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Can generative AI effectively advise business owners and entrepreneurs? With intuitive, chat-based interfaces, GenAI tools are accessible for tasks ranging from marketing to strategy even for those without technical expertise. This accessibility holds promise for entrepreneurs and small business owners seeking an on-demand adviser. Improving entrepreneur performance broadly has been difficult, as personalized mentoring and consulting, though effective, are costly and hard to scale — especially in emerging markets where business support is limited and expensive. A low-cost AI mentor could potentially deliver scalable, quality guidance.
A field study involving 640 small business owners in Kenya tested this by giving half access to a GPT-4-powered adviser via WhatsApp. Results showed that AI access led to a 15% revenue and profit boost for top performers but caused nearly a 10% decline for lower performers. Both groups received similar advice; the difference lay in the users’ judgment to select applicable insights. High performers tailored AI suggestions to their specific business context, while low performers often followed generic advice that hurt margins.
The study highlights that generative AI amplifies human judgment when tackling complex problems. AI’s value depends on users’ ability to filter and apply suggestions effectively, underscoring the risk of widening performance gaps if weaker users adopt AI uncritically. Leaders should cultivate awareness of varying effects, design AI tools with contextual understanding, and provide training and safeguards. Careful implementation and monitoring can help scale AI’s benefits without increasing inequality among business owners.
Florence Nightingale is often remembered as a compassionate nurse, yet her pioneering work transformed healthcare through innovation. Her strategies—powerful data communication, clear public guidance, and formalized training—reshaped nursing and hospital sanitation practices, leaving lessons relevant for today’s leaders.
Born in 1812 in Italy, Nightingale defied societal norms to pursue nursing. During the Crimean War, she and her team improved hospital conditions amidst dire circumstances, using data-driven visuals, like her famous 1858 polar area chart, to advocate for sanitation reforms. She also authored Notes on Nursing, providing accessible, practical advice for caregivers, and led the establishment of the first formal nursing school, professionalizing training and broadening healthcare access.
Nightingale’s impact extended beyond immediate reforms; her work influenced public health policies that increased life expectancy in the UK by nearly 40% over five decades. Her legacy underscores the power of doing things differently, using simple tools effectively, and nurturing disruptive innovation through curiosity, collaboration, and persistence.
Carolyn Geason-Beissel from MIT SMR discusses the contrasting pace of AI adoption across industries, highlighting particularly slow progress in essential sectors like construction, mining, and waste management. These industries often rely on outdated technology and maintain long-standing, stable processes, which fosters skepticism towards AI. Workers may see AI as intrusive, complicated, and unlikely to bring clear benefits, partly due to prior disappointing tech rollouts and fear of change fatigue.
The article identifies three key barriers: AI's perceived inaccessibility and fear factor, the excessive workload associated with integrating new AI tools, and unclear or irrelevant benefits from AI to frontline workers. To overcome these, the author suggests making AI relatable through everyday analogies, embedding AI incrementally into existing workflows rather than imposing new systems abruptly, and measuring AI impact with familiar business metrics that resonate with users.
Success stories include AI integrations that feel natural, such as voice-operated trucking platforms and predictive maintenance tools built within current software systems. Ultimately, organizations that prioritize understanding, gradual integration, and relevant outcome measurement foster higher acceptance and unlock AI’s promise, emphasizing that comfort and trust in users are as crucial as the technology itself.
Are you confident in your ability to manage upward? Navigating relationships with supervisors can be tricky—whether facing micromanagement, feeling sidelined, or unclear about expectations. Effective upward leadership entails active listening to those higher up, influencing them to support your team, and assisting in aligning efforts with organizational goals. In today’s environment, where AI may reduce middle management layers, these skills are even more critical. Upward leaders play three interconnected roles: Buffer, Translator, and Advocate. Balancing these roles helps mitigate frustrations, clarify communications across levels, and persuasively advocate for your team's needs. Improving your skills starts with honest self-assessment—identify which roles you lean on during frustration or flow states and if you can perform them both up and down the hierarchy. Developing buffering, translating, and advocating abilities involves learning emotional resilience, respectful communication, and strategic persuasion. Building candid, trust-filled relationships with superiors, adapting communication style to each leader, and thoughtfully choosing when and how to voice concerns are key to success. Regularly rebalancing your upward leadership approach helps sustain professional growth and organizational impact. Mastering these skills isn't just about career advancement; it's about fostering an environment that encourages open dialogue and engagement, driving better outcomes for teams and organizations alike.
Imagine walking into a dealership with your budget set, only to find the first quoted price creeping into every counteroffer you make. This cognitive bias, called anchoring, causes even seasoned negotiators to get stuck near the initial number presented, limiting their negotiating power. Recent research shows a simple yet effective approach to combat this: adopting a "choice mindset" the moment you hear the first offer. This mindset reminds you that you have multiple options, nudging your brain to explore a broader range of counteroffers instead of fixating on the anchor.
Studies found that a brief prompt — "You can choose any amount you want" — helped participants counteroffer more boldly, moving further from the first price. Preparing several counteroffers ahead of time also reduces anchoring’s grip. Importantly, negotiation should happen when you’re mentally focused, as distractions weaken this effect.
In practice, when faced with a first offer, take a moment to pause, remind yourself of your choices, and brainstorm different counteroffers before responding. This cognitive shift can transform tough negotiations into collaborative problem-solving, letting you set terms that align with your goals rather than reacting to the initial number. Following this, articulate clear reasons for your counteroffers and look for objective criteria to guide the discussion, keeping the negotiation on your terms.
In 2018, a self-driving Uber vehicle in Tempe, Arizona, tragically killed a pedestrian, raising urgent questions about responsibility in the age of autonomous technology. Traditional blame on individuals such as drivers or leaders is no longer sufficient, as decisions now emerge from complex interactions between humans and AI systems. This shift necessitates moving away from assigning fault towards building a shared understanding of responsibility through "narrative responsibility." Unlike classic linear models that pinpoint a single cause, narrative responsibility recognizes accountability as distributed across teams, technology, and organizational culture. It encourages mapping the full story beyond immediate errors, distributing ownership collectively rather than blaming individuals, and embedding continuous reflection about decisions into daily practices. Examples from Google’s handling of AI failures to aviation safety panels and healthcare incident reviews illustrate how organizations can institutionalize this approach to strengthen learning and resilience. While narrative responsibility complements existing legal and ethical standards without replacing them, it offers a vital framework for navigating accountability amid the rapid evolution of AI-driven systems.
Marketing leaders often face significant challenges in obtaining timely and cost-effective consumer insights, traditionally requiring months and substantial budgets. Recent advances in generative AI, particularly large language models (LLMs), are reshaping this landscape by dramatically accelerating research timelines and enhancing qualitative and quantitative methods. AI enables rapid concept testing using synthetic consumer "digital twins," supports large-scale qualitative data analysis, and automates routine tasks such as survey drafting and data visualization. This transformation not only reduces costs but also allows for more frequent, iterative studies aligning with fast-paced decision cycles. Furthermore, integration techniques like retrieval-augmented generation help unify siloed data, creating richer, more dynamic insights. Despite these gains, human expertise remains crucial for guiding research design, ensuring quality, and interpreting AI-generated data. The partnership between human judgment and AI promises a new era of efficient, insightful marketing research, albeit with important considerations around bias, data authenticity, and the evolving role of marketing professionals.
In this episode of the Me, Myself, and AI podcast, Jacqui Canney, Chief People and AI Enablement Officer at ServiceNow, discusses how AI is fundamentally transforming workflows and employee experiences. ServiceNow, with its massive scale of 80 billion active AI-powered workflows, integrates AI to automate routine processes like employee onboarding, enabling staff to focus on more valuable tasks. Canney emphasizes that AI adoption is not just about technology but heavily relies on effective change management, workforce training, and talent development, including personalized AI skills assessments. She reflects on emerging roles, the importance of blending soft skills with technical expertise, and how AI blurs traditional organizational boundaries, pointing to a future where AI and human collaboration reshape leadership and work. The conversation explores practical AI use cases, challenges in scaling AI across large organizations, and the critical role of culture and people in realizing AI's full potential.
Carolyn Geason-Beissel of MIT Sloan Management Review and Minneapolis Institute of Art explores how generative AI is shifting organizational priorities from merely increasing production speed to mastering systematic learning from AI outputs. AI now dramatically lowers the cost of initial drafts, code, and prototypes, but the real value lies in how organizations evaluate and learn from these outputs to improve future iterations. Companies that establish feedback systems to verify, evaluate, and capture insights from AI outputs experience substantial financial benefits and improved organizational learning. This process, termed “return on iteration,” relies on creating infrastructure to support ongoing improvement. By employing specialists as evaluators rather than just producers, and embedding mechanisms for verification and learning capture into workflows, firms can transform AI interactions into compounding assets. Examples include practices like automated self-verification in AI coding and evolving marketing strategies based on feedback loops that refine brand messages continuously. Leaders are urged to measure the learning cycle itself, not just output metrics, to fully realize AI's potential for organizational growth. The central message: AI's promise is best fulfilled not through consumption but through persistent learning and iteration, making expert judgment and infrastructure investment critical to gaining a competitive edge.
In the face of AI-driven workforce changes, many workers experience uncertainty, much like Mike Mulligan and his steam shovel Mary Anne did during the mechanization era depicted in Virginia Lee Burton's classic children's book. As AI reshapes job landscapes, from entry-level positions to roles in HR and project management, the story of Mike Mulligan offers valuable lessons: first, embrace technology to redefine your professional identity; second, pivot by using existing skills to deliver value in new contexts; third, organizational leaders should align AI adoption with the company’s core purpose to support employee resilience. Mike Mulligan’s journey highlights how adaptation and optimism can enable successful career pivots when faced with disruption.
