Unleashing Agentic AI: Transforming Supply Chain, Fintech and Pharma

Imagine an AI assistant that autonomously reroutes shipments to avoid a factory delay, flags fraudulent transactions in real time, or compresses drug launch planning from months into weeks.

Imagine an AI assistant that autonomously reroutes shipments to avoid a factory delay, flags fraudulent transactions in real time, or compresses drug launch planning from months into weeks. This isn’t science fiction – it’s the promise of agentic AI, the next evolution in enterprise AI. Gartner predicts that by 2028, about one-third of all enterprise applications will include agentic AI features. In this blog, we explain what agentic AI is, why it matters, and how visionary companies in supply chain, financial services, and pharmaceuticals are already achieving measurable ROI with LLM-powered agents. We’ll also cover the key benefits realized in each case, the adoption challenges to overcome, and a practical roadmap to get started with agentic AI today – informed by our experience as a custom software engineering firm delivering tailored AI solutions.

What Is Agentic AI (and Why Does It Matter)?

Agentic AI refers to AI systems that act autonomously to achieve goals and solve complex, multi-step problems. As Aisera puts it, “Agentic AI is an AI system that acts autonomously, adapts in real time, and solves multi-step problems based on context and objectives”. In other words, agentic AI goes beyond generating insights: it plans, reasons, and takes action. IQVIA similarly notes that agentic AI can “act independently to perform tasks…make decisions, take action, and even interact with outside environments”.

Practically, agentic AI is built from multiple specialized AI “agents” (often powered by large language models) that break a business objective into subtasks, collaborate as needed, and execute end-to-end workflows. One expert explains that an agentic system can receive a high-level goal (e.g. “optimize our inventory”), autonomously formulate a strategy, and carry out steps (data analysis, planning, execution) with minimal human oversight. In effect, agentic AI “moves AI from suggestion to execution,” giving companies the ability to turn insights into automated outcomes. This shift unlocks dramatic efficiency gains and new capabilities – for example, AI agents can continuously monitor data, adjust plans in real time, and collaborate across tools without constant human intervention.

The practical value is clear: instead of manually charting out each step, managers simply define a goal in plain language, and the agentic AI takes the wheel to achieve it. TechTarget describes a supply chain scenario: if a drought disrupts crops, a supply chain manager could ask the AI agent to “find and deliver the needed quantity of supplies at the lowest cost,” and the agent “not only identify[s] how to do so, but automatically initiates actions to make it a reality.”. In short, agentic AI promises to automate entire workflows in real time – bridging the gap between data and action.

Real-World Use Cases & Impact

Leading companies are already piloting agentic AI (often powered by GPT-style models) in mission-critical workflows. Below we highlight real-world examples and the measurable benefits realized in supply chain, fintech, and pharmaceuticals.

Supply Chain: Autonomous Planning and Risk Management

In complex logistics networks, agentic AI can orchestrate multiple tasks – from demand forecasting to rerouting and risk mitigation – far faster than human teams. For instance, DHL’s Resilience360 platform (used by over 13,000 users) employs AI to provide near–real-time visibility into global supply-chain disruptions. Users report that Resilience360 “empowered swift corrective actions to ensure timely deliveries despite risks”. In practice, this means AI agents continuously scan weather, geopolitical, and supplier data, automatically recommending alternate shipping routes, sourcing options or inventory adjustments when disruptions loom.

Another striking example comes from the air freight sector. American Airlines integrated an AI-based agent for cargo bookings that predicts no-shows with ~90% accuracy. By identifying high-risk shipments days in advance, the airline can proactively rebook space and maintain efficient loading. This AI-driven forecast not only improved cargo space utilization and fuel efficiency, but also protected revenue that would otherwise be lost to empty seats.

Overall, companies deploying agentic AI in supply chain see double-digit gains. Models that were >90% accurate in forecasting demand can cut excess inventory by ~15% and boost on-time delivery rates. Logistics optimization agents have delivered ~12% reductions in shipping costs and significant improvements in delivery reliability. In practice, benefits include faster response to disruption, leaner inventories, lower logistics spend, and greater resilience. In short, agentic AI turns supply chains from reactive and siloed operations into intelligent, continuously improving networks.

Financial Services: Fraud Prevention, Trading and Agentic Commerce

Fraud detection is a standout fintech use case. Mastercard’s AI fraud engine – built on advanced (generative) AI models – now analyzes transaction streams across billions of card and merchant data points. The result: Mastercard reports it can double its fraud detection rate while massively reducing false alarms. In fact, Mastercard’s generative AI “doubles the detection rate of compromised cards” and cuts false positives by up to 200%, all while identifying at-risk merchants 3× faster. These gains translate directly into millions saved: banks are alerted to fraud faster and more accurately, protecting customer funds and reducing investigation costs.

Automated trading and portfolio management is another arena for agentic AI. Modern AI agents ingest vast streams of market data (news, sentiment, price ticks) in real time. Hedge funds deploying these systems report significantly higher returns. For example, strategy-driven AI bots are said to capture micro-trends invisible to humans and can outperform manual trading by ~10–15% annually. (These improvements come from split-second execution and advanced pattern recognition that human traders can’t match.)

Beyond operations, financial institutions are enabling agentic commerce. Visa and Mastercard now offer tools that allow AI “shopping assistants” to make payments on behalf of customers. Consumers can set budgets and preferences, and the AI agent “searches for products, makes recommendations and completes purchases” automatically. This “agentic commerce” model is poised to reshape e-commerce, giving banks and fintechs new channels to serve customers (and collect fees) while streamlining the buying process.

In summary, agentic AI in finance has delivered faster, smarter decision-making and cost savings. In fraud prevention alone, the industry is seeing detection improvements measured in “doubling” the catch rate and eliminating hundreds of percent more false positives. Across trading, lending, and customer service, AI agents yield faster throughput, better risk management, and competitive new services that simply weren’t possible before.

Pharmaceuticals: Faster R&D and More Effective Commercialization

Pharma is notoriously data-rich but time-intensive. Agentic AI offers to slash drug development and commercialization timelines. For example, IQVIA reports that by autonomously orchestrating commercial planning tasks (e.g. market modeling, budget allocation, HCP targeting), agentic AI can compress end-to-end launch cycles from 6–18 months down to as little as 4–5 months. In practical terms, this accelerates time-to-market for new drugs and therapies, letting companies reach patients sooner and capture market share faster.

On the R&D side, experts project massive cost savings from AI-driven processes. Ernst & Young’s life sciences survey found that GenAI (a core component of agentic approaches) could cut drug discovery and development costs by roughly 15–22% within the next 3–5 years, and potentially up to 44–67% at full adoption. In concrete terms, automating tasks like virtual screening, compound design, and trial optimization through AI could reduce labor and laboratory costs dramatically. (EY’s interviewed executives expect target ID and validation stages alone to see ~66–67% cost reductions at peak.)

Beyond R&D, agentic AI also improves field operations. The same segmentation and planning examples that accelerate drug launches in IQVIA’s analysis can help sales teams allocate millions of dollars in marketing spend more efficiently, continuously refining plans based on real-world feedback. The upshot for pharma companies: faster, cheaper innovation and more effective market execution. When each day-to-market can be worth tens of millions of dollars, even small percentage gains in efficiency or accuracy translate to huge ROI.

Benefits Realized

Speed & Agility: AI agents operate 24/7 and react in real time. Forecasts update continuously and actions (e.g. rerouting a shipment or blocking a fraudulent charge) happen instantly. This slashes cycle times (as in pharma launches or supply chain planning) and removes lag in decision loops.
Higher Accuracy & Consistency: Agents aggregate and analyze data beyond human capacity, reducing errors. For instance, Mastercard’s doubled fraud detection and drastically cut false positives. Similarly, AI forecasting models achieve over 90% accuracy, minimizing stockouts and overruns.
Cost Reduction: By automating routine tasks and optimizing processes, agentic AI cuts operational costs. Supply chain pilots report lower inventory carrying costs and logistics spend; pharma budgets see better utilization. EY’s research highlights multi-decadal percentage cuts in R&D spend, while in fintech, faster trade execution and lower fraud losses directly boost the bottom line.
Better Resource Allocation: Humans can focus on creative, strategic work while agents handle data-heavy chores. In customer service or sales, AI assistants provide frontline support and analytics, freeing experts to tackle complex issues.
Scalability & Continuous Improvement: Once built, AI agents can scale across geographies and products, learning and improving as new data comes in. A single supply chain agent template can be cloned for multiple product lines, or a finance agent can adapt to new fraud patterns on the fly.

In each case above, businesses reported measurable outcomes – whether it’s a 15% improvement in on-time delivery, a halving of fraud losses, or months cut from a drug launch timeline. These quantifiable gains underscore that agentic AI is delivering real business value, not just conceptual buzz.

Adoption Challenges

Despite the excitement, large-scale adoption of agentic AI comes with hurdles. Decision-makers must recognize and plan for these challenges:

Talent and Skills Gap: There is a critical shortage of AI expertise. Forrester and IDC research indicate over 90% of enterprises will face severe AI skills shortages by 2026, potentially costing trillions in delays. Specialized roles like data scientists, ML engineers, and prompt architects are scarce. Companies must invest in retraining, hiring, or partnering to bridge this gap.
Data Quality & Availability: Agents need high-quality, curated data to function. A recent survey found ~42% of organizations lack sufficient proprietary data to train next-gen AI models. Inconsistent or siloed data can doom an AI project. Firms must build robust data pipelines, integrate legacy systems, and, if needed, leverage synthetic data to feed their agents.
Infrastructure Costs: Agentic AI, especially LLM-based agents, demands heavy compute (GPUs/TPUs) and storage. Cloud bills can spike due to real-time inference and retraining. Many mid-sized companies worry about the capital needed for scalable AI platforms. Assessing ROI and exploring cost-optimization (e.g. model distillation, spot instances) are essential.
Complex Integration: Enterprise agents must interoperate with numerous existing tools and databases. Gigster notes that “complex system integration” is a top challenge. Many legacy systems lack APIs or were never designed for AI integration, leading to significant engineering work. Expect longer development cycles to connect all the pieces.
Governance & Security: Autonomous agents raise new governance questions. How do you ensure an AI decision is compliant and explainable? Who is accountable if an agent errs? Industries like banking and pharma are heavily regulated, so building in audit trails, security controls and human-in-the-loop oversight from the start is mandatory. Gigster also points to “stringent access control and security requirements” as a barrier.
Organizational Change Management: Finally, adopting agentic AI requires cultural change. Leaders often underestimate the shift in processes and mindset required. Without clear communication, training, and a change management plan, staff may resist the new technology or misuse it. Aligning incentives and demonstrating quick wins helps turn skeptics into champions.

In summary, while agentic AI opens new horizons, companies must tackle skill development, data readiness, technical complexity, and governance in parallel. These are non-trivial efforts, but they’re critical to unleash the full potential of AI automation.

Actionable Steps for Decision-Makers

How can you get started and accelerate agentic AI adoption in your organization? Here are concrete steps we recommend:

Identify High-Value Use Cases: Start by scouting for processes that are multi-step, data-rich, and have clear ROI potential. Good examples are demand forecasting, fraud monitoring, clinical trial matching, or dynamic inventory management. Engage business leaders to prioritize use cases where automation could dramatically improve outcomes.
Assess Data & Infrastructure Readiness: Conduct a data audit. Do you have the historical data, APIs, and compute resources needed? If gaps exist, plan to invest in data integration or high-performance cloud/edge infrastructure. Consider partnering with cloud providers or hybrid solutions to offset upfront hardware costs.
Build the Right Team: Assemble a cross-functional team combining domain experts, data scientists, and software engineers. Given the skills gap, consider strategic partnerships with specialized AI consultancies or firms (like ours) to complement in-house talent. Emphasize ongoing training – upskilling existing staff with AI and ML certifications can pay dividends.
Develop a Pilot Project: Choose one clear pilot – small in scope but end-to-end. For example, an AI agent to optimize one segment of your supply chain, or to triage a subset of customer requests. Develop fast (using Agile methods), test rigorously, and measure results. A successful pilot (even with a simple workflow) builds momentum and reveals technical hurdles early.
Establish Governance & Security: From day one, incorporate governance protocols. Define roles and review processes for AI decisions. Implement monitoring to catch agent anomalies. For regulated industries, involve compliance teams early to ensure data privacy and audit trails. Balancing AI autonomy with human oversight is key.
Iterate and Scale: Use lessons from the pilot to refine your approach. Once validated, gradually expand the agent’s reach or deploy new agents to other use cases. Reuse modular agent components (e.g. an NLP understanding module or a planning module) to accelerate development. Keep measuring performance improvements and adjusting targets.
Foster a Culture of Innovation: Communicate successes internally. Highlight how agentic AI is freeing human teams to focus on creative work. Encourage collaboration between IT and business units. Offer hands-on workshops so staff learn to work with the new agents (and provide feedback). A culture that embraces AI will adopt it more smoothly.
Monitor and Govern Continuously: Even after deployment, actively monitor agentic AI performance and cost. Retrain or update models as needed when business conditions change. Set up a center of excellence or community of practice to share knowledge across the enterprise. This ensures your agentic AI capabilities mature and stay aligned with corporate goals.

By following these steps – and leveraging external expertise where needed – decision-makers can move from exploration to execution.

Key Takeaways: Agentic AI (LLM-powered autonomous agents) is emerging as a game-changer in supply chain, fintech, and pharma, delivering faster decisions, cost savings, and new capabilities. For example, leading firms use AI agents to halve fraud losses, cut supply chain costs by ~10%, and slash drug launch timelines. The main adoption hurdles are skill gaps, data needs, and integration complexity, but they are addressable with clear strategy. We encourage companies to start small, build up data and expertise, and iterate quickly. Our team has guided many enterprises through this journey, designing custom agentic AI solutions that align with each client’s unique domain and goals. By taking practical steps now, your organization can harness agentic AI for real competitive advantage in operations, finance, and beyond.

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