Intelligent automation is rapidly revolutionizing how work gets done across industries. By combining robotic process automation (RPA) with artificial intelligence (AI) and machine learning, intelligent automation systems can learn, adapt, and make decisions to boost efficiency beyond what either technology could deliver alone.
As more companies become aware of intelligent automation‘s vast potential, adoption is accelerating. Gartner predicts the intelligent automation market will reach $232 billion by 2025. But how exactly can businesses put this powerful technology to work?
In this comprehensive guide, we‘ll explore over 26 real-world intelligent automation use cases, examples and applications across business functions and industries. For each use case, we‘ll analyze the key benefits delivered and provide recommendations on how leaders can effectively leverage intelligent automation today.
Table of Contents
Overview: What is Intelligent Automation?
Intelligent automation extends robotic process automation‘s capability to streamline repetitive, rules-based tasks by layering artificial intelligence and machine learning.
Key capabilities of intelligent automation include:
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Understanding Unstructured Data: Leverage computer vision, natural language processing and machine learning to understand context and extract meaning from unstructured data like handwritten documents, images, videos and speech.
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Learning and Adaptation: Continuously learn from data instead of relying on rigid rules; improve results without explicit reprogramming.
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Dynamic Decision-Making: Make complex decisions like a human, considering diverse information sources and adapting recommendations in real-time.
With these capabilities, businesses can harness automation to boost productivity, speed and precision – while also enabling more innovation, higher-value work and enhanced experiences across functions and industries.
Intelligent Automation Use Cases By Business Function
Let‘s explore key automation use cases and examples in some of the highest impact areas within common business functions.
Human Resources
Human resources teams can leverage intelligent automation to reduce repetitive administrative work, hire and onboard talent faster, develop employees more effectively and enhance the employee experience.
Recruitment and Hiring
Talent acquisition typically involves manually screening countless applications and resumes. RPA alone can help parse applicant data to identify qualified candidates. But layering NLP and ML algorithms to deeply understand candidate backgrounds allows bots to drastically cut screening time while improving quality of hire. According to the researchers at Deloitte,
AI-enhanced hiring platforms can deliver 30-60% improvements in candidate applications reviewed per day, time-to-hire and quality of hire.
Bots can also help schedule interviews, send follow up emails, manage pre-employment paperwork and onboard new hires seamlessly.
Employee Development
Using sentiment analysis algorithms in internal communications and surveys enables HR to better understand overall employee engagement and satisfaction levels. Bots can also analyze responses to identify learning needs and growth opportunities; aggregate findings into detailed reports; and even recommend relevant training based on individual roles, interests and goals.
Payroll Management
Payroll processing is extremely rules-intensive yet prone to errors that affect employee morale and engender compliance risks. Intelligent automation delivers immense value here by removing manual work, while continuously adapting to changing tax and labor regulations across global workforce. Industry analysts estimate 60-80% of payroll tasks can be automated using AI-enhanced solutions.
Customer Service
Delivering personalized, omnichannel customer service is challenging today given soaring inquiries and thin staffing. Intelligent automation maximizes human productivity in customer interactions while also providing self-service options.
Conversational Interfaces
Chatbots now harness NLP to understand requests and natural conversations; access data in CRM and other systems to resolve common inquiries directly. If unable to address, they seamlessly connect customers with live agents. Conversational systems are predicted to handle 85% of all customer interactions by 2025 per Gartner.
Content Moderation
Computer vision techniques enable intelligent automation to detect policy violations, toxic content or illegal materials at scale across chat, social media, app reviews and other unstructured data. This protects customers and brands while reducing burden on human reviewers.
Customer Analytics
Understanding customer needs and sentiment is critical yet analyzing surveys, call transcripts, chat logs and feedback at scale is prohibitively difficult manually. Leveraging NLP and speech recognition, bots can rapidly turn unstructured customer data into strategic insights around preferences, pain points and churn drivers. Enriched analytics improves experiences and retention.
Sales and Marketing
End-to-end automation of order to cash, lead generation and campaign management processes using AI facilitates better sales and marketing results with resource efficiency.
Lead Management
Time-consuming tasks like discovering target accounts and contacts from websites and databases, verifying emails, enriching records and segmenting prospects are prime examples where automation delivers immense ROI. Integrating ML for predictive lead scoring and campaign recommendation takes it to the next level by connecting sales with the hottest leads.
Pricing Optimization
Determining optimal price points is incredibly difficult given countless demand drivers and competitive factors. Intelligent automation leverages real-time data on inventory, buying trends, demographics and elasticity modeling to continuously adjust pricing across channels for maximizing yield. Outfittery saw 12% revenue gains within months of implementing AI pricing.
Campaign Orchestration
Coordinating multichannel campaigns with personalized messaging involves synthesizing customer data from CRM, ERP and other systems. RPA handles integration complexity for seamless workflows while ML algorithms optimize channel selection and creative tailored to microsegments for best response and conversions.
Finance and Accounting
Bots and AI adoption in finance aims to embed intelligence and automation into source-to-record-to-report processes for strategic decision support.
Accounting Automation
Vendors like AppZen leverage computer vision and ML to audit 100% of spend automatically – reviewing line items against policies, flagging duplicates across systems, and even learning valid exceptions over time to reduce false positives. This level of automation delivers 10x efficiency gains over traditional spot audits.
Cash Flow Forecasting
Creating weekly and monthly cash forecasts depends on synthesizing data from operations, sales and accounting. While RPA handles data consolidation, ML algorithms can now accurately project future cash positions based on historical patterns, macroeconomic variables as well as business signals like new customer wins.
Anomaly Detection
Finding anomalies or suspicious patterns in expenses, payables, receivables and other financial data is like finding needles in a haystack yet critical for controlling fraud and errors. Unsupervised ML techniques enable bots to baseline normal activity and detect outliers across millions of transactions to prioritize highest risk items for investigation.
Intelligent Automation Industry Applications
Beyond common business functions, automation and AI are enabling transformation across customer-facing industries as well. Let‘s analyze a few major sectors.
Banking and Financial Services
Banks and lenders are using intelligent automation to improve customer onboarding and service while also mitigating risks more effectively amidst growing regulation.
New Account Opening
Opening new deposits, lending or investment accounts requires collecting extensive paperwork and running verification checks which takes weeks manually. AI-based data extraction and validation engines reduce these tediously manual processes down to minutes – seamlessly pulling biographic details from IDs, bank statements etc. while connecting to credit bureaus and watchlists behind the scenes. This slashes onboarding times by over 80%.
Payments Investigation
Resolving disputed credit card or check payments depends on agents piecing together purchase details, merchant information, inventory lookups and more to determine root causes. Leveraging transaction data and unstructured evidence, ML algorithms can automatically assess common dispute scenarios like product not received, quality issues, double charges etc. to expedite resolutions and minimize losses. Investigators experience over 50% efficiency gains with augmented intelligence.
Insurance
Insurers have extensive opportunities to transform underwriting, claims and client interactions leveraging automation.
Underwriting and Pricing
In commercial underwriting, traditional heuristics or one-size-fits all factors for risk classification leads to poor loss ratios and customer experience. New AI techniques enable dynamically pricing policies based on fine-grained modeling of exposure data, geospatial analytics as well as macro trends, delivering up to 30% loss ratio reductions. Startups like Cape Analytics automatically detect property risks from images for home insurance underwriting.
Claims Processing
Systems can automatically classify claims via photos, extract details from documents like police reports or medical bills using OCR and route accordingly – slashing repetitive effort. Computer vision looks for fraud red flags while chatbots provide status self-service. McKinsey estimates end-to-end claims automation cuts 30-50% costs.
Healthcare
Care delivery processes involve vast amounts of complex data and high-risk decisions at every stage – ideal for intelligent automation.
Patient Intake and Assessment
Registering a patient traditionally requires exhaustive information collection and documentation which takes 20 minutes on average. Leveraging speech recognition for medical history and computer vision to extract demographics from IDs shrinks this to under 5 minutes with better accuracy. Symptom checkers then intelligently triage cases to appropriate care teams.
Diagnostic Support
Radiology automation involves using computer vision techniques to process and reconstruct images followed by ML algorithms that analyze scans to detect anomalies, compare against case databases and offer diagnostic recommendations for technicians. Enhancing professionals with automation reduces error rates by over 50%.
Discharge and Monitoring
Ensuring adherence to care instructions and drug regimens after hospital discharge is critical yet challenging. Intelligent bots now call patients post-discharge to verify understanding of physician discharge orders, prescription pickup statuses, answer questions and alert care teams when intervention is appropriate. Early results show 20%+ readmission rate reductions.
Retail
Automation provides limitless potential from optimizing ecommerceshopping experiences to reimagining brick-and-mortar retail operations.
Personalized Recommendations
Product suggestions based on purchase history alone poorly capture evolving customer interests. But by applying deep learning to analyze micro behaviors like email opens, browsing patterns and cart additions, retailers can make recommendations with 2-10x uplift in click and conversion rates.
Intelligent Inventory Management
Determining optimal inventory levels across thousands of products is incredibly complex, depending on historical demand, promotions, pricing, macro trends and more. AI modules now ingest myriad data signals to accurately forecast demand real-time down to the product-location level so retailers avoid stockouts without overstock waste.
Computer Vision Analytics
Sophisticated video analytics leverage object recognition, motion detection and other computer vision techniques to offer customer traffic heatmaps, wait time visibility and even age and gender demographics to inform everything from staffing to merchandising strategies.
Manufacturing
Industrial companies are tapping automation to boost quality, output and equipment effectiveness amidst disruptions.
Predictive Maintenance
Unplanned downtime can cost millions per week yet early failure detection is extremely difficult with traditional reactive maintenance. By connecting manufacturing hardware with AI at the edge, algorithms continuously analyze sensor, vibration and temperature data to detect anomalies, predict failures weeks out and minimize disruptions.
Quality Inspection
Machine vision solutions can automatically scan produced parts against CAD models to identify microscopic defects undetectable to humans without productivity constraints. This drives major improvements in output quality and yield.
Production Planning
Volatility in supply, demand and constraints requires dynamic optimization of manufacturing plans. AI modules now integrate signals from across the value chain including market conditions, logistics delays, asset health, staffing etc. to recommend optimal schedules minimizing disruptions amidst variability.
The Benefits of Leveraging Intelligent Automation
As the use cases demonstrate, businesses realize immense value from increased automation and augmented intelligence:
Greater Efficiency – By taking over repetitive, manual tasks, intelligent automation drives exponential productivity gains across functions like 40-60% in finance processes or even 80% for insurance claims management.
Higher Quality – Bots perform consistently without human errors while AI techniques like computer vision spot defects with super-human acuity for nearly perfect accuracy in everything from document processing to manufacturing quality control.
Improved Experiences – Intelligent chatbots resolve customer inquiries faster with natural conversations powered by NLP while also enabling self-service. In healthcare, automation reduces wait times and drives better health outcomes.
More Innovation – With mundane work and basic decisions automated, human talent has more capacity to focus on high-value analysis, strategy and innovation to move businesses forward faster.
Enhanced Resilience – The machine learning underpinning intelligent automation gives systems the ability to dynamically adapt to changes in data, processes and market conditions for reliable operations despite disruptions.
Best Practices for Implementation
Hopefully this analysis has revealed automation‘s immense potential within your organization as well! But where do you start on the intelligent automation journey?
We recommend four key strategies:
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Take stock – Thoroughly audit processes and pain points across the business to systematically identify automation opportunities. Leverage process mining tools to accelerate analysis.
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Prioritize use cases – Shortlist high impact automation applications based on multiple factors – process criticality, disruption potential, implementation complexity, costs vs. benefits etc. – instead of ad hoc selection.
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Scale across functions – Adopt an enterprise automation strategy beyond isolated deployments within certain groups to maximize benefits through synergies, shared infrastructure and change management. Appoint centralized leadership.
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Focus on augmentation – Design engagements as human-machine partnerships instead of pure labor replacement to realize AI‘s full potential while mitigating risks around job losses that create resistance.
The Future of Intelligent Automation
As technology capabilities advance, expect intelligent automation to reinvent nearly every facet of business in the coming decade. IDC predicts over 50% enterprises will experience hyperautomation by 2025 as emerging areas like process mining, decision intelligence and low code automation augment existing tools.
While change management and building integrated tech stacks remains challenging initially, the rewards will be invaluable. Now is the time to solidify automation strategies or risk losing competitive ground!