From signals to stories: how to leverage AI agents to deliver real-world foresight at scale

Strategic foresight professionals are no strangers to scanning. Every week brings new reports, articles, patents, press releases, and signals of change.

The challenge is rarely the lack of information. It’s making sense of the flood and turning raw signals into meaningful stories that organizations can act on.

This is where AI has stepped in. But many foresight managers and consultants share the same worry: what if the AI only produces generic outputs (what some have called “AI slop”) that still need to be rewritten or even discarded?

Let’s look at how purpose-built foresight AI can avoid this trap and deliver insights that are concrete, credible, and ready to use.

Why generic AI outputs aren’t enough

Generic large language models can summarize and rephrase, but they often fall short when foresight professionals ask for depth, context, and actionable relevance. The risk is that outputs look polished yet lack the rigor or specificity required for strategic work.

As Panu Kause, CEO of FIBRES, put it in our recent webinar on AI agents in foresight: "AI-generated analysis should not be seen as the end result. It should be seen as the starting point for human sense-making."

That starting point has to be solid. It has to provide enough structure and detail to spark new thinking, not force professionals to start from scratch.

From thousands of signals to structured foresight stories

FIBRES’ Foresight Agents take a very different approach from generic tools. Instead of scraping the open web, they work with a curated database of more than 200,000 trusted source publications, including licensed access to paywalled publications. This ensures higher-quality inputs from the outset.

The process with AI looks like this:

• Step 1: Define the brief. Professionals set the research topic (or research problem) and scope.
• Step 2: Generate a plan. The AI proposes detailed research questions and keywords, which can be refined.
• Step 3: Scan and synthesize. Thousands of articles are analyzed, clustered, and summarized into structured outputs.
• Step 4: Deliver foresight stories. The AI produces trend and technology descriptions, each including:
  • definitions and context
  • driving forces and implications
  • opportunities and risks
  • concrete examples and case references
  • links back to the original sources

In the words of Panu Kause: "Nobody can manually process the thousands of signals that AI can synthesize in under an hour. But foresight professionals and subject matter experts still bring the context, judgment, and ownership."

Concrete use cases: what AI foresight really looks like

The most common question we get is simple: can AI foresight deliver real-world examples that matter to my stakeholders?

The answer is yes. Every trend description in FIBRES comes with concrete examples drawn directly from source material. These examples might include a new technology deployment, an emerging policy experiment, or a business model innovation.

Because they’re linked back to original articles, foresight professionals can check, validate, and enrich them for their own contexts.

To illustrate, we’ll embed three example radars directly into this article. Each one shows how signals have been synthesized into foresight stories on topics like the future of B2B SaaS, decarbonization in heavy industry, and AI in healthcare. Readers can explore the trends, dive into the linked sources, and see how the AI presents real-world use cases.

Why context and collaboration still matter

Even with high-quality, structured outputs, foresight is not just about information. It’s about building shared understanding and ownership across teams.

AI can handle the scanning and first-draft synthesis, but only humans can:

• interpret results in the specific context of their organization
• connect insights to strategy, R&D, or risk priorities
• engage stakeholders in workshops and discussions
• create narratives that spark action

If AI is used in isolation, there’s a real risk of losing the collaborative value of foresight. "The real power comes when foresight professionals take these AI-generated deliverables and turn them into shared stories that shape decisions", as Panu notes.

Turning outputs into impact

AI foresight should not replace human foresight work. It should expand its reach. By freeing professionals from hours of manual scanning, it allows them to focus on:

• scenario exploration with stakeholders
• building strategy workshops around concrete cases
• testing assumptions and challenging biases
• embedding foresight outputs into decision-making

When foresight professionals use AI as a force multiplier, they can scale their capacity without scaling their teams. The result is more continuous foresight practices, richer conversations with stakeholders, and ultimately more impact on strategy and innovation.

Get started with your own foresight story

The best way to understand the potential of AI foresight is to see it in action on your own topics. With FIBRES, you can run a research project in less than an hour and get a radar filled with structured trends, implications, and real-world examples.

If you’d like to explore this, you can book a personal demo with our experts. Together, you’ll see how Foresight Agents can turn signals into stories for your organization, and how to use them for real impact.