Human-AI Collaboration

🤝 How Do We Build Trust Between Humans and Agents? Everyone is talking about AI agents. Autonomous systems that can decide, act, and deliver value at scale. Analysts estimate they could unlock $450B in economic impact by 2028. And yet… Most organizations are still struggling to scale them. Why? Because the challenge isn’t technical. It’s trust. 📉 Trust in AI has plummeted from 43% to just 27%. The paradox: AI’s potential is skyrocketing, while our confidence in it is collapsing. 🔑 So how do we fix it? My research and practice point to clear strategies: Transparency → Agents can’t be black boxes. Users must understand why a decision was made. Human Oversight → Think co-pilot, not unsupervised driver. Strategic oversight keeps AI aligned with values and goals. Gradual Adoption → Earn trust step by step: first verify everything, then verify selectively, and only at maturity allow full autonomy—with checkpoints and audits. Control → Configurable guardrails, real-time intervention, and human handoffs ensure accountability. Monitoring → Dashboards, anomaly detection, and continuous audits keep systems predictable. Culture & Skills → Upskilled teams who see agents as partners, not threats, drive adoption. Done right, this creates what I call Human-Agent Chemistry — the engine of innovation and growth. According to research, the results are measurable: 📈 65% more engagement in high-value tasks 🎨 53% increase in creativity 💡 49% boost in employee satisfaction 👉 The future of agents isn’t about full autonomy. It’s about calibrated trust — a new model where humans provide judgment, empathy, and context, and agents bring speed, precision, and scale. The question is: will leaders treat trust as an afterthought, or as the foundation for the next wave of growth? What do you think — are we moving too fast on autonomy, or too slow on trust? #AI #AIagents #HumanAICollaboration #FutureOfWork #AIethics #ResponsibleAI

Improving both human-human and human-AI collaboration is vital. One of the best research domains is Wikipedia. A wonderful new study by Taha Yasseri uncovers recurring patterns of collaboration and conflict, and specifically how to maximize the benefits from using agents and bots in large collaborative pools. Here are some of the key insights in the study "Computational Sociology of Humans and Machines; Conflict and Collaboration" (link in comments): 💡 Collaboration Patterns Reveal Insights into Conflict Dynamics. Platforms like Wikipedia highlight recurring patterns of conflict and cooperation, such as "serial attacks" by experienced editors on novices and "revenge edits" in reciprocated disputes. Bots play a dual role, automating repetitive tasks while sometimes causing unique conflicts like persistent bot-bot reverts. Understanding these dynamics enables better system designs to foster collaboration and reduce friction. Lessons for Maximizing Human-AI Collaboration: 🤖 Bots Streamline Work but Need Thoughtful Integration. Bots effectively automate tasks like vandalism detection, freeing humans for higher-level contributions. However, their impartiality can disrupt social dynamics. Transparent and adaptive bot design fosters trust and smooth integration into workflows. 💡 Shared Goals Drive Consensus and Stability. Aligning human and bot efforts around shared objectives, such as content quality, promotes collaboration. Regularly updating guidelines and managing participant turnover ensure these goals continue to foster harmony. 🌟 Human-AI Synergy Unlocks Greater Potential. When bots function as co-participants, they amplify collective intelligence by processing data and supporting decision-making. Integrating bots at cognitive and informational levels allows teams to achieve results neither could on their own. 🔍 Cultural Context Enhances Bot Effectiveness. Bot behavior mirrors the cultural and linguistic environments they operate in. Tailoring bot frameworks to these contexts reduces friction and maximizes effectiveness in diverse communities. 🛠️ Transparent Design Builds Trust and Equity. Bots that exhibit predictable and clearly communicated behavior enhance human trust and cooperation. Transparent design, coupled with balanced automation and human oversight, ensures productive and fair collaboration. In any collaboration domain the judicious introduction of well-designed AI agents has the potential to result in substantially better outcomes. While there is a lot more research to do, this paper provides an excellent foundation for establishing the principles to apply.

Imagine a virtual office where AI assistants like BrewMaster 2.0 spark both caffeine chaos and meaningful debates. By 2030, workplaces will be defined not just by advanced technology but by the harmony of human-AI collaboration. Agentic AI—autonomous systems with defined goals—is already reshaping industries. Unlike traditional AI, it amplifies human decision-making rather than replacing it, solving complex problems like rerouting logistics or addressing employee burnout. Yet, the rise of agentic AI underscores an urgent need: upskilling. By 2027, 44% of core workforce skills will require transformation. Emotional intelligence, creativity, and AI fluency will be the pillars of success. Enter the D.U.E.T. Model, a roadmap for organizations to design ethical AI, upskill talent, empower humans, and build trust. Together, humans and machines can create workplaces that are not only efficient but also deeply human. #D: Design Human-Centric AI Systems Prioritize ethics, inclusivity, and user needs to ensure AI aligns with organizational and societal values. #U: Upskill to Stay AI-Ready Invest in continuous learning, blending technical skills with emotional intelligence and creativity to prepare the workforce for an AI-driven future. #E: Empower Humans with AI Support Leverage AI to automate repetitive tasks, enabling humans to focus on strategic and creative endeavors. #T: Trust Through Transparency and Ethics Build trust by ensuring AI systems are transparent, accountable, and aligned with ethical standards. Let’s embrace this future—one where heart, humor, and innovation converge.

How Women’s Unique Evaluation Of AI Tools Influences Corporate Culture: “When it comes to adopting AI tools at work, studies have shown that men are more likely to experiment with these tools, while women tend to hesitate. That doesn't mean women are less tech-savvy or less open to innovation. It often means they're asking different questions. And those questions reveal something important about how corporate culture is being shaped in the AI era. Women in the workplace are not saying AI is bad. They’re not rejecting it outright. What they’re doing is pausing. They’re questioning how it works, who created it, what data it was trained on, and whether it could be misused. In many cases, they're also concerned about how others will perceive their use of it. Will they look like they're cutting corners? Will the tool reinforce bias? Will their job become obsolete? That kind of hesitation is discernment and the careful weighing of trade-offs. And it reflects a kind of emotional intelligence and long-term thinking that often gets undervalued in tech conversations. Companies that ignore these perspectives risk designing workflows, cultures, and even ethics policies that leave people behind. If you have a team where the loudest voices are the ones who embrace new tools quickly, and quieter voices are the ones raising concerns, you need to ask yourself: are you hearing the full story? Women may not be the early adopters of every AI tool, but they’re often the first to see unintended consequences. They may be the first to notice that the chatbot is reinforcing stereotypes, or that an AI-powered hiring tool is filtering out qualified candidates based on biased data, which are culture-shaping concerns. I've interviewed hundreds of executives, and the best ones aren't the people who jump on every new technology as soon as it hits the market. They're the ones who ask, ‘Does this make sense for our people? Does it help us do better work? Does it reflect the values we say we care about?’ And more often than not, it’s women who are asking those kinds of questions. Think about what that means in a practical sense. When a company is rolling out a new AI writing tool, a male leader might focus on efficiency. A female leader might ask if the tool risks replacing human insight or if it undermines original thinking. Neither approach is wrong. But they lead to different outcomes.” Read more 👉 https://lnkd.in/enqz6jNy ✍️ Article by Dr. Diane Hamilton #WomenInSTEM #GirlsInSTEM #STEMGems #GiveGirlsRoleModels

Just two years ago, Klarna embraced AI wholeheartedly, replacing a significant portion of its customer service workforce with chatbots. The promise? Efficiency and innovation. The reality? A decline in service quality and customer trust. Today, Klarna is rehiring humans, acknowledging that while AI offers speed, it often lacks the nuanced understanding that human interaction provides. Despite early claims that AI was handling the work of 700 agents, customers weren’t buying it (literally or figuratively). The quality dropped. Trust fell. And even Klarna’s CEO admitted: “What you end up having is lower quality.”   This isn't just a Klarna story. It's a reminder for all of us building the future with AI: - AI can enhance human work, but rarely replace it entirely. - Customer experience still wins over cost savings. - The best “innovation” might just be treating people, customers and workers, better.

Why is there a gender gap in the exploration and adoption of generative AI? Harvard Business School has published a meta-analysis of studies about this gap, concluding that: 😲 The gap is real. Women are about 20% less likely than men to directly engage with this new technology. 😲 The gap is more pronounced when you look at app downloads - women represent only a quarter of all ChatGPT app downloads in the US. 😲 Women report they need training to start using ChatGPT . . . and men do not. 😲 Men are more likely to attempt prompting 2+ times when genAI gives undesired results. 😲 Women are more likely to view AI use as unethical or cheating at work. 😲 Women perceive lower productivity benefits of using genAI at work. But - ❌ Women are NOT more likely to avoid genAI from fears they will become dependent on it or because it could make their job redundant. ❌ Women do NOT have more concern about the risks of using GenAI than men do. There's a lot of work to be done here. Just remember: ❇️ All you need to start is curiosity. ❇️ LLMs are weird. The output is different every time. If you don't like the first response, open a new chat and try a different approach. ❇️ Ask ChatGPT for help writing your prompt. ❇️ Move past "Good Girl Syndrome." There's no gold star for doing things the long way or the hard way.

Women are adopting AI tools at a 25 percent lower rate than men on average. This is according to research by Harvard Business School Associate Professor Rembrand Koning. With my product hat on, I’m curious about why this is. This is “despite the fact that it seems the benefits of AI would apply equally to men and women.” Ok. But is the build designed to address the needs for all… in order to achieve these benefits? Why the gap? The research suggests women are concerned about the ethics of using the tools and may fear they will be judged harshly in the workplace for relying on them. Are these real pain points of women being addressed? A good place to start is at the root, by listening, understanding, and addressing the needs of users. Koning noted: ➡️ Women appear to be worried about the potential costs of relying on computer-generated information, particularly if it’s perceived as unethical or “cheating.” ➡️ Women face greater penalties in being judged as not having expertise in different fields.  They might be worried that someone would think that even though they got the answer right, they ‘cheated’ by using ChatGPT. To design and build for all AND achieve intended outcomes: “It’s important to create an environment in which everybody feels they can participate and try these tools and won’t be judged for [using them],” Koning says. It really comes down to understanding and addressing the real pain points and concerns that women have. #AI #AILiteracy #ProductManagement

🤖 The Gendered Impact of AI: Why Women—Especially from Marginalised Backgrounds—Are Most at Risk As artificial intelligence continues to reshape the world of work, one thing is becoming increasingly clear: the effects will not be felt equally. A new report from the United Nations’s International Labour Organization and Poland’s NASK reveals that roles traditionally held by women—particularly in high-income countries—are almost three times more likely to be disrupted by generative AI than those held by men. 📉 9.6% of female-held jobs are at high risk of transformation, compared to just 3.5% of male-held roles. Why? Many of these jobs are in administration and clerical work—sectors where AI can automate routine tasks efficiently. But while AI may not eliminate these roles outright, it is radically reshaping them, threatening job security and career progression for many women. This risk is not theoretical. Back in 2023, researchers at OpenAI—the company behind ChatGPT—examined the potential exposure of different occupations to large language models like GPT-4. The results were striking: around 80% of the US workforce could have at least 10% of their work tasks impacted by generative AI. While they were careful not to label this a prediction, the message was clear: AI's reach is widespread and accelerating. 🌍 An intersectional lens shows even deeper inequities. Women from marginalised communities—especially women of colour, older women, and those with lower levels of formal education—face heightened vulnerability: They are overrepresented in lower-paid, more automatable roles, with limited access to training or advancement. They often lack the tools, networks, and opportunities to adapt to digital shifts. And they face greater risks of bias within the AI systems themselves, which can reinforce inequality in recruitment and promotion. Meanwhile, roles being augmented by AI—like those in tech, media, and finance—are still largely male-dominated, widening the gender and racial divide in the AI economy. According to the World Economic Forum, 33.7% of women are in jobs being disrupted by AI, compared to just 25.5% of men. 📢 As AI moves from buzzword to business reality, we need more than technical solutions—we need intentional, inclusive strategies. That means designing AI systems that reflect the full diversity of society, investing in upskilling programmes that reach everyone, and ensuring the benefits of AI are distributed fairly. The question on my mind is - if AI is shaping the future of work, who’s shaping AI? #AI #FutureOfWork #EquityInTech #GenderEquality #Intersectionality #Inclusion #ResponsibleTech

Not everyone prompts AI the same way. And that matters. Men often tell AI what to do, while women often ask it for help. “Write a business plan.” vs. “Can you help me write a business plan for my startup focused on women’s health? Thank you.” The difference isn’t just style. It’s confidence, context, and lived experience. Women frequently add emotional context, caregiving roles, or bias navigation into their prompts. They’re not just asking for answers, they’re looking to be understood. But most AI tools have been trained on short, direct, male-leaning language patterns. That means women’s prompts may be misunderstood or generate lower-quality responses. This is where inclusive AI design matters most. Because smarter AI doesn’t just give better answers. It listens better too. Read more in my recent Forbes piece here: https://lnkd.in/gPaNpCfJ 

Should you blindly trust AI? Most teams make a critical mistake with AI - we accept its answers without question, especially when it seems so sure. But AI confidence ≠ human confidence. Here’s what happened: The AI system flagged a case of a rare autoimmune disorder. The doctor, trusting the result, recommended an aggressive treatment plan. But something felt off. When I was called in to review, we discovered the AI had misinterpreted an MRI anomaly. The patient had a completely different condition - one that didn't require that aggressive treatment. One wrong decision, based on misplaced trust, could’ve caused real harm. To prevent this amid the integration of AI into the workforce, I built the “acceptability threshold” framework. Here’s how it works: This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 1. Measure how accurate humans are at a task (our doctors were 93% accurate on CT scans) 2. Use that as our minimum threshold for AI. 3. If AI's confidence falls below this human benchmark, a person reviews it. This approach transformed our implementation and prevented future mistakes. The best AI systems don't replace humans - they know when to ask for human help. What assumptions about AI might be putting your projects at risk?