Generative AI isn’t a promise anymore. It’s here.  

In the momentum of this modern gold rush though, it’s easy to forget a critical truth: the power behind these tools is still human. The quality of generative AI outputs depends on the inputs we feed them, and that begins with the rigor of primary research.  

Since 2023, the use of generative AI by organizations has more than doubled, with 71% of companies leveraging it by 2025. One standout application? Synthetic audiences, a powerful new way for communicators to gain insight into their audiences’ attitudes, perceptions and behaviors. But just like it’s easy to get swept away by the wave of generative AI, it’s easy to think that synthetic audiences are rendering traditional primary research obsolete. Nothing could be further from the truth.  

Synthetic audiences can’t outrun the human source 

Primary research and AI aren’t in competition. They’re codependent. 

The best synthetic audiences are built on the back of great human data. On the other hand, primary research can be made more focused and agile when layered with synthetic audience outputs. Synthetic audiences can extend the life of primary research when we incorporate real-time news or cultural data, keeping the insights fresh and up to date. Understanding the complexities of this relationship enables researchers to maximize benefits of both methods.  

As the old saying goes garbage in, garbage out. 

That’s never been truer than it is today. 

The Human Edge: What AI Still Can’t Simulate 

AI’s emergence has elevated the importance of research design and data quality vigilance, as MRS chief Jane Frost highlights in her article covering the Global Data Quality Initiative. Now more than ever, poorly designed studies don’t just lead to flawed short-term insights; they embed those flaws into synthetic audiences that rely on these studies as crucial training datasets. When applied carelessly, this flawed insight can lead to misinformed decisions that create business or reputational risk. 

This new reality demands that we approach primary research with heightened rigor and foresight. The questions we ask, the participants we recruit and the methodologies we employ must all be optimized not just for their immediate results but for their value as training inputs for AI models that expand the radius of these data.  

The equation is simple: better human data lead to better AI models. Human insights provide texture and nuance that synthetic models currently fail to accurately simulate. 

Cultural Context: AI models struggle to understand deep-rooted and implicit cultural knowledge that humans navigate effortlessly through lived experiences 

Emotional Nuance: The richness and range of human emotional responses remains difficult to synthesize  

Emerging Behaviors: Primary research captures to-the-moment changes or evolutions in human behaviors before they become widespread enough to appear in secondary sources 

Contradictions and Complexity: Humans often hold conflicting views simultaneously; a complexity that enriches our understanding but challenges AI models 

These qualities aren’t “nice to haves.” They’re ingredients for insight that inspire action. The kind of action clients, policymakers and customers can trust.  

The ‘garbage in, garbage out’ dynamic shouldn’t be viewed as loose guidance for fine-tuning virtual audience models; there are real risks involved when primary sources are undervalued (e.g., algorithmic bias, insight homogenization and missed innovation opportunities). 

Reimagining Primary Research for the AI Age 

While critical to the relevance and credibility of AI-driven audience research, traditional primary research isn’t immune to the pressure to adapt and evolve in the age of advancing generative AI. Today’s research must be crafted with dual purposes: 

Delivering precise and actionable insights 

Creating high-quality, scalable inputs for AI systems and synthetic audiences 

This evolution means considering: 

Data Structure: How will this data need to be formatted to serve as effective model inputs? 

Comprehensive Capture: Are we collecting the contextual information AI needs for proper interpretation? 

Longitudinal Value: How will this data remain relevant as behavioral patterns evolve? 

Ethical Considerations: What guardrails ensure our data fuels responsible AI development? 

Final Word 

Forward-thinking organizations recognize that the competitive advantage is not in choosing between primary research and synthetic audiences, but in their purposeful integration. Investing in the quality, design and implementation of primary research is no longer optional. It’s a requirement to fuel the next generation of insights, both human and artificial.  

As we navigate this rapidly evolving landscape, we’re firmly planting the flag:  

Primary research isn’t just still relevant, it’s more important than ever and will improve synthetic audiences.  

When it comes to the success of AI rollouts and adoption, there’s a notable delta between the perspectives of a company’s leaders and its employees.

About a quarter of leaders say their AI rollout has been effective. Only 11% of employees agree.

That’s not just a signal that implementation is lagging – it’s a signal that alignment is lacking. And that gap likely isn’t due to tech – at least not tech alone. More likely, it’s about trust, clarity, consistency, relevance … even identity.

You can launch the right tool with a solid rollout plan behind it. But if employees don’t understand why it matters – or where they fit in – behavior change stalls before it has the chance to take root.

Why Behavior Change Stalls: The Distance Between Intention and Action

Behavior change doesn’t just happen because a tool is available – it has to be intentionally built into the experience. Early. Clearly. With a level of resourcing and support commensurate with what the company has invested in the platform itself.

That means embedding behavior-shaping touchpoints from day one – not waiting for adoption to happen organically. What does this look like in action? Continuous feedback loops to surface and address employee hesitations, leaders modeling new behaviors in visible ways, regular moments of reflection woven into team rhythms, and dedicated roles focused on coaching and practical support – among other things.

People pull back when things feel vague. When the shift doesn’t connect to what they care about, or how they see their role. Even the best tools get overlooked if the environment around them doesn’t support the change they’re meant to create.

Where Behavior Change Breaks Down: The Subtle Signs of Resistance

But where exactly does the friction show up?

Resistance doesn’t always show up as vocal opposition – more often, it shows up in silence. A tool gets rolled out, but questions go unasked. Team conversations sidestep it. Some employees disengage or quietly revert to old habits like manually analyzing large swaths of data or generating meeting summaries and first drafts from scratch.

This refusal isn’t manifested as loud rebellion. It’s slow fade. And in AI transformation, that quiet drift is one of the biggest threats to sustained impact.

Left unchecked, that disengagement can erode tool ROI – dragging down productivity, creating adoption gaps across teams and limiting the career growth of those who hesitate, especially in roles where fluency with AI is quickly becoming table stakes. In short, when employees don’t buy in, the business can’t move forward at the pace it needs to.

The good news? These signals are visible – if you know where to look. Spotting and addressing them early can protect your investment, align your people and accelerate progress where it matters most.

The Three Layers of Hesitation: Enterprise, Team and Individual

For companies struggling to drive AI adoption, this is the moment to step back and start asking simple questions like the ones here.

While the tools will get better and the use cases will expand, none of that guarantees impactful adoption.

Right now, most organizations don’t need a newer generation of the technology. They need better feedback loops. More storytelling and open conversation. A stronger bridge between AI strategy and lived experience.

And more honest signals from leaders – that this isn’t just about the next tool, it’s about how the work is changing, why that matters and how the organization is committed to making space for people to come along with it.

The Final Takeaway: Change Sticks When Conditions Are Right

Adoption doesn’t accelerate just because the tools get better. And AI doesn’t scale well in confusion. These things happen only when the environment is ready – when culture, clarity and context catch up to the ambition.

That’s when change starts to feel real. And when people decide it’s worth leaning in.