SEE. KNOW. INTERVENE.
Instant’s Agentic AI Operating Methodology
Select a Role
Select an Industry
Primary Objectives
SEE
Insight Analytics Suite
Set your KPIs and thresholds once. Every deviation surfaces live, before a report is pulled, not after it.
KNOW
Advanced Predictive Layer
AI traces every variance to its origin, system, team, or transaction, in seconds. No manual investigation.
INTERVENE
Strategic Intelligence Platform
Scenario-modeled recommendations with financial impact pre-calculated and a step-by-step action plan, ready to execute.
AI That Generates Revenue, Not Just Cutting Costs
Three Tier 1-sourced use cases. Measurable, in-production results. Real organizations already running agentic AI to drive revenue.
$4.4T
Potential global value unlocked by AI agents (McKinsey)
74%
Of executives achieve AI ROI within the first year (Google Cloud)
40%
Increase in order intake within 3 to 6 months of agentic deployment (McKinsey)
89%
Of CIOs consider agent-based AI a strategic priority (Futurum Group)
Use Case 01, McKinsey 2026
Hyperpersonalized Marketing Orchestration
McKinsey & Company: Reinventing Marketing Workflows with Agentic AI
10 to 30% Revenue Growth
Multi-agent systems span the full marketing stack, autonomously generating content, testing audiences, and optimizing campaigns live without human intervention at each step.
- Campaigns execute 10 to 15 times faster than traditional manual workflows
- Case Study: Indosat deployed Gemini-powered AI agents into customer journeys, enabling hyper-personalization across millions of subscribers at scale
Use Case 02, Deloitte / McKinsey
Autonomous Dynamic Pricing and Promotion Agents
Deloitte: AI Agents Reshaping the Future of Work; McKinsey Retail Research
2 to 5% Margin Improvement
Agents integrate competitor pricing, inventory, and purchase history to autonomously adjust prices and personalize promotions continuously, maximizing margin without human review.
- Case Study: SPAR Austria achieved 90%+ forecast accuracy across 1,500+stores, reducing food waste and protecting margin
- Case Study: Forbes-recognized retailer generated a $77M annual gross profit improvement and a 9.7% increase in new sales
Use Case 03, McKinsey 2025
Agentic Sales Prospecting and Pipeline Acceleration
McKinsey: Empowering Advanced Industries with Agentic AI
40% Increase in Order Intake
Agents research accounts, prioritize leads, draft outreach, and surface next-best-action recommendations, shifting reps from execution to orchestration and increasing qualified pipeline.
- McKinsey projects $450 to $650 billion in additional annual revenue from agentic AI in advanced industries by 2030
- Case Study: Ford uses AI agents to reduce vehicle engineering simulation time from 15 hours to 10 seconds, accelerating product launches and revenue cycles
In-Production Enterprise Results
Real organizations. Measurable outcomes. Active deployments, not pilots.
Ford Motor Company
Automotive
AI agents reduce engineering simulation time from 15 hours to 10 seconds. Faster vehicle launches tied directly to revenue cycle compression and competitive response time.
CBRE
Commercial Real Estate
Agentic AI deployed across multiple business domains via McKinsey and Google Cloud partnership. Scalable platform targeting significant enterprise efficiency and revenue unlocks.
Indosat
Telecommunications
Gemini-powered AI agents embedded into customer journeys. Always-on campaigns delivering hyper-personalization across millions of subscribers in real-time production environments.
AtlantiCare
Healthcare
Agentic AI applied to clinical and operational workflows with measurable efficiency gains and patient outcome improvements fully deployed in production.
SPAR Austria
Retail / Grocery
90%+ forecast accuracy across 1,500+ stores. AI agents analyzing sales, weather, promotions, and seasonality for continuous inventory optimization and margin protection.
Forbes-Recognized Retailer
Retail (OneReach.ai Deployment)
$77M annual gross profit improvement. 9.7% increase in new sales calls. NPS of 65. 350 production releases nationwide from a single agentic AI deployment.
Measurable ROI and Cost Savings From Agentic AI
Tier 1-sourced benchmarks and in-production results across finance, operations, and supply chain.
30%
Average reduction in financial close cycle time from AI-automated reconciliation (McKinsey)
3 to 5x
ROI achieved within 18 months of enterprise AI deployment (IDC Research)
70%
Reduction in escalation cost when variance is detected at inception vs. period end (Gartner)
$1.4M
Average annual value captured per AI use case deployed in finance functions (McKinsey)
ROI Case 01, McKinsey / Deloitte
Financial Close Acceleration and Period-End Cost Reduction
McKinsey: The CFO Function in 2026; Deloitte: AI Agents Reshaping the Future of Work
20 to 30% Reduction in Close Cycle Time
AI-automated reconciliation and exception detection eliminate the manual steps that drive 40 to 60% of period-end labor cost. Variances caught at inception cost 70 to 80% less to resolve than those found at close.
- Reconciliation exceptions resolved in minutes versus hours of manual investigation
- Case Study: JP Morgan Chase reduced document review from 360,000 hours annually to seconds using AI, with full compliance and audit readiness maintained
ROI Case 02, Gartner / IDC
Operational Efficiency Through Live KPI Intelligence
Gartner: AI in Operations 2025; IDC: Business Value of AI Report 2025
25 to 40% Reduction in Reactive Operational Costs
Live monitoring eliminates the compounding cost of late discovery. Variance detected at inception costs 70 to 80% less to resolve than variance found at period end.
- Organizations report 3 to 5x ROI within 18 months of deploying live KPI intelligence (IDC)
- Case Study: General Electric generated $500M in operational savings from predictive monitoring applied to asset and equipment performance
ROI Case 03, McKinsey / Deloitte
Working Capital and Cash Flow Optimization
McKinsey: Working Capital Excellence 2025; Deloitte: Treasury Intelligence Report
15 to 25% Improvement in Working Capital Efficiency
Live cash monitoring, automated covenant tracking, and AI-driven receivables intelligence let treasury teams optimize float, reduce idle cash, and accelerate collections without adding headcount.
- Days Sales Outstanding reduced by 5 to 8 days on average in organizations with live AR intelligence (McKinsey)
- Case Study: Unilever reduced working capital by $2.8 billion through AI-powered supply chain financing and receivables optimization
Enterprise Cost and Efficiency Results
Documented savings from AI intelligence deployments across finance, operations, and supply chain.
JP Morgan Chase
Financial Services
AI reduced legal document review from 360,000 hours annually to seconds. Full compliance and audit trail maintained. One of the most cited examples of agentic AI delivering measurable cost reduction at scale.
General Electric
Industrial / Manufacturing
$500M in documented operational savings from AI-powered predictive monitoring applied to asset performance, maintenance scheduling, and production efficiency across global facilities.
Siemens
Technology / Manufacturing
40% reduction in financial reporting cycle time through AI-automated variance detection, exception management, and reporting workflows. Significant labor cost reduction and faster close cycles.
Unilever
Consumer Goods
20% working capital improvement through AI-driven supply chain and payment visibility. Hundreds of millions in previously trapped cash identified and released through live intelligence.
Procter & Gamble
Consumer Goods
30% reduction in demand planning cycle time through AI forecasting. Supply chain cost reduction and improved service levels achieved without adding planning headcount.
Maersk
Logistics / Shipping
15 to 20% reduction in logistics costs through AI-powered route optimization and real-time inventory intelligence across global supply chains. Documented ROI within 12 months of deployment.
