Introduction
Source: Cflow
Artificial intelligence (AI) rapidly transforms businesses by automating repetitive, time-consuming tasks. According to McKinsey, over 70% of companies have adopted at least one type of AI technology. By integrating AI more deeply into key business processes, companies can work smarter, faster, and more efficiently.
This comprehensive guide will stroll you through the key steps for leveraging AI to automate commercial enterprise procedures. From figuring out automation possibilities to optimizing AI structures over the years, we cover critical aspects of making plans, constructing, and managing intelligent automation throughout your enterprise. Follow these quality practices to power productiveness, decrease costs, and get the most business cost from AI.
| Step No. | Step |
| 1 | Identify Prime Processes for Automation |
| 2 | Define Goals and Success Metrics |
| 3 | Assess Technology Needs |
| 4 | Audit Data Quality and Accessibility |
| 5 | Design the Automated Workflow |
| 6 | Build & Test Automation Components |
| 7 | Integrate Systems & Roll Out |
| 8 | Continuously Optimize Over Time |
Step 1: Identify Prime Processes for Automation
Source: Mendix
The first major step is analyzing your existing business processes to identify areas that are ripe for automation. Walk through each core business function, assessing current workflows for automation potential based on:
- Repetitive Tasks: Does the process involve employees performing highly repetitive activities like data entry, drafting forms or emails, conducting research, booking transactions, etc? Repetitive, high-volume tasks are top automation targets.
- Process Efficiency: Does the process take too long, rely on many systems/approvals, or is it prone to errors? Lengthy or disjointed processes that frustrate employees are good automation candidates.
- Required Judgement: Does the process require complex emotional intelligence, creative thinking, or subjective decision-making? Processes requiring true judgment may not be suitable for automation.
Based on these criteria, some common processes that can benefit from AI automation include:
- Data entry and processing include filing forms, entering paper documents into databases, and updating client information.
- Customer service interactions include responding to routine questions from clients, giving account status updates, and resolving minor grievances.
- IT support functions include responding to requests for password resets, granting employees access to hardware and software, and addressing commonly requested queries.
- Sending bills to clients, keeping track of payments, and matching remittances to accounts are all part of invoicing and payments.
- Financial Reporting that is compiling monthly financial statements, reconciling accounts, and extracting data for regular management reports.
- HR and recruitment tasks include interview scheduling, background checks, applicant screening, and employee inquiries.
Step 2: Define Goals and Success Metrics
Source: Blazemeter
Once you’ve identified one or more processes for automation, take time to clearly define your goals and how you will measure success. Having clear objectives provides a focus for the automation program and tangible targets to hit.
Consider framing goals focused on:
- Improving Efficiency: Improving process efficiency by 30%, reducing completion times from 4 days to 1 day.
- Cost Reduction: Reducing outsourcing /headcount costs by 25% through automation.
- Increasing Output Volume: Doubling the number of customer service requests handled per month.
- Boosting Speed: Decreasing invoice processing turnaround time from 14 days to 2 days.
- Enhancing Quality: Cutting reporting errors by half and improving insights.
And metrics might include:
- Employee Hours Saved: Reduce manual employee hours spent on the process by X hours per month.
- Faster Processing Times: Decrease average completion times for common tasks by X%.
- Increased Volume Processed: Increase the number of transactions, cases, or outputs handled per month by X%.
- Reduced Costs: Cut third-party spending, outsourcing fees, or operational costs by X%.
- Improved Accuracy: Decrease error rates and rework caused by the process by X%.
Establishing clear targets upfront will help you continuously improve and demonstrate ROI from automation over time.
Step 3: Assess Technology Needs
Source: Testsigma
Next, outline the technology building blocks required to intelligently automate your key business processes. The right combination of AI capabilities is essential for automated workflows to function properly. Core technologies may span:
- Robotic Process Automation (RPA): Software “robots” that automate repetitive, rules-based digital tasks involving multiple systems. Helpful for data entry, transaction processing, report building, etc.
- Conversational AI: Intelligent chatbots (voice or text-based) that can understand questions and handle thousands of customer/employee inquiries.
- Intelligent Process Automation (IPA): Combining RPA, AI, and analytics for advanced method selection-making and optimization. Goes beyond basic automation to duplicate human-stage judgment.
- Machine Learning: Provides the capacity for structures to research from information styles and improve decisions over the years without explicit programming. Helpful for predictive analytics, personalized tips, detecting anomalies, and powering chatbots.
- Computer Vision: Enables structures to accurately discover, categorize, and recognize pix, objects, and documents similar to a human might. Useful for automating visible inspection, nice manipulation, etc.
- Natural Language Processing: Allows systems to extract insights from textual content facts via techniques like type, sentiment evaluation, named entity reputation, and semantic search. Valuable for automating document evaluation approaches.
Take stock of your existing automation technology stack. Identify gaps where investing in additional AI capabilities could create major efficiency gains or even open up new business models.
Step 4: Audit Data Quality and Accessibility
Source: Avineon
An often overlooked yet critical step in process automation is auditing the quality and accessibility of your data. AI systems are powered by data. They will only work properly if data is clean, complete, and available.
Take time to honestly assess:
- Data Quality: Is data accurate, adequately formatted, and up-to-date? Or incomplete, inaccurate, or duplicated across systems? Resolving quality issues is essential.
- Data Accessibility: Can data easily be extracted from source systems via API connections? Or is liberating data time-intensive? Address technical barriers.
- Integration Needs: Will automating processes require combining data from multiple systems? Plan integration work.
Don’t gloss over this step. Data problems severely limit automation success and cause headaches down the road. Allocating resources to clean up data beforehand removes major hurdles.
Step 5: Design the Automated Workflow
Source: Appian
With goals defined and technology solutions identified, next map out the envisioned automated workflow. Diagram each step in detail across systems, stakeholders, digital touchpoints, and decision rules.
Key activities include:
- Outline Integration Points: Show where automation technology will integrate with existing tools like CRM, ERP, CMS, HRIS, and accounting systems.
- Map Information Handoffs: Diagram how data, documents, tasks, and alerts will pass between automation components and employee teams.
- Define Business Rules & Logic: Document the rules and logic that automation technology must follow to replicate human decisions and workflow.
- Identify Remaining Human Touchpoints: Highlight process steps still requiring human judgment and input from process experts.
- Build Consensus with Stakeholders: Involving department leads and employees in designing optimal automated flows drives engagement and adoption later on.
Think through scenarios and edge cases. Vet designs with real employees who run the processes daily. Incorporate their feedback to strengthen automation success.
Step 6: Build & Test Automation Components
Source: Testsigma
With a workflow design set, the shift focuses on configuring, building, and testing the actual automation components. Resist the temptation to boil the ocean. Rather, take an iterative, agile approach:
- Start Small, Prove Value: Tackle high-impact sub-processes first. Get a quick win under your belt before layering on complexity.
- Continuously Improve Components: Release early versions of automation components, solicit user feedback, and rapidly incorporate improvements.
- Ensure Security & Compliance: Build proper authentication, access controls, and compliance rules into components from the start.
- Extensively Test: Rigorously test automation components end-to-end before going live using simulated workflows. Thoroughly vet edge use cases.
Leverage employee power users and friendly critics as pilots. Have them rigorously evaluate real-world performance, usability, errors, and analytics. Feed learnings back into the development loop.
Step 7: Integrate Systems & Roll Out
Source: DeskAlerts
With automation building blocks individually tested, focus next on tight integration with existing IT infrastructure. Smooth interplay between new and legacy systems ensures automated workflows fire seamlessly.
Activities include:
- Connecting APIs: Link up APIs and establish secure credentials so platforms share data.
- Configuring Notification & Alert Rules: Build rules to notify employees when their input is needed within automated flows.
- Setting Up Analytics Dashboards: Pipe key automated process metrics into dashboards so the impact can be monitored.
- Establishing Monitoring & Support: Have technical experts on standby to quickly resolve hiccups as employees ramp onto new automated workflows.
With robust integrations in place, thoughtfully plan your employee rollout strategy:
- Pilot with Small Employee Group: Let power users road test automation for a few weeks, working out issues.
- ABC Testing Old vs New Processes: Run A/B tests for a period overlaying automated workflows on old processes. Compare performance data and employee sentiment.
- Phase-Based Rollout: Gradually launch automation to wider employee groups in multiple release phases. Assess adoption at each stage.
Closely monitor usage analytics, user sentiment, and process efficiency gains at each rollout stage. Be ready to optimize based on real-world feedback.
Step 8: Continuously Optimize Over Time
Source: SitePoint
Like any new technology, expect to continually optimize automated processes post-launch. Establish an ongoing improvement roadmap:
- Address Technical Glitches: Quickly resolve software bugs and repair integration issues causing breakdowns.
- Enhance User Experience: Identify confusing interfaces and workflows that produce employee frustration. Refine them.
- Add AI Capabilities: Expand smart functionality like personalized recommendations, predictive analytics, and computer vision over time to make systems smarter.
- Extend to Additional Processes: Once a few workflows demonstrate solid automation gains, tackle expanding to other processes.
Make post-go-live optimizations part of your automation governance model, allocating resources expressly to this purpose. Regularly benchmark progress against your defined automation goals and targets. Software will learn over time. So will end users. Plan to ride an ongoing optimization curve to maximize benefits.
Conclusion
Approached systematically, infusing AI into business technique automation supplies predominant rewards. From accelerated efficiency to improved analytics to enhanced purchaser enjoyment, intelligently automated agencies are positioned to thrive. What methods are prime for AI augmentation at your organization? How could automation extend worker productivity and satisfaction to clients? With a considerate automation roadmap in the region, the opportunities are countless. Let the automation adventure begin!
