Lean and mean: How teams can do more with less by leveraging AI agents for research

The pressure of doing more with less

Marketing, insights, and brand teams today are facing unprecedented pressure. Budgets are shrinking, timelines are tightening, and leadership is demanding results that rival (or even surpass!) previous years. In other words, teams are expected to do more with less.

This isn’t just a cliché. Across industries, research budgets are down while the demand for consumer insights has never been higher. Retailers are changing their assortments at lightning speed, consumer preferences shift overnight, and competitors are testing new campaigns weekly. For lean teams, keeping up feels nearly impossible.

Enter AI agents for research: specialized, task-oriented tools that can take on significant portions of the work once reserved for humans. These agents aren’t replacing researchers; they’re amplifying them. By taking over repetitive, manual, or highly data-intensive tasks, AI frees up humans to focus on the strategy, creativity, and storytelling that drive business impact.

In this post, we’ll explore how lean teams can transform the way they work by leveraging AI agents across the research lifecycle: from mining historical data to designing surveys, from analysis to deliverable customization.

The reality for today’s research teams

Shrinking budgets and expanding expectations

In years past, a brand team might have worked with a large external agency, dedicating six figures to a single major consumer study. Today, those same teams are often told to “make it happen” with half the budget or less. Leadership expects the same quality of insights but delivered faster, often within days rather than weeks.

At the same time, the number of business questions that need answering has exploded. Shopper teams want to understand changing category behaviors. Brand managers want to test new creative concepts. Marketing directors need to refine audience targeting. The appetite for insights is endless, while the resources to meet those needs are finite.

The rise of “Lean and Mean” teams

Because of this shift, many organizations now run “lean and mean” insights functions. These teams are small, agile, and scrappy by necessity. They’re staffed with professionals who are equal parts researcher, strategist, and project manager. They juggle multiple priorities at once, relying on efficiency and creativity to get things done.

But even the best teams hit capacity. There are only so many hours in the day, and manual tasks, like searching for past reports, drafting survey questions, and cleaning data tables, eat into the time that could be spent on higher-value work.

That’s where AI agents step in.

Why AI agents are the answer

From tools to teammates

AI isn’t just another research tool. AI agents act more like teammates; always available, infinitely scalable, and capable of handling a wide range of tasks. Unlike traditional automation, these agents don’t just follow rigid rules. They can interpret context, adapt to different needs, and generate outputs that are usable in real-world business settings.

For lean teams, AI agents offer a way to:

• Eliminate repetitive, low-value tasks.
• Speed up time to insights.
• Reduce costs by minimizing reliance on external vendors.
• Improve consistency and quality across research outputs.
• Scale the impact of a small team without increasing headcount.

Use cases for AI agents in market research

Let’s break down the specific ways AI agents can transform the work of marketing, shopper, and brand research teams.

1. Querying historical data

Most organizations already sit on a goldmine of research, but that knowledge often goes untapped because it’s scattered across decks, reports, and spreadsheets. When a new business question arises, teams frequently reinvent the wheel, commissioning fresh research instead of leveraging what’s already been done.

AI agents can change this. By indexing past reports and enabling natural language queries (“What do we already know about Gen Z snacking behaviors?”), AI makes historical data instantly accessible. This reduces duplication, saves money, and ensures teams build on past learnings rather than starting from scratch.

2. Finding previous research

Closely related is the ability of AI agents to act as research librarians. Need to locate the results of a packaging test from last year? Or recall the findings of a concept test run in Q2? Instead of digging through shared drives or pinging colleagues, teams can ask an AI agent to retrieve the relevant materials in seconds.

This speeds up workflows and helps teams answer stakeholder questions in real time, without long delays.

3. Creating research plans

When stakeholders come with broad, often vague business questions, like “How do we better understand our Hispanic shoppers?”, teams need to translate those into actionable research plans. This planning process can be time-consuming, requiring deep methodological expertise.

AI agents can generate draft research plans, including recommended methodologies, sample sizes, timelines, and cost estimates. While human researchers still refine and validate these plans, the AI provides a head start that saves valuable time and ensures nothing critical is overlooked.

4. Designing projects

Project design involves defining research objectives, target audiences, sample criteria, and survey flow. AI agents can create project briefs that align with industry standards and best practices. They can suggest which tools to use (quantitative surveys, qualitative interviews, mobile ethnography, etc.) based on the business question.

This helps less-experienced team members ramp up quickly and ensures that even lean teams without deep methodological bench strength can execute sophisticated research.

5. Writing surveys and discussion guides

Drafting surveys and discussion guides is one of the most time-intensive parts of the research process. Questions need to be clear, unbiased, and aligned with objectives. Discussion guides must balance structure with flexibility.

AI agents excel here. With proper prompts, they can generate draft surveys in minutes, complete with logic paths, scales, and wording variations. They can also create discussion guides tailored to different audiences and objectives. Researchers can then fine-tune these drafts, but the heavy lifting is already done.

6. Analyzing data

Data analysis is where many lean teams bottleneck. Cleaning spreadsheets, coding open ends, running crosstabs, and generating charts all take significant time. AI agents can automate much of this grunt work, turning raw data into clean, structured outputs ready for interpretation.

Beyond automation, AI can also identify trends, summarize findings, and even surface unexpected insights that might otherwise be missed. This gives researchers a running start when preparing reports.

7. Customizing deliverables

Different stakeholders require different outputs. A CMO may want a concise executive summary, while a brand manager may need a detailed deck with data tables. Customizing deliverables for each audience traditionally requires hours of manual work.

AI agents can instantly reformat insights into different deliverables: PowerPoint slides, Word summaries, infographics, or even email-ready recaps. They ensure every stakeholder gets what they need in the format they prefer – without multiplying the workload for the research team.

Real-world impact: Doing more with less

Faster time to insights

By reducing manual effort, AI agents compress research timelines dramatically. What once took weeks, like designing and fielding a survey, can now be done in hours, or even minutes. This agility enables teams to respond quickly to market changes, keeping brands competitive.

Cost savings

With AI handling much of the heavy lifting, teams can reduce spend on external agencies and consultants. While there will always be a place for human expertise, AI agents let teams reserve budgets for the most strategic, high-stakes projects.

Increased productivity

Lean teams can manage more projects simultaneously because AI takes on repetitive and resource-intensive tasks. This scales the impact of small teams, allowing them to meet growing demand without burning out.

Empowering non-researchers

Not every stakeholder is a trained researcher, but with AI agents, more team members can participate in the research process. Marketers, product managers, and shopper leads can use AI to generate drafts and ideas, leaving the insights team to guide, refine, and ensure rigor.

Overcoming barriers to adoption

Trust and validation

One common concern is whether AI outputs can be trusted. The key is to treat AI-generated work as a draft, not a final product. Human researchers must review, refine, and validate the outputs. Over time, teams build confidence in AI as they see its consistency and usefulness.

Training and change management

Introducing AI requires a shift in workflows. Teams must be trained not only on how to use the tools but also on how to think differently about their roles. The goal isn’t to replace humans, but to free them up for the strategic and creative work machines can’t do.

Integration with existing tools

To maximize impact, AI agents should integrate seamlessly with the platforms teams already use, like survey tools, knowledge management systems, data dashboards, and collaboration apps. This reduces friction and encourages adoption.

The future of research teams

From doers to strategists

As AI takes on more executional tasks, the role of human researchers will evolve. Instead of spending hours writing survey questions or formatting slides, they’ll focus on interpreting findings, crafting narratives, and advising leadership.

This shift transforms research teams from order-takers into strategic partners who influence decision-making at the highest levels.

Always-on insights

AI enables a future where insights are not one-off deliverables but always-on capabilities. Need to know how shoppers are reacting to a new product launch? Ask the AI agent. Want to compare the latest campaign results with historical benchmarks? Query the system. Insights become immediate, accessible, and actionable.

Lean teams, big impact

With AI, lean teams can punch far above their weight. They can handle the increasing volume and complexity of business questions without adding headcount or compromising quality. In a world where “doing more with less” is the norm, AI makes it possible.

Why now is the time

Marketing, shopper, and brand teams don’t have the luxury of waiting. The demands on insights are only growing, and budgets aren’t bouncing back anytime soon. The good news is that AI agents provide a solution tailor-made for this moment.

By leveraging AI across the research lifecycle – querying past data, designing projects, writing surveys, analyzing results, and tailoring deliverables – lean teams can not only survive but thrive. They can move faster, deliver more, and increase their influence within the organization.

In short: AI isn’t the future of research. It’s the present. And for lean, mean teams trying to do more with less, it’s the key to unlocking their full potential.

Stay tuned

This is only the beginning. In October, we’ll be sharing something new that takes everything you’ve just read about AI agents and makes it even more accessible, powerful, and effortless for teams like yours. Soon, all you’ll have to do is just Ask Suzy and the answers, insights, and research support you need will be right at your fingertips.

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