The Future of Insights: The Industry at an Existential Turning Point

Additional Authors:

Supriya Chaudhury, Head of Content Strategy at CMB

Supriya Chaudhury is an experienced marketing and business leader who has built and grown brands across professional services, consumer, B2B, and SaaS organizations. Over the course of her career, she has held senior leadership roles at Marsh McLennan, Fidelity Investments, Harvard University, Clavis Insight, Kantar Retail, Nielsen, Liberty Mutual, and P&G/Gillette.

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Over the past several weeks, CMB conducted a series of interviews with senior insights across industries, including technology, financial services, media, healthcare, retail, and travel. Leaders spoke openly about both the opportunities and the challenges emerging across the industry. The conversations explored:

1. What does it mean to lead Insights in an AI-first world?
2. Where must the function pivot to stay relevant and valuable?
3. How can Insights evolve from informing decisions to driving business impact?

This synthesis also extends broader industry research examining the transformation of the insights function in an increasingly AI-first world. Together, these perspectives reveal a clear shift from traditional research execution toward strategic decision enablement. This shift has profound implications. If insights teams cannot keep pace with business decision cycles, stakeholders may bypass them entirely.

Core Tensions Shaping the Future of Insights

Pressure for Speed and Rigor: Faster Answers and Uncompromised Quality

The pace of change is making traditional research shelf life shorter; answers relevant today may be outdated by next week. At the same time, AI-driven research is allowing insights teams to address fast-moving decisions in ways that were not possible, in a context where LLMs are delivering the instant gratification of “good enough” answers. Stakeholders expect partial answers in real time and within days of fieldwork and rapid synthesis of findings to support real-time decision-making.

“You can immediately get an answer to a pretty complex question by typing it into a chatbot…and you get compared to that”

“If stakeholders can move faster without research, research is at risk”

AI is a primary catalyst for this shift. By automating foundational tasks and enabling faster analysis, AI has redefined what is possible. However, the true value of insights lies not in speed alone but in the ability to interpret data, connect disparate signals, cull noise and hallucinations from true signals and provide strategic clarity.

Demand for Strategic Partnership and Efficient Delivery

AI is pushing insights upstream. When the same LLM that helps answer a research question can also generate the concept or strategy based on the research, big decks and slow decisions are no longer acceptable. But speed is not the same as judgment. It is important not to miss nuance and context, as that can result in wrong conclusions. This makes the insights role more critical to interpret deeply, guide strategy and ensure faster decisions are still the right ones.

“People are overwhelmed with information. Stories that help people make decisions matter more”

“Executives want three pithy bullets they can act on”

Leaders increasingly expect concise, actionable recommendations that translate insights into strategy. This evolution reflects a broader shift in expectations. Insights teams are no longer viewed solely as research providers; they are or must become strategic partners responsible for guiding priorities and enabling confident decision-making.

Leveraging AI Acceleration and Managing Risk

AI is transforming workflows and addressing a growing share of foundational questions, yet adoption remains uneven due to governance constraints and ongoing concerns around data integrity. Leaders must work to expand capabilities in a dynamic, changing environment.

“These tools handle about 80% of basic questions we used to research”
“The process for bringing in AI solutions can take 10-12 months

Yet AI also introduces new challenges. Concerns around data integrity, governance, and transparency are top of mind for insights leaders. Insights teams increasingly need a voice in how their data is used to train LLMs, as poor-quality inputs can lead leaders to flawed decisions.

That also creates a bigger opportunity for insights to improve the inputs, close critical gaps, and guide where AI should and should not shape strategy. The greatest impact comes not from automation alone but from the thoughtful integration of artificial and human intelligence.

Emerging Operating Modes for Modern Insights Teams

As the insights function evolves, organizations are adopting distinct operating modes shaped by their strategic priorities, organizational constraints, and levels of AI maturity. Insights leaders are approaching this transition from different starting points, and while no organization fits neatly into a single category and may situationally transition between them, four common operating modes emerge, shaped by the constraints teams face today. Each reflects a unique set of pressures, needs, and expectations for solutions and external partnerships.

Agile Insight Operators are focused on delivering real-time intelligence to keep pace with rapidly evolving business environments. These teams face intense pressure to provide immediate answers while maintaining methodological rigor, often benchmarked against AI-powered tools.

Tensions and Needs

• Speed and Depth: Stakeholders expect immediate, real-time answers, while teams must still maintain rigor.
• Continuous Learning: Pressure for faster, iterative learning loops; the business moves faster than the insights process.
• Human against AI benchmarking: Insights teams are increasingly compared to chatbots and LLMs that deliver instant answers.
• Capacity and credibility: Small teams are under pressure to both automate and be credible.

Responsible Innovators need to deliberately balance governance with innovation to manage risk exposure. Operating within highly regulated environments or organizations with strict legal, privacy, and procurement requirements, these teams are eager to leverage AI and emerging technologies but must also manage internal guardrails.   

Tensions and Needs

• Innovation under Governance: The need to move quickly is constrained by legal, privacy, security, and procurement friction
• Opportunity and Reputation Risk: Desire to leverage AI is tempered by fear of leaks and “black box” methods
• Speed with Risk Management: Faster experimentation is limited by model-risk constraints and long approval cycles
• AI Transparency: Transparency on what AI is doing and what data is used; teams are frustrated by slow momentum

Strategic Translators need to drive impact through synthesizing insights into clear, decision ready narratives that drive action. These teams face complex stakeholder dynamics and high expectations for clarity, relevance, and influence. Their success is measured not by the volume of insights produced, but by their ability to translate insights into actionable strategies that drive business outcomes.

Tensions and Needs

• Complexity and Clarity: High stakeholder politics and executive cadence demand simple, clear, and focused outputs
• Iteration with Efficiency: Endless revisions reflect the need for precision, while creating pressure to move faster
• From Insight to Activation: Generating insights is not enough; impact depends on how well they get integrated into decisions
• Pride and Pressure: Teams take pride in their craft, while feeling urgency and frustration to influence decisions

Capability Builders work to modernize while maintaining credibility through transition. They need to focus on strengthening operating models and evolving skills and approaches amid organizational change. These teams often face resource constraints, talent gaps, and evolving leadership expectations, creating both urgency and opportunity for transformation.

Tensions and Needs

• Modernizing through instability: Evolving a strong operating model with new, updated tools, in the face of flux
• Capability and Continuity: Organizational churn resulting in capacity, continuity, capability and talent pipeline gaps
• Change and Continuity: Adapting to new priorities, leadership, and expectations with a stressed workforce
• Transformation with Confidence: Building internal confidence in adopting AI and other new tools amongst team

The New Future

The future of market research presents both challenges and opportunities. To remain relevant and influential, insights leaders must evolve their operating models and capabilities in several keyways.

1. Build Hybrid Human-AI Operating Models
   Integrate AI with human expertise to accelerate analysis and enhance strategic impact.
2. Prioritize Decision-Driven Research Portfolios
   Align research investments with business priorities and decision timelines to maximize value.
3. Invest in Activation and Stakeholder Alignment
   Ensure insights travel effectively across the organization, influencing strategy, innovation, and execution.
4. Strengthen Governance, Quality, and Transparency
   Establish robust frameworks that protect data integrity and support responsible AI adoption.

Grounded in the voices of senior leaders across industries, this perspective highlights a clear but not easy path forward.  Insights leaders cannot afford to be passive observers of the AI shift. They need to actively define how AI is used in research, where it can responsibly accelerate decisions, and where relying on a generic LLM creates risk for the business. That starts with understanding which AI solutions fit their operating model, pushing leadership to establish clear standards for responsible use, and making the case that research activation is not just about generating answers faster, but about ensuring those answers support decisions.

At this pivotal moment, it is important that insights teams do not undersell their own value, allowing the business to settle for fast, shallow answers. The opportunity is to do the opposite: to own the change and lead it by playing a larger role in connecting data to strategy, activating knowledge across the enterprise, and bringing the judgment that turns information into influence.