How AI Transforms the Best Low-Code Use Cases in Pharma, CPG and Finance

Before cameras met the internet, video was something you recorded and showed to a few people during a family gathering. Once those worlds converged, it became something you could stream to millions and even build a business around. The same happened when GPS met the smartphone: a niche, military-grade utility turned into a tool we use every day without thinking about it. Some technologies grow on parallel tracks, useful but limited, until one day they meet and unlock entirely new possibilities. 

Low-code and AI follow this pattern. One grew around speed and accessibility, the other around intelligence and decision making. Now these two worlds are finally coming together. It is a moment of technological coevolution, where the combination delivers more than either could achieve alone. 

In this article, we look at what this convergence means in practice through real low-code use cases in Pharma, CPG and Finance, and how AI elevates each of them to a new operational level. 

Pharma: Low Code for Speed, AI for Precision 

Pharma has always relied on structured processes. Workflows, approvals and compliance rules define how almost every task is done. Low-code fits naturally into this environment because it makes it possible to digitize steps, build apps fast and connect data across teams. AI adds something that the industry has always struggled to scale: the ability to interpret information, detect patterns and make decisions in real time. 

Below are the Pharma use cases where the combination brings the clearest benefits. 

Patient Hub Data Intake and Management 

This refers to the process of collecting, validating and organizing patient and HCP data for services such as onboarding, support programs or education. Patient data often arrives incomplete or inconsistent, which slows onboarding and creates compliance risks. 

How low-code helps 
Low-code platforms make it easy to build apps that collect patient and HCP data, validate fields and connect the information with CRM systems such as Veeva. Teams can adjust forms, rules and workflows without a long development cycle. 

How AI enhances it 
AI agents can check incoming records, detect duplicates, highlight missing or inconsistent fields and confirm that consent is captured in the right way. Another agent can monitor compliance rules that vary by market. 

What we see in practice 
These solutions help teams improve data quality and reduce manual reviews. In our projects, we have seen improvements of up to 60 to 80 percent in review effort, depending on the complexity and volume of incoming records. 

Clinical Trial Site Enrollment 

This is the workflow used to onboard study sites, investigators and documents before a clinical trial begins. Enrollment timelines often slip because site readiness is hard to predict and operational data is scattered. 

How low-code helps 
A low-code app can bring onboarding, documentation and investigator portals into one workflow. Study teams can manage tasks, upload files and track progress without relying on email. 

How AI enhances it 
A forecasting agent can analyze site performance, past studies and operational data to predict enrollment delays. It can suggest early corrective actions so teams can react before timelines slip. 

What we see in practice 
Organizations gain better visibility into site readiness and can react earlier to delays. In our projects, we have seen improvements of up to 20 to 40 percent in activation timelines, although results vary by study design. 

Medical Affairs Evidence Request Portals 

This is a digital point of contact where HCPs or internal teams request publications, evidence or scientific information. Teams handle a growing volume of requests, and keeping response quality consistent is difficult. 

How low-code helps 
Medical Affairs teams often need a lightweight portal where HCPs or internal stakeholders can request evidence, publications or scientific clarification. Low-code makes it easy to build such portals with automated routing and status tracking. 

How AI enhances it 
A triage agent classifies each request, extracts the key question and prepares a draft response based on approved scientific sources. This reduces the manual load on medical writers and keeps response times consistent. 

What we see in practice 
Teams handle higher request volumes with more consistent responses. In our experience, SLA performance can reach above 95% when triage and drafting are supported by AI. 

Product Launch Readiness Tracking 

This is the set of tasks, dependencies and indicators used to prepare for a new product launch. Launch plans involve many functions, and even small delays can ripple across the entire timeline. 

How low-code helps 
Brand and launch teams use low-code dashboards to monitor tasks, dependencies and deliverables during a product launch. The platform handles reminders, approvals and data collection from many functions. 

How AI enhances it 
An AI agent monitors KPIs and flags anything that deviates from the launch plan, such as delays in creative assets or out-of-range market feedback. It serves as an early warning system. 

What we see in practice 
Launch teams coordinate more smoothly, identify risks earlier and maintain a clearer view of dependencies. In our work, this has translated into faster, more predictable launch timelines across functions. 

Pharmacovigilance Adverse Event Capture 

This is the process of collecting and processing adverse event reports submitted by HCPs, field teams or patients. Free text reports vary widely in quality and completeness, making early signal detection difficult. 

How low-code helps 
Low-code supports mobile-first reporting forms and integrations with safety systems. Field teams or HCPs can submit adverse events quickly in a consistent format. 

How AI enhances it 
An NLP extraction agent can read free text, classify the event type and detect signals that may indicate a serious case. The agent can also check completeness before the case moves to safety review. 

What we see in practice 
Safety teams benefit from cleaner intake, faster case processing and earlier detection of significant signals. Our projects show that automated extraction can meaningfully accelerate initial triage. 

Field Sales Visit Planning 

This covers the planning and prioritization of HCP visits based on segmentation, performance and strategy. Reps often rely on manual planning and intuition, which leads to inconsistent focus and missed opportunities. 

How low-code helps 
Sales teams rely on apps that integrate segmentation, CRM data and visit planning. Low-code makes these apps easy to adapt to changing priorities. 

How AI enhances it 
A next best action agent suggests which HCPs to visit, what messaging is appropriate and how to optimize routes. These suggestions are based on performance data, interactions and past behavior. 

What we see in practice 
Sales reps make more efficient decisions about where to focus time and how to tailor messages. In our experience, call effectiveness can increase by 5 to 15 percent depending on the maturity of targeting. 

Manufacturing Deviations and CAPA 

This refers to the process of logging, investigating and resolving deviations on the production floor. Root causes are often hidden across many small events, and manual reviews miss patterns. 

How low-code helps 
On the shop floor, low-code apps digitize deviation reports, CAPA workflows and audit trails. Operators can log issues in real time. 

How AI enhances it 
A root cause analysis agent looks at deviations across batches and lines. It identifies clusters, recurring patterns and possible systemic issues. 

What we see in practice 
Manufacturing teams gain better insight into repeated issues and address root causes sooner. This leads to fewer recurring deviations and stronger audit readiness across plants. 

Sample Accountability and Compliance 

This is the controlled process of tracking samples provided to HCPs in line with regulatory requirements. Sample distribution is highly regulated, and monitoring unusual patterns is time consuming. 

How low-code helps 
Low-code enables self-service portals for sample requests, tracking and documentation. All records stay in one structured system. 

How AI enhances it 
An AI audit agent looks for unusual spikes, spending patterns or anomalies that may require investigation. It helps companies stay compliant in highly regulated markets. 

What we see in practice 
Organizations improve transparency, spot anomalies earlier and reduce compliance risks in highly regulated markets. Automated checks support more consistent monitoring across regions. 

CPG: Low Code for Scale, AI for Accuracy 

CPG companies manage fast moving operations with many partners, retailers and distribution points. Low-code helps them digitize processes and capture data from the field. AI adds the ability to recognize patterns, analyze images and predict demand with more accuracy. Together they improve both execution and decision making across the value chain. 

Trade Promotion Planning and Claims 

This refers to the workflow used to plan promotions, collect claims from distributors and validate them against contracts. Claims are often submitted in inconsistent formats, and evaluating the real ROI of each promotion is difficult. 

How low-code helps 
Low-code portals make it easy for distributors and retailers to submit claims and for internal teams to track and validate them. 

How AI enhances it 
A pricing and ROI agent reviews historical data, compares promotion performance and recommends the most effective discount structures. 

What we see in practice 
Companies gain clearer visibility into promotion performance and reduce manual claim validation work. In our projects, this has translated into revenue uplifts from 3 to 10 percent on selected promotions, although results vary by category and channel. 

Sustainability and Packaging Compliance 

This refers to the reporting of recycled content, packaging materials and CO2 footprints in different markets. Requirements vary across regions, and collecting accurate data from suppliers can be slow and inconsistent. 

How low-code helps 
Teams can build data capture apps for recycled content, material specifications and emissions reporting, all in one place. 

How AI enhances it 
A monitoring agent checks submissions for completeness and flags entries that do not meet regulatory thresholds. 

What we see in practice 
Teams streamline reporting, reduce data gaps and stay aligned with changing regulations across markets. Automated validation helps maintain accuracy at scale. 

Quality Issue Tracking from Retail 

This refers to the process of capturing product defects reported from stores, often through photos and short notes. Feedback arrives in many formats and manual review slows down decisions about recalls or corrective actions. 

How low-code helps 
Low-code mobile forms allow merchandisers and store staff to submit quality issues with photos and descriptions. 

How AI enhances it 
A vision agent identifies defect types directly from images and categorizes them for faster triage. 

What we see in practice 
Feedback loops become faster and more reliable, enabling quicker decisions about quality actions and potential recalls. 

Merchandiser Retail Execution 

This refers to store visit routines, planogram checks and shelf condition reporting performed by field merchandisers. Manual checks are slow, inconsistent and difficult to consolidate across regions. 

How low-code helps 
Offline ready mobile apps allow merchandisers to record visits, take photos and complete checklists. 

How AI enhances it 
A shelf recognition agent compares photos with expected layouts and highlights gaps or misplaced items. 

What we see in practice 
Field teams deliver more consistent in store execution, improve shelf accuracy and reduce time spent on manual checks. In our experience, this can drive improvements in shelf performance from 10 to 30 percent for prioritized categories. 

Demand Forecast Adjustments During Events 

This refers to short-term demand corrections during promotions, weather changes or marketing campaigns. Traditional forecasts react too slowly to real-world events and local conditions. 

How low-code helps 
Low-code dashboards integrate sales and supply chain data so planners can review performance daily. 

How AI enhances it 
A forecasting agent uses weather patterns, campaign data and historical trends to recommend volume adjustments. 

What we see in practice 
Planners react more quickly to real-world conditions and adjust inventory with greater confidence, resulting in better service levels. 

Vendor Collaboration Portal 

This refers to the digital workspace where suppliers onboard, submit documents and participate in sourcing processes. Managing many vendors adds administrative load, and manual bid evaluation can be inconsistent. 

How low-code helps 
Low-code enables supplier portals, onboarding flows and RFQ processes with fully trackable documentation. 

How AI enhances it 
A negotiation agent compares bids, evaluates scoring criteria and suggests optimal choices. 

What we see in practice 
Procurement teams work with suppliers more efficiently, shorten sourcing cycles and make more informed decisions based on structured data. 

OTIF (On Time In Full) Monitoring 

This refers to tracking whether shipments arrive on schedule and in the expected quantity. Visibility is often limited, and manual tracking makes it difficult to spot patterns early. 

How low-code helps 
A low-code visibility app consolidates shipment data and status updates from multiple carriers. 

How AI enhances it 
A pattern detection agent analyzes delays, route issues and incomplete deliveries to flag risks before they impact customers. 

What we see in practice 
Supply chain teams detect risks earlier, improve delivery reliability and minimize disruptions that affect customers. 

Finance: Low Code for Control, AI for Intelligence 

Financial processes depend on accuracy, transparency and regulatory consistency. Low-code helps teams digitize workflows, reduce manual steps and centralize information. AI adds real-time analysis, anomaly detection and decision support, which makes complex processes faster and more reliable. 

Digital Loan Origination and Underwriting 

This refers to the process of capturing loan applications, verifying documents and assessing customer risk. Manual reviews slow approvals and make it difficult to detect inconsistencies or early fraud signals. 

How low-code helps 
Low-code forms and workflows support KYC data collection, document uploads and review steps in one system. 

How AI enhances it 
A risk scoring agent analyzes applicant data, reads ID documents and highlights anomalies that require attention. 

What we see in practice 
Financial institutions speed up approvals, reduce fraud risk and make underwriting decisions with more confidence. 

Expense Reconciliation and Audit 

This refers to the submission and review of employee expenses across departments. Teams often struggle with policy compliance because receipts and justifications arrive in many different formats. 

How low-code helps 
Mobile expense apps built with low-code make it easy to submit receipts, categorize expenses and automate approvals. 

How AI enhances it 
An audit agent compares each submission against policy rules, flags inconsistencies and highlights unusual spending behavior. 

What we see in practice 
Expense reviews become faster and more consistent, with fewer policy violations slipping through manual checks. In our projects, AI assisted controls have reduced manual review workload by up to 70 percent, depending on the starting process design. 

Regulatory Reporting for AML, ESG or MiFID 

This refers to preparing regulatory reports that must pull data from multiple systems and meet strict standards. Inconsistent data quality and manual checks make the process slow and error prone. 

How low-code helps 
Low-code workflows pull data from core systems, validate fields and generate structured reports. 

How AI enhances it 
A compliance agent audits data integrity, identifies missing elements and checks whether reporting rules are met. 

What we see in practice 
Reports become more accurate and complete, and teams spend less time correcting data issues before submission. 

Customer 360 Relationship Portal 

This refers to creating a unified view of each client across products, interactions and performance. Data silos make it difficult to identify opportunities or understand client behavior. 

How low-code helps 
Low-code platforms integrate CRM, sales and service data into one front end experience. 

How AI enhances it 
A recommendation agent identifies upsell and cross sell opportunities based on patterns in client behavior. 

What we see in practice 
Organizations gain a fuller view of each client and identify growth opportunities more effectively. In our experience, this can support increases in revenue per client from 5 to 12 percent, depending on the product portfolio and sales model. 

Claims Processing and Appeals 

This refers to the workflow for receiving, categorizing and evaluating financial claims. Manual classification slows down processing and affects customer satisfaction. 

How low-code helps 
Low-code case management apps structure claim submissions and move them through standardized review steps. 

How AI enhances it 
An NLP agent reads claim descriptions, extracts key information and classifies the claim automatically. 

What we see in practice 
Cases move through review steps faster and with fewer inconsistencies, improving both accuracy and customer experience. 

Financial Planning and Forecasting 

This refers to collaborative budget planning across business units. Manual spreadsheets make it hard to test assumptions and assess the impact of changes. 

How low-code helps 
Low-code apps centralize inputs, support version control and streamline the planning cycle. 

How AI enhances it 
A predictive agent recalculates impacts, tests scenarios and highlights trends that may influence decisions. 

What we see in practice 
Business units plan more efficiently, test scenarios more easily and make decisions based on clearer insights. 

Payment Dispute Resolution 

This refers to the process of receiving, reviewing and resolving customer disputes about payments or charges. Prioritization is difficult when cases arrive in large volumes with different levels of urgency. 

How low-code helps 
Low-code portals collect dispute details, organize evidence and support internal review. 

How AI enhances it 
A sentiment analysis agent identifies frustrated or high risk cases so teams can prioritize them quickly. 

What we see in practice 
Teams handle disputes faster, prioritize urgent cases more accurately and improve customer satisfaction. 

The Transformative Impact of AI in Low Code 

Low-code and AI started as separate technologies with different goals. One focused on building applications fast, the other on understanding information and making decisions. Now they are entering a phase of coevolution where each amplifies the other. Low-code gives organizations the structure and speed needed to digitize processes, while AI adds the intelligence that turns those processes into something adaptive and responsive. 

The use cases across Pharma, CPG and Finance show that this convergence is already delivering practical value. In our projects we have seen faster processes, higher data quality and more confident decision making, although the exact results always depend on the specific context and the maturity of each organization. What matters most is that the combination consistently moves teams away from manual work and toward workflows that learn and improve over time. 

As these technologies continue to converge, the value they create will keep expanding. Companies that explore this space early will be the ones that benefit the most from the synergy of low code and AI.