Impact of Automation

Klarna bet big on AI. Now they're rehiring humans. After their valuation plunged from $45B to $7B in 2022, the company faced enormous pressure. One cost-saving measure was replacing 700 customer-service roles with AI. Then they learned a critical lesson: Some AI savings carry steep human costs. "It’s critical that customers know there will always be a human if you want." – Sebastian Siemiatkowski (CEO) The insight is strategic, not operational: → AI is transactional → Humans are relational → Automation optimizes predictable interactions → Humans manage unpredictable trust moments → AI builds efficiency → Humans build loyalty Firms that find balance will outperform those blindly bolting on technology. The new service blueprint: → Clearly map trust vs. transactional moments → Position humans strategically, not universally → Use AI to complement rather than to replace → Measure success beyond cost savings → Prioritize trust metrics (retention, advocacy, loyalty) Beyond fintech: → Consulting faces the same trust dilemma → Legal automation risks client trust → Finance must automate tasks, not judgment The winners won't automate fastest. They'll automate everything except trust itself. Because trust, judgment, and empathy never scale. And that's exactly why they're valuable.

🤖 The European Union needs to rapidly upskill its citizens if it's going to capitalise on the benefits that artificial intelligence can bring, according to a report from LinkedIn's Economic Graph team. AI has been hailed as a technology that can help humans with everything from boosting office productivity to drug discovery. But a lack of talent is one of the biggest hurdles. 🗒 AI talent makes up just 0.41% of EU workers, LinkedIn's report, AI in the EU, found. While that's a 126% increase on 2016, and more than the UK (0.35%) and the US (0.34%), the bloc still needs more people who know how to get the most out of the technology. 📍 As it stands, just 26.3% of the EU's AI talent is female, which is less than the UK (27.7%) and the US (29.8%). It will take 162 years to reach gender parity if the gap keeps on closing at the current rate, according to the report. Addressing the gender imbalance in AI is one way the EU could try and close the skills gap, according to the report. In terms of AI's impact on the workforce, women are likely to be disproportionately impacted by AI, and generative AI (gen AI) in particular, which is capable of creating a variety of content including emails and presentations. Gen AI is poised to impact a number of jobs that tend to be held by women including medical clerks, clinical research assistants and sales operations assistants. 🗣️ What’s your take on these findings? Are you aware of AI’s impact and its presence within the EU workforce? We’d love to hear your thoughts in the comments. Full report: https://lnkd.in/g3_EhhiP 🖊️ Sam Shead  📸 Getty Images #AIInTheEU

How AI Risks Widening the Gender Gap — And What We Can Do About It AI is transforming industries, but it's also at risk of deepening gender disparities. While 40% of business leaders are prioritizing AI, we must ask: are we considering the impact on women in the workforce? The challenges we need to address include: 🔹 One of the key risks posed by AI is it’s tendency to perpetuate gender biases, especially in sectors where women are already underrepresented. AI systems are typically trained on historical datasets, and if these datasets reflect societal or institutional biases, the AI will likely replicate them. This has already been observed in areas such as recruitment, where AI tools that analyse CVs have demonstrated a preference for male candidates over their female counterparts. 🔹 Surprisingly, studies have shown that women use AI-driven tools such as ChatGPT significantly less than men, even when they hold similar roles. This gap may have long-term implications for women's career trajectories, particularly as AI becomes more embedded in day-to-day business processes. Several factors are at play here. First, there is a perception gap: women tend to express greater scepticism about AI’s potential benefits. For instance, surveys suggest that women are more concerned about the societal risks posed by AI, including job displacement, privacy concerns, and ethical issues. Women often report feeling less confident in their ability to navigate AI technologies, frequently citing the need for additional training before feeling comfortable using these tools. 🔹 Many of the jobs most vulnerable to automation are disproportionately held by women — Goldman Sachs has found that nearly 80% of women’s jobs are at risk of being automated, compared to 58% of men’s jobs. Sectors such as office administration, customer service, and healthcare support roles often referred to as "pink-collar jobs" — are seeing significant shifts as AI-driven systems take over tasks like scheduling, data entry, and customer interactions. But there’s hope! With more diverse datasets, inclusive development teams, and reskilling opportunities, we can ensure AI empowers everyone. Read my full article on how we can address these challenges and build a more equitable future, and please share your view! I look forward to having different perspectives on this critical topic. Thanks sincerely and kind regards, Fabio #AI #GenderEquality #Inclusion #FutureOfWork #Diversity #Leadership

GenAI won’t take your job. It’ll turn it into something you didn’t sign up for. ⤵ Most people fear job loss. But the real disruption is subtler - it's job mutation. AI isn’t firing people. It’s reshaping what they do, behind the scenes: 1/ ➠ Exposure is Everywhere ➝ 1 in 4 jobs already show signs of GenAI exposure ➝ Clerical roles carry the highest risk ➝ Women are hit harder than men ↳ In high-income countries, nearly 10% of women work in top-risk roles The automation wave isn't just a tech issue - it's a gender and equity issue too. 2/ ➠ Don’t Fear Unemployment - Fear Restructuring ➝ Full job replacement is rare ➝ GenAI automates tasks, not entire roles ➝ It eats away at predictable, repeatable work ↳ Think: invoices, form responses, basic research This is about transformation, not elimination. 3/ ➠ What’s Vulnerable? ➝ Cognitive and white-collar jobs are on the frontline ➝ Professionals, tech workers, clerks - all at risk ➝ GenAI handles digital workflows fast ↳ The more structured your work, the more exposed you are It's not just about low-skill labor anymore. 4/ ➠ This Isn’t a Forecast - It’s Data ➝ Over 30,000 tasks analyzed across 2,500 occupations ➝ 1,640 workers surveyed, plus expert validation ➝ GenAI predictions refined with human judgment ↳ This is the most detailed global exposure index yet These numbers aren’t guesses - they’re grounded. 5/ ➠ Forget Replacement - This Is Transformation ➝ Jobs won’t disappear, but they’ll evolve ➝ Your job title may survive ➝ But the content? That’s already changing ↳ Adaptation isn’t optional - it’s the new baseline The shift is already happening. Quietly. Relentlessly. What GenAI leaves behind may still be called “your job” - but it may no longer feel like it. ▶ What happens when GenAI handles half your to-do list? ▶ Does your value shrink, or shift? ▶ Can you compete with a tool that works 24/7? ▶ What if your job still exists - but without you? // Repost this ⇄ // Source: ILO Working Paper 140 – Generative AI and Jobs (May 2025) // Follow me for daily posts on emerging tech and growth: https://lnkd.in/gqzS_9Tf

If your AI rollout still forces people to double-check every answer, congratulations — you’ve automated nothing. The hidden tax here is verification cost. When trust isn’t engineered in, work shifts from creation to endless auditing, burning the very hours automation was meant to save. Verification cost lurks on every P&L, unbudgeted yet brutal. We watched an industrial predictive-maintenance tool collect dust even though its forecasts were spot-on. A redesign that surfaced “why,” exposed confidence levels, and invited feedback flipped usage from ignored to indispensable. In banking, an AI advisor’s stiff tone and opaque logic drove clients away — until we added explanations and a “skip” button. Engagement jumped 60 %. Before you chase higher model accuracy, audit your verification hours per user this quarter. Where is trust debt quietly killing ROI? https://lnkd.in/e4kQCS8g #AI #Technology #Transformation #Business #Trust

It’s hard to trust a manager who doesn’t care. Even harder when that “manager” runs on code. New research shows that employees perceive AI managers as less benevolent than human ones—and that this perceivedlack of care deeply undermines trust. Across four studies—from real-world delivery riders to controlled lab experiments—the evidence is consistent: 🤖 AI managers are trusted less not because they lack skill or fairness, but because they lack the ability to feel. 💔 In emotionally charged situations—like asking for time off after a bereavement—this matters even more. 👀 The result? People often prefer human managers, even if both follow the same rules. Why? Because trust isn’t just about competence. It’s about connection. And connection, in human psychology, still runs through empathy. As AI tools take on more leadership functions, here’s the call for all of us building the future of work: 👉 Don’t confuse efficiency with humanity. 👉 Use AI where empathy isn’t required—but be cautious where care is core. 👉 And remember: when people feel unseen, they disengage—even if the algorithm is technically right. Leadership is more than logic. It’s a moral and emotional contract. AI may assist, but it cannot yet feel. https://lnkd.in/dVNe6gPJ (Note though, this is a snapshot in time. As long as humans do not know that the other side is an AI, they more often than not evaluate its responses as more empathetic than humans. Who is to say how long we will devalue machines. One day, we may want to rather rely on their "benevolence" than that of our fellow humans).

🤖 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

Imagine a world where AI handles repetitive tasks, freeing women to excel in areas where human ingenuity truly shines. This shift can empower women to take on leadership roles and contribute more significantly across various sectors. The International Monetary Fund recently warned that AI could disproportionately displace women in emerging economies. Nearly two-fifths of women in these regions are employed in sectors highly susceptible to automation. This highlights a critical challenge: ensuring AI empowers, rather than displaces, women. By equipping women with the skills and knowledge to leverage AI, we can unlock a future where AI augments their work, allowing them to focus on higher-level tasks like creative problem-solving and strategic thinking. AVPN recognises the importance of mitigating the potential negative impacts of AI on workers. Through our AI Opportunity Fund, we aim to support workers of all genders across Asia Pacific who are most vulnerable to workforce transitions driven by AI. While many of us enjoy the benefits of AI in our daily lives, we must not forget those who may be negatively impacted. More can be done. Let me know what other ways we can ensure a just #AI transition. #womeneconomicempowerment #AItransition

Last month, Empathetic AI turned two. Two years, two products, a lean team, and an incredible group of domain experts and supporters who have shaped this journey. This still feels like just the beginning. When I first started, AI for tax did not seem like the obvious choice. It's not the most talked-about industry when people think about AI transformation. After many client meetings, the deeper I got into this space, the more obvious it became. Here are my whys - Because tax is: - Mandatory: Every taxpayer, business or individual, has to deal with tax. (Who doesn’t?) - Repetitive: Same deadlines, same processes every year - Complex: Compliance keeps getting tougher - Data-driven: A perfect field for AI to learn from and improve As a founder doing B2B sales firsthand, I’ve learned some biggest lessons along my journey: 💚AI adoption in businesses starts with trust, not just technology. No one adopts AI just because it looks smart, fast or efficient. Trust is what drives adoption in businesses. Trust does not come from just having the best model. It comes from: - Involving domain experts early - Letting real-world feedback shape the product - Having a proper evaluation framework Along my journey, I started every client meeting the same way: "I used to work in finance before founding Empathetic AI, we speak the same language" And the meetings often ended with the same request: “We need more domain experts like yourself in the AI space. Can I invite you to join our expert hub?" Most of the time, these meetings are not just about product demos. They turn into insightful conversations on how AI will reshape the future for finance and tax as a profession. 💚AI in accounting today feels like cloud accounting 10 years ago, but much bigger Before cloud, accounting was slow and manual. Then cloud accounting softwares changed that situation. Now AI is that shift, but on a much larger scale. The real challenge is not just proving AI works. It’s proving it’s intelligent, reliable, transparent and built for aligning with professionals’ workflows. 💚In B2B, credibility matters more than product features The real questions firms ask are not about AI models. They care about: - "Can this be trusted?" (Explainability) - “Is my data safe?” (Data security) - "How will it affect my job?" (Risk assessment) - "Who else is using it?" (FOMO) Therefore, we have been focusing on: - Being in the right industry spaces: Partnerships, speaking engagements, case study reports - Building credibility: Published the first vertical domain AI evaluation framework with CSIRO's Data61 - Creating a network effect: This is where the flywheel begins. Two years in, and it still feels like day one. As AI models become commodities, the real value comes from deep industry expertise, user feedbacks and trust. I look forward to working with more accounting, tax and finance professionals to rethink how AI will shape the profession.

90% of sales are automated today.   But is it helping us connect or pushing customers away? Automation can boost speed… but does it hurt trust? (Lets find out.)   In my 24 years in marketing, I’ve watched tech transform the industry.   Automation has brought speed and convenience, but it’s also introduced a challenge: How do we keep the human side alive?   Sales isn’t just about closing deals; Its about connecting with people.   We buy from those we • Trust • Connect with • Feel valued by.   Customers remember how we make them feel. A transaction fades but a connection lasts.   I’ve seen companies lose clients because they focused too much on automation and not enough on the personal touch.   Automation is a fantastic tool → when used right. But when overused, it can push clients away.   Here are 3 things I’ve learned to keep clients engaged:   Make Clients Feel Heard ↳ Use automation for follow-ups only. ↳ Start with real, genuine conversations. ↳ Listening builds trust, automation doesn't.   Balance Tech with Real Connection ↳ Automate simple tasks, personalize important ones. ↳ Clients remember thoughtful, small gestures. ↳ A personal touch always stands out..   Focus on Meaningful Conversations ↳ Quality beats quantity every time. ↳ Real connections build strong loyalty. ↳ Sincere messages matter more than many. Automation should support not replace the human element. It’s about finding the right balance. How are you keeping the human touch alive in sales? Drop your thoughts below!