Impact of Automation

Are manual sales processes lengthening your sales cycle? Automating with CRM integration might be the key to faster deals. A logistics firm I consulted for was bogged down by manual sales processes, leading to extended sales cycles. We introduced ActiveCampaign integration to automate routine tasks, freeing up the sales team to focus on closing deals. Automating sales processes not only sped up the sales cycle but also allowed the team to handle more leads efficiently, leading to increased revenue. Manual processes can be a drag on productivity. Automating these tasks feels like lifting a weight off your shoulders, allowing your team to move quickly and efficiently. Try these five tools and strategies to automate sales processes: 1️⃣ Use CRM tools like ActiveCampaign for sales automation. 2️⃣ Automate follow-up emails and reminders to ensure timely communication. 3️⃣ Set up workflow automation for routine tasks like data entry. 4️⃣ Integrate CRM with e-signature tools to speed up contract signing. 5️⃣ Use CRM analytics to identify bottlenecks and optimize the sales process. How have you used automation to reduce sales cycles? Share your tips and experiences in the comments. Let's discuss how automation can streamline the sales process! #MarketingStrategy #DigitalStrategy #Marketing #DigitalMarketing #CRMintegration ----------- Like this post? Want to see more? Ring the 🔔 on my Profile ⬆️ Connect with me

When work is automated, will purpose become the new paycheck? Almost every AI conversation I have eventually lands on the same promise: we'll automate more work, save more time, and become more productive. And every time I hear it, I find myself asking a different question. What happens when work is no longer where people find their purpose? The question stayed with me because I realized I've been guilty of making the same assumption. Like many people, I've often measured my contribution by what I produce, the projects I complete, and the role I hold. But the more I think about it, the more I believe we've confused work with something much deeper. Perhaps AI isn't creating a purpose crisis. Perhaps it's exposing one that has always existed. For decades, our jobs quietly answered questions we rarely stopped to ask ourselves. Who am I? Where do I create value? Why do I matter? We borrowed those answers from our careers because work happened to be the easiest place to find them. That's why I don't think the biggest impact of AI will be unemployment. I think it will be identity. In Irreplaceable, I introduced the Joy and Growth Principle, which suggests that if a task doesn't bring us joy or help us grow, AI should probably do it instead. Looking back, I think there's an even deeper implication that I didn't fully appreciate at the time. The time AI gives us isn't the reward. The real question is what we choose to become with the time it gives back. If we simply fill those extra hours with more consumption, more meetings, or more distractions, we've only become more efficient. But if we invest them in what I call the Humics by strengthening our creativity, our critical thinking, and our relationships with others, then automation doesn't diminish our humanity. It expands it. That's the shift I've been trying to make myself. ✔️ I ask whether AI is saving me time or helping me become a better thinker. ✔️ I deliberately reinvest part of every hour AI saves into learning, reflection, or conversations that no technology can have for me. ✔️ And I remind myself that purpose isn't something an employer gives us. It's something we build through the choices we make every day. Maybe we've spent decades preparing people for careers. Maybe we should have been preparing them for meaning. What do you think? If AI eventually automates much of our work, will purpose become the new paycheck, or will we simply discover new ways to tie our identity to what we do? #HumanAgentOrchestrator #Humics #AIReadiness #LeadershipInAIEra #PurposeAtWork

AI is changing the economics and speed of cyberattacks. What once took threat actors days or weeks can now happen in minutes: automated reconnaissance, AI-assisted exploit development, credential targeting, lateral movement, and highly personalized phishing at scale. This is why Palo Alto Networks believes so strongly in the concept of autonomous resilience. The traditional model of security operations: fragmented tools, manual escalation paths, and human-speed response cycles - was not designed for machine-speed threats. Autonomous resilience means building security architectures that can continuously reduce exposure, validate trust, and contain threats in real time. What does that look like in practice? 🔸 Minimize attack surface Continuously identify and remediate exposed assets, misconfigurations, vulnerable APIs, and unmanaged cloud resources before attackers can weaponize them. For example, AI-driven exposure management can detect an internet-facing development environment created outside policy and trigger automated remediation immediately. 🔸 Secure every identity Trust must extend beyond employees to machine identities, workloads, APIs, and AI agents. This means enforcing least privilege, adaptive access controls, and continuous identity validation to stop credential misuse and token theft before attackers gain persistence. 🔸 Defend the software supply chain AI-assisted attacks increasingly target CI/CD pipelines, open-source dependencies, and code repositories. Organizations need runtime protections, code integrity validation, and automated policy enforcement to prevent manipulated code from reaching production environments. 🔸 Constrain blast radius Zero Trust architectures become even more critical in an AI-driven threat landscape. Microsegmentation, continuous inspection, and behavioral analytics help prevent attackers from moving laterally across environments once initial access is achieved. 🔸 Detect and respond in real time Security teams cannot rely on analysts manually correlating thousands of alerts. AI-driven SOC operations can automatically prioritize incidents, enrich telemetry, isolate compromised assets, and initiate containment workflows within minutes — dramatically reducing operational fatigue and response time. The outcome is not “fully autonomous security.” The outcome is resilient organizations that can adapt, contain, and recover faster in an increasingly automated threat environment. Cybersecurity is evolving from reactive defense into continuous operational resilience. The organizations preparing for that shift now will be far better positioned for what comes next.

As AI is replacing early-career jobs, the economy's productivity in the short-run increases, but productivity and welfare in the long run may decline. In a new paper, Enrique Ide argues that we may be witnessing "socially excessive automation of early-career work. Such automation may deliver immediate productivity gains, but it also erodes the skills of future cohorts and constrains long-run growth." Here's the abstract of his paper: "Recent advances in Artificial Intelligence (AI) have sparked expectations of unprecedented economic growth. Yet, by enabling senior workers to accomplish more tasks independently, AI may reduce entry-level opportunities, raising concerns about how future generations will acquire expertise. This paper develops a model to examine how automation and AI affect the intergenerational transmission of tacit knowledge—practical, hard-to-codify skills critical to workplace success. I show that the competitive equilibrium features socially excessive automation of early-career tasks, and that improvements in such automation generate an intergenerational trade-off: they raise short-run productivity but weaken the skills of future generations, slowing long-run growth—sometimes enough to reduce welfare. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could reduce the long-run annual growth rate of U.S. per-capita output by 0.05 to 0.35 percentage points, depending on its scale. I further show that AI co-pilots can partially offset lost learning by assisting individuals who fail to acquire skills early in their careers. However, they may also weaken juniors’ incentives to develop such skills. These findings highlight the importance of preserving and expanding early-career learning opportunities to fully realize AI’s potential." What can policy do? In the concluding remarks, the paper offers several ideas: - government subsidies for "mentorship, apprenticeship, and other entry-level training arrangements" - "taxing entry-level automation" - reducing minimum wages for young workers - promoting AI systems that complement rather than replace entry-level jobs. Universities could also play a role by placing "greater emphasis on providing undergraduate students with opportunities to gain practical experience before they formally enter the labor market. Such initiatives would complement the traditional focus of undergraduate programs on codifiable knowledge and help foster the early development of tacit skills." Read the full paper here: https://lnkd.in/eMq3uktX (open access)

"When automation removes the simpler tasks (as accounting software did for bookkeeping clerks), the remaining work becomes more specialized, wages rise, and fewer workers qualify. When it removes the harder tasks (as inventory management systems did for warehouse workers), the job becomes more accessible, employment expands, and wages fall. Same technology, opposite labor market outcomes, depending on which part of the job gets automated." - Alex Imas, The University of Chicago Booth School of Business from What Will Be Scarce? https://lnkd.in/eqdKHk2u

🤖 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

𝗧𝗵𝗲 𝗙𝗼𝗿𝗴𝗼𝘁𝘁𝗲𝗻 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗘𝗻𝘁𝗿𝘆-𝗟𝗲𝘃𝗲𝗹 𝗝𝗼𝗯𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 AI promises massive productivity gains. But it may also be quietly eroding how expertise is built. As AI enables senior employees to do more on their own, many entry-level roles—the primary source of learning by doing—are disappearing. This matters because the most valuable workplace skills are often 𝘁𝗮𝗰𝗶𝘁: absorbed through experience, not taught in classrooms or manuals. According to a recent study, today’s rush to automate early-career work may be socially excessive. While automation boosts short-term productivity, it also disrupts the intergenerational transfer of tacit knowledge. The result is a trade-off: higher output now, but weaker skills in the next generation—ultimately slowing long-term economic growth and, in some cases, reducing overall welfare. The implications are not trivial. Even modest levels of AI-driven automation at the entry level could lower long-run U.S. per-capita growth by an estimated 𝟬.𝟬𝟱 𝘁𝗼 𝟬.𝟯𝟱 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗽𝗼𝗶𝗻𝘁𝘀 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆. Over time, that compounds into a meaningful economic drag. AI co-pilots offer a partial remedy. They can help workers who missed early learning opportunities catch up later in their careers. But they also introduce a new tension: if AI makes skill gaps easier to mask, it may reduce incentives for juniors to develop those skills in the first place. 𝗧𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗶𝘀 𝗹𝗼𝘂𝗱 𝗮𝗻𝗱 𝗰𝗹𝗲𝗮𝗿: 𝗔𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗵𝗼𝘄 𝘄𝗼𝗿𝗸 𝗶𝘀 𝗱𝗼𝗻𝗲, 𝗯𝘂𝘁 𝗵𝗼𝘄 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝘀 𝗳𝗼𝗿𝗺𝗲𝗱. 𝗔𝗻𝗱 𝗴𝗿𝗼𝘄𝘁𝗵 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝗯𝗼𝘁𝗵. To capture AI’s full potential, policy, firms, and universities must protect and expand early-career learning—through mentorships, apprenticeships, practical education, and AI systems that complement junior roles rather than erase them. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝗸𝗶𝗹𝗹 𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀—𝗶𝘁’𝘀 𝗯𝗼𝗿𝗿𝗼𝘄𝗲𝗱 𝗴𝗿𝗼𝘄𝘁𝗵. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: Ide, Enrique. (2025). Automation, AI, and the Intergenerational Transmission of Knowledge. 10.48550/arXiv.2507.16078.

There’s a common belief that AI will close the skill gap between beginners and experts. But a new Stanford-Harvard study shows it’s more complicated. Researchers studied three groups inside an organization, all asked to write web articles: • Insiders: SEO web analysts who regularly wrote articles • Adjacent outsiders: marketers in the same department who didn’t usually write • Distant outsiders: technologists whose work was unrelated to writing AI didn’t boost every group equally. It helped adjacent outsiders close the gap and perform at insider level. But distant outsiders (technologists) still fell short. Even with AI fine-tuned on company documents, their articles scored lower than insiders’. They hit what the researchers call the “GenAI wall.” They lacked too much of the marketers’ tacit knowledge: the instinct for tone, the craft of building a narrative, the ability to weave ideas coherently, and the judgment of what makes an article “good.” AI couldn’t fill that gap or replicate the intuition that insiders had built through experience. One of the next big challenges, and opportunities, for AI is learning tacit knowledge. Bridging the gap between novices and experts requires more than data. It means capturing the shortcuts, sequences, and judgment calls that turn a draft into a finished product. Only when AI understands the real workflows, rhythms, and processes of an organization can it start to absorb that hidden know-how—and begin to shift the “GenAI wall.”

As more companies become AI-first and adopt AI workflows and AI co-workers, today’s Anthropic / Claude outage begs the question: If your team is “all AI”, what happens when the systems go down? Do you have a human in the loop, a backup workflow, or do operations simply stall? At Blue Tulip Ventures, we are experimenting with an AI Chief of Staff to automate some of our work that is manual and time‑consuming. Right now, us humans can still do all the work the AI Chief of Staff is doing. But moving forward that may not always be the case. With a human‑centric AI lens, these are exactly the questions we need to ask. As we redesign companies around AI coworkers and AI workflows, we also need to design for resilience. AI can be the engine, but should there be a human‑driven plan B?

Hands up if you’ve asked yourself whether you’ll lose your job because of AI 🤚 If you’re a woman, the odds say you’re more likely to. And if you’re a woman, you’re less likely to get AI training to help you stay ahead. Women are still less likely to enter tech, to be promoted, or to reach leadership roles. And somehow, in 2025, we’re still talking about the gender pay gap. It’s shocking (but not surprising) that the same imbalance now extends into AI. In high-income countries, women’s jobs are three times more likely to be automated by AI than men’s, largely because women are overrepresented in administrative and clerical roles. Yet, Randstad studies show that women are less likely to receive access to AI or skills training. But research from Harvard Business School from a few months ago shows that women are adopting AI tools at a 25% lower rate than men, partly due to ethical concerns and fear of being judged at work for using them. So it’s not just discrimination shaping our careers, it’s our mindset too. We’re often afraid to ask for a pay rise, worried about being judged, and that fear can hold back our progress more than we realise. So what’s the solution? 🤔 It’s not waiting for someone to offer training. It’s going out there and getting it yourself. Upskill and don’t worry about what others think. Because soon, the only professionals who will thrive in the job market are the ones with AI skills.