Getting started with agentic analytics

How do you get started with Agentic Analytics? Analytics driven by autonomous AI agents is no longer a futuristic idea reserved for tech giants or the enterprise business. It’s rapidly becoming a competitive advantage for organizations ready to turn insight into action at scale.

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But here’s the truth: jumping straight into full automation is a recipe for stalled projects and skeptical stakeholders. That’s why the smartest analytics leaders, transformation teams, and AI champions are embracing a phased, low-risk path to adoption.

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In this guide, we’ll walk you through a crawl-walk-run approach to agentic analytics that helps you deliver value fast, build trust across your teams, and scale automation when the time is right.

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Why Agentic Analytics isn't an all-or-nothing play

When looking at Agentic Analytics, there is one major question that immediately arises: “Is agentic analytics hard to implement all at once?”

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The answer is simple: Yes—but it shouldn’t be. One of the biggest misconceptions about agentic analytics is that it requires a massive, organization-wide transformation from day one. That’s not only unrealistic—it’s unnecessary.
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Agentic Analytics Is a Journey, Not a Switch

Agentic analytics, by definition, introduces AI agents into your analytics stack—automating decisions, triggering workflows, and learning from data. But like any strategic capability, it follows a curve of maturity. Trying to jump to full autonomy without foundational readiness often leads to:

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• Confusion or distrust among end users
• Poorly scoped use cases
• Automation without oversight

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Instead, adopting a phased approach like the AI crawl-walk-run model lets you build AI capabilities step by step—starting with observable insights, then layering in AI-driven recommendations, and finally moving to full analytics automation.

Why You Should Start Small and Scale with Confidence

Greatness comes from small beginnings. The crawl-walk-run approach works for Agentic Analytics because it aligns AI implementation with:

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• Your team’s comfort level with automation
• The reliability of your data sources
• The complexity of your business processes

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In short, agentic analytics isn’t all or nothing. It’s a structured evolution. And if you’re wondering how to start your journey, the next sections walk you through exactly what to do at each stage.

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What Is the Crawl-Walk-Run Framework for Agentic Analytics?

How do you implement agentic analytics in phases? The crawl-walk-run framework is a phased approach to adopting agentic analytics—a model where AI agents increasingly take on roles in monitoring, recommending, and eventually acting on data.

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This framework helps you move from simple AI-driven summaries and alerts (crawl) to decision support (walk) and finally to autonomous execution (run). It ensures your team gains value and confidence at every step, without needing to commit to full automation immediately.

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Here’s how the AI crawl-walk-run model works:

Crawl: Build trust with AI-powered monitoring, alerts, and summaries—no automation yet.

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Walk: Introduce AI recommendations into workflows, with human approval.

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Run: Let autonomous agents take defined actions based on data and thresholds.

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This staged approach forms the backbone of your analytics automation strategy, reducing risk while accelerating maturity. Let’s dive into why this approach works—and how to start.

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Crawl: Start with Real-Time Monitoring and AI Summaries

What is the first step in getting started with agentic analytics?

The crawl phase focuses on building awareness and trust in AI. Instead of jumping straight into automation, you start by enhancing your existing analytics environment with real-time monitoring, AI summaries, and proactive notifications.

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This foundational step in the agentic analytics maturity model helps teams understand what’s happening in the business—before introducing AI-powered decisions or actions.

What to Implement in the Crawl Phase

Natural language summaries of dashboards or reports (e.g., “Sales dropped 12% in region A, mostly driven by product X”).

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AI-generated alerts when key metrics move outside defined thresholds.

Proactive insights delivered in Slack, Teams, or email—without needing to log into BI tools.

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Use of leading platforms like Tableau Pulse, Einstein Copilot, ThoughtSpot, or Power BI with Azure AI.

Why This Phase Works

This phase is intentionally low-risk and high-visibility. It helps:

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• Build trust in AI output
• Give stakeholders a shared view of what’s happening now
• Identify useful metrics for future automation

You’re not automating decisions yet—you’re using AI to watch, summarize, and notify. That gives teams the clarity they need without changing how they operate.

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By the time you’re ready to move into recommendation or automation territory, your users will already be familiar with AI’s voice—and likely asking for more.

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Walk: Add AI Recommendations with Human-in-the-Loop Approval

What is the next step after monitoring agentic analytics?

Once you’ve established real-time visibility and AI-driven summaries, it’s time to empower your team with AI recommendations—but with human approval still in the loop.

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This is the “walk” phase in the AI crawl-walk-run model, where AI shifts from observing to advising.

What to Implement in the Walk Phase

Embed AI-generated recommendations directly into the tools your teams already use—like dashboards, CRMs, or Slack.

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Use agents to suggest next-best actions such as follow-ups, pricing adjustments, inventory decisions, or customer outreach.

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Incorporate lightweight approval workflows where a human validates or adjusts the AI’s suggestion

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Example: A marketing dashboard could highlight underperforming campaigns and recommend pausing them—while routing the decision to a campaign owner for one-click approval.

Why This Phase Matters

This stage brings measurable efficiency gains without sacrificing control. It helps your team:

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• Build confidence in AI decision-making
• Maintain accountability and transparency
• Document feedback loops for future learning

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By keeping a human in the loop, you reduce the risk of rogue automation while laying the foundation for analytics automation at scale. This is where agentic analytics starts delivering not just awareness—but real, scalable decision support.

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Run: Empower Autonomous Agents to Act on Data

What is the final stage of agentic analytics implementation?

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The final stage of the AI crawl-walk-run framework is the “run” phase—where autonomous AI agents don’t just recommend actions, but take them. This is where your organization unlocks the full potential of agentic analytics: automated, continuous, and intelligent operations that scale without manual intervention.
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What to Implement in the Run Phase

Agents that trigger actions in business systems based on predefined rules or real-time signals.

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Integration with platforms like Agentforce, Snowflake, HubSpot, or Tableau Pulse to connect insight to action.

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Autonomous execution of tasks like: Reordering inventory based on demand signals, escalating high-risk accounts to retention teams, or launching personalized campaigns based on behavioral data.

Why This Phase Unlocks Full Analytics Automation

This is not about removing humans—it’s about freeing them from repetitive, rule-based decisions so they can focus on strategy and innovation. By the “run” stage, AI agents:

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• Operate within clearly defined boundaries
• Learn from feedback loops to improve over time
• Deliver exponential value through time savings, faster response rates, and always-on execution

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Is Your Team Ready for Autonomous Analytics?

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If you’ve validated outputs in the “crawl” phase and tested decision logic during “walk,” this final step will feel like a natural evolution—not a leap.

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Letting AI agents act autonomously isn’t reckless—it’s responsible scaling, made possible by the trust and structure you’ve built along the way.

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Readiness Checklist + Next Steps

How do you know if your organization is ready for agentic analytics?

Before progressing from crawl to run, it's critical to evaluate your data, culture, and systems. This agentic analytics readiness checklist helps you assess whether you're set up for success—or where you might need to pause and reinforce.

Agentic Analytics Readiness Checklist

To make life easier for you, we've built a complete checklist for you that's available for download here:

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Download Readiness Checklist

✔️ Data is clean, consistent, and accessible across your key systems.

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✔️ Dashboards and reports are trusted by stakeholders.

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✔️ Workflows are clearly defined and documented (so they can be automated).

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✔️ Your team is open to AI involvement and willing to experiment.

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✔️ You have tools in place that can support embedded AI (e.g., Salesforce, Tableau, Snowflake).

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✔️ Governance and approval rules are clear enough to guide AI agents.

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✔️ KPIs for success are defined and measurable.

If you're not checking every box yet—don't worry. That’s why the crawl-walk-run model works.

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You don’t need full readiness to get started; you just need to know where you stand and how to progress.