What is Intelligent Automation (IA)? It’s the combination of Robotic Process Automation (RPA) and Artificial Intelligence (AI) to automate complex business processes, reduce errors, and save time. IA processes large datasets, learns from patterns, and adapts in real-time to changing conditions.
Key Benefits of IA:
- Cost Savings: Companies report up to 32% cost reductions and save 24 working days annually by automating tasks like financial closing.
- Efficiency: Handles repetitive tasks like invoice processing, payroll, and supply chain operations faster and with fewer errors.
- Scalability: Enables businesses to grow without increasing operational costs.
- Improved Accuracy: Reduces human errors in sensitive tasks like financial reporting and supply chain management.
How IA Works:
- Technologies Used: Natural Language Processing (NLP), Optical Character Recognition (OCR), Process Mining, and AI-powered tools.
- Core Difference: Unlike traditional automation, IA can learn, adapt, and handle unstructured data, making it ideal for dynamic workflows.
Real-World Impact:
- Finance: AI tools reduce invoice processing time from hours to minutes and improve cash flow with automated payment reminders.
- Healthcare: Automates patient intake, eligibility verification, and appointment scheduling to optimize operations.
- Manufacturing: AI-driven systems improve productivity, reduce defects, and streamline supply chains.
Quick Comparison: IA vs. Traditional Automation
| Feature | Traditional Automation | Intelligent Automation (IA) |
|---|---|---|
| Decision-Making | Rule-based | AI-driven, adaptive |
| Learning Ability | None | Learns and improves |
| Task Complexity | Simple, repetitive tasks | Complex, dynamic processes |
| Human Oversight | High | Minimal |
| Scalability | Limited | Highly scalable |
Why it matters: By 2025, 70% of businesses will fully implement AI to streamline operations, reduce costs, and stay competitive. Ready to transform your business? Read on for actionable steps and examples.
Core Concepts of Intelligent Automation
Basic Elements of IA
Intelligent Automation (IA) brings together advanced technologies to create effective business solutions. It combines cognitive automation, natural language processing (NLP), process mining, intelligent document processing, and computer vision [3].
These core technologies enable IA to:
- Simulate human-like decision-making with cognitive automation
- Understand and process language using NLP
- Analyze workflows through process mining
- Manage and interpret documents efficiently
- Recognize and analyze images with computer vision
These tools form the backbone of IA’s capabilities [3]. Let’s see how IA stacks up against traditional automation.
How IA Differs from Basic Automation
Traditional automation operates on fixed, rule-based systems, following predefined instructions. IA, on the other hand, introduces a more advanced, flexible approach. Here’s a comparison:
| Aspect | Traditional Automation | Intelligent Automation |
|---|---|---|
| Decision Making | Based on fixed rules | Uses AI for adaptive decisions |
| Learning Capability | None | Learns and improves with ML |
| Task Complexity | Handles simple, repetitive tasks | Manages complex, dynamic processes |
| Human Intervention | Requires frequent adjustments | Needs minimal oversight |
| Scalability | Limited by rigid rules | Adjusts to evolving conditions |
“AI injects ‘intelligence’ into automation, enabling systems to perform complex tasks, interpret data, decide effectively, and learn continuously” [4].
This leap in functionality allows IA to handle tasks that would be impossible for traditional systems.
AI’s Function in Process Automation
AI takes process automation to the next level by leveraging its advanced technologies. Here’s how it transforms operations:
- Predictive analytics: Identifies patterns to forecast outcomes and improve processes
- Real-time decision-making: Evaluates situations and adjusts automatically
- Complex data processing: Handles unstructured data from diverse sources
By 2025, it’s expected that half of enterprises will integrate AI into their operations, and 70% will fully implement it [5].
“Adaptive AI systems support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise. These systems aim to continuously learn based on new data at runtime to adapt more quickly to changes in real-world circumstances” [5].
This integration enables organizations to redirect human resources toward more strategic efforts. For instance, in February 2021, Notable Health showcased IA’s potential by streamlining COVID-19 vaccine administration. They used intelligent automation to process data from hospital electronic health records, facilitating patient identification, education, and vaccination scheduling efficiently.
Business Impact and ROI
Cost Reduction and Efficiency
Intelligent Automation (IA) helps businesses save money by optimizing operations and boosting productivity.
For example, one large company revamped its material purchasing process using process mining, RPA, BPM automation, and analytics – resulting in annual savings of $40 million [6].
Here’s how IA improves efficiency in specific areas:
| Process Area | Benefit |
|---|---|
| Invoice Processing | Processes hundreds in minutes instead of hours [2] |
| Payroll Management | Cuts cycle time by 80% [2] |
| Supply Chain Operations | Enables 24/7 operations with instant task execution [2] |
In addition to saving money, IA improves accuracy across operations.
Error Reduction and Quality Control
IA minimizes human errors in repetitive tasks, which is critical for industries managing sensitive data or complex calculations.
“Process intelligence has opened up the eyes of many of our clients to show them things they did not know.” – Joe Surprenant, sales leader across Deloitte’s AI and Data Ops practices [6]
For instance, a retail company used process intelligence to address supply chain issues. By creating a unified customer journey app, they achieved:
- 23% fewer order returns
- $46 million in reduced sales risk
- 7% better net promoter score [6]
Beyond reducing mistakes, IA supports scalable growth across various business functions.
Growth and Process Scaling
IA allows businesses to expand operations efficiently without sacrificing quality. A recent study shows AI adoption in business functions grew from 50% in 2020 to 56% in 2021 [7]. Key areas of impact include:
- Customer service and experience: 74% report positive ROI
- IT operations and infrastructure: 69% see favorable ROI
- Planning and decision-making: 66% report benefits [7]
“A lot of the RPA solutions are very much focused on recording how a process works, and automating it. But if there’s an exception in that process, as there often is, all you’ve done is delay solving the problem by putting a bot between me and the person who will actually resolve my issue.” – Matt McLarty, global field CTO and VP of the digital transformation office at Mulesoft [6]
This highlights the importance of solutions that handle exceptions and adapt to changing business needs. In fact, 46% of organizations plan to adopt AI automation technologies within the next three years [6].
The ability to scale is especially important during peak periods. For example, accounting firms can use RPA during tax season to process higher volumes of documents quickly and accurately, ensuring service quality remains consistent [2].
Main IA Technologies
RPA Systems
Robotic Process Automation (RPA) forms the backbone of Intelligent Automation (IA) by handling repetitive tasks and connecting systems across platforms.
A Deloitte survey shows that 53% of businesses are already using RPA, and another 19% plan to adopt it within the next two years [8]. This growing interest is driven by RPA’s ability to simplify operations across industries:
| Industry | RPA Application | Results |
|---|---|---|
| Banking | Mortgage Processing | Automates data collection and document verification [8] |
| Insurance | Policy Management | Handles renewal reminders and premium calculations [8] |
| Retail | Inventory Control | Integrates ERP systems for stock tracking and replenishment [8] |
While RPA ensures efficient execution, adding AI and ML takes automation to the next level by enabling learning and adaptability.
AI and ML Components
Artificial Intelligence (AI) and Machine Learning (ML) enhance basic automation, creating systems that learn and improve over time. Goldman Sachs estimates global investment in AI technologies could hit $200 billion by 2025 [8], highlighting their rising influence.
Key features include:
- Self-learning algorithms that analyze data to improve performance [1].
- Advanced analytics for tackling complex decision-making challenges.
- Adaptability to process variations and exceptions.
A standout example is JPMorgan Chase‘s COiN platform, which combines AI with RPA to process thousands of legal documents in seconds. This approach has cut costs and reduced errors significantly [9].
Adding tools like NLP and computer vision enables IA systems to handle unstructured data, making the ecosystem even more powerful.
Text and Image Processing
Natural Language Processing (NLP) and computer vision allow IA systems to work with unstructured data effectively. The global computer vision market alone was valued at $11.44 billion in 2020 [10].
Here are some practical applications:
- Insurance Claims: Computer vision automates damage assessment through photo analysis, speeding up claims processing [10].
- Manufacturing: Foxconn‘s AI-driven assembly lines improved productivity by 25% and reduced defects by 15% [9].
- Retail: Sephora‘s Virtual Artist app uses computer vision for virtual makeup trials, boosting conversion rates by 15% [9].
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Steps to Deploy IA
Process Selection
Work closely with process owners and subject matter experts (SMEs) to pinpoint processes that are well-suited for automation. Prioritize processes with the following traits:
| Process Characteristic | Impact on Automation Success |
|---|---|
| Rule-based | Simplifies automation with clear decision-making paths |
| High-volume | Offers higher ROI potential due to scale |
| Error-prone | Presents opportunities to enhance quality significantly |
| Time-sensitive | Enables quicker processing, including 24/7 operation |
Research indicates that 30% to 50% of RPA projects fail to meet their goals [11]. This makes selecting the right processes a crucial factor for success.
After identifying suitable processes, the next step is forming a skilled IA team to drive the initiative forward.
Building an IA Team
Assembling a capable IA team requires a thoughtful approach to structure and expertise. Establishing a Center of Excellence (CoE) is often the best way to combine technical know-how with business insights.
A CoE-style team should leverage both internal talent and external expertise through a hybrid model to address technical and operational needs [11]. This approach includes:
- Team Composition Bring together process experts, IT architects, and innovation leaders to form a well-rounded CoE team [11].
- Implementation Model Choose from three main operational models:
- In-house operations
- Third-party outsourcing
- Hybrid approach
Staff Training and Results Tracking
Ongoing training for the IA team is essential to achieve and sustain automation success. Well-designed training programs play a critical role, as highlighted by HDI Versicherung‘s experience:
“Thanks to the combination of Robotic Process Automation (RPA) and Artificial Intelligence, we are now able to intelligently automate the entire process end-to-end.” – Christoph Wetzel, HDI Versicherung [13]
Effective training programs should include:
- Role-specific learning paths for developers, analysts, and IT professionals [13].
- Automated Learning Management Systems (LMS) for consistent training delivery [12].
- Real-time feedback tools to assess and refine learning outcomes [12].
Track success by:
- Gathering user feedback through surveys.
- Monitoring key performance metrics.
- Conducting regular evaluations for continuous improvement.
IA in Practice
Finance Department Use Cases
Intelligent Automation (IA) is making waves in finance by drastically reducing processing times and lowering resource demands. For example, a global investment management firm managed to cut remittance processing from 2 hours to just 25 minutes while halving the resources needed for investor reporting [15].
Gembah has also leveraged AI to streamline its sales contract lifecycle, from integrating with QuickBooks to automating payment collection. Zach Leonard, Gembah‘s Founder, highlights the impact:
“Thoughtful AI introduced a solution that eliminated a glaring gap in our billing workflow. An AI Agent is now part of our accounting team, performing this vital role more efficiently and at a fraction of the cost of paying a full salary to an actual employee” [14].
| Financial Process | Automation Impact |
|---|---|
| Invoice Processing | Real-time validation and anomaly detection |
| Payment Collection | Automated reminders and improved cash flow |
| Financial Reporting | Automated data aggregation and analysis |
| Contract Management | End-to-end processing with minimal manual effort |
Healthcare and Factory Examples
IA is also transforming healthcare and manufacturing by boosting efficiency and cutting down on repetitive tasks.
In healthcare, automated systems are making a difference by:
- Streamlining patient intake and eligibility verification, reducing wait times and minimizing claim denials [16].
- Optimizing clinic operations through appointment management systems that lower no-show rates [17].
- Supporting clinical decisions with automated alerts and tools for medication adherence tracking [17].
Manufacturing is seeing equally impressive results. Siemens, for instance, reduced X-ray testing for printed circuit boards by 30% using AI-driven inspection protocols [18]. In food production, Soft Robotics‘ AI helps robots handle tasks like picking single, wet chicken wings from a pile with precision [18].
The automotive sector showcases even more advancements:
- A Tier 2 automotive supplier doubled production line output by using AI to identify and resolve cycle time issues [18].
- An automotive OEM increased throughput per shift by 5% while reallocating 20% of its workforce through station optimization [18].
“Predictive maintenance is one of the first things to implement with AI in an industrial setting” [18].
What is Intelligent Automation? Everything You Need to Know
Conclusion
Intelligent Automation (IA) is reshaping how businesses operate, with the market expected to hit $51.26 billion by 2032 [20]. Companies that want to stay ahead need to embrace this shift. The numbers back it up – Accenture found that well-executed RPA initiatives can slash processing costs by 40–80% and reduce processing times by 80% [21].
Take Microsoft as an example. From 2010 to 2020, the company increased its revenue by 145%, but its finance team grew by only 15%. What’s more, a forecasting process that once required 100 employees working for a month now takes just 2 employees two days to complete [23].
To successfully implement IA, a clear strategy is key. Here’s a breakdown of the critical steps:
| Implementation Pillar | Key Actions | Expected Outcome |
|---|---|---|
| Governance | Define frameworks and operating models | Better control and consistency |
| Stakeholder Engagement | Involve teams early | Stronger adoption and support |
| Technology Selection | Choose flexible, integrable tools | Smooth system integration |
| Training & Support | Offer thorough upskilling programs | Long-term operational success |
Looking forward, Gartner predicts that by 2024, combining hyperautomation technologies with redesigned processes could cut operational costs by 30% [21]. But it’s not just about cost savings – ethical considerations and data privacy are also essential [20].
“Intelligent automation has gained steam in recent years, not only because it incorporates today’s most sophisticated AI technologies but also because it offers a significant improvement in business productivity.” – eWeek [19]
As workflows become more autonomous, the focus should be on strategic improvements, not just functionality [22]. With 70% of business leaders now seeing AI-driven automation as critical for future success [20], the real challenge lies in implementing IA effectively while driving sustainable growth and staying competitive.