisions with less manual rule setting over time. In practice, ML is often embedded inside larger automation solutions. For example, in Intelligent Document Processing (IDP), ML helps the system recognize document types such as invoices, purchase orders, or claims, extract key fields like invoice number, due date, and amount, and improve accuracy as more documents are reviewed and validated.
• Gen AI is a category of AI that creates new content, including text, images, audio, video, code, and structured outputs. Large Language Models (LLMs) are one form, or subset, of generative AI focused specifically on language. Rather than “knowing” information the way humans do, generative AI identifies patterns in data and predicts the most likely output based on a prompt. Common business uses include drafting emails and summaries, generating images and design concepts, producing audio and video content, and creating reports or other structured outputs.
• Agentic AI commonly called Agents are AI systems built on top of Large Language Models (LLMs) that add memory, planning, tool use, and multi-step decision-making. Rather than simply generating content from a prompt, agentic AI can pursue a goal, take action across systems, and adapt as conditions change. This enables organizations to create persistent, intelligent digital workers that can support long-running processes such as research, case management, service operations, and workflow coordination, while still operating within defined guardrails, approvals, and human oversight.
• Intelligent Process Automation (IPA) is a blend of RPA, AI, and machine learning that helps organizations streamline operations, reduce errors, and cut costs. IPA goes beyond routine tasks by coordinating bots, AI models, and agents across end-to-end, long-running workflows that may span systems, decisions, and human approvals.

Putting It All Together: Agentic Orchestration

Similar to an orchestra comprised of music instruments working together, agentic orchestration connects bots, agents, systems and people into long-running workflows with full visibility and accountability. Agentic orchestration allows companies to automate more complex, end-to-end processes while maintaining governance, security, and auditability.

FAQs

What are the consequences of employees not being trained in AI?

A lack of employee training and company AI governance can make untrained users a liability and create organizational risk including accidental release of proprietary data, or disclosure of personal identifiable information like names, addresses and phone numbers. A lack of training can also result in employees missing AI errors like inaccurate information, hallucinations, and incomplete or biased data. In most cases, the risk isn’t intentional but instead is related to an AI fluency gap.

What happens if companies lack an AI strategy?

An even riskier proposition is AI adoption without a clear implementation strategy. Companies can easily invest thousands of dollars into AI, but and see little return on investment without a clear strategy.  

What industries are affected by AI?

Nearly every industry can benefit from AI. The use cases vary, but organizations with large volumes of data, repetitive processes, complex decisions, or high customer interaction often see value fastest. From financial services and retail to healthcare, marketing, and technology, AI is broadly relevant. What changes is how each organization applies it and how prepared its workforce is to use it responsibly.

Conclusion

AI’s trajectory is staggering — a projected $267 billion market by 2027 and $15.7 trillion added to the global economy by 2030. The technology is ready. The question is whether the people deploying it are. As AI grows more sophisticated, the widening gap between what organizations adopt and what their workforce actually understands has become the single greatest barrier to realizing its value. The future of AI isn’t a technology problem. It’s a human one.

About the Author:
David Hickey is a principal in Baker Tilly’s digital solutions practice, where he helps organizations modernize operations through intelligent process automation, AI, and digital transformation.

Read more from the author:

Agentic AI: What CFOs need to know | CFO.com, March 17, 2025

How agentic AI is poised to transform financial services | CFO.com, Sept. 2, 2025

Transforming the billing process with intelligent process automation | Baker Tilly, Oct. 13, 2025

Beyond bots: building adaptive workflows with intelligent process automation | Baker Tilly, Dec. 16, 2025

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