Market research operational excellence in the agentic era

What does market research operational excellence, in the age of agentic AI mean? We can start to answer that by asking another question. What would you do with an extra 12 hours each week? It’s not a trick question. Recent estimates suggest that roughly 30% of all work activities could be automated by 2030, potentially adding around 12 hours of capacity per employee per week. 

And this is a general estimate; for market research, the numbers look even more extraordinary.  

In this article, I want to share my personal take on operational excellence in the era of agentic AI, especially as it relates to market research teams. At Forsta, we’ve been blending powerful technology with human expertise, and I’ve seen firsthand how a confident, candid approach can help research organizations work smarter without losing their human touch. Let’s talk about what that looks like in practice – how AI can augment our processes; how it can streamline workflows by up to 50%; and why the future of insights will still depend on human judgment, storytelling, and empathy. 

AI as an augmentation

One thing I’m adamant about is helping clients improve without overhauling everything they do. We just don’t need to. The goal isn’t to rip up or replace well-honed methods, but to augment them where it makes sense. Think of AI as an accelerator and assistant, not an existential threat. In fact, industry experts agree: McKinsey’s research on operational excellence finds that the highest-performing organizations use technology to “augment human capabilities, rather than simply replacing humans with machines”. 

AI can take on the repetitive, time-consuming tasks; it can clean your data overnight. This doesn’t make human researchers obsolete. I love how a recent World Economic Forum piece put it: AI doesn’t remove humans; it removes friction. It cuts out the drudgery that prevents humans from doing their best work. 

Crucially, this means you don’t need to discard your existing processes overnight. We can plug in AI to streamline operations. Your core expertise, methodological rigor, and knowledge remain intact. For example, if your team has a solid survey design process, an AI assistant can take a questionnaire draft and script it into a survey platform in seconds, preserving your question flow but sparing you the laborious coding. In my experience, this augmentative approach lowers the fear factor significantly.  

And incremental change is often the smartest path. Organizations, especially large ones, evolve incrementally even as technology leaps exponentially. Instead, we pilot, test, and iterate, giving our teams time to relearn and build trust in AI outputs. This risk-minimizing transformation makes adoption more sustainable. People get to see AI as a helpful colleague rather than a disruptor. 

Transforming with AI

First, sticking with wider operational excellence practices, it helps to ground the transformation in some data and reality. We know the long-term potential of AI is huge. McKinsey pegs the opportunity at up to $4.4 trillion in added value for companies globally. For research, meeting that sweet spot is a measured approach: set ambitious goals for efficiency and quality, but break the journey into safe, pilot-sized steps. For instance, start by automating one piece of the workflow (maybe try Word Importer on a small project, Research Agent to refine slides for a low-pressure presentation) and measure the impact. In parallel, invest in governance and upskilling. Deloitte’s research emphasizes that many companies hit barriers because they lack clear governance models for AI and haven’t adjusted operating processes to support it. So, establishing guidelines (for quality checks, for data privacy, for when human review is required) reduces the risk of using AI. It sets guardrails so people trust the new tools. 

Also key is simplifying the message and focusing on the bigger picture. I’ve found that when introducing AI changes, they’re most enthusiastically received when we frame it in terms of story and purpose: What is the story we’re trying to tell with these improvements? Usually it’s something like, “Imagine if we could deliver client results in half the time, with higher confidence – what would that do for our business?”  When people see the big-picture narrative – faster insights, happier clients, more room to innovate – the tools become just a means to that end. As research leaders, we must be storytellers to drive change and meet ever-evolving operational excellence needs. Simplify the message: AI is here to take the boring stuff off our plate so we can shine in the interesting stuff. Period. That clarity helps everyone see why we’re doing this, not just how, and it creates a shared vision that makes adoption a mission, not a mandate. 

From data to story: Keeping the human touch

Let’s talk about what truly excites me: the storytelling and insight that humans – and only humans – can deliver. We often say internally that freeing up time with automation is only half the equation. The other half is, what do you do with that time saved? This is where operational excellence transcends efficiency and becomes about elevation of work. 

When AI slices out 50% of your production timeline, it forces a new question: if the late nights of data prep are gone, how will you redeploy that energy? I put this challenge to my teams and our clients’ teams alike. The answer we keep coming back to is: focus on the story, focus on the why. In a world where AI can churn out decent-looking charts or basic analysis in seconds, the differentiator becomes the human narrative and interpretation. We need to spend our reclaimed hours connecting the dots, finding the “so what” in the data, and crafting the story that will resonate in the boardroom or with the end client. 

Read more: Transform data visualization: Speak the language of leadership

In fact, at Forsta we explicitly designed our Research HX platform to help researchers focus on strategic storytelling and delivering deeper insights, rather than getting stuck in operational weeds. As I noted during our launch, the goal is to remove the roadblocks of outdated systems so teams can “focus on strategic storytelling, deliver deeper insights, and create tangible value”. That means using the time and mental bandwidth AI gives us to elevate the research deliverable – turning findings into a narrative that decision-makers can act on. It’s the difference between handing over a data dump versus telling an insights-driven story that inspires action. The latter requires empathy, context, and creative thinking – qualities uniquely human. 

There’s also a bigger-picture benefit here: differentiation. If every firm has access to the same AI analysis tools, reports could start to look eerily similar. (Large language models, after all, tend to produce the same “plausible prose” based on common training data.) This is the challenge of automation: you get efficiency, but you risk losing originality. The part where you infuse the findings with meaning, prioritize what matters for that particular client, and add the creative “spark” is where human researchers make the difference. It’s our job to ensure that efficiency doesn’t come at the cost of a bland, one-size-fits-all output. So operational excellence must include excellence in storytelling. We’ve got to see the forest for the trees and help our stakeholders see it too. 

Beyond automation: Elevating your team (and yourself)

Now, let’s circle back to that tantalizing notion of extra time and what we do with it. If operational excellence in the AI era gives you breathing room, use it to invest in your people and your own growth.

Read more: The future of insights leadership

For teams, this means reskilling, coaching, and thought leadership. The tasks that AI automates are often the entry-level, mechanical tasks that junior researchers used to cut their teeth on. So how do new researchers learn and grow when AI handles, say, the first pass of data cleaning or chart-making? The answer is we train them differently. We elevate their starting point. Instead of spending weeks on drudgery, entry-level team members can spend more time interpreting data, learning to ask the right business questions, or honing their storytelling skills. We need to coach teams to elevate their game – teaching things like how to critically evaluate an AI-generated insight, how to cross-examine multiple data sources, how to inject human empathy into an analysis. These are higher-order skills, and developing them early is a huge win for the next generation of researchers. 

From a leadership perspective, I also ask: what new questions should we be asking now that AI is picking up the slack? One big question is how to ensure human judgment and empathy stay front and center. Our clients entrust us with understanding human behaviors and emotions – that doesn’t change in the AI era. In fact, it becomes even more important. The Forsta ebook “Human Experience in the AI Era” makes a compelling point: as AI gets more powerful, the premium on uniquely human traits like empathy, imagination, and ethics only increases.  

At the end of the day, operational excellence isn’t just about efficiency metrics – it’s about building a smarter, more resilient, more human-centric operation. That includes technology and people in harmony. Yes, we streamline processes and cut out waste (who doesn’t love a 50% time savings?), but we also invest that dividend back into our teams’ growth and our clients’ success. 

Embracing augmented excellence

If there’s one takeaway I want to leave you with, it’s this: the future of AI in research is bright and it’s fundamentally human. We have an unprecedented opportunity to redesign how our teams work. By letting AI handle the rote and routine, we unlock time and creative headspace for the work that truly differentiates us. That’s the work that fuels innovation, strengthens client relationships, and drives growth. 

At Forsta, we’re excited (and frankly, honored) to be on this journey with our clients as a forward-thinking partner. We’re blending powerful technology with human expertise because we believe that’s the winning formula. The early results are validating, but what’s truly gratifying is seeing researchers light up at what they can do now. When a team realizes they can deliver a project in days instead of weeks or finds themselves brainstorming insights instead of battling Excel at midnight, it’s like a whole new world opens up. 

If you haven’t started, start small but start now. Pilot an AI tool on one of your processes. Encourage your team to experiment. And remind everyone (including yourself) that the goal isn’t to work faster for faster’s sake.  It’s to work smarter and create space for what truly matters. 

Discover more about what it means to use AI and agentic to enhance your workflow by exploring our solutions.