Beyond the Hype: AI for Strategic Foresight

Product professionals are now being held to a higher standard as they are increasingly expected to deliver commercial outcomes and drive strategic value. While the development of product strategies built on Strategic Foresight may have once been a nice-to-have value add, it is now increasingly essential. (Read The Four Capabilities Product Managers Need Now and Into the Future for our perspective on Strategic Foresight, and 3 other critical capabilities.) Ultimately, if we aren’t considering the forces that impact the future and how those forces may impact our products, then we risk being blindsided.

But this is an arena where AI can help. AI in this context can extend our ability to think beyond the present by finding data points that we didn’t even know existed and then by bringing them to us as a potentially valuable input to wrestle with.

Why Strategic Foresight Matters More Than Ever

Product people who focus only on what customers said last quarter, what competitors are doing now, or what feature is next risk missing the bigger picture. Market-defining shifts rarely show up in sprint reviews. They build gradually, then change everything. We’ve seen global and regulatory shifts, climate impacts, new customer behaviours and emerging tech all disrupt even the most established products.

Strategic Foresight helps product teams look further ahead. It sharpens their ability to anticipate disruption, adapt strategy, and respond with clarity instead of reactivity.

In fact, the study “Corporate foresight and its impact on firm performance: A longitudinal analysis” found that “Future-prepared” firms outperformed the average by a 33% higher profitability and a 200% higher growth. Product teams that build foresight into their planning consistently outperform those who rely only on retrospective analytics or short-term priorities.

Foresight also builds confidence. It gives teams more weight when imagining possible futures and designing strategies that are innovative, adaptive and resilient.

For a deeper look at Strategic Foresight and how product teams are embedding it into their planning, culture and decision-making, read Strategic Foresight: An Edge Product Teams Need Now.

Let’s now look more closely at how AI is providing additional clarity and context when developing foresight.

AI as a Partner in developing Foresight

Strategic Foresight is most powerful when thinking across multiple time horizons:

• Near-term (1–2 years): emerging customer behaviours, competitive positioning, AI-driven product innovation
• Mid-term (3–5 years): industry changes, platform shifts, new market entrants, changes in customer values
• Long-term (5–10+ years): regulatory transformations, societal shifts, technological disruption

AI can assist in synthesising volumes of data and in identifying weak signals or early indicators of change, subtle patterns, trends, or emerging phenomena, that may not yet be obvious to humans scanning the landscape. It can generate hypotheses across all the horizons—expanding the field of view far beyond the next release.

Leading product professionals aren’t just using AI as a search tool—they’re pairing with it. They’re engaging in back-and-forth conversations, feeding it context, asking for rationale, and debating results. In essence, they’re treating AI like a thinking partner.

Product people may ask “Who are the competitors in my market?” and validate the results against their existing analysis, or against other AI tools to start mapping tools with tasks. They continue, “Provide a SWOT analysis on every one of those companies.” This gathering of data and synthesis is helpful to get the bullet points, but they recognise that they have to do more human research to deeply understand the competitors and more thought to make sense of it all. Ultimately, the work still needs us, for validation, perspective, sanity checks and human creativity.

To do this well:

• Ask multi-turn prompts: challenge assumptions, explore contrarian perspectives, identify interdependencies
• Feed the AI rich context: product vision, market insights, key assumptions
• Combine AI outputs with human synthesis: validate insights, map implications, and prioritise action

Three Strategic Foresight Techniques to Explore with AI

While are many techniques that can be used to gain Foresight, here are three that help Product teams with some starter prompts to use when pairing with AI in this realm.

• Horizon Scanning – Spotting early signals of changeHorizon Scanning involves systematically searching for weak signals, first seeds or symptoms of a future trend or change that usually need another to 10 years to mature into a trend and emerging trends that could impact your product or market. In her series on Weak Signals, Elina Hiltunen explores the factors to consider when evaluating Weak Signals and Emerging trends and notes that by combining weak signals, you find trends. Useful sources are not just about industry news—it’s about widening your lens to include adjacent domains, technological patents, regulatory debates, academic research, and even cultural shifts.
  • As an example, in the mid-2000s desktop computing was still prevalent and the mobile internet was in its early stages. Google, which was already dominant, had weak signals and a belief that a mobile OS would help greatly expand its core search and ad businesses well beyond its PC platform. It acquired Android Inc. in 2005, which while originally pitched as creating to create an operating system for digital cameras was, by then, focused on making an open-source OS for phones. See https://www.androidauthority.com/google-android-acquisition-884194/
• Scenario Planning – Exploring multiple possible futuresScenario Planning helps teams stress-test their strategies by exploring how different forces might combine to create alternate futures. AI can support this by generating scenario narratives, identifying wildcard events, and suggesting second-order effects.The authors of Harvard Business Review’s “Use GenAI to Improve Scenario Planning” note that GenAI can help address many of the core limitations of traditional scenario planning approaches in three stages: scenario creation, narrative exploration, and strategy generation. In a simulated example with a fictional company, GenAI analysed risks across supply chains, technology, and policy shifts, built detailed scenarios on AI integration, and iteratively generated tailored strategies. Through this dynamic and interactive process, planners could explore complex futures more comprehensively, engage stakeholders effectively, and develop actionable strategies at a speed and scale that traditional methods could not match.
• Backcasting – Working backward from a desired future to determine what must happen todayBackcasting starts with a vision of a preferred future and works backward to map the milestones and decisions required to achieve it. As an example, Interface Inc. was a flooring business with a bold vision. It set a vision to becoming a fully sustainable company by 2020 though doing so “would be like summiting a mountain higher than Everest.” The initiative called Mount Sustainability backcasted what would be necessary to achieve the goal and identified seven key areas – the “Seven Fronts” where it needed to progress to achieve the vision. Things like eliminating all forms of waste in every area of business, eliminating toxic substances from products, vehicles and facilities and operating facilities with 100% renewable energy. They also knew they had to set up metrics to track their progress. They reported success on those goals in Lessons for the Future: The Interface guide to changing your business to change the world.Pairing with Generative AI can aid backcasting by helping product teams think more boldly and plan more effectively. It can assist in defining future scenarios by drawing on trends, patents and customer insights; scan market research to identify key enablers or gaps; suggest milestone pathways based on industry precedents; and map dependencies across technology, teams and processes. It can also monitor for changes, like new regulations or market shifts, and update the plan as new signals emerge.

We have made it easy to try some prompts related to each of these techniques to get you started in exploring the concepts and the tools. Download AI Prompts for Strategic Foresight for prompts you can use for these three techniques.

Findings in Practice

In our work with Australian Product Professionals of all levels who are active with GenAI, we’ve found that the AI-pairing “discussions” provide a wide variety of additive data to incorporate into our thinking – some useful, some not – but in evaluating outputs we are gain perspective and greater confidence in our overall understanding of the factors at play.

In doing so, product teams are using AI to challenge biases, generate contrarian perspectives, and rapidly prototype ideas. This insight makes strategies more resilient, uncovers blind spots that could have derailed roadmaps, and, frankly, just makes products better.

There are AI-powered tools that accelerate these methods like GPT-based models to generate alternative future scenarios or employ trend platforms to synthesise emerging patterns from global datasets. There are also products like Futures Platform which can help.

Product people pairing with AI have reported that their research sessions are more productive, entertaining and engaged than traditional research. The result of this is product people who are more attuned to the market and insights beyond their existing knowledge which results in better strategies and stronger product outcomes.

Managing the Risks

In doing so, Product Managers cannot outsource their role and responsibility. As with all things, they need to mitigate risks while leveraging the benefits. It’s the critical thinking, human judgment, and creativity that are their superpowers.

AI is a powerful mirror as it can both reveal and amplify biases. Recognising that, we must:

• Use multiple models or sources to avoid echo chambers

  • Relying on a single model risks reinforcing the same patterns of thinking or bias baked into its training data. It also helps to evaluate the timeliness of the data – basing Foresight on old data isn’t as valuable as current knowledge. By comparing outputs from multiple models or sources, you can spot discrepancies and broaden perspective. You might run the same queries through different LLMs (such as ChatGPT, Gemini or Perplexity) and supplement AI insights with human-sourced expert reports, market research, and customer feedback.

• Review sources and ensure source transparency

  • AI often generates insights without clear citations. Understanding where information originates helps us judge its reliability and detect when biased or low-quality sources are being reflected back. Choose AI tools or plugins that provide citation links or use follow-up prompts to explicitly ask the model to reveal its sources or reasoning path; then follow the path to make sure that’s real and says what you think it says.

• Recognise that Deep Research may reference hallucinated content

  • Even when AI-generated research appears thorough, it may include fabricated facts or references. We must remain vigilant and critically assess these outputs rather than taking them at face value. Double-check key facts against sources of truth and validate unfamiliar concepts with trusted databases, peer-reviewed literature, or expert consultation.

• Intentionally ask AI to surface dissenting views

  • Because AI tends to optimise for coherence or popularity, it may overlook minority or counter perspectives. Explicitly prompting for dissenting views may help to ensure that blind spots and alternative futures are brought into the conversation. Prompt the AI with questions like: “What might critics of this view say?” or “What are possible risks or opposing arguments that are under-represented?”

• Cross-validate AI outputs with domain experts and empirical data

  • AI can surface surprising insights, but without validation from human expertise and real-world data, these insights risk being misleading or misapplied. Pairing AI with rigorous validation anchors foresight and decision-making in reality. Share AI-generated outputs with internal experts, run validation workshops, test assumptions through user research, or triangulate findings with independent data sources.

We all know that there are risks we need to mitigate with any new technology. Gen AI hallucinates and sometimes invents “facts” in very confident ways. That’s not just inconvenient, it’s dangerous if left unchecked. It is also constantly learning, and employees need to ensure they are respecting their business’ intellectual property and working in secure ways in secure environments.

Shaping the Future

Strategic Foresight isn’t about predicting a single future, it’s about preparing for many possible futures. The key thing to remember when pairing with AI for strategic foresight is that we aren’t outsourcing our creativity or decision-making, but are increasing the inputs and our own understanding in order to create a vision that is grounded in reality.

The value of foresight lies in shaping better decisions. Use foresight outputs to inform strategic priorities and product roadmaps and translate scenarios into signals to monitor. AI can even automate this tracking and build adaptive strategies that remain resilient across multiple futures.

The human role remains critical. AI may collect and arrange all the puzzle pieces, but it’s up to us to create a beautiful picture that also solves meaningful problems both now and in the future.