The art of possible
Real-world applications of our AI agents and agentic workflows
Last updated
Was this helpful?
Real-world applications of our AI agents and agentic workflows
Last updated
Was this helpful?
Agentic flows are intelligent, modular analytics processes composed of AI Agents that understand your questions and deliver insights without writing a single line of code.
The Steps section in Composer lets you describe any workflow like the ones below in natural language. Our AI agents will do the rest: gather the relevant data, run the analysis, generate visuals, and surface insights.
Each workflow becomes a living, reusable intelligence asset in your Agent Library, ready to trigger when you (or anyone in your team) asks the right question (as mentioned in the Triggers section).
Below are real, industry-relevant examples showing what you can build—organized by use case domain.
Analyze payer plans underperforming on TRx, segment by access restrictions or HCP tiers, and suggest data-backed contract renegotiation points.
Identify territories or HCPs (Health Care Professionals) where a new launch is lagging, then break down messaging gaps, rep engagement, or access barriers.
Investigate market share erosion by segment, uncover if it's driven by new competitors, loss of exclusivity, or formulary exclusion.
Analyze sampling and TRx data together to pinpoint why high sample volume isn't translating into prescriptions, by HCP or specialty.
Automatically compute and visualize variances by department or cost center and identify drivers like overspend in labor or marketing.
Track invoice approval SLAs, highlight vendors or departments causing delays, and suggest automation or escalation points.
Break down decreasing margins by region or SKU and isolate root causes like price cuts, cost increases, or channel mix shifts.
Identify underperforming SKUs with low margin and high cannibalization impact, and generate a shortlist for discontinuation.
Measure uplift from trade promotions by channel or retailer and highlight where discounting didn’t drive incremental sales.
Track and explain frequent stockouts at a product or store level, connecting issues to supplier lead times or DC inefficiencies.
Match rep performance to territory opportunity, flag underperformers in high-potential areas, and suggest redeployment.
Identify pipeline leaks by stage, region, or deal owner and determine whether the issue is conversion, qualification, or velocity.
Analyze why certain reps or teams missed quota—whether due to low volume, high-value deal loss, or lack of activity.
Track late deliveries, segment by carrier or warehouse, and surface which combinations are failing SLA the most often.
Identify aging or excess inventory by category, link to misaligned demand forecasts, and quantify holding costs.
Highlight vendors with increasing lead times, compare to historical norms, and recommend vendor evaluation or escalation.
Diagnose delays in claims processing by team or system bottlenecks (manual review, missing info etc.), and suggest routing or staffing optimizations.
Use attrition analytics to flag customer segments with high churn risk, identify shared behaviors, and suggest outreach.
Break down recent fraud spike by transaction type, location, and device to find patterns and recommend rules.
Analyze drop-offs by device, page load times, and payment method to pinpoint why customers abandon checkout.
Break down return reasons by product, geography, or channel to find quality or sizing issues affecting profitability.
Understand why open or click rates dropped—by send time, device, campaign type—and test alternatives.
Segment leavers by department, tenure, and performance score to uncover common causes behind churn.
Track where candidates drop off in the hiring process and determine whether it's role fit, offer rejection, or slow feedback.
Identify teams or locations where overtime is growing unsustainably, and highlight workload or staffing issues.
