A Day in the Life: An Example of How Agentic OS is Transforming the Insights Buyer Journey

Editor’s Note: This piece is a little different from our usual fare. While authored by my colleague Lenny Murphy, the “day in the life” that follows was generated using AI ... prompted and shaped by Lenny to explore what the agentic future might actually look like for an insights leader. It’s not a case study, but it’s grounded in real tools, trends, and conversations we’re having across the industry. Think of it as a near-future scenario that’s less science fiction and more strategic preview, offering a glimpse of what’s not only possible, but likely coming very soon.

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We've all seen the writing on the wall: the insights industry is bifurcating between tech-driven efficiency and service-led strategy. But what if the real game-changer isn't just AI in research, it's AI as the new operating system for businesses? Imagine agentic systems, autonomous AI that doesn't just answer questions but plans, executes, and adapts entire workflows across organizations. These aren't futuristic fantasies; they're emerging now, poised to become the central hub for tasks, transactions, and decisions.

From OpenAI's browser launch to Perplexity's Comet and xAI's Grok 4, the macro-dynamics are clear: efficiency imperatives (40-70% cost reductions in procurement, per IBM and BCG), data ubiquity & dependence, and technological convergence are driving this revolution. Investments hit $46.5 billion in Q1 2025 alone, with the agentic AI market projected at $50 billion by 2030. Adoption is accelerating: 40-51% of enterprises are piloting agents in 2025 with a goal of achieving 80% autonomy by 2029 . This isn't hype; it is clear we are well into the transition of deploying a new OS for business, where agents orchestrate everything, and in the insights & analytics space that includes the process from supplier discovery to project execution.

But what does this look like in practice for insights buyers? Well, using an AI platform, I asked it to build off of this thesis and adopt the persona of a research buyer fully leveraging this agentic process. The output is both mind blowing in its implications and entirely credible. And here the kicker; I have heard from multiple sources this future is unfolding very quickly right now. 

That tidbit is the point of this entire exercise: it’s not just the research process that is being transformed, it is the fundamental business management process that is. What is the role of marketing, sales, project managers, etc… in this agentic system? How are services and consulting contracted in an entirely automated procurement process? These are not abstract questions; buyer organizations are pushing suppliers to adapt to this now. This has profound implications for many aspects of the industry; it’s time to start preparing yesterday.   

So, let's step into a day in the life of a consumer insights leader at a mid-sized CPG company, fully immersed in an agentic ecosystem. This isn't speculation; it's grounded in today's tools and trends, showing how the buyer journey compresses from months to weeks, blending traditional methods with emerging AI for unprecedented speed and scale.

A Day in the Life: Orchestrating a New Product Launch with Agents

As the Head of Consumer Insights at a mid-sized CPG company, my world has transformed since we fully embraced an agentic AI ecosystem six months ago. Gone are the days of endless vendor emails, spreadsheet comparisons, and months-long project timelines. Now, my "team" includes a suite of autonomous agents—powered by platforms like Microsoft Copilot for orchestration and integrated with tools from OpenAI and Salesforce Agentforce—that handle everything from ideation to execution. Today, I'm tackling a new plant-based snack concept, covering the full lifecycle: ideation, optimization, testing, targeting, campaign development, ad testing, performance measurement, and customer experience. It's a high-stakes project with a $500K budget and a three-month launch window, but with agents, I expect to shave 60% off costs and deliver insights in weeks, not quarters. Let me walk you through my day, bringing the efficiencies—and a few new hurdles—to life.

7:30 AM: Kickoff with Agent-Led Planning (Internal Focus)

I start my day at home with a quick coffee, logging into our centralized agent dashboard—a sleek interface that feels like a supercharged Slack, but with AI "colleagues" ready to collaborate. My primary agent, "Insight Orchestrator" (built on Azure AI with custom integrations), greets me via voice: "Good morning, Alex. Ready to plan the plant-based snack project?" I dictate my high-level brief: "Develop a full research plan for a new vegan protein bar: ideation through CX, budget $500K, timeline three months, prioritize sustainability targeting."

The agent springs into action, decomposing the task into phases using multi-agent reasoning—spawning specialized sub-agents for each step. Internally, we handle ideation using emerging tools: a synthetic data agent (powered by LlamaIndex) generates 50 concept ideas by simulating consumer behaviors from our first-party CRM data and public trends. It cross-references with GRIT reports for market gaps, producing a ranked list in minutes. Cost efficiency: This would have taken a week and $10K with a traditional brainstorm firm; now, it's free and instant.

But here's a challenge: Synthetic data can introduce biases if not grounded—last project, it overemphasized urban millennials, ignoring rural demographics. I flag this for human review later, setting a KPI: 90% concept diversity score based on demographic coverage.

9:00 AM: Concept Optimization and Initial Testing (Hybrid Internal/Outsourced)

Back at the office, I review the agent's output on my dashboard—an interactive report with visualizations from integrated tools like Tableau. For optimization, the agent runs A/B simulations using synthetic panels, testing variations on flavor, packaging, and pricing. This internal phase leverages emerging tech: The agent pulls behavioral data from our loyalty app, synthesizing 1,000 "virtual respondents" to predict preferences, cutting testing time from two weeks to two hours and costs by 70% (no real panel fees yet).

For validation, I outsource to a traditional qualitative partner like an online qualitative platform—a full-service provider with human moderators who specialize in focus groups. The agent auto-generates an RFP, sends it via GEP's procurement platform, and selects the platform based on past performance (KPI: 4.5+ rating on response time/quality). Expectations: Deliver moderated sessions with 50 real participants for attitudinal depth; core KPIs: 85% alignment with synthetic predictions, turnaround in 5 days. Profile: Mid-sized qual firm with AI transcription tools, ensuring hybrid efficiency without full disintermediation.

Challenge: Integration hiccups—last time, the agent's synthetic data clashed with the platform's real insights, requiring manual reconciliation. Efficiency win: Overall phase cost drops to $50K from $150K.

11:00 AM: Targeting and Campaign Development (Outsourced with Agent Oversight)

Mid-morning meeting with my team: We dive into targeting. The agent analyzes first-party data (e.g., purchase history) and outsources fresh behavioral sampling to a data provider like a large panel firm with AI-augmented samples (e.g., blending real and synthetic for scale). The agent issues a micro-RFP for 2,000 targeted respondents, expecting 95% match rate on demographics (KPI: Data freshness <7 days old).

For campaign development, the agent ideates concepts internally using AI synthesis (e.g., Midjourney for visuals, Copy.ai for messaging), then outsources to a strategic consultancy like a high-end insights advisory blending human strategy with agentic tools; expectations: Refine 10 campaigns with ROI projections. KPIs: 2x projected engagement lift, delivery in 1 week.

Efficiency: Agents coordinate seamlessly, reducing vendor back-and-forth by 80%; cost savings: 50% via synthetic pre-testing. Challenge: Over-reliance on agents can stifle creativity—humans still catch nuanced cultural fits.

1:00 PM: Ad Testing and Performance Measurement (Internal with Emerging Tools)

Over lunch at my desk, I monitor ad testing. The agent runs multivariate tests using synthetic audiences (emerging tool: Google's Gemini for predictive modeling), simulating views across channels. For real validation, it outsources quant testing to a quantitative provider like a polling and data analytics firm—a quantitative provider with agentic APIs. Profile: Data-heavy firm specializing in polls; expectations: Test 5 ads with 1,500 respondents. KPIs: 80% predictive accuracy, cost under $30K.

Performance measurement shifts internal: The agent sets up dashboards (integrated with Qualtrics XM) for real-time tracking, using AI to forecast CX metrics. Efficiency: From monthly reports to daily insights, saving 60% time. Challenge: Data privacy—ensuring agents comply with GDPR during ingestion adds oversight.

3:00 PM: Customer Experience Optimization and Wrap-Up (Hybrid)

Afternoon: The agent synthesizes all data into a unified report, flagging CX gaps (e.g., post-purchase feedback loops). I outsource final qual checks to a qual provider like a virtual focus group platform—a qual provider for in-depth interviews. Profile: Niche firm with virtual focus groups; expectations: 20 interviews for experiential insights. KPIs: 90% satisfaction alignment, 3-day turnaround.

Internally, the agent iterates the plan, projecting 2x ROI from optimized targeting. End-of-day: I review efficiencies—total timeline: 6 weeks vs. 6 months traditionally; costs: $300K vs. $800K. Challenges: Agent hallucinations required 10% manual corrections; balancing synthetic/real data to avoid biases.

In this agentic world, my role evolves from manager to strategist—overseeing a symphony of AI and partners. For our organization, it's a game-changer: Faster launches, better decisions. But success hinges on KPIs like 90% accuracy and human oversight for the unpredictable.

This glimpse into the agentic future isn't distant—it's unfolding now, with adoption accelerating and tools like these reshaping how we drive innovation. At Greenbook, we're committed to guiding the industry through it. 

What does your day look like in this new era? Share your thoughts in the comments, and sign up for updates to stay ahead of the curve.