Businesses today face relentless pressure to deliver excellent customer experiences, bring innovative products and services to market faster, and optimize costs – all while navigating an increasingly complex technology landscape. Many companies are turning to intelligent automation solutions to help transform key business processes and unlock new levels of speed, quality and efficiency.
What is Intelligent Automation?
Intelligent automation refers to a new generation of software tools that combine fundamental robotic process automation (RPA) capabilities with artificial intelligence technologies like machine learning, natural language processing, and more. The net result is automated systems that can handle unstructured data inputs, adapt to changing conditions, and get smarter over time.
Where basic RPA tools excel at replicating manual, rules-based human tasks, intelligent automation allows for more complex workflows. These tools can extract meaning from documents, interpret requests, ask clarifying questions, and handle fuzzy judgment calls in ways that traditional programming cannot.
Key Capabilities of Intelligent Automation Tools
Leading intelligent automation platforms share some common key capabilities:
Robotic Process Automation (RPA) – At their core, these tools have the ability to integrate with software applications at the presentation layer and mimic human interactions like keystrokes and mouse clicks. This allows them to complete repetitive digital tasks just as a human would. RPA bots follow programmed rules and workflows without getting bored or distracted.
Natural Language Processing (NLP) – NLP algorithms help systems analyze, understand, and derive meaning from human language. Instead of just scanning text for keywords, NLP extracts contextual meaning and intent.
Conversational AI – Chatbots and other conversational interfaces powered by AI give automation tools more intuitive ways to interact with human users. These tools can understand questions, clarify ambiguous requests, and hold conversations.
Process Mining – By monitoring real-world data from corporate software systems, process mining tools can map out actual operational processes. This shows where inefficiencies exist, exceptions occur, and improvements can be made.
Optical Character Recognition + Document Understanding – OCR extracts text from scanned documents and images, while document understanding tools classify document types and extract relevant data fields using NLP. Together, they convert unstructured data like forms and invoices into machine-readable, actionable data.
There are other emerging capabilities as well, like task mining, digital twin modeling, hyperautomation orchestration tools, and integrated connectivity. Enterprise-class automation platforms give users options to build bots with different combinations of capabilities suited for specific use cases.
Emerging Capabilities
Self-Healing Bots – Leading platforms like UiPath are developing self-healing capabilities that allow bots to detect disruptions and automatically apply predefined remedies without needing human intervention. This prevents critical processes from stalling.
Hyperautomation – This emerging paradigm refers to automating as much of an end-to-end process as possible using a combination of tools. For example, an invoice processing workflow may connect RPA bots, OCR document data extraction, chatbots to handle vendor inquiries, APIs to update accounting systems, machine learning for document validation, and analytics dashboards to monitor KPIs.
The Benefits of Intelligent Automation
Implementing intelligent automation tools offers a growing list of benefits:
Improved Efficiency – By automating repetitive tasks in business processes, workflows run faster with fewer errors. Software bots don’t get interrupted, distracted or fatigued. They can work around the clock without downtime. Intelligent automation also gives human workers more time to handle strategic, value-added activities.
According to McKinsey, intelligent process automation can deliver efficiency gains upwards of 30-50% in areas like finance, IT, customer service, and HR. These tools generate additional bandwidth for innovation and customer focus.
Enhanced Customer Experiences – Chatbots and NLP document processing tools deliver quick, convenient self-service options to customers around the clock. Automating repetitive internal tasks also gives human agents more time to handle complex issues. Surveys show organizations leveraging automation see much higher customer satisfaction scores.
Cost Savings – Faster process throughput means less employee time spent on routine tasks. Scalable software bots don’t add overhead expenses as operations grow. Because they perform tasks consistently without errors, intelligent automation also minimizes costly mistakes.
On average, departments using automation cut operating costs by 20-25% within the first year according to Institute for Robotic Process Automation. Expanded use cases deliver exponential savings over time.
Increased Agility – With modular automation handling baseline tasks, companies can shift human focus to innovation, new products, and custom use cases that provide competitive advantage. New processes can be automated and reconfigured extremely fast.
Improved Compliance & Control – Detailed activity logs provide complete transparency and evidence trails for regulated activities. Automated processes apply rules consistently without human lapses or overlooked steps.
Enriched Data & Analytics – Automation tools feed back detailed process data that wasn’t accessible before. This massively enhances reporting, forecasting, customer insights and other analytics applications, enabling data-driven decision making.
Industry Adoption Trends
Intelligent process automation has already seen high levels of global adoption, but is projected to accelerate further as companies embrace digital transformation:
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As of 2022, over 90% of large corporations already used some form of RPA or intelligent automation in their operations according to Deloitte.
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The worldwide intelligent process automation market is predicted to grow at a 32.5% CAGR through 2027 according to Grand View Research.
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By 2024, Gartner estimates automation and AI techniques will be used pervasively in over 60% of large organizations – 4x higher compared to 2020 levels.
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Over 80% of shared services leaders say process automation is critical to their financial transformation initiatives in a recent PwC survey.
Leading Intelligent Process Automation Solutions
The intelligent automation landscape has grown rapidly beyond traditional IT vendors, with focused pure-play solution providers gaining strong market traction. Here is an overview of leading options:
| Solution | Key Strengths |
|---|---|
| UiPath | Global end-to-end RPA platform leader. Strong NLP, process analytics & security. Free community edition to fuel growth. |
| Automation Anywhere | Rapid no-code bot building powered by IQ Bot. NLP & RPA tightly integrated. |
| Microsoft Power Automate | Tight integration across Microsoft stacks. Low barrier to entry with per-user pricing. |
| Blue Prism | Robust enterprise-grade capabilities for highly regulated industries. Compliance focus. |
| IBM | Leverages Watson‘s NLP leadership. FusedML delivers AI/automation synergies. Integrates automation technologies from recent acquisitions. |
| Pegasystems | Pega Platform provides low-code app dev with RPA. Industry solutions for insurance, govt. & healthcare. |
| WorkFusion | Smart process automation orchestrating RPA, AI and human-in-the-loop. Expertise across financial services, insurance and healthcare. |
The list just scratches the surface as focused intelligent automation pure-plays like FortressIQ, Servicetrace, Another Monday, and Boost.ai are driving compelling innovation alongside legacy tech giants like Microsoft, SAP, Oracle and VMware making big automation bets.
Criteria for Selecting Intelligent Automation Tools
With many automation platforms now available, here are key factors companies should consider when evaluating options:
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Integration – The system should seamlessly connect with your existing enterprise app mix with APIs, pre-built connectors and non-invasive technology. Cloud-native is ideal.
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Ease of Use – Even non-technical staff should be able to build automation routines with drag-drop interfaces and minimal code. Some coding options still beneficial.
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Total Cost – Seek predictable pricing like bot licensing models without hidden fees. Calculate hard cost offsets and productivity gains when determining ROI.
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Scalability – The platform should scale cost-effectively while maintaining performance as use cases expand over time. True cloud platforms offer best scalability.
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Security – With bots accessing sensitive systems and data, security is critical – including role-based access, encryption, and measures against threats or unauthorized activities.
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Analytics & Monitoring – Embedded process and task analytics, automation metrics, audit logs, control room dashboards and real-time notifications allow teams to monitor progress, demonstrate hard value, and identify new improvement opportunities.
Embracing Hyperautomation
While companies gain experience from initial departmental RPA implementations, automation is evolving into “hyperautomation” – the practice of rapidly automating processes end-to-end by orchestrating multiple technologies.
Forrester defines hyperautomation as “the unprecedented combination of technologies like RPA, low-code development platforms, AI, process mining, analytics, and more to rapidly identify, vet and automate as many processes as possible.”
Rather than RPA bots performing standalone tasks, hyperautomation connects modular tools so that end-to-end processes execute touchlessly at high velocity. Human staffers shift to managing this automated value chain and handling exceptions.
Real-World Use Cases
Intelligent automation delivers high-impact outcomes across virtually every industry and function:
IT Operations – Automating IT infrastructure monitoring, event ticket creation and routing, system updates and technical inquiries. AI resolves common help desk calls while auto-scaling optimizes cloud capacity utilization and costs.
Financial Close Processes – Software bots can reconcile accounts daily, handle intercompany transactions, process invoices, support external audits and consolidate results – accelerating period close while increasing control.
Insurance Claim Processing – Bots can initiate claims, importing documents like police reports and medical records. Natural language processing classifies claim types then extracts relevant data fields using AI document understanding. Bots route info to appropriate downstream systems for fraud analysis, coverage validation and payment.
Banking – Automating new account opening procedures by filling forms, combining customer data from online and back-office systems, validation checks, KYC verification, account activations and documentation. Greatly accelerates customer onboarding.
Healthcare Payer Operations – Intake bots handle patient insurance enrollment details, validate coverage eligibility, initiate medical records requests while coordinating across care management and billing systems. Dramatically shrinks account set-up cycle times.
Field Services – Chatbots provide agents faster answers for issues encountered onsite like parts ordering or diagnosis guides. Knowledge bots behind the scenes predict parts likely needed for service calls based on tech notes, IoT sensor data patterns and resolution history analytics.
Driving Successful Adoption
Drawing from client experiences around intelligent automation programs, here are some leading practices:
Start Small, Demonstrate Value – Launch targeted pilots that solve defined pain points. Let early wins build business case momentum for long-term roadmaps.
Map Processes First via Discovery Tools – Conduct process analyses using process and task mining before wholesale automation. Update flows based on actual as-is states rather than outdated assumptions or tribal knowledge. Document new workflows in central repos to enable governance.
Involve Frontline Teams – Collaboratively map processes with input from ops staff that execute them. Address their enhancement ideas, overcome skepticism through co-creation and upskill staff to complement automation capabilities rather than compete.
Commit Resources to Scale Skills – Knowledge transfer from implementation partners is short term. Ultimately organizations need in-house expertise across tools, integration, bot config, management and enhancement. Develop or import technical and automation talent.
Implement Oversight Early – Governance around security, access, system connections, enhancements, business continuity and compliance helps de-risk and provides needed oversight as usage grows. Document control policies, sticking to architectural guard rails and data protection standards that apply for manual processes too.
Overcoming Challenges with Intelligent Automation
While delivering tremendous upside, automation initiatives also come with common challenges to anticipate and overcome:
Initial Complexity – Early RPA adopters often underestimated technology ramp-up needs. Staff require training across process analysis, tools, bot configuration/enhancement and change management. Build competency deliberately rather than assuming overnight success.
Integration Sprawl – Too many disconnected bot integrations with various systems leads to tangled “spaghetti” complexity. Institute centralized control over architecture and connections using API managers and iPaaS tools. Reuse approved integrations wherever possible.
Tech Debt Accumulation – Technical debt piles up as ongoing bot revisions and patches accumulate without periodic cleansing. Schedule regular bot enhancement releases along with some redevelopment to control entropy via DevOps-style thinking.
Overautomation Risks – Removing humans excessively from processes can lead to undesirable outcomes from bots missing nuances. Keep staff engaged in oversight roles to leverage human judgment and manage exceptions until AI maturity reaches human capabilities.
Improper Change Control – Modifying systems connected to bots without notification creates process breaks. Follow formal change protocols and check-in/check-out procedures for maximum business continuity. Use automation analytics to spot issues emerging.
Economic & Job Market Impact
Automation naturally brings questions around AI eliminating human jobs rather than simply displacing roles. The reality is that automation both destroys repetitive jobs and creates net-new specialized ones.
According to Gartner, through 2025 some 70% of organizations adopting artificial intelligence will ultimately increase their headcount. While automated software and machines handle high volume, rules-based tasks, a growing need emerges for analytics skills to manage AI systems along with creative roles to drive innovation.
Forrester also analyzed automation and AI’s macroeconomic impacts, forecasting these emerging technologies boosting global workforce productivity enough to create payrolls funding 58 million net-new jobs in 2025 compared to 2018 employment levels.
Rather than overall job losses, automation facilitates economic expansion that fuels business growth. Net job creation is typically amplified around higher value creative and technical roles versus mundane tasks prime for software bot replacement.
Skills Needed to Scale Automation
Companies succeeding with automation at scale develop expertise across these key domains:
Technical – Hands-on skills to administer automation platforms, build and enhance software bots, customize configurations, integrate toolsets and develop APIs. Includes RPA developers, data and integration engineers.
Analytics – Business intelligence talent to derive insights from process mining and task mining tools, create management dashboards, continuously improve solutions and quantify hard savings linked to automation performance.
Process Innovation – Combination of operations analysis, workflow re-engineering and lean thinking to identify new automation opportunities and holistically transform processes for end-to-end improvement.
Management & Governance – Program owners to oversee roadmaps, compliance, access protocols and drive change management plus automation CoEs guiding standards and guardrails to control risk.
Both upskilling existing talent and importing new specialist resources helps build comprehensive in-house capabilities over time.
The Future of Intelligent Automation
As intelligent automation delivers tangible benefits across functions, these technologies will continue evolving quickly alongside AI and analytics innovations. We can expect:
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Bots Becoming Increasingly Autonomous and Smart – Machine learning models allow systems to self-improve performance, adapt to new unstructured use cases and handle complex decisions independently.
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Conversational Interfaces Mainstreaming – More natural language bots and speech-powered digital assistants take on higher reasoning customer service, employee support and expert advisory tasks.
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Rise of Hyperautomation – Connecting automation, AI and analytics building blocks will rapidly transform end-to-end value chains touching customers, suppliers and internal teams simultaneously.
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Embedded Intelligence & Predictive Recommendations – Forward-looking analytics like projected inventory stockouts and predictive part replacement recommendations will emerge natively from automated systems rather than separate reporting tools.
With intelligent automation executing essential processes accurately around the clock, human staffers across organizations gain capacity to focus on creative, relationship-based interactions and innovative projects that drive business competitiveness through both efficiency and strategic differentiation. The future looks bright for companies thoughtfully leveraging automation to reinvent themselves!
