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[!summary]Executives should learn how AI works by using it themselves and appointing AI champions to lead projects. Start with one important task and build AI tools that solve real problems, then improve and scale them. AI success depends on ongoing effort, teamwork, and clear ownership, not just tools or quick fixes. [!note]
Excelente insumo para pensar en cómo ayudar a una organización a incorporar IA de manera óptima en sus procesos.
Highlights
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what we see in our sessions is that leaders who haven’t gotten their hands dirty don’t clearly understand the practical opportunities and challenges of AI.
Las personas en altos cargos tienen que desarrollar una intuición respecto de cómo funcionan los modelos de lenguaje, para así entender sus capacidades.
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AI usage in the workplace is now widespread, but it’s an altogether different ballgame to build organizational capability that truly realizes financial gains.
El que la IA se utilice en las organizaciones no quiere decir que las haga más eficientes o efectivas. Gran parte del uso es por usuarios individuales en tareas triviales que no agregan mucho valor. Es mucho menos común que las organizaciones tengan una estrategia implementada que involucre encadenamiento y optimización progresiva de procesos.
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The METR chart shows just how far the technology has progressed, but we’ve seen that many organizations implementing AI haven’t kept up with the sea change.The bottleneck for AI adoption has moved from model capability to chart shows just how far the technology has progressed, but we’ve seen that many organizations implementing AI haven’t kept up with the sea change. The bottleneck for AI adoption has moved from model capability to organizational capability.
Este es un muy buen framing para promover la idea de la consultoría en uso de IA. Útil para cualquier pitch: no te sirve de nada pagar por tokens si no sabes usarlas bien.
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we’ve fundamentally altered our trainings to support executives and teams in this new era. For instance, we’ve retooled our sessions on prompting into workshops on setting up agents, skills, and workflows that can be owned, tested, and maintained. We’re working with executives on building that organizational muscle and turning raw model capability into reliable, repeatable workflows.
Útil tip para pensar en el diseño de experiencias de aprendizaje efectivas en la línea del desarrollo de capacidades organizacionales para el uso de la IA.
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AI implementation starts with executive fluency. That doesn’t mean executives need to become day-to-day AI builders. What’s important is that you spend enough time with the tools to understand what you’re asking your teams to do.
Las jefaturas deben tener nociones generales sobre cómo funcionan los modelos de lenguaje, para que puedan entender cómo pueden apalancar su organización. AI Fluency contextualizada, a eso debe apuntar un programa de formación personalizado.
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Understanding the roles and perspectives of IT and security are a critical part of AI fluency.
Esto es algo que noto que para mí es medio invisible, pero que es parte fundamental de lo que te permite desarrollar soluciones potentes mediante AI: qué es un servidor y la diferencia entre frontend y backend son un par de vacíos concretos con que me he topado en otros.
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A skill is a set of reusable capabilities that define how an agent performs tasks; in order to reliably reproduce a workflow, it needs instructions, examples, reference materials, and a clear picture of what good and bad output look like. Of course, the same is true of people. If you cannot explain your standard of excellence to your chief of staff, you’ll certainly struggle to explain it to an AI system. This is why as a leader, you’re better positioned to use AI than you may think. High-performing executives already know how to set direction, allocate resources, define standards, and judge whether work is good enough. Executives do not need to have all the answers. But you do need enough AI fluency to ask the questions that will help you decide where AI belongs in your company’s strategy
Sobre por qué los líderes con experiencia profunda en un campo son quienes están mejor posicionados para apalancar el valor agregado de la IA en el trabajo.
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Who will own the systems we build?
Esta es una pregunta muy importante, sin cuya respuesta es imposible desarrollar soluciones sostenibles. El trabajo que estamos haciendo en comercial lo está tomando en consideración.
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Champions shepherd a project from initial idea to completion by experimenting with and iterating on workflows, teaching others what success looks like, and gathering support across the organization for AI implementation. Their job is to decide what gets built, what gets maintained, what gets improved, and what gets killed.
Está figura difiere de lo que yo habría pensado inicialmente en el sentido de que también se deja en manos de estas personas la decisión respecto de qué es lo que se construye… Tengo sentimientos encontrados al respecto, ya que no sé si lo que a una persona con perspectiva local le parece la mejor movida a nivel organizacional es necesariamente lo más estratégico. Aunque, por otra parte, solo se pueden desarrollar buenas soluciones con conocimiento íntimo del problema.
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AI champions do not need to be the most technical people in the organization. They don’t need to be engineers or have experience using AI for years. They do, however, need to be curious. Great champions constantly ask questions, probe how processes work, and want to understand “what excellence looks like” for different tasks and functions.
Otro desafío a mis supuestos… yo me hubiese imaginado que es importante que tengan competencias técnicas a la base (como Allison y Gonzalo), pero me hace sentido que no sea un requisito, ya que no siempre se puede cumplir.
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The best AI champions also understand that AI implementation is fundamentally a people issue — and they care about the people they work with.
AI Champions. Importante la máxima de que la implementación de la IA es, fundamentalmente, un problema humano. Es un buen framing para la consultoría.
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the strongest champion is someone who’s close to the workflow the company is solving—a marketer who knows where campaign analysis gets stuck, for example, or a customer support lead who understands ticket triage.
Desarrollador descalzo. Bien similar la propuesta de valor del experto situado como la persona mejor posicionada para entender la mejor solución.
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This is where many AI programs fail. Executives identify enthusiastic people and ask them to help with AI on top of their day job. The result: The work gets squeezed into evenings, deprioritized during busy periods, and sometimes abandoned altogether.
La implementación de procesos de IA necesita condiciones, no es “magia”.
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What champions need is protected time—at least two days a month, in our experience—and a clear mandate. They should be responsible for a small number of workflows in their domain, with enough authority to make decisions about how those workflows are documented, tested, and maintained. They should also have a clear escalation path when they need support from IT, security, leadership, or another function.
Condiciones necesarias para los AI Champions
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AI implementation works better when you resist the urge to build the “whole body” at once. Instead, start with one artery of the workflow, a narrow, painful piece of the puzzle that can be tested, improved, and then trusted before expanding from there. Good candidate workflows are often unglamorous—categorizing support tickets or summarizing vendor updates—but are frequent enough to act as valuable test cases.
Es mejor comenzar con problemas locales y sencillos, para ir construyendo momentum en base a las victorias. El caso de locales que estamos trabajando en comercial, con un bot que evalúa las propuestas antes de enviarlas, es un súper buen ejemplo.
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Think of an AI agent less as a machine that runs forever and more as an employee you’re onboarding. You have to give it instructions, show it examples, correct its mistakes, and clarify what excellence looks like. Over time, it’ll become more useful, but only if it’s managed correctly.
Metáforas para agentes IA.
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The next step sounds obvious, but you’d be surprised how many executives get it wrong: Only scale what works. This is important from a resource perspective, but it’s also key for internal adoption. While many executives begin with a company-wide mandate that everyone start using the tools, the better path is to foster one visible win by choosing the right champion, workflow, and standards, and building from there. When a team experiences an AI workflow that solves a real and painful problem, AI stops being an abstract productivity promise and becomes a practical solution. That experience creates pull across the organization, and other teams start asking what could work for them.
Sólo escala lo que funciona. El principio detrás de esta idea es que la mejor forma de movilizar a las personas a adoptar IA para automatizar o asistir procesos es a través de casos de éxito concretos y con evidencia clara de su utilidad.