Practical Expert Articles on Applied AI
Technical analyses, regulatory assessments, and strategic perspectives – based on Jaroona’s hands-on development work in applied AI, computer vision, and data-driven business processes.
Agentic AI
Automation
LLM
Agentic AI: When AI Doesn't Just Answer, But Gets Things Done
Many AI applications stop at well-formulated answers. Agentic AI goes a step further: it receives a goal – such as “Create a complete proposal for client XY” – and works toward it autonomously: it plans sub-steps, calls APIs, reads databases, executes code, and checks intermediate results until a reliable outcome is achieved. The principle: Goal → Plan → Action → Review → Adjustment → Result. Instead of just producing text, this AI acts like a digital employee.
Goal Orientation
The system works toward a clearly defined outcome – e.g., a finished report, a completed booking, or a validated data object
Planning & Decomposition
A goal like “Analyze quarterly data and create an executive summary” is automatically broken down into 5–10 sub-steps and processed sequentially
Tool Usage
Access to REST APIs, SQL databases, web browsers, Python interpreters, email systems, CRM and ERP – depending on the task
Feedback Loop
After each action, the system checks the result against the goal: Is the result correct? If not → correction and retry
Context & Memory
Intermediate results, decisions made, and failed attempts are stored in a persistent working memory (e.g., vector store)
Control & Guardrails
Policies define which tools may be used, which actions require human approval, and when the process is terminated
Classic Chat AI
- Reactive: Question → Answer
- Usually a single output
- Barely any autonomous actions
- Limited to text generation
Agentic AI
- Active: Goal → Process execution
- Multiple iterative steps
- Uses tools and verifies results
- Delivers a verifiable result
Typical Enterprise Use Cases
Business Automation
Automatically generate proposals from CRM data (Salesforce, HubSpot), analyze tenders and create proposal drafts, run complete onboarding workflows for new clients
Data & Analytics Agents
Pull raw data from data warehouses, clean it, run models, and output a management summary with recommendations in natural language – without manual intermediate steps
IT Ops / DevOps
Analyze logs from Splunk or Datadog, triage incidents by severity, automatically execute runbooks, and create escalation tickets in Jira when needed
Research Agents
Search patent databases, scientific publications, and competitor websites, extract relevant content, and condense it into a structured research report – in minutes instead of days
Key takeaway: Agentic AI = a digital employee with a goal, a toolbox, and self-control. It doesn't just produce text – it completes tasks from start to finish, verifiably and transparently.
What Really Matters in Practice: Guardrails
For agentic AI to be used productively, a clear security and governance design is required. These safeguards are not optional – they are critical for safe operation:
Human-in-the-loop
Actions with external effects (sending emails, deleting files, triggering payments) require explicit approval
Least Privilege
Each agent receives only the minimum necessary access rights – no write access where read access suffices
Logging & Audit-Trails
Every action, every tool call, and every decision is logged with a timestamp and justification – GDPR-compliant
Rate- & Budget-Limits
Maximum API calls per hour, token budgets, and cost limits prevent uncontrolled spending or infinite loops
Termination Criteria
After N failed attempts or when confidence falls below a threshold, the agent stops and escalates to a human
Advantages
- End-to-end execution: A process like “Analyze tender → Create proposal → Send” runs fully automatically – time savings: 60–80% compared to manual processing.
- Robust for multi-step workflows: Self-correction on errors reduces manual rework by up to 70%.
- Scalable: Hundreds of parallel agent instances simultaneously handle different tasks without quality loss.
- Consistent data quality through automated validation and cleansing at every process step.
- Employees focus on exceptions and strategic decisions – the AI handles routine tasks.
Risks to Keep in Mind
- Hallucinations in tool calls: The model calls an API with incorrect parameters – result: faulty data or unintended actions. Countermeasure: output validation before every action.
- Permission creep: Overly broad access rights enable unintended data access. Countermeasure: least-privilege principle and regular access audits.
- Compliance risks: Automated data access must be GDPR-compliant – audit trails and data protection impact assessments are mandatory.
- Overreach: Without clear termination criteria, the agent executes unauthorized actions – e.g., sending emails to external recipients.
How Jaroona Supports You
Jaroona develops agentic AI systems that integrate directly into existing enterprise infrastructures – from SAP and Salesforce to internal databases and cloud platforms. We accompany the entire journey: from process analysis and feasibility study through proof-of-concept to production-ready solutions with full monitoring. The focus is on four core areas:
Operationalization
From idea to production-ready solution: We define process boundaries, build in robust error handling, and ensure the agent runs stably even with unexpected inputs – with SLA monitoring and automatic retraining.
Tool Integration
We develop custom tool adapters for your systems: REST APIs, SQL databases, SAP modules, Microsoft 365, Salesforce, internal knowledge bases, and proprietary interfaces.
Governance & Guardrails
We implement role-based access models, human-in-the-loop workflows for critical actions, complete audit trails, and GDPR-compliant data storage – documented and auditable.
Measurability
We define KPIs before go-live: throughput time, error rate, cost savings, degree of automation. Dashboards in Power BI or Grafana make ROI transparent and continuously traceable.
With Jaroona, you transform real business processes – not just pilot projects. Our agentic systems run in production, are secure, and deliver measurable ROI.
Pharma
Drug Discovery
Predictive Analytics
AI in the Pharmaceutical Industry
Pharmaceutical companies generate immense amounts of data every day, ranging from R&D laboratories and clinical trials to production processes, quickly reaching terabytes in volume. This data is typically distributed across various isolated silos, such as LIMS (Laboratory Information Management Systems) for managing lab samples and test results, EDC (Electronic Data Capture) for electronic capture of clinical trial data, ERP (Enterprise Resource Planning) for company-wide resource planning, CRM (Customer Relationship Management) for customer interactions, specialized clinical databases, and direct outputs from laboratory equipment.
Furthermore, the data is heterogeneously structured, ranging from image data (e.g., histopathological scans, microscopy images, medical imaging such as MRI/CT) to text data (research reports, patient histories, regulatory documents, scientific publications) and time series (sensor data from wearables, biomarker progressions, production parameters) through to complex molecular structures (chemical formulas, 3D protein structures). This fragmentation and the variety of formats pose significant challenges for comprehensive analysis and the extraction of valuable insights.
This results in:
Inefficient Data Integration
Limited reusability of insights
Long Development Cycles
10–15 years to market launch
High Dropout Rates
In clinical trials
Lack of Transparency
In supply chains and production planning
Objectives
Through the targeted use of AI and cloud technologies, the entire value chain is to be optimized:
Automated Data Analysis
Of clinical, genomic, and image-based information
Predictive Forecasting
For demand, production, and study planning
Generative Modeling
Of new active compounds and proteins
Secure Platform Architecture
With end-to-end governance
Solution Architecture & AI Components
Data Analysis (Data Analytics & Knowledge Extraction)
Architecture:
- Raw data is stored in a data lake (Azure Data Lake Gen2 / AWS S3 Bucket).
- Transformation with Databricks + Delta Lake, curated via dbt (Data Build Tool).
- Semantic processing with knowledge graphs (Neo4j, RDF triple store) for linking patient, laboratory, and study data.
Models & Methods:
CNN Models
(ResNet, EfficientNet) for analyzing histopathological images
Transformer-Based Models
(BioBERT, ClinicalBERT, PubMedBERT) for extracting medical entities from study protocols and literature
Autoencoder
For detecting anomalies in sensor data (e.g., from wearables)
Goal: AI detects patterns, side effects, and risk correlations in clinical data significantly faster than manual analysis.
Forecasting (Predictive & Prescriptive Analytics)
Algorithms:
- Time series models: Prophet, LSTM, Temporal Fusion Transformer (TFT).
- Multivariate regressions and Bayesian forecasting for simulating clinical parameters.
- Reinforcement Learning (RL) for adaptive study planning and production control.
Pipeline:
- Real-time data from ERP, MES, and SCM systems is ingested into the data lake via Kafka Streams.
- Models are automatically trained and deployed using Azure ML Pipelines or Kubeflow Pipelines.
- Results feed into dashboards (Power BI, Tableau, Streamlit).
Benefit: Reduction of production bottlenecks by up to 25 %, more precise demand planning, and dynamic resource allocation.
Generative AI (Drug Discovery & Automation)
Models & Frameworks:
GANs & Diffusion Models
Generative Adversarial Networks (GANs) and diffusion models for generating new molecular structures.
Graph Neural Networks
Graph Neural Networks (GNNs) for molecular representation & property prediction.
Reinforcement Learning
Reinforcement Learning for Molecules (RLfM) for optimizing chemical properties (e.g., lipophilicity, binding affinity).
AlphaFold Integration
AlphaFold 2/3 integration for protein structure prediction.
LLM-Based Generation
Document generation based on locally developed large language models (e.g., Llama-3 fine-tuning) for the automated creation of complex regulatory reports.
Pipeline:
- Models run in GPU clusters (NVIDIA A100/H100) with TensorFlow 2.0 + PyTorch Lightning.
- Molecular databases (ChEMBL, PubChem) are automatically converted into feature embeddings.
- Results are validated through in-silico simulations and feedback loops (active learning).
Benefit: Reduction of the drug design cycle by > 40 %, higher success rate in molecular screenings.
Technical Implementation
Results & Key Metrics
Conclusion
Artificial intelligence is transforming the pharmaceutical industry into a precise, data-driven, and adaptive organization. The combination of data analytics, forecasting, and generative AI enables:
Efficient Research
Through data-driven insights
Early Identification
Of opportunities and risks
Accelerated Development
With controlled quality
With an integrated MLOps architecture, AI can be operated transparently, securely, and scalably – a decisive success factor for modern pharmaceutical companies.
Renewable Energy
Predictive Maintenance
IoT
AI-Powered Optimization of Renewable Energy Systems
- Volatility as a challenge: PV, wind & battery storage require real-time control beyond rule-based systems
- AI as the solution: Adaptive analysis, forecasting, and control of high-dimensional interactions in real time
- Measurable results: Up to 30% efficiency gains and 40% fewer unplanned outages
Why AI for Renewable Energy?
Mastering Volatility
AI overcomes the limitations of rule-based systems by analyzing and controlling the intrinsic volatility of renewable energy in real time.
Increasing Efficiency
Through precise forecasting and adaptive control, efficiency gains of up to 30% and a 40% reduction in unplanned outages are achieved.
Creating Smart Systems
Artificial intelligence transforms energy systems into intelligent, self-learning entities that continuously optimize and adapt.
AI Application Areas in Renewable Energy Systems
Forecasting & Prediction
Precise predictions of power generation (PV, wind) and storage state (SoC/SoH) to optimize operations and grid integration.
Adaptive Control
Real-time optimization of turbines, storage charge management, and system-wide coordination for maximum yields and grid stability.
Early Fault Detection & Maintenance
AI-based analysis of sensor data for predictive maintenance and reduction of unplanned downtime.
Grid Integration & Stabilization
Coordination of various energy sources and storage systems to ensure grid stability and handle frequency deviations.
Self-Consumption Optimization
Intelligent demand-side management in buildings to increase self-consumption rates and reduce external energy purchases.
Successful AI Applications in Practice
Exemplary Efficiency Gains Through AI
30%
Self-consumption rate through intelligent charge management
40-60%
Fewer unplanned outages through predictive maintenance
20-25%
Battery lifespan through optimized charging cycles
12%
PV yield through data-driven cleaning strategies
15-20%
Fewer grid load peaks through dynamic energy management
Conclusion: AI, cloud, and IoT make renewable energy systems not only smarter, more robust, and more reliable, but also significantly more economical. Data is used in real time to continuously make better, adaptive decisions and sustainably optimize operations, which can lead to an overall ROI of over 20% within 3–5 years of implementation.
Identifying Hidden Yield Losses in Your PV Portfolio?
Find out how Jaroona uses SCADA, inverter, irradiance, and weather data to make economically relevant performance losses visible.
ERP
Machine Learning
Data Intelligence
Intelligent ERP Systems: Your Data as a Competitive Advantage
Your ERP systems hold immense, untapped potential. Every day they collect vast amounts of data – on finances, customers, inventory, production, and more.
These massive datasets, often fragmented across departments and systems, represent an untapped goldmine. With Artificial Intelligence (AI) and Machine Learning (ML), you transform this information from mere numbers into a strategic tool that enables informed decisions and sustainable growth.
AI models can identify complex patterns in this data that remain invisible to the human eye, generating valuable insights.
Why AI for ERP?
Smart Decisions Instead of Gut Feeling
Predictive analytics helps you identify trends early and make informed strategic decisions. By analyzing historical data and recognizing patterns, future developments can be precisely forecasted, giving you a decisive competitive advantage.
Automation of Routine Tasks
Machine learning models take over time-consuming, repetitive analyses and processes that previously required significant time and resources. This frees your employees from monotonous tasks and allows them to focus on more complex, value-adding activities.
Competitive Advantages Through Data Intelligence
Proactive rather than reactive action is the key to success in dynamic markets. AI-powered systems enable your company to respond quickly to changes, seize market opportunities, and minimize risks early on.
Integrating AI into your ERP systems goes beyond mere efficiency gains. It enables a profound transformation of your business processes and creates new opportunities for value creation. From supply chain optimization to personalizing the customer experience – the potential is virtually unlimited.
AI Application Areas in ERP Systems
Forecasting and Prediction
Stay ahead of the curve with data-driven planning: revenue forecasting through regression analyses, precise inventory & demand planning via time series analysis, and detailed financial forecasts using various ML models. This minimizes overstocking, prevents supply bottlenecks, and optimizes your capital commitment.
Customer Segmentation
Targeted customer outreach through K-Means clustering to identify homogeneous customer groups, optimized marketing strategies through behavioral analysis, and customer feedback analysis with Natural Language Processing (NLP). This allows you to address your customers in a highly personalized way and increase customer satisfaction and conversion rates.
Fraud Detection
Protect your company with AI-based security: anomaly detection in transaction data, fraud pattern analysis with neural networks, and improved risk control through deep learning algorithms. This helps prevent financial losses and maintain compliance guidelines.
Process Optimization
Increase the efficiency of your internal processes: AI models can identify bottlenecks in production, optimize maintenance schedules through predictive maintenance, and improve resource allocation. This leads to shorter throughput times, lower operating costs, and higher productivity.
Supply Chain Management
Revolutionize your supply chain: AI enables the prediction of delivery delays, optimization of routes and transportation costs, and efficient inventory management across the entire supply chain. This creates a more resilient and responsive supply chain that can also react to unforeseen events.
Conclusion: Data Becomes Action.
Artificial intelligence transforms ERP data from an administrative obligation into a true business booster. Companies that invest in intelligent data analysis and AI-powered processes today not only secure long-term market opportunities, but position themselves as pioneers in their industry. Invest in AI to make the most of your business data and achieve a sustainable competitive advantage.
Finance
Risk Management
RegTech
Intelligent AI Solutions for Risk, Returns, and Regulation
The financial industry is experiencing massive innovation pressure from digital technologies and high customer expectations. At the same time, increasingly complex and growing regulation (e.g., MiFID II, Basel III, GDPR, AML) hampers operational agility and causes high compliance costs. Financial institutions must also efficiently manage enormous volumes of data – from transactions and customer data to unstructured texts – and extract value from them. Competition from agile FinTechs (neobanks, payment service providers, robo-advisors) intensifies this pressure, as they threaten the market shares and margins of traditional institutions with lower cost structures and superior digital user experiences.
Our AI solutions address these challenges in a targeted way: they enable financial institutions to assess risks more precisely (e.g., detect fraud patterns with over 95% accuracy), automate decision-making processes (e.g., reduce loan processing times by up to 80%), and identify market opportunities in real time. This leads to proactive adaptation to market dynamics and the development of new revenue streams through precise forecasts and data-driven insights.
Why AI for Finance?
Faster Decisions
AI models detect patterns in market, customer, and transaction data before they become visible to humans.
Reduced Risk
Deep learning models detect credit, market, and fraud risks at an early stage.
Regulatory Certainty
AI-based RegTech solutions support compliance with MiFID II, Basel III, IFRS, and ESG requirements.
Higher Efficiency
Automated processes reduce costs in compliance, reporting, and portfolio management.
AI Application Areas in Finance
Risk Assessment & Forecasting
We enable precise forecasting of credit defaults, market volatility, and customer churn. This empowers financial institutions to make informed decisions in lending, investment strategies, and risk management, significantly minimizing operational risks.
Technical Implementation: We use advanced machine learning algorithms such as Gradient Boosting, Random Forests, and specialized Long Short-Term Memory (LSTM) networks for time series analyses.
Document Analysis & Compliance
We enable the efficient and automated processing of unstructured financial documents such as contracts, financial reports, and complex regulatory texts. This not only ensures seamless regulatory compliance, but also uncovers valuable, previously hidden insights for strategic decisions.
Technical Implementation: We use state-of-the-art NLP technologies such as BERT, RoBERTa, and transformer models to ensure precise text analysis and extraction.
Portfolio & Trading Optimization
We enable the dynamic optimization of portfolio compositions and trading strategies in real time. This leads to the maximization of returns and effective risk minimization, as financial institutions proactively respond to market changes and make data-driven investment decisions.
Technical Implementation: We use Reinforcement Learning, advanced optimization algorithms, and high-performance real-time data processing.
Fraud Protection & Identity Verification
We enable robust protection against fraudulent activities through highly precise detection of forged documents, manipulated signatures, and suspicious transaction patterns using visual AI models. This increases the security of your systems and effectively protects your customers.
Technical Implementation: We use advanced computer vision, Convolutional Neural Networks (CNNs), Optical Character Recognition (OCR) models, and state-of-the-art anomaly detection methods.
Regulatory Monitoring
We enable proactive and automated compliance with complex regulatory requirements. Through AI-powered analysis of laws, guidelines, and internal reports, we identify potential violations or deviations at an early stage, minimizing compliance risks.
Technical Implementation: We use Knowledge Graphs for semantic linking of regulatory data, complemented by rule-based systems and intelligently automated compliance workflows.
Practical Examples
AI-Based Fraud Detection
A combination of deep neural networks and graph analytics detects complex fraud patterns across multiple accounts with a detection rate of >99%. The response occurs in real time (<100 ms) to immediately block common types of fraud such as credit card fraud, money laundering, and identity theft, reducing losses by up to 80%.
Credit Risk Scoring
Adaptive models calculate default probabilities incorporating historical transaction data, social media, and macroeconomic indicators. This leads to 20-30 % higher prediction accuracy and reduces loan application processing time from days to minutes, enabling faster decisions for customers and increasing operational efficiency.
Real-Time Portfolio Management
Reinforcement-learning algorithms dynamically optimize asset allocations and respond to market changes within milliseconds. This enables a 5-10 % higher alpha generation and a 15 % reduction in portfolio volatility, while risk metrics such as the maximum drawdown are actively minimized.
Automated ESG Reporting
NLP models analyze annual reports, extract ESG-relevant passages, and classify them according to EU Taxonomy, SFDR, TCFD, and CSRD standards. This enables an automation rate of 80-90 % and reduces reporting time from weeks to just a few days, minimizing compliance risks and increasing transparency.
Your Benefits at a Glance
Conclusion: Our AI solutions create measurable competitive advantages:
lower risks, automated compliance, and intelligently managed returns. Jaroona combines technological excellence with regulatory expertise – for financial institutions that rely on precision, security, and speed.
Stay informed
Would you like to stay up to date on the latest developments, publications, and events from Jaroona? Subscribe to our newsletter or follow us on our social media channels.
We regularly share valuable resources, expert articles, and news from the world of AI and cybersecurity. Don’t miss any important updates!
newsletter.jaroona.com
Sign Up for Our Newsletter
Never miss the latest news with our newsletter. Sign up now and stay up to date!
Your Partner for AI & Machine Learning
Tailored AI solutions for businesses — from strategic consulting to successful implementation.
© 2026 Jaroona. All rights reserved.