Leading organisations have, for a few years now, have employed AI to accelerate the environmental scanning process that underpins strategic foresight. As a very public example, the World Economic Forum's strategic intelligence platform connects thought leaders across industry and government together with entrepreneurs in service of addressing today's complex, systemic challenges. It is a powerful tool to visualise the complex interconections between technological advances and societal trends across systems.
Machine learning's capability for pattern recognition across the vast number of data points being generated daily across the globe, whether from new entries into patent databases, new scientific research publications, new applications of technologies, is enabling the accelerated identification of weak signals of change at the strategic periphery.
So, what foresight tasks that humans do today might AI do in the near future?
Without question, AI's superiority at scanning and analysing huge amounts of data to detect the weak signals ahead of big shifts, will reduce the human hours currently invested in desktop research and subsequent analysis.
It is likely that the rapidly developing capabilities of generative AI will increasingly help us to imagine future scenarios, accelerating and enhancing our creativity in scenario planning. Generative AI can rapidly create text, images and even video from simple text prompts, which provides inspiration for further iteration and the creation of futures narratives and visualisations. Perhaps it may even be able to identify the tipping points that will lead to any one scenario to eventuating over another.
In the near future, humans will still retain their superiority for sensemaking, generating insight and critical thinking around the priorities. But with ever expanding neural networks, AI will surely advance over the longer term in its ability to curate the trends, connect the dots, and perhaps uncover new innovation opportunity spaces, and to accelerate assessment and human decision making.
In an ever more complex operating environment, access to these capabilities will be a key source of advantage.
What are some of the challenges?
Possibly the most pressing challenge for many organisations will be access to data, first of all proprietary data, and then to third party data, which is an essential precursor to applying AI to ‘connect the dots’ within.
Data security is a key concern, whilst storage and processing are currently extraordinarily energy intensive.
Others include our ability to remove bias from models that are trained on human activity that has inherent bias, and figuring out how exactly we humans will interface and collaborate with AI to amplify our human creativity and problem-solving ability.
Transparency in the algorithmic models and underlying data will be essential for senior leaders and Board to have confidence in the thoughtful implementation of AI derived recommendations.
What capabilities will organisations need to develop?
AI and data literacy will be vitally important to develop throughout the workforce. Much as every business has become a digital business over the last two decades, AI will become is a critical competency to develop during the next decade.
Not every organisation will need to develop the capability to build its own models, but organisations must develop a baseline understanding of AI and how it can be applied to generate business outcomes. Those that don’t have the capability to apply AI techniques will start to see their competitive edge eroded by those that do.
The past three years have heightened the need to develop resilience and agility with organisations. Organisations must plan not towards a singular future extrapolated from the present, but for a variety of scenarios that may.
This requires developing capability in strategic foresight, and tightly integrating this into organisational systems. AI’s potential to accelerate environmental scanning and robust scenario development by a significant factor and at scale will be transformational.