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Futures Thinking es un método estratégico y creativo que sirve para explorar múltiples futuros alternativos, con los que dar sentido propio al cambio.

Futures Thinking es una metodología mediante la cual exploramos múltiples futuros alternativos, analizamos los riesgos y beneficios comparativos de cada uno, e identificamos oportunidades para tomar hoy decisiones rentables para el negocio y positivas para la sociedad y el entorno.

Entre los beneficios de incorporar el Futures Thinking al pensamiento estratégico, más obvio es que permite a la empresa ampliar su visión, la claridad, el entendimiento y la agilidad con las que se enfrenta al mundo y a su propio futuro.

El futuro empieza hoy

El futuro no está definido, podemos darle forma: cada acción que tomamos hoy construye el mañana.
Por eso, el Futures Thinking no pretende hacer predicciones; eso querría decir que el futuro es inevitable.

El objetivo final de la metodología Futures Thinking es darnos poder y diligencia: protegernos de los vicios del cortoplacismo, que nos hacen altamente vulnerables a los cambios y a la incertidumbre, y poder así promover y hacer real el cambio positivo desde hoy.

¿Qué es Futures Thinking y cómo funciona? ¿Qué significa el pensamiento de futuros?
Futures Thinking es:
Un proceso continuo que consta de 3 fases
Futures Thinking es una metodología cíclica de trabajo que se divide en tres fases: Foresight " Insight " Acción.

El proceso de trabajo es cíclico porque ante el cambio incesante al que estamos expuestos, es necesario hacer un escaneo situacional continuo.

Veamos en qué consiste cada una de las tres fases.

Fase 1 :: FORESIGHT

Comprender las dinámicas y direcciones del cambio y descubrir perspectivas posibles de futuro.

El Futures Thinking es una de las metodologías que se basan en el Foresight y que toman como punto de partida la volatilidad, la incertidumbre, la complejidad y la ambigüedad ( VUCA).
Futures Thinking es:
Comprensión del cambio
Todos pertenecemos a un mundo que se enfrenta a una gran variedad de desafíos tecnológicos, sociales, ambientales, económicos y políticos.

Un Mundo VUCA -volátil, incierto, complejo y ambiguo-, en el que la incertidumbre es en muchos momentos extrema.

Ciertas tendencias, drivers y dinámicas del cambio que están dando forma al futuro, están fuera del control directo de cualquier empresa.

Investigarlas y comprenderlas es fundamental; además, entender las incertidumbres críticas que están generando para la organización, nos permitirá anticipar sus consecuencias para estar preparados.
Futures Thinking es:
Scanning del entorno
En Futures Thinking, para investigar esas tendencias y fuerzas del cambio, se realiza un escaneo sistemático del entorno externo, o lo que se llama Horizon Scanning o Environmental Scanning. Hablaremos de ello en profundidad más adelante, pues supone la tarea fundamental y la materia prima de cualquier proyecto de Foresight o Strategic Foresight, para identificar posibles oportunidades, desafíos y posibles desarrollos futuros relevantes para la compañía.

En medio de la complejidad, existen señales del cambio, de que el futuro podría ser diferente.

Escanemos el entorno para descubrir conexiones no obvias en los múltiples dominios del cambio, y para detectar tendencias y cambios económicos, sociales, culturales, ambientales, de salud, científicos, tecnológicos, políticos, etc. Los valores y "variables de futuro".
Futures Thinking es:
Hacerse preguntas provocativas y profundas
Responder a la incertidumbre profunda requiere plantearse preguntas también profundas y provocativas, para ampliar nuestra visión y comprensión.

Futures Thinking consiste en pensar de forma más creativa y estratégica.

Desligarse de historias, sesgos y suposiciones familiares para expandir la mente y abarcar un espectro mucho más amplio de posibilidades.

Comprender el pasado y el presente, y conectarlos con el futuro.

Descubrir incluso lo que parece imposible hoy y, en realidad, podría ser posible en la próxima década.
Futures Thinking es:
Sentir el cambio
El ser humano es la única criatura capaz de pensar y planificar el futuro, aunque los científicos han descubierto en esta última década que nuestro cerebro presenta una serie de obstáculos neurológicos que nos impiden pensar en el futuro de manera efectiva: pueden llevarnos a hacer suposiciones erróneas sobre qué futuros son probables y qué futuros son improbables, y pueden hacernos sentir que tenemos menos poder para moldear e influir en el futuro de lo que realmente tenemos. Por eso necesitamos técnicas que nos ayuden a sentir el cambio, para poder comprenderlo en profundidad.

En Futures Thinking desarrollamos escenarios, es decir, convertimos las señales de cambio en puntos de vista plausibles, provocativos y lógicos del futuro para poder sumergirnos en ellos y sentir el cambio.

Los escenarios describen cómo se podría transformar el mundo, como resultado de los cambios actuales y de la acción de las discontinuidades emergentes. De nuevo, no pretendemos elaborar predicciones, sino simulaciones que amplíen nuestra mirada y pensamiento.

En este proceso la creatividad cobra mayor importancia. La investigación de futuros se basa en hechos y datos reales del presente, que no son suficientes para extrapolar tendencias, elaborar hipótesis e imaginar otros posibles futuros alternativos. No existen datos del futuro, solo del pasado; necesitamos imaginar lo inimaginable para que, si sucede, no nos pille desprevenidos, o para liderar su creación.

Fase 2 :: INSIGHT

Dar sentido propio al cambio traduciendo la prospectiva en las alternativas estratégicas más convenientes, no sólo para la organización, sino también para la sociedad. Definición del Futuro Deseable de la compañía.
Futures Thinking es:
Desvelar oportunidades
Una vez que hemos comprendido el mundo en un contexto más amplio, las dinámicas del cambio, hemos desarrollado perspectivas a futuro y nos hemos sumergido en ellas, podemos darle a todo un sentido propio.

Explorando esos múltiples futuros alternativos, analizamos los riesgos y beneficios comparativos de cada uno, para identificar las consecuencias internas de los retos descubiertos en el foresight.

Qué oportunidades tenemos para tomar decisiones rentables para el negocio y positivas para la sociedad.
Futures Thinking es:
Definir tu Futuro Deseable para cambiar el mundo
Con todo, no sólo podemos identificar los retos urgentes a los que se enfrenta tu negocio y las amenazas futuras que debemos evitar. Podemos también identificar los retos a los que se enfrenta el mundo.

De esta manera, podemos definir una visión propia, positiva, convincente, diferente y medible de cómo crear impacto positivo tanto en tu empresa como en la sociedad: el Futuro Deseable de tu compañía.

Visualizar un futuro deseable es el primer paso para crear una estrategia y planificación poderosas para lograr un propósito en concreto. Aclarar una visión es uno de los mecanismos más poderosos para involucrar a un equipo, organización o comunidad y entusiasmarlos para avanzar hacia un nuevo territorio.

Si perseguimos impactar positivamente con nuestro trabajo, nuestros productos, servicios y políticas, en los pensamientos, valores y comportamientos de la sociedad y de la cultura, crearemos una sociedad menos frágil y reactiva, y más sostenible y resistente.

Fase 3 :: ACCIÓN

Planificación estratégica del Futuro Deseable de la organización. Activar acciones transformadoras en el presente.
Futures Thinking es:
Tomar decisiones y acciones hoy. Planificación estratégica
Tras desvelar insights, dar sentido propio al cambio y definir el Futuro Deseable de la organización entre las alternativas posibles, estamos preparados para tomar decisiones y acciones más informadas, más contextualizadas y menos cortoplacistas.

Es el momento de movilizar colaboradores y recursos en torno a un objetivo común. De construir agilidad estratégica a corto plazo, a la vez que se trabajan y desarrollan oportunidades prometedoras de crecimiento e impacto positivo a largo plazo.

Con una visión clara, y revisando nuestras capacidades y limitaciones, podemos identificar obstáculos, reclutar aliados, recopilar recursos, identificar nuevas áreas de inversión y planificar las acciones necesarias para convertir nuestra visión en realidad.

Recuerda: Futures Thinking no es simplemente imaginar nuevas posibilidades; es la práctica de descubrir lo que es posible para avanzar hacia un futuro más deseable. Y el punto crítico para convertirlo en realidad es pasar a la acción.

Futures Thinking es crear futuros hoy. Es aplicar el Foresight para llevar a cabo un cambio positivo real en tu vida, en tu trabajo y en el mundo que te rodea.

. . .

Referencias:

Get There Early: Sensing the Future to Compete in the Present, Bob Johansen.

Foresight Manual, Empowered Futures, Global Centre for Public Service Excellence.

Foresight: The manual, United Nations Development Program.

Making organisations fit to drive desirable futures, René Rohrbeck, Camelia Ram.

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Metodología Futures Thinking. ¿Qué es? - Futuros Deseables

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Es un método estratégico y creativo que sirve para explorar múltiples futuros alternativos, con los que dar sentido propio al cambio.

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The latest annual McKinsey Global Survey on the current state of AI confirms the explosive growth of generative AI (gen AI) tools. Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards' agendas. What's more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI-related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.

The organizations that have already embedded AI capabilities have been the first to explore gen AI's potential, and those seeing the most value from more traditional AI capabilities-a group we call AI high performers-are already outpacing others in their adoption of gen AI tools.

The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations' adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.

Table of Contents

1. It's early days still, but use of gen AI is already widespread

2. Leading companies are already ahead with gen AI

3. AI-related talent needs shift, and AI's workforce effects are expected to be substantial

4. With all eyes on gen AI, AI adoption and impact remain steady

5. About the research

1. It's early days still, but use of gen AI is already widespread

The findings from the survey-which was in the field in mid-April 2023-show that, despite gen AI's nascent public availability, experimentation with the tools is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they've had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.

Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function-meaning that 60 percent of organizations with reported AI adoption are using gen AI. What's more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board's agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research, these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.

In these early days, expectations for gen AI's impact are high: three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry's competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. Industries relying most heavily on knowledge work are likely to see more disruption-and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI-adding value equivalent to as much as 9 percent of global industry revenue-knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI's strengths in language-based activities, as opposed to those requiring physical labor.

Responses show many organizations not yet addressing potential risks from gen AI

According to the survey, few companies seem fully prepared for the widespread use of gen AI-or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees' use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they're mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we've seen in previous years, most respondents say their organizations are not addressing AI-related risks.

2. Leading companies are already ahead with gen AI

The survey results show that AI high performers-that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use-are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities-including more traditional machine learning capabilities, robotic process automation, and chatbots-AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

AI high performers are much more likely than others to use AI in product and service development.

Another difference from their peers: high performers' gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations' top objective for gen AI is to create entirely new businesses or sources of revenue-and they're most likely to cite the increase in the value of existing offerings through new AI-based features.

As we've seen in previous years, these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.

While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.

The findings offer further evidence that even high performers haven't mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that's a much larger share than the 19 percent of respondents from other organizations who report that practice.

Many specialized MLOps technologies and practices may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver-and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.

3. AI-related talent needs shift, and AI's workforce effects are expected to be substantial

Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists-all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers-the most-hired role last year-than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

4. With all eyes on gen AI, AI adoption and impact remain steady

While the use of gen AI tools is spreading rapidly, the survey data doesn't show that these newer tools are propelling organizations' overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations' EBIT last year was attributable to their use of AI-essentially flat with the previous survey-suggesting there is much more room to capture value.

Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

About the research

The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations' AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP.

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