CIOs are caught between employee AI fatigue and leadership expectations

In 2024, when cloud-based software company BlackLine implemented its Buckie AI agent, a knowledge base that employees could ask HR- or IT-related questions, the company didn’t expect to move away from the tool within a year.

“The technology was moving so fast,” CIO Sumit Johar says, and the company needed a different system to scale for the future.

By June the following year, BlackLine had migrated to Google Gemini enterprise, and today, employees organization-wide have built nearly 300 AI agents themselves.

The rapid clip at which organizations are adopting AI is compounding challenges for CIOs. And for employees, being bombarded with new tools and processes is leading to AI fatigue, a feeling of burnout from added workflows and unmet promises of time savings.

At the same time, corporate boards are putting increased pressure on CEOs to deploy AI and deliver results. So CIOs are caught in the middle, balancing board and leadership expectations with employee reality on the ground. They’re pressured to move quickly — a strategy that, in reality, often backfires, according to Doug Gilbert, CIO and chief digital officer at global business technology consultancy Sutherland. He says AI implementations currently have up to a 90% failure rate. “Doing AI right may sound slower, but in the long run, it’s going to be faster,” he says.

Why employee fatigue happens

Riley Stricklin, founder and chief strategy officer at AI integration firm Cadre AI, agrees that AI fatigue is a growing problem across companies. It’s not necessarily because employees are anti-AI, but rather they’re overwhelmed with new tools, new expectations, and constant change, he says.

The initial steps to implement AI take time, temporarily adding to employee workloads before delivering promised time savings, a common complaint Johar hears. Then, the moment teams feel they’re settling in with a new technology, understanding how they can organize their business processes and maximize value, something new comes up that changes everything. “That’s why there’s exhaustion, because things are moving so fast,” he says.

Gilbert adds that AI fatigue most commonly arises when AI is clunky, when organizations bolt AI on top of an existing process, rather than implement it as an in-line solution. Employees could be asked, for instance, to copy and paste data from their programs into a separate LLM like ChatGPT. But the method doesn’t take. “You’re frustrating the heck out of the employee,” Gilbert says.

On top of that, he adds that when AI isn’t properly integrated with a company’s data, or it lacks broader organizational context, the LLM can hallucinate, delivering outputs that, as he puts it, are kind of crap.

Stricklin also says when AI is an added layer instead of an integrated solution, it compounds friction when the purpose is to reduce it.

So the most successful CIOs don’t simply plug AI into existing systems and expect transformation, he says. They rethink the entire workflow and build AI into operations. And in the most seamless AI integrations, Gilbert says employees don’t really think about AI; they simply use a process and get better, faster results.

CIOs pressured from all sides

Gilbert says the clunky approach often happens because of a top-down push that ripples throughout the organization. Boards and CEOs may see case studies or articles of what other companies are doing related to AI, and want to jump on the bandwagon. The AI request trickles to the CIO, who then feels pressure to deploy a solution quickly, rather than take the time to develop an in-line system.

“The reality is you’ll never meet the false expectations they have in their heads,” Gilbert says, adding that boards and CEOs often have a utopian mindset of AI capabilities. Likewise, company investors often expect AI to slash costs, which pressures leadership to demonstrate immediate ROI from AI, Johar adds.

“They don’t always understand that you have to incur the cost before you save any cost,” he says.

In fact, a recent McKinsey survey shows that of the companies that participated, only 39% reported AI‑related impact on their earnings at the enterprise level, suggesting the majority of AI programs have yet to deliver meaningful financial results.

In addition to top-down pressure, sometimes CIOs are feeling stress from the ground up. Despite employee fatigue around AI, Johar’s team at BlackLine has seen requests for AI-based tools from other departments increase by up to 25%.

The higher volume of requests creates fatigue for the IT team itself, as they evaluate myriad tools. Increasing the challenge, the fast pace of change with AI means the team’s processes to evaluate technology have to evolve, too. By the time IT makes a decision to procure a technology or select a supplier, it’s possible the tech is already obsolete, Johar says.

BlackLine has also trained employees on how to build their own agents for specific departmental functions, and to date, employees have built nearly 300 AI agents. CIOs and their teams bear the responsibility of bringing governance and structure to the flood of agents, as Johar puts it, ensuring they meet corporate policies around data privacy or security.

As tech features such as vibe coding continue to gain traction, Johar anticipates additional questions will arise for CIOs related to software oversight.

Framing the AI narrative

Delivering business value continues to be a top priority for tech leaders, and Stricklin says the most successful CIOs establish clear business objectives — whether it’s increasing revenue or margins, or reducing cycle time — before an AI deployment.

But when persuading employees to embrace AI that ultimately creates business value, CIOs may need a different tact than touting the benefits.

Johar says CIOs should frame AI’s benefits as compelling from an employee point of view, like helping employees do their jobs more effectively and building skillsets. “Once you position it that way, employees become a lot more accommodating to invest their time,” he says.

In this kind of climate, Gilbert says CIOs need to reassure employees that AI isn’t a means to headcount reduction but about flipping the narrative to how AI will work alongside employees, not replace them. Gilbert adds that humans should always be in the loop to fine-tune models and improve the accuracy of AI’s outputs over time.

Finding the right balance is key, given the gap that still exists between leader and employee sentiment around AI. Executives are 15% more likely to say AI has had a significant positive impact on their companies than their employees are, according to a survey commissioned by Google Workspace.

Stricklin also advises CIOs to have a focused strategy for how they adopt AI instead of trying to boil the ocean and immediately implement AI organization-wide. So they should pick two to three priority areas to use AI over the next six months, and get employees involved with the best course of action. “Trying to address everything simultaneously will cause more harm than wins,” Stricklin says, adding that equally important is selecting areas in which an organization won’t pursue AI.

Gilbert agrees that not every facet of a business is enhanced by gen AI. CIOs should be mindful of that and not be afraid to push back against CEOs or boards if they suspect an AI deployment is unnecessary. “Sometimes AI isn’t the answer,” Gilbert says.