Why trend decks rarely change decisions, and how the knowledge spiral could be used to explain the gap

In my previous post, I described a pattern I see often: foresight work that is active, structured, even high quality, and yet, it doesn’t lead to better decision when ignored. In this one, I’ve tried to go deeper into how we could move from foresight data into futures knowledge, as knowledge becomes part of decision-making when produced and used together.

How to utilize the classical knowledge spiral into your foresight activities for bridging the gap between foresight results and decision-making?

More trend information is not the problem

When foresight struggles to scale, the first instinct is often to increase input:

• track more signals,

• expand sources,

• scan more frequently,

• produce more detailed outputs.

This makes sense. It improves visibility, shows results or foresight work. But it rarely solves the underlying issue of using futures knowledge in decision-making.

In many organizations, the challenge is not lack of information. It’s that information does not turn into intelligence. And without that transformation, foresight remains something you deliver, not something the organization learns from.

The missing mechanism: how knowledge is actually created

To understand this, it helps to step away from tools and look at how organizations create knowledge. Let’s go back to the very basics of knowledge creation. One useful lens comes from Nonaka and Takeuchi’s (1995) work on the knowledge spiral. In simple terms, knowledge does not move in a straight line, as it evolves through a continuous cycle between tacit and explicit understanding. This spiral amplifies knowledge, but stalls if externalization or combination fails, like trend decks gathering dust without decisions.

In foresight work, this cycle is highly visible. We can think of it as four stages:

1. Socialization: sensing together

This is where weak signals first appear.

• observations from different teams,

• early signs from the market, technology, policy,

• informal discussions, intuitions, field insights.

At this stage, knowledge is mostly tacit. It lives in people’s heads, conversations, and scattered notes. If this stage is weak, you don’t have a sensing system. You have isolated observers and small groups.

2. Externalization: making it visible

Here, signals are captured and articulated for wider audience:

• turning observations into documented signals based on discussions,

• writing summaries for sharing ideas, reports, facilitating workshop documentation,

• describing early patterns and different point of views.

Tacit knowledge becomes explicit. If this stage is weak, insights stay informal and only few become aware of those. It is hard to share or built on.

3. Combination: connecting the dots

This is where foresight work becomes recognizable:

• clustering signals and linking them to what is already known,

• defining phenomena links to strategic topics, and describing their implications for your organisation,

• linking developments across domains,

• building scenarios or concepts for piloting ideas,

• using results in different decision-making processes.

Explicit knowledge is combined into structured insight. If this stage is weak, you get lists, fancy trend reports, less of patterns describing relevance for you. It becomes part of noise, if the meaning is not described.

4. Internalization: using it in decisions to change or transform

This is the step most often overlooked.

• applying insights in strategy discussions in a way that leads to changes,

• stress-testing assumptions with practical pilots in the market,

• shaping innovation priorities

• influencing risk assessments

Explicit knowledge becomes embedded again: in decisions, habits, and actions. If this stage is weak, you have great foresight insight without (measurable) impact.

This is the stage for assessing foresight ROI with questions such as:

• How many tests lead to success?

• How many new product ideas did foresight activities land to?

• How many many strategy stress tests have been completed (by using X number of scenarios)?

• After 2-3 years: To how many surprises did we manage to be well-prepared for?

• After 5-10 years: How many new business streams did we manage to test and successfully launch?

..ultimately, benefits of foresight activities should be seen in high performance and long term survival. If you are doing as much of foresight efforts as the volatility and complexity of your operating environment requires, you are on the right track: Future-prepared firms outperformed the average by a 33% higher profitability and by a 200% higher growth (Rohrbeck, & Kum, 2018).

Where foresight typically breaks

Most organizations are reasonably strong in the middle of this cycle. They can externalize (capture signals) and combine (create trends and reports). Still, many struggle with all the steps.

This spiral amplifies knowledge, but stalls if:

1. socialization happens seldom (many of signals are not shared, nor discussed if focus is on business as usual and most probable events),

2. externalization is not structural and organized,

3. combination results are ignored (e.g. trend decks gathering dust without further use),

4. internalization is left to people, who are not familiar with foresight results or do not know how to use them.

This creates a very specific pattern: Insights are there, but not collected, or shared, implications not created… and even if reports are built, they might not be absorbed by decision-makers. And when that happens, the cycle is not continuous, thus the futures intelligence and knowledge does not accumulate.

So even if the content is strong, the system around it is incomplete. This is why many teams experience the same frustration: “We’ve already done this work. Why are we starting again?” Because their input never fully circulated.

A simple knowledge spiral-based quiz

If you want to understand where your foresight process is breaking, it helps to look at each stage directly.

Socialization (Sensing)

• Are multiple teams contributing signals regularly? Is it easy to share your insights on latest signals or trends?

• Do people discuss and share observations beyond formal projects?

Externalization (Capturing)

• Are signals stored in a structured, accessible way?

• Can others understand and reuse them easily?

Combination (Sensemaking)

• Can signals be linked, updated, clustered, and compared over time?
• Do insights evolve, or are they recreated each time from scratch?

Internalization (Decision and use)

• Are insights available and used when decisions are made?
• Can decision-makers trace results back to original material and find evidence?

Most organizations don’t need to fix everything. They need to identify where the spiral breaks and restore continuity there.

What changes when the cycle is intact

When all four stages are connected, foresight starts to behave differently.

1. sensing becomes collective, not individual (having a shared place to capture and access signals),

2. insights build on each other, instead of resetting (adding deep dive analysis, sharing results),

3. knowledge becomes accessible across time (one place, easy to use, connected to other databases and discussions),

4. decisions are grounded in visible, shared context and lead to changes (access to foresight within decision contexts, traceability and testing).

At this point, foresight is no longer a sequence of outputs. It becomes a learning system for continuous futures knowledge creation.

This is where many teams begin to move toward a dedicated foresight workspace. Platforms like FIBRES are designed around this cycle: supporting continuous sensing, structured sensemaking, and decision-linked intelligence in one environment. Not as a repository for outputs, but as infrastructure for the spiral itself. If you'd like to see FIBRES in action, you're welcome to book a personalized walkthrough of the foresight platform with me.

Foresight does not scale when information increases. It scales when learning becomes continuous, which only happens when insight is not just created, but shared, connected, and used.

References

Nonaka, I. & Takeuchi, H. (1995). The knowledge creating company. New York: Oxford University Press.

Rohrbeck, R., & Kum, M. E. (2018). Corporate foresight and its impact on firm performance: A longitudinal analysis. Technological Forecasting and social change, 129, 105-116.