AI Isn’t Synonymous with Efficiency: 4 Ways to Conquer the AI Productivity Paradox

August 6, 2024

The possible future promised by generative AI is compelling: work can be done faster, knowledge work can be democratized, and the workforce has access to an always-on collaborator.

Business leaders have recognized this potential in generative AI, and they’re embracing it wholeheartedly. The latest research—surveying 2,500 C-suite executives, full-time employees, and freelancers—shows that 85% of leaders are already mandating or encouraging their employees’ use of AI tools. And 96% of executives expect AI to improve overall productivity.

So why, then, are nearly two-thirds of employees struggling more than ever to meet their productivity goals?

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AI isn’t synonymous with efficiency

Since OpenAI released ChatGPT to the mainstream in late 2022, there’s been an ongoing public conversation about generative AI and efficiency.

The two are often discussed as if they’re the same thing—as if generative AI equals efficiency. But this isn’t true.

Yes, generative AI can support and enhance processes for better efficiency, but the technology by itself isn’t a magic pill. It can’t create efficiency where there isn’t any to begin with—or replace the strategic guidance of a leader who recognizes the nuances of a particular context.

If you try to insert AI into inefficient processes, you run the risk of making work harder on your team members. In this scenario, AI can wind up exacerbating, not reducing, friction points while employees struggle to meet higher productivity demands. It’s no surprise that workers often feel frustrated or resistant to new technologies that impose higher productivity demands. Without system-wide changes and opportunities for upskilling, these advancements can feel overwhelming and unattainable.

It’s this very productivity paradox that’s led 77% of full-time employees to feel like their workload is heavier after the introduction of AI.

For many, AI isn’t helping; it’s hurting. But that doesn’t have to be your reality.

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Innovative AI adoption that avoids burnout

Increasingly, leaders are being asked to supply their employees with the tools and resources they need to do great work while also supporting their well-being. This much is apparent—our research shows that 84% of C-suite leaders highlight the importance of employee well-being over productivity at work.

But with 71% of full-time employees reporting a sense of burnout, something’s getting lost in translation.

By looking to work innovators for inspiration, we can better learn how to close the gap between leaders’ goals and employee experiences.

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Work innovators and AI

Work innovators are high-performing companies typically led by action-oriented leaders. They share several attributes, including a willingness to use new technology throughout their organizations.

But work innovators aren’t just introducing a new tool and asking employees to use it. The leaders at these companies are actively adjusting their existing strategy with AI in mind—and training their teams on how to use generative AI tools for peak efficiency.

In fact, work innovators are 3.8 times more likely than their counterparts to have a well-defined generative AI strategy. They’re also 1.9 times more likely to have a generative AI training program in place.

As a result, 47% of work innovator companies had already incorporated generative AI into their daily operations by Q4 2023—making them early to figure out the secrets to successful AI adoption without burnout.

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Approach AI like a work innovator

By adopting the mindset of a work innovator, you can help to improve the rollout of generative AI tools at your own company and ultimately reduce the likelihood of added stress or burnout across your teams.

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1. Understand where generative AI shines at work

The “jagged technological frontier” is a term used to describe the impact of AI on knowledge worker productivity and quality. It refers to the fluctuation between tasks that can be sped up with the use of AI versus other tasks that are slowed down by the introduction of AI.

Understanding this difference in your own operations can help you truly maximize your teams’ efficiency. Rather than requiring your employees to use AI on tasks better suited to a human, you can create clear delineation between:

Tasks to be fully handled by generative AI
Tasks to be done by employees with the help of AI
Tasks to be executed entirely by employees or flexible talent, without AI

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2. Evaluate existing workflows before introducing AI

In some instances an AI tool can be helpful, even with minimal training. This can include:

AI acting as a companion tool during the information gathering and research process
AI serving as a virtual collaborator, including organizing team members’ input and scanning data for repeating patterns
AI supporting coaching and training efforts—such as by reviewing a junior developer’s code for errors

In all of these instances, AI isn’t replacing a human task. Workers are still going about their normal processes; they’re just using AI to help them find or sort information—like a more robust version of a web browser or Excel formula.

But when AI is used to fully automate certain tasks, or serve as a new step in an existing process, more preparation, strategy adjustment, and training is required to make the deployment smooth. This could involve:

Reworking processes to create steps specifically for AI use
Updating policies about sharing of information with third-party tools
Gradually introducing generative AI into non-critical parts of workflows before expanding its use
Implementing mandatory or encouraged training programs to help employees understand how to best use the AI tools at their disposal
Co-creating new productivity goals with the input of your employees who will be using the tools

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3. Rely on flexible talent for help with core functions

As part of your evaluation process, you may discover that you simply can’t meet increased productivity goals with your existing full-time headcount alone. Rather than relying fully on AI to boost productivity, consider bringing in flexible talent as well.

Once you establish your company’s internal “jagged edge”—which tasks can be outsourced to AI, and which must remain with humans—you can then bring in flexible talent with specific skill sets to fill gaps.

This is another work innovator attribute. Innovative companies use flexible talent models to respond to changing demands and have established processes for smooth knowledge transfer between flexible talent, full-time employees, and generative AI.

You don’t have to choose between hiring more full-time team members or attempting to force certain tasks through generative AI processes. Flexible talent can fill skill and headcount gaps as they happen, giving you the ability to scale up and down as needed.

You can also turn to flexible talent for as-needed consulting opportunities. This is particularly useful when planning how to integrate AI into your operations without overwhelming workers.

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4. Focus on doing more with more, not less

Ultimately, using AI like a true work innovator can mean you have to shift from a mindset of doing more with less to doing more with … more.

This doesn’t mean you have to expand your full-time headcount or invest large sums into multiple AI solutions. Instead, you can retain your existing employees, tap flexible talent to fill strategic skill gaps, get help selecting the best generative AI solution for your needs, and bring in experts to train your team in AI best practices.

By taking the time to lay the groundwork for successful and efficient AI use now, you can begin scaling up your output over time—and outpace your competitors who haven’t yet figured out the secret to true AI productivity.

This article originally appeared on Upwork.com Resource Center (Upwork is a company that helps businesses find talent and people find work) and was syndicated by MediaFeed.org.

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