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As artificial intelligence (AI) tools continue to enter the workplace at record speed, leaders and employees alike are asking an important question: Is AI actually helping us work smarter, or is it adding layers of complexity under the guise of productivity?
A growing body of evidence suggests the answer isn’t as simple as we’d like. While AI has the potential to streamline workflows and support decision-making, poor implementation and hype-driven adoption may be slowing teams down rather than speeding them up.
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Productivity or Just Noise?
According to a February Pew Research Center report, workers using AI mostly say it helps them complete tasks faster, but not necessarily better. A separate May survey from Gartner found that even many executive leaders lack the expertise to successfully roll out AI tools within their organizations.
Doug Gilbert, Chief Digital Officer at Sutherland, points to a common pitfall: layering AI onto outdated systems. “Too often, IT departments bolt AI onto legacy platforms, expecting results. But this mismatch creates friction instead of efficiency,” he explains.
Instead of simplifying workflows, AI becomes a clunky tool that frustrates users and delivers limited returns.
The Key: Workflow-First AI Integration
The most successful AI integrations share one trait, they are embedded directly into existing workflows. AI should support how people already work, not demand brand-new processes or steep learning curves.
“AI should remove friction, not add to it,” says Gilbert. He stresses the importance of building AI solutions with user needs in mind. This “empathetic automation,” as he calls it, puts human experience at the center of design.
That approach is echoed by Davit Baghdasaryan, CEO and co-founder of Krisp, who says AI must deliver clear, immediate value to workers. “Minimize disruption. Maximize usefulness. That’s how you earn trust and adoption,” he notes.
At Sutherland, co-designing tools with frontline staff ensured their AI flagged only high-priority issues rather than overwhelming users with constant alerts.
Finding the Signal in the Noise
In the right setting, AI can dramatically reduce workload, especially in roles that involve high-volume communication or repetitive tasks. Examples include customer service centers, healthcare support, and sales teams where AI can assist by providing quick, relevant information in real time.
But leaders must be cautious. “Too much information is just as dangerous as too little,” says Baghdasaryan. That’s why AI tools must act as curators, highlighting what matters most and filtering out the rest.
Gilbert agrees, adding, “AI should amplify good decisions, not drown them out.”
Dan Root, Head of Global Strategic Alliances at Barco ClickShare, also urges companies to compare the usability of new AI tools against what employees already use. “If adoption is your goal, design and user experience matter just as much as the tech itself,” he says.
Leadership Must Set the Right Expectations
Leadership plays a critical role in how AI is implemented and perceived within an organization. According to Gilbert, one of the most damaging mistakes is believing that simply “adding AI” is a solution in itself.
“Executives sometimes demand AI for AI’s sake,” he says. “But AI is a tool, not a magic fix.”
To make the most of these tools and work better with them, leaders must clearly communicate what problems the technology solves, why it’s being introduced, and how it ties into overall business goals.
That clarity builds trust, which is essential for adoption.
Baghdasaryan emphasizes that successful AI rollouts depend heavily on onboarding and training. “A thoughtful, gradual rollout beats a flashy launch every time,” he adds. Root cautions that errors and false outputs (known as hallucinations) can still happen, so teams must be taught when to verify AI-generated results.
Not All Tasks Need AI
One of the biggest risks in today’s AI rush is the urge to apply it to everything. Gilbert warns that AI often gets misused when leaders are caught up in the hype.
“Force-fitting AI into the wrong process adds stress instead of reducing it,” he says. He suggests organizations evaluate each use case and deploy the right kind of automation. Robotic Process Automation (RPA) may be better suited for repetitive tasks, while generative AI works best for creative tasks.
Baghdasaryan points out that employees are more likely to engage with tools that support their work, not ones built to replace them.
“The best AI tools empower humans. They reduce stress and elevate performance without removing the human element,” he says.
Feedback and Flexibility Make the Difference
Another common mistake? Treating AI as a “set it and forget it” system. Instead, leaders should build in feedback loops that allow tools to evolve and improve over time.
Gilbert encourages companies to set clear success metrics, like reduced task-switching or faster responses, to measure if AI is actually doing its job.
He adds, “If AI floods users with low-value alerts, it becomes a distraction, not a tool.”
Root recommends that leadership maintain transparency throughout the adoption journey. Internal communications, such as spotlight stories showing how AI improves workflows, can build enthusiasm and alignment across teams.
Final Thought: Human First, AI Second
AI is a powerful tool that could really boost the team performance and help them work better, but only when applied intentionally and with users in mind. When built around real workflows, aligned with strategic goals, and continuously improved, it becomes an ally.
But if applied recklessly or treated as a quick fix, AI can easily turn into just another overhyped distraction.
Companies that prioritize thoughtful design, employee training, and strong leadership will be the ones that unlock AI’s full potential, without losing sight of the people it’s meant to support.
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