Latest news with #AlbertBandura
Fast Company
2 days ago
- Business
- Fast Company
Using AI at work requires confidence. Here's how to build it
The Little Engine That Could wasn't the most powerful train, but she believed in herself. The story goes that, as she set off to climb a steep mountain, she repeated: 'I think I can, I think I can.' That simple phrase from a children's story still holds a lesson for today's business world—especially when it comes to artificial intelligence. AI is no longer a distant promise out of science fiction. It's here and already beginning to transform industries. But despite the hundreds of billions of dollars spent on developing AI models and platforms, adoption remains slow for many employees, with a recent Pew Research Center survey finding that 63% of U.S. workers use AI minimally or not at all in their jobs. The reason? It can often come down to what researchers call technological self-efficacy, or, put simply, a person's belief in their ability to use technology effectively. In my research on this topic, I found that many people who avoid using new technology aren't truly against it—instead, they just don't feel equipped to use it in their specific jobs. So rather than risk getting it wrong, they choose to keep their distance. And that's where many organizations derail. They focus on building the engine, but don't fully fuel the confidence that workers need to get it moving. What self-efficacy has to do with AI Albert Bandura, the psychologist who developed the theory of self-efficacy, noted that skill alone doesn't determine people's behavior. What matters more is a person's belief in their ability to use that skill effectively. In my study of teachers in one-to-one technology environments —classrooms where each student is equipped with a digital device like a laptop or tablet—this was clear. I found that even teachers with access to powerful digital tools don't always feel confident using them. And when they lack confidence, they may avoid the technology or use it in limited, superficial ways. The same holds true in today's AI-equipped workplace. Leaders may be quick to roll out new tools and want fast results. But employees may hesitate, wondering how it applies to their roles, whether they'll use it correctly, or if they'll appear less competent—or even unethical—for relying on it. Beneath that hesitation may also be the all-too-familiar fear of one day being replaced by technology. Going back to train analogies, think of John Henry, the 19th-century folk hero. As the story goes, Henry was a railroad worker who was famous for his strength [as a steel driver]. When a steam-powered machine threatened to replace him, he [competed against] it—and won. But the victory came at a cost: He collapsed and died shortly afterward. Henry's story is a lesson in how resisting new technology through sheer willpower can be self-defeating. Rather than leaving some employees feeling like they have to outmuscle or outperform AI, organizations should invest in helping them understand how to work with it—so they don't feel like they need to work against it. Relevant and role-specific training Many organizations do offer training related to using AI. But these programs are often too broad, covering topics like how to log in to different programs, what the interfaces look like, or what AI 'generally' can do. In 2025, with the number of AI tools at our disposal—ranging from conversational chatbots and content creation platforms to advanced data analytics and workflow automation programs—that's not enough. In my study, participants consistently said they benefited most from training that was 'district-specific,' meaning tailored to the devices, software, and situations they faced daily with their specific subject areas and grade levels. Translation for the corporate world? Training needs to be job-specific and user-centered—not one-size-fits-all. The generational divide It's not exactly shocking: Younger workers tend to feel more confident using technology than older ones. Gen Z and millennials are digital natives —they've grown up with digital technologies as part of their daily lives. Gen X and boomers, on the other hand, often had to adapt to using digital technologies mid-career. As a result, they may feel less capable and be more likely to dismiss AI and its possibilities. And if their few forays into AI are frustrating or lead to mistakes, that first impression is likely to stick. When generative AI tools were first launched commercially, they were more likely to hallucinate and confidently spit out incorrect information. Remember when Google demoed its Bard AI tool in 2023, and its factual error led to its parent company losing $100 billion in market value? Or when an attorney made headlines for citing fabricated cases courtesy of ChatGPT? Moments like those likely reinforced skepticism—especially among workers already unsure about AI's reliability. But the technology has already come a long way in a relatively short period of time. The solution to getting those who may be slower to embrace AI isn't to push them harder, but to coach them and consider their backgrounds. What effective AI training looks like Bandura identified four key sources that shape a person's belief in their ability to succeed: Mastery experiences, or personal success Vicarious experiences, or seeing others in similar positions succeed Verbal persuasion, or positive feedback Physiological and emotional states, or someone's mood, energy, anxiety, and so forth In my research on educators, I saw how these concepts made a difference, and the same approach can apply to AI in the corporate world—or in virtually any environment in which a person needs to build self-efficacy. In the workplace, this could be accomplished with cohort-based trainings that include feedback loops —regular communication between leaders and employees about growth, improvement, and more—along with content that can be customized to employees' needs and roles. Organizations can also experiment with engaging formats like PricewaterhouseCoopers' prompting parties, which provide low-stakes opportunities for employees to build confidence and try new AI programs. In Pokemon Go!, it's possible to level up by stacking lots of small, low-stakes wins and gaining experience points along the way. Workplaces could approach AI training the same way, giving employees frequent, simple opportunities tied to their actual work to steadily build confidence and skill. The curriculum doesn't have to be revolutionary. It just needs to follow these principles and not fall victim to death by PowerPoint, or end up being generic training that isn't applicable to specific roles in the workplace. As organizations continue to invest heavily in developing and accessing AI technologies, it's also essential that they invest in the people who will use them. AI might change what the workforce looks like, but there's still going to be a workforce. And when people are well trained, AI can make both them and the outfits they work for significantly more effective.
Yahoo
6 days ago
- Business
- Yahoo
The biggest barrier to AI adoption in the business world isn't tech – it's user confidence
The Little Engine That Could wasn't the most powerful train, but she believed in herself. The story goes that, as she set off to climb a steep mountain, she repeated: 'I think I can, I think I can.' That simple phrase from a children's story still holds a lesson for today's business world – especially when it comes to artificial intelligence. AI is no longer a distant promise out of science fiction. It's here and already beginning to transform industries. But despite the hundreds of billions of dollars spent on developing AI models and platforms, adoption remains slow for many employees, with a recent Pew Research Center survey finding that 63% of U.S. workers use AI minimally or not at all in their jobs. The reason? It can often come down to what researchers call technological self-efficacy, or, put simply, a person's belief in their ability to use technology effectively. In my research on this topic, I found that many people who avoid using new technology aren't truly against it – instead, they just don't feel equipped to use it in their specific jobs. So rather than risk getting it wrong, they choose to keep their distance. And that's where many organizations derail. They focus on building the engine, but don't fully fuel the confidence that workers need to get it moving. Albert Bandura, the psychologist who developed the theory of self-efficacy, noted that skill alone doesn't determine people's behavior. What matters more is a person's belief in their ability to use that skill effectively. In my study of teachers in 1:1 technology environments – classrooms where each student is equipped with a digital device like a laptop or tablet – this was clear. I found that even teachers with access to powerful digital tools don't always feel confident using them. And when they lack confidence, they may avoid the technology or use it in limited, superficial ways. The same holds true in today's AI-equipped workplace. Leaders may be quick to roll out new tools and want fast results. But employees may hesitate, wondering how it applies to their roles, whether they'll use it correctly, or if they'll appear less competent – or even unethical – for relying on it. Beneath that hesitation may also be the all-too-familiar fear of one day being replaced by technology. Going back to train analogies, think of John Henry, the 19th-century folk hero. As the story goes, Henry was a railroad worker who was famous for his strength. When a steam-powered machine threatened to replace him, he raced it – and won. But the victory came at a cost: He collapsed and died shortly afterward. Henry's story is a lesson in how resisting new technology through sheer willpower can be self-defeating. Rather than leaving some employees feeling like they have to outmuscle or outperform AI, organizations should invest in helping them understand how to work with it – so they don't feel like they need to work against it. Many organizations do offer training related to using AI. But these programs are often too broad, covering topics like how to log into different programs, what the interfaces look like, or what AI 'generally' can do. In 2025, with the number of AI tools at our disposal, ranging from conversational chatbots and content creation platforms to advanced data analytics and workflow automation programs, that's not enough. In my study, participants consistently said they benefited most from training that was 'district-specific,' meaning tailored to the devices, software and situations they faced daily with their specific subject areas and grade levels. Translation for the corporate world? Training needs to be job-specific and user-centered – not one-size-fits-all. It's not exactly shocking: Younger workers tend to feel more confident using technology than older ones. Gen Z and millennials are digital natives – they've grown up with digital technologies as part of their daily lives. Gen X and boomers, on the other hand, often had to adapt to using digital technologies mid-career. As a result, they may feel less capable and be more likely to dismiss AI and its possibilities. And if their few forays into AI are frustrating or lead to mistakes, that first impression is likely to stick. When generative AI tools were first launched commercially, they were more likely to hallucinate and confidently spit out incorrect information. Remember when Google demoed its Bard AI tool in 2023 and its factual error led to its parent company losing US$100 billion in market value? Or when an attorney made headlines for citing fabricated cases courtesy of ChatGPT? Moments like those likely reinforced skepticism – especially among workers already unsure about AI's reliability. But the technology has already come a long way in a relatively short period of time. The solution to getting those who may be slower to embrace AI isn't to push them harder, but to coach them and consider their backgrounds. Bandura identified four key sources that shape a person's belief in their ability to succeed: Mastery experiences, or personal success Vicarious experiences, or seeing others in similar positions succeed Verbal persuasion, or positive feedback Physiological and emotional states, or someone's mood, energy, anxiety and so forth. In my research on educators, I saw how these concepts made a difference, and the same approach can apply to AI in the corporate world – or in virtually any environment in which a person needs to build self-efficacy. In the workplace, this could be accomplished with cohort-based trainings that include feedback loops – regular communication between leaders and employees about growth, improvement and more – along with content that can be customized to employees' needs and roles. Organizations can also experiment with engaging formats like PricewaterhouseCoopers' prompting parties, which provide low-stakes opportunities for employees to build confidence and try new AI programs. In 'Pokemon Go!,' it's possible to level up by stacking lots of small, low-stakes wins and gaining experience points along the way. Workplaces could approach AI training the same way, giving employees frequent, simple opportunities tied to their actual work to steadily build confidence and skill. The curriculum doesn't have to be revolutionary. It just needs to follow these principles and not fall victim to death by PowerPoint, or end up being generic training that isn't applicable to specific roles in the workplace. As organizations continue to invest heavily in developing and accessing AI technologies, it's also essential that they invest in the people who will use them. AI might change what the workforce looks like, but there's still going to be a workforce. And when people are well trained, AI can make both them and the outfits they work for significantly more effective. Greg Edwards does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
Forbes
05-05-2025
- Business
- Forbes
Agentic AI At Work: What It Means, Why It Matters, And Who Should Worry
The term agentic AI has been tossed around a lot lately. I didn't realize I had already learned about it until I thought back to a conversation I had when the great Albert Bandura invited me to his home years ago. As one of the most influential psychologists of the 20th century, Bandura was best known for developing Social Cognitive Theory and the concept of agency. During our conversation, he spoke about how people are not just shaped by their environment but can actively shape it. That same idea is now being applied to machines. Companies are starting to use AI that doesn't wait to be told what to do. Instead, it figures out what needs to happen and takes the next step on its own. Gartner predicts that by 2028, one in three enterprise software applications will include agentic AI, compared to less than one percent today. But what does it actually mean? And why is it sparking both optimism and unease? What Agentic AI Actually Does getty You've probably used a chatbot when ordering something online. It might help you track a package or answer a basic question. That kind of AI responds to a request and stops when the task is done. Agentic AI works differently. It can figure out what's needed next, make decisions along the way, and keep going without someone guiding each step. Instead of handling one task at a time, agentic AI can take on a full objective. That might mean planning a meeting, gathering background materials, sending the invites, and even creating a follow-up summary. It connects across tools, manages steps in the right order, and moves things forward without being told exactly what to do. This kind of autonomy is what separates agentic AI from earlier tools. It is no longer just helping people get work done. In many cases, it is taking the lead. Why Some People Are Excited About Agentic AI getty Many people see agentic AI as a major step forward. It offers a way to take care of time-consuming processes without constant supervision. Instead of managing each task one by one, someone can assign a larger goal and let the system handle the details. That frees up time for deeper thinking, collaboration, or simply catching up on work that has been sitting in the background. In fast-paced environments, the appeal is obvious. A sales team might use agentic AI to prepare custom pitches for each client. A marketing department could delegate campaign setup, content scheduling, and data tracking without doing each part manually. The goal is to increase output without increasing burnout. With less need for step-by-step oversight, teams can approach problems with more flexibility, adjust faster to changes, and focus on strategy instead of repetitive tasks. Why Some People Are Concerned About Agentic AI getty The same autonomy that makes agentic AI appealing also makes it risky. When a system is trusted to take action on its own, it becomes harder to trace decisions, catch errors, or understand what influenced a particular outcome. That lack of transparency is one of the biggest concerns. There are also questions about control. If a system can act without permission at every step, who is responsible when something goes wrong? And what happens when multiple AI agents are interacting with each other, making choices faster than any person can follow? In my article about Gibberlink, I shared how two machines carried on a conversation. It raises new concerns about visibility, oversight, and who is ultimately in charge. Some worry that introducing agentic systems into sensitive areas like finance, healthcare, or hiring could lead to decisions that are difficult to audit or reverse. The concern is about bad outcomes and not knowing how those outcomes were reached in the first place. What Agentic AI Means In The Workplace getty As agentic AI becomes more common, the impact on workplace roles, responsibilities, and culture will grow. For leaders, this means shifting from task-based management to outcome-based delegation. Rather than assigning work one piece at a time, leaders will need to get comfortable defining goals clearly, setting parameters, and trusting the system to handle the process. For employees, it may mean working alongside systems that make suggestions, take initiative, or even direct next steps. This creates opportunities to focus on more strategic or creative efforts but also raises concerns about deskilling or reduced decision-making authority. Transparency and communication will be key. People need to understand what the system is doing, why it is doing it, and when to step in. Organizations should start preparing now by identifying where agentic tools are already being used, whether officially or informally. They should also evaluate their comfort level with AI autonomy and assess whether current teams have the skills to supervise and collaborate with these systems. Training, guidelines, and open discussions about responsibility will help avoid confusion later. Key Agentic AI Takeaways For Leaders And Teams getty Being clear, intentional, and curious about how agentic AI fits into your team will make a real difference. Here are a few ways to stay ahead of that shift: Before these systems become more embedded in daily work, it is important to clarify roles, communicate expectations, and reinforce the human judgment that still needs to guide outcomes. Staying proactive can make the difference between using AI well and being surprised by what it decides to do. What To Watch As Agentic AI Continues To Grow getty Agentic AI is still evolving, but it is already influencing how work gets done. That means leaders, teams, and individuals need to start asking different questions. What level of autonomy is appropriate? How are decisions being made? What safeguards are in place to keep things aligned with goals and values? The original idea of agency was rooted in human choice. Bandura believed people could shape their own path by setting goals, building confidence, and taking action. As AI systems begin to show similar patterns of behavior, it forces a new conversation. The challenge now is to stay involved in the right places. Not every decision should be handed off, and knowing where to stay involved may be the most important skill leaders and teams develop next.
Forbes
05-04-2025
- General
- Forbes
Giving Helpful Feedback At Work: How To Have Constructive Conversations
Giving Helpful Feedback At Work: How To Have Constructive Conversations Feedback has the power to motivate and improve performance, but it often backfires. Instead of sparking growth, it can trigger defensiveness or lead to silence. Some people soften their words so much that the message gets lost. Others stay quiet entirely, fearing conflict. Introverts often tell me they leave meetings thinking, 'I wish I had said something,' because they prefer to process before speaking. Extraverts tend to speak while thinking and later say, 'I wish I hadn't said that.' In both cases, feedback is challenging. Delivering it in a way that is both honest and well-received requires more than good intentions. It demands emotional intelligence, cultural awareness, and a deeper understanding of how people interpret and respond to input. Why Do People Take Feedback Personally? People like to think of themselves as rational professionals, but they are human first. Feedback can feel like a threat, especially when it touches on identity or performance. Even well-meaning input can trigger the brain's defense mechanisms. Individuals become more focused on protecting their ego than listening to the message. When I interviewed Dr. Albert Bandura, one of the most cited psychologists of all time, he explained that people are driven by a need for self-efficacy. They want to feel capable, respected, and in control of their own actions. When feedback challenges that self-view, it can trigger moral disengagement or avoidance behaviors. Bandura's research showed that people tend to reframe or rationalize their actions to protect their self-image, even when the facts suggest otherwise. That is why a comment that seems simple to one person can feel like a personal attack to another. It is not just about what is said, but how it threatens the story people tell themselves about who they are. What Role Do Emotional Intelligence And Curiosity Play In Feedback? Emotional intelligence involves being aware of your own emotions and recognizing those in others. It helps you choose the right words, notice when someone is shutting down, and pivot when a conversation is not going well. When I interviewed Dr. Daniel Goleman, author of Emotional Intelligence, he emphasized that self-awareness is the keystone of emotional intelligence. He explained that we often miss the mark because we are not paying attention to what is going on within us or around us in the moment. Mindfulness, he noted, helps strengthen that internal radar. And without it, people tend to speak from assumption rather than attention. In my research on curiosity, I found that people often avoid giving or asking for feedback because of fear or assumptions. They assume someone will be upset or that they already know the answer. That internal voice can block the kind of open conversation that leads to growth. Curiosity changes the dynamic. Instead of delivering a judgment, you are inviting a conversation. A simple shift from 'You need to improve your communication' to 'I noticed some challenges in how the message was received; what do you think was going on?' can lead to a very different outcome. How Can Cultural Awareness Improve Feedback Conversations? In some cultures, directness is valued. In others, indirect communication is the norm. Even within the United States, different regions, industries, and generations interpret tone and body language in different ways. What one person sees as helpful candor, another might experience as harsh criticism. Being culturally aware means thinking carefully about how your message might be received. When I interviewed Ricardo González, CEO of Bilingual America, he explained that cultural mastery goes beyond basic competence. It is a continuous journey; one that requires self-reflection, adaptability, and empathy. Leaders must move past just knowing historical facts about a group and begin to understand what truly gives people meaning in their daily lives, what they value, celebrate, and believe. This requires what researchers call cultural metacognition, or cultural mindfulness: the ability to reflect on one's assumptions and adapt behavior based on how others may interpret the interaction. My own work on perception highlights that this reflection is part of a larger process, what I describe as EPIC: evaluating, predicting, interpreting, and correlating information. Without this deeper awareness, feedback may unintentionally clash with someone's cultural expectations. CQ, cultural intelligence, is a skill that, like EQ, can be developed. Leaders who embrace cultural mindfulness, not just as a concept but as a practice, will have more productive conversations across diverse teams. Feedback lands better when it is shaped with an understanding of how someone's cultural lens may shape their reaction. When I interviewed Joe Lurie, Executive Director Emeritus of UC Berkeley's International House and author of Perception and Deception, he emphasized that what we perceive is deeply shaped by what we believe. Misunderstandings often arise not from what is said, but from how it is interpreted across cultural and perceptual filters. That is why curiosity and cultural mindfulness are so important. Feedback not only delivers information. Feedback is about connecting in a way that resonates. What Feedback Prompt Can I Use To Start A Constructive Conversation? Feedback does not have to come as a surprise. In fact, it works best when it feels like part of an ongoing dialogue rather than a sudden performance review. A good starting point sounds less like a warning and more like an invitation. One way to begin is: 'I had a thought about something I noticed. Would now be a good time to share it?' This approach gives the other person a moment to prepare mentally without suggesting they can opt out of the conversation entirely. It also signals respect and consideration, setting the tone for a more productive exchange. What If I Need To Give Feedback On Something That Really Frustrated Me? If you need to give feedback on something that genuinely frustrated you, begin by calming your own emotions so that the conversation stays productive rather than reactive. Instead of venting, shift your goal to improving the outcome. One way to start is with, 'I want to make sure we can collaborate effectively. Can we talk about what happened during [specific situation]?' Focus on the behavior, not the person. Rather than saying, 'You were unprofessional,' say, 'During the presentation, it seemed like we were not on the same page, and that caused confusion for the client.' Then invite reflection by asking, 'What was your take on how that went?' This helps create space for a more open and constructive dialogue. How Do I Handle Feedback When I Know The Person Will Be Defensive? If you know someone may become defensive, it helps to approach the conversation with care while still being clear. Acknowledge their strengths and frame your intention around shared success. You might say, 'You contribute a lot to this team, and I wanted to share something that could help us work even better together.' From there, focus on the impact rather than the intent. For example, say, 'When the deadline passed, it created a domino effect that put a lot of pressure on the rest of the team,' instead of, 'You missed the deadline again.' Then invite collaboration with a question like, 'What can we do differently to avoid that in the future?' What Should I Say When Giving Positive Feedback That Does Not Feel Generic? Positive feedback is just as important as critical feedback, but it often lacks the specificity that makes it useful. Saying 'Great job' may sound encouraging, but it does not tell the person what they did well or what to continue doing. A better approach is to recognize the specific action and explain why it mattered. For example, you might say, 'I want to acknowledge how you handled the client meeting. The way you paused to ask clarifying questions showed real attention to detail and helped us avoid extra work later.' This kind of feedback reinforces effective behavior, builds confidence, and creates clarity about what success looks like. BusyWhat Happens When You Get Feedback Wrong? It is okay to miss the mark. If it becomes clear that your feedback caused unintended harm, take responsibility and make space for clarification. You might say, 'I've been thinking about our conversation earlier, and I am concerned that what I said may not have come across the way I intended. Can I clarify?' Taking accountability in this way supports psychological safety and signals that you are open to learning and improving the dialogue. Why Feedback Is A Skill Worth Practicing Constructive conversations rely on trust, empathy, and a shared commitment to improvement. Feedback is most effective when it is specific, thoughtful, and rooted in curiosity. It helps clarify expectations, uncover blind spots, and strengthen working relationships. Rather than viewing it as a risk to avoid, it helps to treat feedback as a responsibility that builds a healthier culture over time.
