There appears to be a massive disconnect in the corporate world when it comes to AI literacy. While an overwhelming majority of workers are eager to develop their generative AI skills and put them to work, only a fraction of companies are stepping up to provide the necessary training. 

This isn’t just a minor oversight — it’s a critical strategic blind spot that could expose companies to serious data privacy and security risks while also significantly impacting their organizational effectiveness and competitiveness in today’s rapidly evolving AI-augmented world of work.

Recent research from Microsoft — covering over 31,000 people across 31 countries — found that 75% of knowledge workers are already using generative AI at work and 78% of them are bringing their own AI to work. On top of that, more than half of the people using generative AI at work are reluctant to admit it.

This creates a concerning situation where employees are secretly leveraging these powerful tools for work without proper guidance or support.

The benefits of using GAI are too compelling to ignore

Some companies have taken action to limit access to generative AI tools like Copliot, ChatGPT, Claude, or Gemini at work, but let’s be realistic — they can only effectively do that on work-provided devices. Even with policies prohibiting public AI tools, there’s no effective way to monitor and block use on personal devices. And if a company hasn’t provided employees with private and secure alternatives, it’s easy to understand the motivation for bringing their own AI to work.

The benefits of using generative AI for work are simply too compelling for people to ignore. Microsoft’s research found that when people can use generative AI at work:

• 90% say it helps them save time
• 85% say it helps them focus on their most important work
• 84% report it helps them be more creative
• 83% say they enjoy their work more 

You’ve probably seen the controlled studies like the one from Wharton and Harvard and the one from MIT showing how knowledge workers can do more work in less time and at higher quality and enjoy their work more. Perhaps one of the most important findings from these studies is that generative AI can act as the great leveler — less experienced workers using AI can often perform at the same level as their more senior colleagues.

The reality we need to face is this: If you’ve provided your employees with private and secure generative AI tools and comprehensive training in their safe and effective use, you’re likely in good shape. 

For everyone else, there’s cause for concern.

Provide the tools and training that remove the risk of shadow AI use and potential data issues

On the one hand, you have employees who are eager to use generative AI for work because they can do more and better work. On the other hand, you have four types of companies:

1. The Prepared: Those that provide both secure AI tools and comprehensive training, enabling their workforce to safely maximize the value of these powerful tools
2. The Partially Prepared: Those that have provided secure tools but no training, limiting the potential value and creating risk through improper use
3. The Restrictive: Those that haven’t provided any secure AI tools or training, forcing their workforce to choose between productivity and compliance
4. The Avoidant: Those that are simply ignoring the situation entirely

Any approach except the first creates the perfect storm for shadow AI use and potential data privacy and security risks.

Whether or not you provide secure generative AI solutions for your employees, you have to recognize that some employees are likely to use generative AI tools on their mobile devices for work, whether they're formally allowed to or not. That’s why I believe providing training on safe and responsible use is absolutely critical. And this applies across the board — whether companies are still working on their AI strategy or have already provided their workforce with private, secure AI tools. 

The key is ensuring employees have the knowledge they need to use these tools effectively and safely to realize their full potential.

Legitimate security concerns don’t have to paralyze your organization

The primary concern of most companies in the use of generative AI is the misuse of data. The fact of the matter is that there are a great number of use cases of generative AI that do not involve entering personally identifiable information (PII) or sensitive, confidential, or proprietary information. If you’re not entering any of those kinds of data, there simply isn’t a data privacy or information security concern.

While data security concerns are legitimate, they shouldn’t paralyze organizations from embracing generative AI. Many valuable use cases don’t involve entering any sensitive information at all. Consider these examples:

• Writing and improving job descriptions to attract diverse talent
• Generating creative sourcing strategies and strings for hard-to-fill roles
• Creating candidate outreach messages and follow-up templates
• Developing interview questions and screening frameworks
• Analyzing public labor market trends and compensation data
• Writing engaging job posting headlines and social media content
• Creating onboarding checklists and new hire documentation
• Drafting employee engagement survey questions
• Developing training materials for hiring managers
• Writing inclusive workplace policies and employee handbooks

These are just a few examples of how generative AI can enhance HR and recruiting work without touching sensitive data. Of course, there are other use cases that do involve sensitive or confidential information.

So, the principal challenge is helping your teams understand the difference between the types of tasks that pose no risk and those that do involve sensitive information. This is exactly why it is ideal to provide employees with safe and responsible use guidelines by role and specific use, clearly stating which use cases are acceptable and which use cases are not allowed and why. 

The key lies in providing clear, role-specific guidelines that delineate:

• Generative AI do’s and don’ts
• Approved use cases with examples
• Prohibited scenarios and the rationale behind restrictions
• Best practices for data handling
• Procedures for handling edge cases
• Reporting mechanisms for potential issues

Final thoughts: You can create an environment where your workforce feels confident it is using AI appropriately

It all comes down to this: Your employees want to use these powerful tools and they’re going to find ways to do so. 

The real question is whether they’ll do it with proper guidance or in the shadows. By providing clear, role-specific guidelines about what’s acceptable and what isn’t, along with the reasoning behind these decisions, you can create an environment where people feel confident using AI tools appropriately. 

This isn’t just about minimizing risk — it’s about maximizing the potential of both your tools and your talent while keeping your organization’s data secure and being fully compliant with privacy regulations.
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Originally published on LinkedIn

Work smarter, not harder. 

There’s no “I” in “Team.”

People don’t leave jobs. They leave managers. 

By now, most talent professionals have heard these stock phrases so many times they’re practically background music. 

Yes, you know what it takes to make a great team. You certainly know what it means to do more with less. And the wisdom about managers? Well, everyone knows that’s true. Who wouldn’t want to leave a job with a horrible boss? 

But sometimes it’s worth examining these timeworn phrases more closely to unpack just how much wisdom — or lack thereof — they contain. So in the spirit of this examination, we asked five talent leaders the following question: There’s an expression in the talent world that “People don’t leave jobs. They leave managers.” What are the qualities of managers that inspire people to stay? 

Below you’ll find their thoughtful, and even surprising, answers: 

Stacey Gordon: The best managers truly value their team members 

“I almost feel like this is a trick question,” says Stacey Gordon, executive advisor at Rework Work, “because we are all well aware that our actions, our behavior, and the way that we treat people has a direct impact on whether they stay. Or, at least, we should be. More than 50% of employees say they have quit a job due to a bad manager — bad meaning they create unnecessary work and cause them stress. 

“With my clients, I see that the managers who truly value their team members have increased retention. Knowing a person’s strengths and weaknesses, understanding their competencies, and appreciating their contributions only happens when a manager invests in their growth, demonstrates empathy, and cultivates psychological safety. 

“These are all soft skills that a good manager cannot lead without. Unfortunately, there is a level of emotional intelligence that is often overlooked. As a manager, taking the time to develop your skills in these areas will directly affect your ability to lead effectively, while reducing the number of times you have to fill a vacated position.”

Stacy Zapar: They share one trait that makes people want to stay

“The best managers I’ve worked for over the years,” says Stacy Donovan Zapar, founder of The Talent Agency, “all shared a common trait: They were quick to take responsibility when things went wrong and slow to take credit when things went right.

“That kind of leadership built trust and made us feel safe trying new things and taking risks. These managers not only recognized the efforts and achievements of team members, they made sure other leaders noticed them too! Knowing our hard work would be acknowledged and appreciated motivated us all to do our best work and created a team culture that was more collaborative than competitive.

“Working for a manager who has your back, is invested in your career success, and creates a positive team environment? That builds loyalty and makes staying an easy choice!”

Tim Sackett: It’s a myth that people leave jobs because of bad managers

“First, I reject the statement, ‘People don’t leave jobs. They leave managers,’” says Tim Sackett, president of HRU Technical Resources. “This is the biggest lie we’ve sold to leaders in the past three decades. It’s based on flawed research that basically said, if everything was equal (pay, benefits, work location, etc.), why would you leave your job? Oh, in that case, yeah, bad boss! 

“In reality, people work for bad bosses all the time and don’t leave because they love their pay or benefits or work location or coworkers, etc. But, as we know, it’s never equal. Employees don’t make retention decisions in a vacuum. 

“If all things were actually equal and my staying was utterly dependent on my manager, why would I stay working for that manager?

“Hmmm . . . 

1. They cared about my career aspirations and helped me achieve them.
2. They cared about my high performance and helped me achieve it consistently.
3. They cared about me as a human and worked to build a lasting relationship with me.
4. They let me fail just enough to help me learn but not so much that it hurt me professionally.
5. They spoke my name in rooms when I wasn’t there in a positive way.

“That would be a fantastic manager to work for.”

Hung Lee: The best managers handle conflict well and help teams work through it

"An underestimated aspect of great people management,” says Hung Lee, curator of the Recruiting Brainfood newsletter, “is the ability and willingness to handle conflict. The best managers are not conflict avoidant but recognize tension in the team as an opportunity to resolve issues early before they become destructive to objectives.

“Being able to fairly resolve difficult disagreements elevates someone from being administrative manager to being an active leader. Doing this consistently builds trust, engenders loyalty, and strengthens team bonds, even amongst those who were having the disagreement.

“Conflict management skills from the leader play a huge role in retention."

J.T. O’Donnell: Blaming departures on managers oversimplifies a more complex issue

“‘People don’t leave jobs; they leave managers.’ I hear this all the time,” says J.T. O’Donnell, founder and CEO of Work It Daily, “and while it holds some truth, it oversimplifies a much more complex issue. 

“Yes, poor management is often a significant factor in employee turnover. A manager who doesn’t provide clear direction, recognition, or support can create a toxic environment, leading people to seek greener pastures. However, it’s rarely the only reason people leave.

“In my 20-plus years as a career coach, I’ve found that people often leave jobs because of misaligned expectations, stagnant growth opportunities, or a lack of purpose. Even with a great manager, if the role no longer aligns with someone’s values, skills, or long-term goals, they’ll eventually move on.

“That said, managers do play a pivotal role in retention. A great manager fosters trust, advocates for employees, and creates pathways for development. But even the best manager can’t fix a bad company culture or a role that doesn’t fit.

“The key takeaway? Don’t oversimplify your career decisions — or assume your manager is the only variable. Take time to reflect on what truly matters to you in your career. And if you are a manager, remember: Your leadership can make or break someone’s experience — but it’s not the whole story.” 
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Originally Published on LinkedIn

Despite being the top economy in Africa, South African workers are largely unproductive, producing less per hour than the world average.

This is according to data visualised in the latest Nedbank Pulse, which examines which country’s workers produce the most GDP per hour worked.

Using data from the International Labour Organisation (November 2023), the bank charted the top-ranked countries by GDP per hour worked—or labour productivity—measured in International dollars (Int$).

International dollars are a hypothetical currency used to compare the purchasing power of different countries by adjusting for price differences.

Nedbank found that workers in Luxembourg and Ireland are the most productive in the world, by quite some margin, earning Int$146 and Int$143 per hour, respectively – significantly higher than third place, Norway, at Int$93 per hour.

Looking at the top 25, Luxembourg is nearly three times more productive than Qatar (25th) due to its strong financial services sector.

Nine of the top ten countries are in Europe, with Singapore as the exception.

“Europeans work fewer hours than Americans to achieve similar output levels per capita. Many wealthy European countries have smaller populations and high-value industries, boosting per capita productivity,” Nedbank noted.

The EU’s strong worker protections and generous paid time off may also enhance productivity, it said.

South Africa

South Africa often gets a reputation for having hard workers who work some of the longest hours in the world.

Previous research has gone some way to back this up, showing that South Africans aren’t afraid to work long hours and often go beyond the legislated maximum of 45 hours per week.

However, the ILO’s data shows that South Africans don’t actually work that much longer than much of the world, averaging 42.6 hours per work—not even cracking the top 50 of hardest workers.

Even when measuring ‘excessive’ hours work (49 hours or more per week), only 17% of the working population clocks these numbers—again, not even within the top 50.

Not only is the working population not hard-working, relative to other countries, by this metric, South Africa also emerges as being highly unproductive, producing only Int$21 per hour.

This is below the global average of Inl$26 per hour and about seven times lower than Luxembourg.

The table below outlines the top 15 countries regarding working hours, the percentage working extremely long hours (49 hours+), and the value of their work measured as GDP output per hour- and where South Africa fits in.

The graphic designer came into work knowing that the client account was overdue for a project meeting. But there was no one to call the meeting: His boss had been let go in a round of layoffs a month earlier. And his boss’s boss had been laid off the year before. The designer now reported to a hands-off executive who didn’t know the details of the project.

A new reality is emerging in the modern workplace—one that in some cases features no manager. Last year, in a sign of the aggressiveness with which firms are removing them, middle managers represented 31.5% of all layoffs, and an average of 22% between 2018 and 2022, according to job tracker Live Data Technologies. And when middle managers depart voluntarily, they are not being replaced, which creates a void in leadership. “If you cut and cut and cut, but don’t change mindsets, you can accelerate vertical hierarchy,” says Mark Arian, CEO of Korn Ferry Consulting. “You can end up in a bit of a death spiral.”

The disappearing layer of middle management is especially common in professional services, where the top of the traditional org-chart pyramid is growing rather sharp. In theory, trimming the middle layer can strengthen workflows: Autonomy and decision-making extend downward, and customer responsiveness improves, along with accountability and morale. But “that doesn’t necessarily happen,” says Arian. In practice, sometimes authority coalesces at top levels, leaving underlings awaiting signals.

To be sure, rumors about imperiled middle management have been circulating for a decade. But experts say today’s middle managers are under unprecedented pressure. Half are burned-out. Thirty percent are too stressed to support their teams, according to employees participating in LinkedIn’s Workforce Confidence survey. “They’ve been inundated,” says sustainability and ESG expert Cheryl D’Cruz-Young, senior client partner at Korn Ferry. Many struggle to prioritize and escape the so-called frozen middle. 

The middle manager heave-ho is being driven by a number of practical forces, beginning with financial realities: Top-line growth has stalled while labor costs have jumped, meaning that companies need to make adjustments in order to maintain their margins. Some professional-service firms are “delayering”—an old-school staff-reduction technique of thinning middle management in order to improve flexibility and responsiveness. At the same time, some firms are eager to preserve the nimbleness and adaptability that teams displayed during the pandemic. Lastly, massive technological and workforce skill changes have lessened the need for constant managerial overhead. Experts say that shifting org charts are to be expected, and should be implemented alongside massive technological and workforce changes. “The structure is evolving, as it should,” says D’Cruz-Young.

Experts advise firm leaders to proceed with great caution. “When you get rid of middle managers, the margin for error becomes minuscule,” says JP Sniffen, practice leader at Korn Ferry’s Military Center of Expertise. “They’re often the common-sense police.” Rather than making deep cuts, experts advise piloting studies of new organizational structures. At Korn Ferry, clients are creating multidisciplinary, agile teams that are customer-centric. “It’s the opposite of bureaucracy,” says Arian. The idea is to train teams to be adaptive and integrative.

Experts advise training teams to be inclusive and knowledgeable about the expertise and leadership of each trainee. This means creating norms that allow leadership to shift from person to person depending on the scenario, says Andrés Tapia, global DE&I strategist at Korn Ferry. Ideally, teams will learn to be self-regulating. “We need to get used to a very dynamic flow of power,” he says.

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Original Korn Ferry content

Imagine a world where your digital colleague handles entire workflows, adapts to real-time challenges, and collaborates seamlessly with your human team. This isn’t science fiction — it’s the imminent reality of AI agents in the workplace. 

As Sam Altman, CEO of OpenAI, boldly predicted at their annual DevDay event, “2025 is when AI agents will work.” 

But what does this mean for the future of human labor, organizational structures, and the very definition of work itself? 

According to research by The Conference Board, 56% of workers use generative AI on the job, and nearly 1 in 10 use generative AI tools daily. 

As we move into this AI-enabled stage of business, it’s essential to understand the transformative potential of AI agents, explore how they’re set to revolutionize the workplace, and challenge ourselves to reconceptualize the human-machine partnership in the world of work.

Ultimately, AI’s involvement in the workplace can be categorized into three buckets: Bots, AI Agents, and Digital Workers. Here is a guide to how each of these is impacting the world of work. 

1. Bots: Driving customer support and so much more

Bots, short for robots, are software applications programmed to perform automated tasks. In the business context, chatbots are often used to streamline operations, enhance customer service, and improve internal processes. Most chatbots are programmed using Natural Language Processing (NLP) to interpret and understand human language and machine learning, which allows the chatbots to learn and improve from data over time. 

Over the past decade, the adoption of bots in business environments spanning healthcare, retail, banking, and a range of other industries has seen exponential growth. 

The chatbot market has experienced remarkable growth and is expected to expand from $396.2 million in 2019 to $27.3 billion by 2030. This surge in bot usage can be attributed to advancements in NLP, increased demand for 24/7 customer support, and the growing recognition of how bots can enhance operational efficiency across various business functions.

However, chances are you know the feeling of smashing the zero key in frustration as you try to exit chatbot support and connect with a human. While bots offer numerous benefits, they also come with limitations that businesses must consider. 

One primary challenge is the potential for misunderstanding complex or nuanced queries, which can lead to user frustration and incorrect information dissemination. Additionally, bots may struggle with context-dependent situations or emotionally sensitive issues, areas where human empathy and judgment are crucial. Lastly, there are concerns about data privacy and security, as bots often handle sensitive information, requiring robust safeguards to protect against potential breaches or misuse of data.

2. AI agents: They’re poised to take automation soaring

An AI agent is an autonomous software entity or program designed to perceive its environment, make decisions, and take actions to achieve specific goals or objectives without direct human intervention. 

Reading about AI agents and their transformative potential in the workplace can feel like looking into the future — but this future is already knocking on our door.

AI agents are poised to take automation to unprecedented levels, transcending simple task completion to instead manage complete, complex, adaptive workflows. This shift represents a quantum leap from traditional automation technologies. Here are a few factors to consider when weighing the future of AI agents:

• Scope of capability: Unlike simple bots, AI agents can handle entire processes, making decisions and adjusting their actions based on real-time data and changing circumstances.
• Integration potential: A recent Capgemini survey found that 82% of tech executives plan to integrate AI-based agents across their organizations within the next three years, signaling a seismic shift in workplace technology adoption.
• Value perception: The same survey found that 71% of executives believe AI agents will help shift focus to more value-added activities, indicating a strategic view of AI as a tool for enhancing human potential rather than replacing it.

This rapid embrace of AI agents is driven by their potential to dramatically increase productivity and efficiency. A report from McKinsey states that “about half of the activities (not jobs) carried out by workers could be automated,” with AI agents playing a crucial role in this transformation. 

“Agentic AI allows us to provide customers with accurate information within context,” says Marcus Sawyerr, CEO of EQ.app, an AI tool that helps recruiters find and engage talent. “It fulfills tasks precisely and improves based on user context and real-world data. This helps us find the right people for the right roles in real life.”

This distinction between automating tasks versus entire jobs is critical — it suggests a future where human-AI collaboration becomes the norm, rather than wholesale replacement.

As we weave agentic AI capabilities into our businesses, we will likely deconstruct jobs into individual tasks and then identify the tasks that can be fully automated by these new AI technologies and agents. 

3. Digital workers: The world isn’t quite ready for them

And then there are “digital workers,” who are capable of handling entire workflows, adapting to real-time needs, and collaborating with humans in ways that weren’t imaginable a few years ago. If the phrase “digital worker” makes you uneasy, you’re not alone.

Earlier this year, Lattice, an AI powered HR platform, came under fire for proposing a feature that would let organizations make employee records for AI workers.

“Today Lattice is making AI history,” CEO Sarah Franklin shared in a blog post announcing this new feature. “We will be the first to give digital workers official employee records in Lattice. Digital workers will be securely onboarded, trained, and assigned goals, performance metrics, appropriate systems access, and even a manager. Just as any person would be.”

Three days later, Lattice posted an update stating they would no longer pursue digital workers in the product.

The future was here, but it wasn’t a future we were ready for. Perhaps this is because of psychological barriers. The concept of “digital workers” highlights the psychological barriers to accepting AI as part of the workforce. It underscores the need for careful, thoughtful integration that respects human concerns while leveraging AI’s potential.

There are very real barriers to adoption

The integration of AI agents into the workforce is not without its challenges, particularly when it comes to human perception and adaptation. Here are three realities decision-makers need to account for as we move forward with adopting, and adapting to, AI:

• Job displacement concerns: A Goldman Sachs report estimates that AI could replace the equivalent of 300 million full-time jobs, fueling anxiety about widespread unemployment.
• Partial automation reality: The same report found that two-thirds of jobs will experience partial automation, translating to the replacement of about one-quarter of work tasks in the U.S. and Europe.
• Current AI usage: Research by The Conference Board found that 56% of workers already use generative AI on the job, with nearly 1 in 10 using it daily. This indicates the gradual integration of AI into daily work processes has already begun.

Deconstructing jobs into their constituent tasks is not merely a theoretical exercise — it will become a critical strategy for optimizing workforce efficiency in the age of agentic AI. 

By breaking jobs into discrete tasks, organizations can identify which components are prime candidates for AI automation (typically routine, data-driven tasks) and which require uniquely human skills like emotional intelligence, creative problem-solving, or complex decision-making. 

This task-level approach allows for more nuanced workforce planning, enabling companies to redesign roles that leverage both AI efficiency and human expertise. Successfully transitioning to this coming hybrid future is not just about parsing jobs for efficiency’s sake, it’s about preparing employees, aligning them with the right processes, and tracking the impact on organizational goals.

Ultimately, it’s re-architecting work itself. 

“The AI revolution in the workplace began with tools like ChatGPT, introducing employees to AI’s potential,” says Timur Meyster, cofounder and chief product officer of OutRival, an AI-powered platform for customer experience teams. “Now, we’re witnessing the rise of AI agents that can actually perform tasks, not just assist with them. This shift is even redefining success metrics, with businesses now measuring outcomes like enrollments, bookings, or appointments on a per-employee basis, showcasing the powerful synergy between human expertise and AI capabilities.”

Orgs need to make sure that AI is augmenting the work of human rather than making it obsolete

I believe that in the new hybrid workforce, combining human intuition and emotional intelligence with AI precision will open doors for businesses to be more agile and productive. But the question we should be asking isn’t just how we adopt these technologies, it’s how we do it in a way that augments — rather than making obsolete — human potential. This means determining where and how we won’t leverage AI is as important as where we will.

Here are five considerations leaders should weigh when making these decisions:

• Skill development: Organizations must invest in upskilling and reskilling programs to prepare employees for roles that complement AI capabilities. This includes enhancing skills like creative problem-solving, emotional intelligence, and strategic thinking.
• Ethical considerations: Integrating AI agents raises critical ethical questions about data privacy, decision-making accountability, and the potential for bias. Organizations need robust frameworks to address these issues.
• Organizational restructuring: The advent of AI agents will likely necessitate changes in organizational structures, workflow processes, and management practices. Companies need to be proactive in redesigning their operations to maximize the benefits of human-AI collaboration.
• Performance metrics: New ways of measuring productivity and success in a hybrid workforce will be necessary. This might include evaluating the effectiveness of human-AI teams rather than individual performance alone.
• Change management: Perhaps most critically, organizations need comprehensive change management strategies to help employees adapt to and embrace AI agents as collaborators rather than threats.

The integration of AI agents into the workforce represents a paradigm shift in how we conceptualize work. While the challenges are significant, the potential benefits in terms of productivity, innovation, and job satisfaction are immense. 

Final thoughts: The task at hand is to create a seamless partnership between people and technology

For business leaders and HR professionals, the task ahead is clear: Prepare your organizations and workforce for a future where human creativity and AI efficiency work in tandem. This means investing in technology, yes, but more importantly, investing in your people — their skills, their adaptability, and their capacity to work alongside intelligent machines. 

Now is the time to demystify AI to your employees to proactively address the anxiety that comes with a change of this magnitude. 

Perhaps the rise of AI agents isn’t just a technological shift, but a philosophical one — challenging us to rediscover the essence of our humanity in the face of artificial intelligence.

In dissecting jobs and reassembling them with AI, we’re not merely changing work — we’re evolving our understanding of human value in work itself. This process challenges us to question not just what we do but why we do it, pushing us toward a future where work becomes a true expression of our uniquely human capabilities.

This article was originally published by Fast Company.