Backcasting to the Futures

In recent decades, the future has often been portrayed as an inevitable nightmare, an impending apocalypse. From the Y2K scare of the 90s to anxieties about climate change, nuclear war threats, dystopian pop culture scenarios, and today’s fear that artificial intelligence will strip us of jobs and wellbeing, we live immersed in a narrative steeped in pessimism. It’s as if we are passive spectators of a foretold disaster, a fate beyond our control.

Projecting excessively negative (or, conversely, excessively positive) futures creates a domino effect on people. It leads to paralysis, anxiety, and inaction, triggering adverse outcomes on both individual and organisational levels, such as “disempowerment”, “self-fulfilling prophecies”, or “decision paralysis”. In short, it’s far from ideal. On the other hand, excessively utopian projections risk inspiring reckless choices.

Curiously, even in prosperous times, we tend to look to the future pessimistically. In 1997, a time of optimism, Wired published an article envisioning a prosperous future for humanity—provided ten negative scenarios did not come to pass. Despite the hopeful atmosphere of that era, many of those predictions have materialised, proving that merely projecting a future is not enough. Action is the strategic and tactical key for businesses and societies to prevent the worst and bring about the best.

Many negative scenarios identified in the Wired article have occurred because they were “Black Elephants”—the proverbial elephant in the room. These were known issues, ignored until they became so impactful they redefined the present and the future.

We must remember that a negative perception is not reality. The future is not written, nor is it an independent entity inexorably moving toward doom. On the contrary, it is a space we can still shape. Action is the critical element.

By applying Futures Thinking, we can analyse trends and signals of change that may impact tomorrow and, consequently, possible, probable, and preferable futures.
The method centres on identifying preferable scenarios, and from there, the exciting part begins: figuring out how to achieve the best possible future. The technique here is backcasting.

Working backwards from a hypothetical future prompts practical questions: Do we have the right tools? Are our resources sufficient? Should we start investing in certain technologies? Do we need to empower our teams to grow and develop expertise in specific areas? What obstacles could hinder this scenario’s realisation?

Concrete action becomes crucial at this stage. Wired’s 1997 predictions proved accurate because we ignored the “elephant in the room”, allowing foreseeable problems to materialise without intervention.

When we delve deeper into backcasting, it becomes clear that defining a future 10 years from now involves asking, “Where should we be in seven years? In five years? In three years?”
To bring these steps to life, we need to identify the requirements and obstacles to achieve the desired future and turn them into clear objectives. Then, we repeat the process from the beginning.

This process simplifies the realisation of strategic roadmaps and actionable short-term initiatives. Such structured planning enables the design of products and services today that will not become obsolete tomorrow.

Today, artificial intelligence, with its ability to process vast amounts of data and detect patterns invisible to humans, can be a powerful ally in this mission. If the dystopian fears surrounding AI stem from our biases and present-day emotions, AI can provide a data-driven perspective to help overcome these distortions. It can identify emerging trends, weak signals, and hidden opportunities, offering a compass in a sea of uncertainties and guiding us toward a more objective future.

But that’s not enough. AI, trained on human-generated data, inevitably inherits our biases. These are not intrinsic to the technology but reflect our societies, choices, and historical and cultural preconceptions. Therefore, AI must be guided by positive, inclusive and ethical values—a critical human input. It’s up to us to ensure that the data we use to train these systems is representative, balanced, and free from harmful biases. We must steer technology toward goals that reflect our highest ideals, understanding that the ethical quality of AI’s output depends on the ethical quality of our input. 

Undoubtedly, AI will change the world, but how can we ensure it helps us shape a better future? This calls for profound political, cultural, and social work.