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RPA vs. AI Agents for SAP Automation: The Definitive Comparison
Struggling to optimize your SAP processes?> The choice between Robotic Process Automation (RPA) and advanced AI Agents can feel overwhelming. Both promise efficiency, but their capabilities, costs, and long-term strategic value differ significantly. This comprehensive guide cuts through the hype, providing a clear, data-driven comparison to help you make the most informed decision for your enterprise SAP landscape.<
In today's competitive business environment, leveraging automation isn't just an advantage—it's a necessity. For SAP-centric organizations, the quest for greater efficiency, accuracy, and scalability often leads to two powerful contenders: Robotic Process Automation (RPA) and AI-powered Autonomous Agents. But which technology truly delivers the most value for your specific SAP automation needs? Is it the quick, structured automation of RPA bots, or the intelligent, adaptive capabilities of AI agents that can learn and evolve?
This page will demystify the core differences, explore real-world applications within SAP, and equip you with the insights needed to select the optimal solution, or combination of solutions, to drive your digital transformation forward. Prepare to understand the nuances, evaluate the leading tools, and chart a clear path to superior SAP operational excellence.
RPA vs. AI Agents for SAP: At a Glance
Before diving deep, here's a quick overview of how these two powerful automation paradigms stack up against each other in the context of SAP.
Feature/Aspect
Robotic Process Automation (RPA)
AI Agents (Intelligent Automation)
Core Functionality
Mimics human actions on UIs; rule-based, repetitive tasks.
Understands context, learns, makes decisions, adapts to changes.
SAP Integration
UI-based (screen scraping, click automation), API integration for structured data.
Deep API integration, natural language processing (NLP) for unstructured data, cognitive services.
Task Complexity
Best for high-volume, repetitive, rule-based tasks with stable processes (e.g., data entry, report generation).
>RPA is like building a digital workforce of software robots that mimic human interactions with computer systems. For SAP, this typically means bots logging into SAP GUI, Fiori, or web portals, navigating screens, entering data, clicking buttons, and extracting information—exactly as a human user would, but much faster and without errors.<
Key Characteristics of RPA in SAP:
Rule-Based: RPA excels at processes with clear, predefined rules and steps.
Non-Invasive: It doesn't require changes to underlying SAP systems, making deployment relatively fast.
User Interface (UI) Centric: Primarily interacts with SAP through its presentation layer, mimicking keystrokes and mouse clicks.
Structured Data Focus: Best suited for tasks involving structured data input and output.
Auditable: Every action performed by an RPA bot is logged, providing a clear audit trail.
Common RPA Use Cases in SAP:
Master Data Management: Automating the creation, update, and deletion of customer, vendor, material, and employee master data (e.g., transaction codes like XD01, FK01, MM01, PA30).
Financial Operations: Automating journal entry postings (FB50), invoice processing (MIRO), vendor payments (F-53), bank reconciliation, and closing activities.
Supply Chain & Logistics: Automating order processing (VA01), goods receipt (MIGO), purchase order creation (ME21N), and inventory checks.
HR Processes: Automating employee onboarding/offboarding, payroll data entry, and time sheet processing.
Report Generation & Data Extraction: Automatically logging into SAP, running specific reports (e.g., FBL5N, S_ALR_87012164), extracting data, and distributing it.
Leading RPA Platforms for SAP Automation:
While many RPA tools exist, a few stand out for their robust SAP integration capabilities and enterprise-grade features.
Pros of RPA for SAP
Rapid ROI: Quick to implement for high-volume, repetitive tasks, leading to fast cost savings.
Low Technical Barrier: Often citizen-developer friendly with low-code/no-code platforms.
Non-Disruptive: Integrates at the UI level without altering core SAP systems.
Improved Accuracy: Eliminates human error in data entry and processing.
Cons of RPA for SAP
Fragile: Highly susceptible to UI changes in SAP (e.g., Fiori updates, SAP GUI version changes).
Limited Intelligence: Cannot handle exceptions or unstructured data without human intervention or advanced add-ons.
Scalability Challenges: Each bot typically handles one process, scaling can become resource-intensive.
Not Truly Cognitive: Lacks understanding or decision-making capabilities beyond predefined rules.
Platform Spotlight:
UiPath
UiPath is a market leader known for its comprehensive platform and strong community. For SAP, UiPath offers specialized activities and connectors that go beyond simple UI automation. It provides dedicated SAP activities for both SAP GUI and Fiori, enabling more robust and resilient automation. Its AI Fabric also allows for the integration of machine learning models to handle unstructured data or complex decisions, blurring the lines towards intelligent automation.
SAP Integration: Native SAP GUI activities, SAP BAPI/RFC integration, Fiori automation.
Key Features: StudioX (citizen developer), Studio (pro developer), Orchestrator (management), AI Fabric, Document Understanding.
Pricing: Subscription-based, often tiered by bot type (attended/unattended) and usage. Entry-level packages can start around $10,000-$20,000 annually for basic enterprise use, scaling significantly for large deployments.
Best For: Organizations seeking a scalable, end-to-end automation platform capable of handling both simple and increasingly complex SAP processes, often as a stepping stone to more intelligent automation.
Looking to see UiPath in action for your SAP processes?
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Automation Anywhere
Automation Anywhere offers a cloud-native RPA platform (Automation 360) with a strong focus on intelligent automation through its IQ Bot and AARI (Automation Anywhere Robotic Interface). Its SAP automation capabilities are robust, with specific commands and recorders designed for SAP applications. IQ Bot is particularly useful for processing unstructured SAP-related documents like purchase orders or invoices by leveraging AI.
SAP Integration: SAP-specific commands, recorder for SAP GUI, API integration, IQ Bot for document processing.
Key Features: Cloud-native platform, IQ Bot (AI/ML for documents), AARI (human-bot collaboration), Bot Store.
Pricing: Subscription model, often user-based and bot-based. Enterprise pricing is custom, but typically comparable to UiPath, starting in the low five figures annually.
Best For: Enterprises prioritizing a cloud-first approach and looking to integrate AI for document processing directly into their RPA workflows for SAP.
Explore Automation Anywhere's intelligent automation for SAP.
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Microsoft Power Automate
Microsoft Power Automate, especially with its Desktop RPA capabilities, provides a compelling option for organizations already invested in the Microsoft ecosystem. It offers connectors for SAP ERP and SAP ECC, allowing for both UI automation and API-based integration. Its strength lies in its seamless integration with other Microsoft products (Office 365, Azure AI), making it a powerful tool for end-to-end business process automation that touches SAP.
SAP Integration: Dedicated SAP connectors (for API/BAPI), UI automation for SAP GUI/Fiori, integration with Azure Logic Apps.
Key Features: Low-code/no-code, Desktop flows (RPA), Cloud flows, AI Builder (for forms processing, text recognition), Dataverse integration.
Pricing: Included in some Microsoft 365 plans for basic use, premium connectors and unattended RPA bots require separate licenses (e.g., ~$15/user/month for attended, ~$500/bot/month for unattended).
Best For:> Microsoft-centric organizations looking for a cost-effective, integrated solution for automating SAP processes, particularly those involving other Microsoft applications.<
See how Microsoft Power Automate can streamline your SAP operations.
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AI Agents for SAP Automation (Intelligent Automation)
>AI Agents represent a paradigm shift from simply mimicking human actions to understanding, learning, and making autonomous decisions. For SAP, this means intelligent systems that can interpret complex business scenarios, interact with SAP systems via APIs, process unstructured data, and even predict outcomes or suggest optimal actions, often without direct human supervision. These agents leverage technologies like Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, and advanced analytics.<
Key Characteristics of AI Agents in SAP:
Cognitive Capabilities: Can understand context, interpret intent, and learn from data.
Adaptive and Resilient: Can adjust to changes in processes or data, handle exceptions intelligently.
Data-Driven Decision Making: Utilizes algorithms to make informed choices based on patterns and predictions.
Unstructured Data Processing: Excels at interpreting and acting upon data from emails, documents, voice, etc.
Deep SAP Integration: Primarily interacts with SAP through APIs (BAPIs, OData, RFCs) for robust, data-level interaction, though UI interaction can be part of a broader solution.
Common AI Agent Use Cases in SAP:
Intelligent Invoice Processing: Beyond OCR, an AI agent can read invoices (PDFs, scans), extract line items, match them against purchase orders and goods receipts in SAP (MM/FI modules), identify discrepancies, and even initiate workflows for resolution or automatically post the invoice.
Predictive Maintenance: Analyzing sensor data from equipment, historical maintenance records in SAP PM, and operational data to predict failures, automatically create maintenance orders (PM01), and trigger parts procurement.
Supply Chain Optimization: AI agents can analyze demand forecasts, inventory levels (SAP MM/SD), supplier performance, and logistics data to recommend optimal ordering quantities, routes, and even dynamically adjust pricing or production schedules.
Proactive Customer Service: AI-powered chatbots or virtual assistants integrated with SAP CRM/SD can understand customer queries (natural language), access relevant customer data, provide personalized responses, and even initiate transactions (e.g., order status, returns).
Fraud Detection: Monitoring SAP financial transactions (FI, CO) for anomalies, patterns indicative of fraud, and flagging them for investigation or automatically blocking suspicious activities.
Automated Contract Management: Reading and interpreting clauses in contracts, comparing them against SAP terms, identifying deviations, and triggering alerts or workflows.
Leading AI Agent & Intelligent Automation Platforms for SAP:
The "AI Agent" space is more diverse, often involving platform stacks rather than single products. These platforms usually integrate with SAP using APIs and leverage various AI services.
Pros of AI Agents for SAP
True Intelligence: Can learn, adapt, and make decisions, handling complex, variable processes.
Handles Unstructured Data: Critical for many real-world SAP business processes (e.g., invoices, emails).
Strategic Value: Enables predictive analytics, process optimization, and new business models.
Resilient: Less prone to breaking from minor UI or process changes due to API-centric interaction and learning.
Scalability: Can process vast amounts of data and manage complex interactions across systems.
Cons of AI Agents for SAP
Higher Initial Investment: Requires significant data, expertise (data scientists, ML engineers), and infrastructure.
Longer Implementation Cycles: Model training, validation, and integration are time-consuming.
Data Dependency: Performance is directly tied to the quality and quantity of training data.
Explainability: "Black box" nature of some AI models can make auditing and compliance challenging.
Requires Strong Governance: Ethical considerations, bias, and continuous monitoring are crucial.
Platform Spotlight:
SAP Intelligent Robotic Process Automation (iRPA) + SAP AI Business Services
SAP's own offering combines RPA with a suite of AI services. SAP iRPA provides the robotic component for structured, repetitive tasks, similar to other RPA vendors. However, its true power lies in its seamless integration with SAP AI Business Services, which includes capabilities like Document Information Extraction (for invoices, purchase orders), Business Entity Recognition, Service Ticket Intelligence, and Conversational AI (SAP Conversational AI). This allows for a holistic approach where RPA handles the transactional execution, and AI handles the cognitive aspects within the SAP ecosystem.
SAP Integration: Native, deep integration with SAP S/4HANA, ECC, and other SAP cloud solutions.
Key Features: Desktop Studio, Cloud Factory, Pre-built bots for SAP, SAP AI Business Services (ML models), Conversational AI.
Pricing: Based on consumption (e.g., number of bot executions, API calls for AI services). Specific pricing available upon request from SAP, but generally competitive for existing SAP customers leveraging their cloud platform.
Best For: Organizations with a strong SAP footprint and strategy, looking for an integrated, vendor-supported intelligent automation solution that leverages their existing SAP investments.
Discover SAP's integrated approach to intelligent automation.
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Google Cloud AI Platform / Azure AI / AWS AI Services (Hyperscalers)
These hyperscaler platforms offer a vast array of AI and ML services that can be leveraged to build custom AI agents for SAP. This approach involves integrating SAP with services like Google Cloud's Document AI, Vision AI, Dialogflow; Azure's Cognitive Services (Form Recognizer, Language Understanding, Bot Service); or AWS's Textract, Comprehend, Lex. While not "off-the-shelf" agents, they provide the building blocks to create highly customized, intelligent solutions that interact with SAP via APIs (e.g., SAP Gateway, OData).
SAP Integration: API-driven (via middleware like SAP Cloud Platform Integration, or custom connectors).
Key Features: Comprehensive suite of AI/ML services, scalable infrastructure, pre-trained models, custom model training, MLOps tools.
Pricing: Consumption-based (pay-as-you-go) for individual services. Can range from very low for small use cases to significant for large, complex deployments requiring custom ML models and extensive compute.
Best For: Enterprises with strong in-house data science capabilities, complex, unique SAP automation challenges, and a preference for building highly customized, scalable AI solutions leveraging leading cloud infrastructure.
Process Mining & Intelligent Automation Platforms (e.g., Celonis, Appian, Pega)
These platforms offer a broader perspective, combining process mining capabilities with intelligent automation. Tools like Celonis help identify and understand process bottlenecks in SAP before automation. Platforms like Appian and Pega Systems offer Intelligent Business Process Management Suites (iBPMS) that blend RPA, AI, and human workflows. They can orchestrate complex, end-to-end SAP processes, leveraging AI for decision support, dynamic case management, and adaptive workflows.
SAP Integration: Deep data connectors for process mining, API integration for workflow orchestration.
Key Features: Process discovery, real-time analytics, low-code development, workflow orchestration, AI services, RPA integration.
Pricing: Enterprise-grade, typically subscription-based with custom quotes. Can be substantial due to the comprehensive nature of the platforms, often starting in the high five to six figures annually.
Best For: Large enterprises focused on end-to-end process transformation, requiring deep process insights, orchestration of complex human-bot-AI workflows, and a unified platform for digital operations across SAP and non-SAP systems.
Understanding the cost implications and the best fit for your organization's size and complexity is crucial.
SMBs and Mid-Market (Up to $500M Revenue)
RPA Suitability: High. RPA offers a relatively low entry barrier and quick ROI for specific, high-volume SAP tasks. Tools like Microsoft Power Automate can be particularly cost-effective if already within the Microsoft ecosystem. UiPath and Automation Anywhere also offer flexible licensing that can scale.
AI Agents Suitability: Limited for custom-built solutions. Pre-packaged AI services (e.g., SAP AI Business Services for existing SAP customers, or specific cloud AI APIs for document processing) can be viable for targeted use cases, but building complex AI agents from scratch is often cost-prohibitive due to the need for data scientists and significant data.
Typical Investment:
RPA: $10,000 - $100,000+ annually, depending on the number of bots and complexity.
AI (Targeted): $5,000 - $50,000+ annually for specific AI services or pre-built solutions.
Large Enterprises ($500M - $5B+ Revenue)
RPA Suitability: High, but often as part of a broader intelligent automation strategy. RPA is excellent for "quick wins" and automating the "long tail" of repetitive processes that don't warrant full AI development. Large enterprises often deploy hundreds or thousands of bots.
AI Agents Suitability: Very High. Large enterprises have the data, budget, and talent to invest in sophisticated AI agents that deliver strategic value, competitive differentiation, and transformative outcomes. They can leverage hyperscaler platforms, SAP's integrated AI, or iBPMS solutions for complex, end-to-end automation.
Typical Investment:
RPA: $100,000 - $1M+ annually for enterprise-wide deployments.
AI Agents: $200,000 - $5M+ annually, including platform licenses, data science teams, and infrastructure.
Global Corporations (Multi-billion Revenue, Complex Operations)
RPA Suitability: Essential component, but always integrated with AI. RPA handles the sheer volume of transactional tasks, while AI provides the intelligence, adaptability, and strategic oversight needed for global operations.
AI Agents Suitability: Critical. Global corporations require the adaptive, decision-making capabilities of AI agents to optimize complex supply chains, manage diverse customer interactions, detect sophisticated fraud, and drive innovation across geographies. Custom AI solutions, often built on hyperscaler platforms, are common.
Typical Investment:
RPA & AI (Hybrid): $500,000 - $10M+ annually, representing a significant strategic investment in a unified intelligent automation fabric.
"The future of SAP automation isn't about choosing RPA *or* AI; it's about intelligently combining them. RPA handles the 'do,' while AI handles the 'think and adapt.' A true intelligent automation strategy leverages both." - CTO, Global Manufacturing Firm
Who Should Use What? Persona Matching for SAP Automation
Your role, strategic objectives, and immediate challenges will heavily influence the best automation path for your SAP landscape.
Challenge: Slow, error-prone manual processes, high operational costs, lack of scalability, inability to adapt to market changes.
Recommendation:
Start with RPA: For immediate cost savings and efficiency gains in high-volume, repetitive SAP tasks. Focus on areas like finance, HR, and master data.
Strategize for AI Agents: As a long-term play for competitive advantage. Identify complex, strategic processes that require judgment, prediction, or dynamic adaptation (e.g., supply chain optimization, customer experience). Invest in data infrastructure and AI talent.
Hybrid Approach: The ultimate goal should be an intelligent automation fabric that combines RPA for execution and AI for intelligence and orchestration.
Tools to Consider: UiPath, Automation Anywhere (RPA); SAP iRPA + AI Business Services, Hyperscaler AI, Celonis/Appian (AI/iBPMS).
RPA First: Automate GL postings, invoice data entry (MIRO), bank reconciliation, report generation. This delivers immediate accuracy improvements and faster cycles.
AI for Intelligence: Implement AI agents for intelligent invoice matching, fraud detection, predictive cash flow analysis, and dynamic dispute resolution. This moves beyond basic automation to strategic financial insights.
Tools to Consider: UiPath, Automation Anywhere, Power Automate (RPA for finance); SAP AI Business Services (Document Information Extraction), Hyperscaler AI (for custom invoice processing and analytics).
For the Supply Chain Manager / Head of Procurement:
RPA for Transactions: Automate purchase order creation (ME21N), goods receipt (MIGO), vendor master data updates.
AI for Optimization: Implement AI agents for predictive demand forecasting, dynamic pricing, inventory optimization, supplier risk assessment, and proactive logistics management. This drives resilience and cost reduction across the supply chain.
Tools to Consider: UiPath, Automation Anywhere (RPA for SCM); Hyperscaler AI (for predictive models), Celonis (process mining for SCM bottlenecks).
For the HR Director / CHRO:
Challenge: High administrative burden for onboarding/offboarding, manual payroll processing, employee data management.
Recommendation:
RPA for Administration: Automate employee master data updates (PA30), new hire provisioning in SAP, time entry processing, report generation.
AI for Employee Experience: Leverage AI-powered chatbots (integrated with SAP SuccessFactors) for HR queries, intelligent talent matching, and sentiment analysis for employee engagement.
Tools to Consider: Microsoft Power Automate, UiPath (RPA for HR); SAP Conversational AI, Hyperscaler NLP services.
Implementing Your SAP Automation Strategy: A Getting Started Guide
Embarking on SAP automation requires a structured approach. Here’s a practical guide:
Define Your Strategic Objectives:
What are you trying to achieve? Is it cost reduction, improved accuracy, faster cycle times, enhanced customer experience, or competitive differentiation? Clear objectives will guide your technology choice.
Example: "Reduce manual invoice processing time by 40% and improve data accuracy by 95% within 12 months for SAP FI."
Identify and Prioritize Processes:
Don't automate for automation's sake. Conduct a thorough process discovery. Use tools like process mining (e.g., Celonis, UiPath Process Mining, SAP Signavio) to identify bottlenecks, variations, and the highest-impact processes for automation. Prioritize based on:
ROI Potential: High volume, repetitive, error-prone tasks.
Complexity: Start with simpler, rule-based processes for RPA.
Strategic Impact: Processes that can benefit from AI's cognitive capabilities.
Assess Your Current SAP Landscape & Data:
Understand your SAP versions (ECC, S/4HANA, Fiori), existing integrations, and data quality. For AI, data availability, cleanliness, and volume are paramount. Do you have sufficient historical data to train ML models?
Checklist: SAP GUI vs. Fiori usage, API availability (BAPIs, OData), data governance policies, data security requirements.
Pilot Project & Proof of Concept (PoC):
Start small. Choose a well-defined process with clear boundaries for your first automation project. This allows you to test the technology, build internal expertise, and demonstrate value quickly.
RPA PoC: Automate a simple SAP data entry task (e.g., creating a new vendor).
AI PoC: Develop a simple document extraction model for a specific invoice type.
Build Your Automation Center of Excellence (CoE):
Establish a dedicated team or cross-functional group responsible for strategy, governance, development, and maintenance of automation initiatives. This CoE will manage the pipeline, ensure best practices, and scale automation across the enterprise.
Roles: Automation Architect, Process Analyst, RPA Developer, AI Engineer/Data Scientist, Business Stakeholder.
Choose the Right Technology & Partner:
Based on your objectives, process analysis, and PoC results, select the platform(s) that best fit your needs. Consider vendor support, scalability, integration capabilities, and total cost of ownership. Engage with reputable implementation partners if internal expertise is lacking.
Vendor Comparison: Leverage detailed comparison tools and analyst reports.
Partner Selection: Look for partners with proven SAP automation experience.
Scale and Iterate:
Once your pilot is successful, scale up. Continuously monitor performance, gather feedback, and identify new automation opportunities. Intelligent automation is an ongoing journey of continuous improvement.
Metrics: Track ROI, process cycle time, error rates, compliance adherence, employee satisfaction.
Ready to Transform Your SAP Operations?
The decision between RPA and AI Agents for SAP automation isn't about choosing one over the other, but understanding where each excels and how they can combine to create a powerful, intelligent automation ecosystem. Whether you're aiming for quick efficiency gains or long-term strategic transformation, the right tools are available.
Q1: Can RPA and AI Agents work together for SAP automation?
A: Absolutely, and this is often the most powerful approach, known as "Intelligent Automation" or "Hyperautomation." RPA bots can handle the structured, repetitive task execution within SAP (e.g., logging in, navigating, data entry), while AI agents provide the cognitive capabilities (e.g., interpreting unstructured data, making decisions, predicting outcomes) that guide the RPA bots or handle exceptions. For instance, an AI agent might analyze an incoming invoice, extract relevant data, identify discrepancies, and then instruct an RPA bot to post the invoice in SAP or initiate a dispute resolution workflow.
Q2: Is my SAP data secure with RPA and AI solutions?
A: Data security is paramount. Reputable RPA and AI platforms implement robust security measures, including encryption, access controls, audit trails, and compliance with industry standards (e.g., GDPR, ISO 27001). For SAP, this means ensuring that bots and AI agents access data only with appropriate authorizations, mirroring human user permissions. Cloud-based solutions from hyperscalers (Azure, AWS, Google Cloud) and SAP itself have enterprise-grade security built-in. Always review a vendor's security certifications and ensure your implementation adheres to best practices for secure access to SAP.
Q3: What's the typical ROI for SAP automation using RPA or AI?
A: ROI varies significantly by use case and implementation. For RPA, organizations often see ROI within months, driven by reduced manual effort, increased accuracy, and faster processing times. Typical benefits include 20-50% cost savings and 50-80% faster process execution. For AI agents, the ROI can be higher but takes longer to realize, as it often involves strategic benefits like improved decision-making, predictive capabilities, enhanced customer experience, and competitive advantage. Expect to measure ROI not just in cost savings, but in business outcomes, such as reduced inventory write-offs, improved customer retention, or faster time to market.
Q4: How do I get started if I have a complex, highly customized SAP environment?
A: A highly customized SAP environment favors solutions that offer deep API integration rather than relying solely on UI automation. While RPA can still handle some UI-based tasks, leveraging SAP's BAPIs, RFCs, and OData services through platforms like SAP iRPA, Hyperscaler AI services, or iBPMS tools will provide greater resilience and scalability. Start with a thorough process discovery phase, ideally using process mining, to understand the exact variations and dependencies introduced by your customizations. Engage experienced SAP automation consultants who understand both the technical nuances of your SAP system and the capabilities of advanced automation technologies.
Q5: Will automation replace my SAP users?
A: Automation, particularly intelligent automation, is designed to augment human capabilities, not entirely replace them. It takes over the dull, dirty, and dangerous tasks, freeing up your SAP users to focus on more strategic, creative, and value-added activities that require human judgment, problem-solving, and empathy. Roles will evolve; some employees may transition into "bot supervisors," "process analysts," or "AI trainers." The goal is to create a more engaged and productive workforce, leveraging human intelligence where it matters most, and machine intelligence for repetitive or cognitive heavy lifting.
Q6: What's the role of process mining in choosing between RPA and AI for SAP?
A: Process mining is a foundational step for any intelligent automation initiative. Tools like Celonis or SAP Signavio analyze event logs from your SAP system to visualize actual process flows, identify bottlenecks, variations, and compliance deviations. This data-driven insight is crucial for:
Uncovering AI Opportunities: Discovering complex, variable processes where AI can add significant value (e.g., predicting future bottlenecks, intelligent exception handling).
Measuring Impact: Providing baseline metrics to accurately calculate the ROI of your automation efforts.
It ensures you're automating the *right* processes in the *right* way, rather than simply automating inefficient ones.
