When Every Research Firm Uses AI: A Quiet Note on Reflexivity and Disruption

Exploring the Future of Industry Research in the Age of AI-driven Prediction

This piece follows an earlier reflection titled What’s Still Mine? A Knowledge Worker’s Quiet Question in the Age of AI.

There, I explored what it means to keep one’s voice in a world where machines can predict and produce so much.

In this note, I move from the personal to the systemic. I shift from that inner sense of disorientation to the broader implications for research itself.

It began with a simple question:

What happens when industry research and consulting firms widely adopt AI? Not just to organize data or identify trends, but to launch apps and build interactive platforms. What will this industry become?

This is not a forecast.

It is a quiet moment of sensing the future, when it presses closer and begins to shift the ground beneath us.

1.  What Products Will Future Research Firms Offer?

AI and platformization are reshaping the form of consulting services. Where we once delivered reports and slide decks, the offerings may soon look like this:

1.1  Insight-as-a-Service Platforms

Clients type in a question such as, “How will China’s restrictions on rare earths affect the EV supply chain?” The platform then generates data summaries, trend charts, cross-industry analysis, and strategic recommendations. These tools turn one-off reports into ongoing dialogues.

1.2  Auto-Generated Competitive Briefs

Clients input a competitor’s name and receive a ready-made briefing, including financials, market positioning, core strategies, and threat analysis. Output formats may include PDF, PPT, or direct integration into internal databases.

1.3  Semantic Monitoring Platforms

These tools track not just keywords but shifts in tone and intent. For instance, a system might detect how NVIDIA’s language around edge AI has evolved across earnings calls, and notify clients when new signals like “rising cost pressure” emerge.

1.4  Narrative-led Scenario Models

These combine AI with futures thinking. They help companies model multiple paths based on strategic narratives, such as: “If Apple stops developing its own AI chips, how will the supply chain reorganize?”

1.5  Analyst-as-a-Personality

Clients can choose which kind of analyst to interact with: a cool-headed strategist, a contrarian observer, or an East Asia industry expert. Each persona interprets data through a distinct frame of reference, offering a range of perspectives.

2.  How will This Market Evolve?

2.1  Short term (1–3 years)

Traditional report-based firms will face pricing pressure and delivery challenges. Companies with proprietary databases and engineering capacity will rapidly move toward platform and API offerings. Clients will increasingly favor real-time, interactive, and demand-driven insight platforms.

2.2  Medium term (3–5 years)

Analysts will evolve into prompt designers and content curators. They will:

• Help clients shape the right questions
• Design data extraction and response formats
• Translate technical output into human-centered strategic stories

Consulting value will shift toward strategic framing and cultural-context translation. Insight becomes a stylized product. Smaller firms without technical strength will rely on narrative and tone to differentiate.

2.3  Long term (5–10 years)

The traditional report delivery model will fade. Firms that fail to become platforms will be marginalized. Enterprises will build internal insight studios. External consultants will become embedded coaches. Independent analysts with a unique voice and framing may gain loyal followings.

3.  When Everyone Uses AI to Predict, What Happens?

This may be the most uncertain and most profound question.

When every firm, advisor, and strategist uses AI to predict others’ behavior, we enter the realm of reflexivity. This is not a technical flaw, but a logical paradox: once predictions become widely adopted, they start changing the reality they attempt to describe.

This idea traces back to George Soros’s theory of reflexivity. Market participants act on forecasts, and in doing so, reshape the market itself. The prediction becomes false by becoming true.

If everyone believes a stock will fall and sells it, it will fall because the crowd made it happen, not because the model was correct.

When AI models are trained on similar data and deployed to anticipate mass behavior, we may see:

• Strategy convergence and rapid saturation
• Trend bubbles inflated by self-reinforcing feedback
• Black swan events that no one is prepared for

AI has a blind spot. It can extrapolate from the past but:

• It does not realize it is altering the future it predicts
• It cannot grasp that publishing a forecast may change the behavior it observes
• It struggles with layered reflexivity: knowing that others know they are being predicted

4.  What Will This Do to Industry Research?

4.1  From Behavior to the Behavior of Predictors

Research will no longer center solely on “What will consumers do?” but instead ask, “When companies predict what consumers will do, how do they react and how does that reshape the market?”

4.2  Competitive Advantage Will Shift

The edge will not lie in who predicts best, but in who understands the bias and blind spots of dominant models.

4.3  Real Insight Will Come from Deviation and Renaming

“This market didn’t cool down. It overheated to the point that participants lost their agency.” That is not a line an AI is likely to generate. But a person can.

The role of the analyst will evolve from someone who observes trends to someone who observes how predictions are made, and eventually, someone who disrupts the model itself.

Conclusion: In an Age of Predictive Collapse, What Can We Still Do?

Individual behavior is unpredictable. Collective behavior once was. But when everyone uses AI to anticipate the collective, even that becomes distorted.

We are no longer studying markets. We are shaping them. The researcher becomes a participant, then a disturber.

The ones who remain won’t be those with the most accurate models, but those who can see when and why prediction breaks.

We won’t just write reports or output results. We may become designers of narrative, translators of context.

Insight will no longer mean knowing the most. It will mean knowing what still matters.

When everything becomes common sense, our job is to redefine what deserves our attention.

This note is not just about the future of industry research. It is about the quiet evolution of those who still care to ask: What is worth naming, when prediction becomes the norm?

This article is part of our Cultural Signals and Emerging Trends series.
It explores how subtle shifts in culture, behavior, and values, especially around work, identity, and technology, may quietly reshape the future.
These reflections aim to capture early signals, not as predictions, but as prompts for deeper understanding.

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