Within modern healthcare enterprises, operational prowess transcends mere efficiency; it directly dictates patient welfare, fiscal stewardship, and unwavering adherence to regulatory mandates. The complex tapestry of clinical and administrative workflows, extending from the initial patient admission to the final claims adjudication, ceaselessly presents formidable challenges. It is precisely amid this intricate environment that Business Process Management in Healthcare solidifies its position as an undeniable strategic imperative. For leaders in accounting, finance, and technology within leading healthcare organizations, discerning BPM’s transformative capacity is indispensable for propelling patient care excellence and safeguarding enduring fiscal viability.
This article aims to precisely elucidate the pivotal concept of Business Process Management in Healthcare. It will meticulously define BPM within this vital context, articulate its critical importance and unparalleled suitability for dynamic healthcare operations, and detail its profound, transformative advantages. These benefits encompass a profound elevation of patient care, a substantial enhancement of operational efficiency, a significant refinement of data management practices, rigorous assurance of regulatory compliance, and a notable curtailment of operational costs. By meticulously showcasing a diverse array of real-world applications and impactful use cases (e.g., patient scheduling, claims processing, revenue cycle management, integrated care coordination), this blog delivers a comprehensive synthesis. In essence, the article functions as an indispensable foundational resource for healthcare organizations striving to embrace or optimize BPM solutions, championing its pivotal role in fostering continuous improvement and elevating patient outcomes.
Business Process Management in Healthcare represents a systematic discipline wholly dedicated to meticulously discovering, abstractly modeling, rigorously analyzing, precisely measuring, iteratively improving, and ultimately optimizing the myriad end-to-end clinical and administrative processes woven throughout healthcare organizations. Its fundamental objective is to fundamentally enhance organizational performance, streamline efficiency, and cultivate profound agility by meticulously aligning every process with overarching strategic goals, invariably leading to superior patient care delivery.
In the healthcare arena, a “process” can span a patient’s entire journey through a clinical encounter, the intricate management of a complex surgical calendar, or the precise adjudication of an insurance claim. Effective Business Process Management in Healthcare seeks to ensure these sequences operate with maximal fluidity, unwavering precision, strict compliance, and optimal cost-effectiveness. This goes beyond simply mapping linear steps; it embodies instilling a pervasive culture of continuous, data-driven improvement in every facet of how healthcare services are rendered.
The healthcare industry confronts a unique confluence of pressures, rendering Business Process Management in Healthcare not merely advantageous but absolutely indispensable for survival and growth.
These multifaceted factors unequivocally underscore why healthcare business process management is a non-negotiable strategic imperative for patient-centric, fiscally responsible, and competitive healthcare providers.
Implementing a comprehensive BPM in Healthcare strategy bestows profound advantages that directly impact the patient experience, amplify operational efficiency, and bolster financial viability. These constitute the core BPM in Healthcare benefits.
These compelling BPM in Healthcare benefits unequivocally illustrate why BPM forms the bedrock of modern healthcare excellence.
While Business Process Management in Healthcare furnishes the overarching strategic framework, Business process automation in healthcare (BPA) serves as the dynamic engine propelling its transformative power. BPA systematically leverages technology to autonomously execute defined tasks and sequences within healthcare workflows, decisively moving beyond manual processes.
Traditional automation in healthcare, typified by Robotic Process Automation (RPA), often manages repetitive, rule-based duties like data entry. However, the inherent complexities of healthcare—encompassing unstructured medical narratives, nuanced clinical judgments, and dynamic patient journeys—demand a far more intelligent form of automation. Modern Business process automation in healthcare, especially when empowered by advanced Artificial Intelligence, possesses the capability to navigate these intricacies, orchestrating entire patient journeys from admission to seamless discharge and beyond, with minimal human intervention. This enables true operational scalability for BPM in healthcare.
The practical application of Business Process Management in Healthcare is both expansive and profoundly impactful, poised to redefine core operational paradigms and enhance the patient experience. Here are prominent BPM in Healthcare examples that vividly showcase its transformative influence:
These diverse BPM in Healthcare examples unequivocally demonstrate the pervasive and transformative impact of BPM across the entire healthcare ecosystem.
A robust healthcare business process management solution, particularly one leveraging intelligent automation, typically boasts an expansive suite of Healthcare BPM features meticulously designed to streamline intricate clinical and administrative workflows.
These sophisticated Healthcare BPM features are absolutely vital for cultivating truly effective Business Process Management in Healthcare.
While numerous BPM in Healthcare solutions offer basic digital workflow management, Kognitos presents a fundamentally distinct and profoundly more powerful methodology. It is specifically engineered for the nuanced, document-intensive, and frequently exception-laden realities inherent in healthcare operations. The platform empowers sophisticated BPM in Healthcare by:
By leveraging Kognitos, healthcare organizations can definitively transcend traditional BPM in Healthcare approaches, ascending to a realm of truly intelligent, remarkably adaptive, and profoundly human-centric process management. This bestows unparalleled efficiency, measurably improves patient outcomes, and cultivates formidable operational agility.
The trajectory of Business Process Management in Healthcare points unequivocally towards even greater inherent intelligence, hyper-personalization, and seamless integration. The future of healthcare business process management will be fundamentally characterized by:
By embracing intelligent automation platforms like Kognitos, healthcare organizations can truly transform their approach to Business Process Management in Healthcare, converting operational challenges into decisive strategic advantages that enhance care quality and ensure long-term viability.
Twenty-five years ago, digital transformation meant replacing filing cabinets with databases and rolling out enterprise software. It was about converting analog information—think healthcare records, research, data—and processes into a digital format. In the 2010s, the rise of cloud computing expanded this vision, enabling organizations to centralize and retire infrastructure, modernize legacy systems, and scale faster than ever before.
Over the last decade, digital transformation has matured. Companies have shifted their focus from digitizing operations to rethinking them entirely. The goal now is operational agility to enable faster decision-making, greater resilience, and real-time responsiveness to customer needs and market changes.
This shift has made digital transformation a top priority in sectors where disruption is constant—supply chain, logistics, retail, finance, and healthcare, to name a few. For these organizations, digital transformation is more than a technology initiative; it’s a strategy for sustainable business growth.
AI automation is redefining the meaning of transformation, not by replacing humans, but by elevating them. It takes the digital foundation many companies have already built and turns it into something dynamic. Imagine systems that learn, reason, and adapt alongside your business.
AI automation enables true digital transformation. Where traditional automation tools like robotic process automation (RPA) focus on task repetition, AI-powered platforms like Kognitos can understand processes, handle exceptions, and collaborate with people to improve workflows over time.
According to McKinsey, organizations embedding AI into core operations are more likely to see cost savings, productivity gains, and competitive differentiation by shifting how work gets done.
| Benefit | Impact on Digital Transformation |
| Dynamic SOP Execution | Keeps operational documentation current and usable in real-time |
| Institutional Knowledge Retention | Documents critical process knowledge in plain English |
| Business User Empowerment | Reduces strain on IT teams while retaining AI governance |
| Adaptive Exception Handling | Asks for input and ;earns from each exception to improve over time |
| Process Visibility and Auditability | Offers transparency into what’s happening—and why |
Most organizations have standard operating procedures (SOPs) buried in manuals, PDFs, or static wikis. They’re necessary for compliance and quality, but they’re rarely dynamic. As teams and tools evolve, these SOPs become outdated and irrelevant.
Kognitos flips this model. The platform operationalizes SOPs and enables non-technical users to improve them using natural language. When procedures are actually used rather than just documented, teams gain visibility into how work is performed and where it can be improved.
The result is that best practices can evolve with your business, not behind it.
Whether it’s a retiring employee, unexpected turnover, or siloed teams, the loss of undocumented expertise is a major risk.
Kognitos captures and stores institutional knowledge as part of the standard process of automating a workflow. Business users describe processes in plain English. The system executes them, learns from variations, and builds an evolving knowledge base accessible by any member of the organization.
With traditional automation like RPA, business users must submit requirements to specialized developers, wait through their backlog of development tasks, and endlessly maintain fragile workflows just to get a process automated. By the time it’s done, the process may have already changed.
Kognitos removes this bottleneck. Its natural language interface enables business users to automate processes themselves, while maintaining IT governance. No code, no backlog, no IT ticket required.
This creates a new dynamic. Operations teams, finance leads, procurement managers can all drive innovation directly. They don’t need to become developers. They just need to know how the work should be done.
Automation often fails at the edge cases. The “what-ifs” that define real-world operations can be insurmountable. RPA bots break when something unexpected happens, leading to exorbitant maintenance costs and driving up TCO.
Kognitos doesn’t. Its patented exception handling allows the AI to reason through anomalies based on simple inputs from users who know the process. These users correct the system once, and the platform adapts for the future without additional programming or maintenance.
That means fewer manual interventions, faster resolution, and smarter systems that get better with every use.
Digital transformation has always been about evolution. In the early days, it meant going paperless. Later, it meant going cloud-native. Today, it means being intelligent—building systems that don’t just run the business, but continuously learn how to run it better.
Kognitos is helping enterprises lead the next chapter of digital transformation. With an AI platform built for natural language, patented exception handling, and user-driven automation, it offers something rare: a transformation strategy that’s as flexible as the people driving it.
For companies looking to make digital transformation a reality—not just a roadmap—Kognitos is the partner to help you get there faster, smarter, and with every process stronger than the last.
Modern manufacturers and distributors rely on Epicor to manage complex operations, but manual data entry, invoice processing, and repetitive enterprise resource planning (ERP) tasks can drain productivity and introduce human error. Kognitos, the leading AI automation platform, enables business users to automate Epicor processes using plain English, eliminating the need for code or technical expertise while maintaining IT governance.
This blog explores how Kognitos automation can supercharge Epicor efforts through a native integration that showcases tangible business value.
Epicor is an ERP solution designed to be the operational hub for organizations in the automotive, building supply, distribution, manufacturing, and retail industries. ERP systems help businesses manage and automate operations in core business areas including:
Epicor is a popular ERP with mid-sized and enterprise organizations because of its modular nature, so organizations can pick and choose the functionality they need to keep their business running smoothly.
The purpose of an ERP software like Epicor is to move away from multiple fragmented systems with isolated data. Epicor provides organizations with a unified view of their business so they can make more informed and agile decisions.
Kognitos is an AI automation platform that transforms plain English process documentation into powerful AI agents. Unlike point solutions such as intelligent document processing (IDP) or traditional robotic process automation (RPA) tools, Kognitos manages the entire lifecycle of automation through the hyperautomation lifecycle (HAL) methodology.
Kognitos moves beyond the limitations of traditional automation—specialized developers, outdated programming languages, lengthy implementation, and brittle bots that require constant maintenance. This results in lower total cost of ownership, fast speed to production, and a transparent solution that records every decision in natural language.
Imagine taking a solid Epicor foundation and pairing it with an automation solution that understands your business processes as intuitively as your own team. That’s the power of Kognitos. The platform empowers your team to return to high-value strategic activities by automating everything from repetitive tasks to complex workflows.
Kognitos offers a seamless integration with Epicor, leveraging built-in AI technologies like natural language processing (NLP), optical character recognition (OCR), and intelligent document processing (IDP) to reduce the need for corresponding point solutions. With Kognitos, business users can:
Kognitos can quickly tackle a variety of use cases within organizations using Epicor ERP, from order journey management to inventory optimization to invoice processing.
Kognitos allows organizations to boost operational efficiency, improve data accuracy, and enhance customer satisfaction with a scalable AI automation solution.
Ready to see how Kognitos can streamline your Epicor operations? Explore the full breadth of enterprise-grade integrations, reach out to the Kognitos team to schedule a personalized demo, or see for yourself with a free trial of our community version.
In today’s dynamic corporate world, finance and accounting departments face unprecedented demands. Managing vast data volumes, ensuring regulatory compliance, and delivering swift, accurate insights are critical. Traditional manual processes often fall short. This urgent need drives the widespread adoption of AI tools for finance and accounting, fundamentally transforming financial operations. Embracing AI in finance and accounting moves beyond simple automation, integrating intelligence into every financial workflow. For any organization striving for operational excellence and a decisive competitive edge, exploring these sophisticated AI finance tools is now essential. This article highlights leading brands offering smart AI tools for finance and accounting teams.
The market for AI tools for finance and accounting is rich with innovative solutions, each tailored to specific financial challenges. Here we spotlight prominent brands reshaping financial operations with advanced AI accounting software and other critical AI finance tools. These represent some of the best AI tools for finance, empowering finance professionals and enhancing overall finance automation.
Kognitos: This platform redefines how finance and accounting teams automate complex processes using natural language, eliminating the need for intricate code. It is highly valued for its user-friendliness for non-technical finance professionals, allowing them to describe workflows in plain English. Its rapid deployment cycle allows businesses to implement sophisticated automations quickly, significantly reducing the time to value. It also substantially reduces reliance on specialized coding expertise, democratizing access to powerful AI for business users across various departments. However, it requires an initial setup time for optimal integration within existing financial systems, which can involve initial data mapping. Its full benefits genuinely depend on proper implementation strategies and diligent user adoption across the team.
Glean: Primarily known for enterprise search and knowledge management, Glean offers powerful AI-driven information retrieval crucial for finance teams. Its ability to swiftly find relevant financial data across disparate systems immensely benefits AI tools for a finance professional needing quick access to reports, historical data, or specific transaction details for AI for financial analysis. It accelerates information discovery, significantly reducing time spent searching for scattered financial data, improving overall efficiency. It also enhances team collaboration around financial knowledge, making it easier for teams to share insights. However, its core focus is on data retrieval and knowledge synthesis, meaning its direct financial transaction processing or automation capabilities for core accounting functions are limited.
Cast.ai: This platform specializes in AI-driven cloud cost optimization for Kubernetes environments. While not directly AI accounting software, finance teams overseeing significant cloud infrastructure budgets can benefit immensely from its cost-saving capabilities and detailed financial visibility into cloud spending. It provides substantial cost reductions on cloud spend, achieved through automated resource allocation and optimization, directly impacting the bottom line for cloud-heavy organizations. It also offers granular cost monitoring and reporting, helping finance teams track spending trends. However, its highly specialized focus on cloud infrastructure management means it does not cover broader traditional financial operations or core accounting tasks like reconciliation or invoicing.
Ema: Positioned as an enterprise AI assistant, Ema automates tasks for knowledge workers across various departments, including finance and accounting. For finance teams, this can mean assisting with routine financial queries, generating basic financial summaries from reports, or intelligently routing financial documents for approval processes. It offers strong versatility across various business functions, making it adaptable to different departmental needs beyond just finance, allowing for broad internal applications. Its user-friendly conversational interaction for employees also enhances adoption and simplifies task execution for common inquiries. However, deep, specialized financial integration and complex accounting functions might require additional configuration or custom development to fully meet specific finance-specific requirements.
Orby.ai: This tool focuses on AI agents for automating finance helpdesk operations, significantly improving efficiency in resolving internal and external financial queries. It helps streamline tasks like ticket routing, information gathering for resolutions, and providing quick, automated answers to common finance-related questions. It leads to a significant reduction in manual labor for finance helpdesk teams, resulting in faster ticket resolution and improved accuracy in responses to common financial queries. It also enhances internal customer satisfaction by providing quicker, consistent responses from employees. However, its specialized application means it does not directly perform core accounting functions such as ledger management or financial statement generation.
Paramark: An AI platform dedicated to marketing attribution and spend optimization. Given the close collaboration between finance and marketing on budget allocation and return on investment, Paramark provides crucial financial oversight on marketing ROI for finance professionals. It helps finance teams better allocate marketing budgets by providing data-driven insights into campaign performance and profitability, optimizing spend efficiency. It also offers clearer performance metrics directly linked to financial outcomes, improving budget accountability. However, its specialized focus on marketing analytics means it does not offer broad financial management or core accounting features like general ledger management.
Ascertain: This platform leverages AI for robust risk assessment and compliance management, a highly relevant area for finance and accounting. It helps identify potential financial risks, detect fraudulent activities by analyzing transaction patterns, and ensures stringent adherence to regulatory frameworks. It enhances fraud detection capabilities through advanced pattern recognition, helping to prevent significant financial losses and secure assets. It also improves audit readiness through comprehensive data analysis and strengthens overall compliance postures. However, it critically requires highly accurate and unbiased data input to ensure the AI model’s effectiveness and avoid flawed risk assessments or skewed compliance checks.
Chartahealth: While primarily focused on healthcare data and clinical insights, its underlying AI capabilities for complex data analysis can indirectly support finance teams within healthcare organizations. This might involve optimizing patient billing processes, analyzing cost structures related to clinical outcomes, or improving revenue cycle management specifically in a healthcare context. It offers specialized processing of vast healthcare data for financial applications, providing nuanced insights relevant to healthcare economics. It can improve billing accuracy and optimize financial flows within a healthcare setting, impacting revenue and cost management. However, its inherent focus on a specific industry means it lacks direct features for general corporate finance and accounting functions applicable across all sectors.
Numeric.io: Specifically designed to enhance financial data quality and automate reconciliation, Numeric.io uses AI to streamline these critical accounting tasks. It directly addresses common pain points in the financial close process by automating matching and identifying discrepancies across ledgers. It provides highly accurate data reconciliation, significantly reducing manual effort during closing periods and accelerating the financial close. It also contributes to vastly improved data integrity across financial systems, building greater trust in financial reports. However, its narrower, specialized focus means it does not encompass broader, end-to-end financial management suites that cover a wider range of accounting operations beyond reconciliation and data quality.
Poolside.ai: This platform uses AI for code generation and accelerating software development. While not a direct AI accounting software, finance teams that rely on custom internal tools, complex data integrations, or bespoke reporting systems can benefit from Poolside.ai by speeding up the development of such solutions. It enables significantly faster development cycles for internal financial applications, allowing finance departments to quickly build tools tailored to their unique analytical or reporting needs. It also reduces reliance on specialized coding personnel. However, its indirect application to core financial processes means it does not offer out-of-the-box financial solutions.
Thoughtful.ai: As an intelligent automation platform, Thoughtful.ai aims for hyperautomation, integrating various AI capabilities to automate complex processes across an organization. For finance, this means end-to-end process automation involving cognitive tasks like intelligent document processing, automated invoice handling, or decision-making workflows. It offers comprehensive automation capabilities across various finance workflows, allowing for seamless integration of multiple steps within a complex financial process. It also provides strong flexibility in handling intricate, dynamic processes, adapting to changes in financial procedures. However, there is a potential for a steeper learning curve for its more advanced and integrated features compared to simpler, more niche tools designed for a single function.
The role of AI in accounting extends beyond simple automation. It encompasses intelligent data processing, predictive analytics, and enhanced decision support. AI for finance and accounting helps finance professionals by automating data capture from diverse sources, performing reconciliations with greater accuracy, and flagging anomalies. This significantly improves efficiency and reduces manual workload, demonstrating how AI is used in finance to streamline operations. The benefits of using AI in finance and accounting are evident across the entire financial ecosystem. This includes substantial cost reduction through optimized operations, improved accuracy by minimizing human error, and enhanced analytical capabilities that provide deeper insights into financial performance. AI is used in finance to power various functions like fraud detection, credit scoring, algorithmic trading, and personalized financial advice. This empowers finance teams to transition from data entry specialists to strategic advisors.
For financial forecasting, AI plays a crucial role in predicting future financial performance with greater accuracy. The AI technology most commonly used in financial forecasting is machine learning, particularly algorithms designed for time series analysis and regression. These models analyze vast historical financial data, market trends, and external macroeconomic factors to identify complex patterns and predict future values for revenue, expenses, and cash flow. This capability is paramount for AI for financial analysis. Machine learning models learn from past forecast errors and continuously refine their predictions, providing finance professionals with more reliable future outlooks. This dynamic approach is invaluable for strategic planning and resource allocation, ensuring better prepared financial strategies and improving the overall quality of financial analysis.
How to implement AI in finance & accounting involves several key steps. It typically begins with identifying specific pain points and defining clear objectives for AI integration. This includes assessing data readiness by ensuring clean, accessible, and sufficient data for AI model training. The next stage involves selecting the appropriate AI accounting software or AI finance tools that align with these objectives and can integrate with existing systems. The best AI tool for finance and accounting is not a one-size-fits-all solution but rather the one that best addresses an organization’s specific needs and seamlessly integrates with its infrastructure. Implementation then involves training AI models, integrating them into daily workflows, and providing comprehensive training for finance professionals. This approach ensures effective finance automation.
How much does AI cost for businesses? The cost of AI for businesses can vary significantly depending on the complexity of the solution, the need for customization, and the scale of deployment. It ranges from subscription fees for off-the-shelf AI accounting software to substantial investments for custom-built enterprise solutions. Ongoing costs include data storage, processing power, and specialized talent for maintenance and optimization. Despite the initial investment, the long-term benefits in efficiency, accuracy, and strategic insights often yield a strong return, making it a valuable investment for finance automation.
AI’s presence in the workplace is expanding rapidly, impacting everyday tasks across departments. In finance, AI is being used in the workplace to automate reconciliation, detect fraud, and generate predictive models, significantly changing day-to-day operations for AI tools for a finance professional. Beyond finance, AI assists human resources in talent acquisition and employee engagement, aids marketing in personalization, and supports customer service with intelligent chatbots. The overarching goal of AI in the workplace is not to replace humans, but to augment human capabilities, allowing employees to focus on more strategic and creative endeavors.
How will AI be used in business in the future? We can anticipate AI moving beyond assisting humans to taking on more autonomous, complex, and even creative roles within finance. Future uses will likely include hyper-personalization for customers, where AI systems deliver highly tailored financial products and services. AI will drive more sophisticated predictive and prescriptive analytics, enabling finance teams to not only foresee trends but also to automatically take corrective actions. Autonomous operations will expand, with AI managing entire financial workflows or optimizing portfolio management with minimal human oversight. Furthermore, AI will become a co-creator, working alongside finance professionals in areas like complex financial modeling, strategic problem-solving, and identifying new market opportunities. This signifies a profound shift in how financial teams will operate, focusing more on high-value analytical and advisory tasks.
The integration of smart AI tools for finance and accounting teams is no longer a futuristic concept but a strategic imperative for modern businesses. By leveraging AI in finance and accounting, organizations can transcend the limitations of manual processes, achieving unprecedented levels of efficiency, accuracy, and insight. From automating routine tasks with advanced accounting software to providing powerful predictive analytics, the advantages are clear and tangible.
While challenges in implementation exist, the transformative potential of artificial intelligence in business far outweighs these hurdles. Companies that strategically adopt these intelligent tools will be better positioned to optimize operations, reduce costs, enhance decision-making, and ultimately drive sustainable growth in an increasingly competitive global economy. The future of finance is inherently intelligent, driven by these groundbreaking technological advancements.
Enterprise data can be overwhelming. It resides in multiple systems and comes from a number of sources including customer interactions, market trends, and operational processes. Mastering data management and analysis is crucial for gaining a competitive edge.
Data-driven decision-making replaces guesswork with evidence-based insights, empowering leaders to make informed, accurate, and consistent business decisions. Using data in real-time allows organizations to allocate resources efficiently and react quickly to market changes. This is why CIOs prioritize clean data for the types of insights that will drive organizational growth.
Accurate and complete data is essential in making data-driven decisions, and this is where many organizations run into challenges. On average, 80% of the data collected by organizations is unstructured, making it hard to interpret into actionable insights.
Lack of data leads to inefficiencies in decision-making, operational bottlenecks, and missed opportunities. However, too much data—particularly unstructured data—causes challenges, as well, in data overload, poor quality, and unstructured systems. Either way, decisions are often delayed and strategic initiatives are undermined. CIOs have to walk the line between navigating these complexities and ensuring their organizations remain agile.
Enterprise process automation allows CIOs to tackle data challenges using AI agents to improve data quality, streamline processes, and deliver actionable insights in real-time. In this article, we explore the challenges CIOs face due to the absence of data-driven decision-making within their organizations, and how AI automation helps CIOs address them.
| Challenge | Benefit of AI Automation |
| Poor Data Quality | Clean, validate, and enrich data in real-time using AI-powered workflows |
| Siloed Systems | Integrate fragmented datasets across departments to create a unified view for analytics |
| Unstructured Data Volume | Process text, images, and videos to extract actionable insights |
| Delayed Decision-Making | Accelerate time-to-action with real-time analytics and predictive insights |
| Compliance Risks | Enforces privacy safeguards like encryption and data masking to meet regulatory standards |
When CIOs work off of inaccurate or incomplete data, their insights are skewed and they’re more prone to missteps. Because data systems are so fragmented, reliability is inconsistent, at best. Further adding to the challenge, data is scattered across departments and business lines. All of these factors combined severely undermines their decision-making ability.
Enterprise process automation addresses these challenges head-on by using intelligent algorithms to detect anomalies like duplicate records or missing fields. AI agents are capable of validating incoming datasets continuously, rather than in batches, so corrections are immediate. This real-time data cleansing and validation helps to maintain higher quality data, which leads to more reliable insights
Disconnected tools lead to fragmented data. This obstructs collaboration and hinders enterprises from easily viewing unified metrics. CIOs need seamless integration across multiple systems to inform organizational decisions. When teams operate in silos and use different tools, information is scattered, leading to barriers in collaboration, inefficient execution, and lack of shared understanding.
AI automation leverages robust API capabilities to connect legacy infrastructure with new applications for seamless data flow. In opposition to legacy solutions like Robotic Process Automation (RPA)—which struggles with complex, cross-functional workflows—AI automation introduces adaptive agents capable of orchestrating dynamic interactions between systems. Workflows are monitored continuously, so information moves fluidly between departments and systems. Fragmented, siloed systems become interconnected processes, enabling CIOs to unlock new levels of efficiency.
Unstructured data includes everything from social media posts and documents to photos, videos, and even emails and chat logs. This disorganized information presents another major challenge for CIOs: traditional analytics tools are not equipped to extract actionable insights from such diverse and media-heavy sources. As a result, this data often sits untouched, leading to the potential for missed opportunities and inefficient analysis.
AI automation like Kognitos uses natural language processing (NLP) to unlock the value hidden in unstructured data. Some of the capabilities include:
Delayed insights can hinder an organization’s ability to respond to dynamic market conditions, a major concern for CIOs, who are responsible for driving rapid innovation and maintaining a competitive edge. Decision-making is often slowed by manual data collection, batch processing, and static reporting, which results in missed opportunities and increased risk when the markets shift unexpectedly.
APA addresses these challenges by leveraging AI agents that can continuously analyze live data streams. Unlike traditional automation solutions like RPA, which relied on fixed rules and scheduled updates, Agentic systems are adaptive and context-aware, using machine learning (ML) and large language models (LLMs) to interpret real-time information, predict outcomes, and recommend optimal actions. APA solutions can monitor and process incoming data instantly, identifying emerging trends, anomalies, or disruptions as and when they happen. This enables proactive adjustments rather than reactive firefighting.
CIOs are navigating an increasingly complex landscape where the innovation of AI technology must be balanced carefully with regulatory compliance. For instance, failure to comply with frameworks such as HIPAA (Health Insurance Portability and Accountability Act), GDPR (General Data Protection Regulation), or CCPA (California Consumer Privacy Act) risks legal consequences and even reputational harm.
By embedding compliance into operational DNA rather than treating it as an add on, AI automation enables CIOs to scale AI initiatives without compromising regulatory integrity. AI agents continuously monitor for policy violations like unauthorized data access or atypical processing patterns and trigger real-time alerts. AI automation can even leverage predictive capabilities to identify emerging compliance gaps.
For CIOs striving to root their business decisions in data, AI automation offers a transformative solution for addressing critical challenges head-on. By improving data quality, integrating siloed systems, unlocking unstructured data insights, accelerating decisions, and ensuring compliance, AI automation empowers CIOs to drive measurable business outcomes while fostering enterprise innovation.
Ultimately, data empowers CIOs to navigate complexity, maintain a competitive edge, and achieve sustained growth. If you’re a CIO or technology leader prioritizing data-driven decision-making, reach out to the Kognitos team to see how our AI automation platform can set your organization up for long-term strategic success.
The insurance industry, known for its paper-heavy processes and manual workflows, is undergoing a profound transformation. At the heart of this shift is the widespread adoption of automation, particularly in the realm of claims. For insurance executives, CIOs, and IT leaders, understanding how to leverage these advancements is no longer optional; it’s a strategic imperative to remain competitive and enhance customer satisfaction. Insurance companies are increasingly recognizing that to thrive in today’s fast-paced environment, they must embrace automated claims processing insurance.
The manual handling of claims, from initial notification to final settlement, is fraught with inefficiencies. It leads to delays, higher operational costs, inconsistent outcomes, and often, frustrated policyholders. This necessitates a move towards intelligent automation, where technology streamlines operations and elevates the customer experience. A report by BCG states that organizations that AI will transform claims management, reducing costs by 20% and increasing the speed of claims by 50%.
This article will delve into how insurance companies are successfully automating claims, offering insights into how your organization can achieve similar efficiencies and improved service.
Automated claims processing insurance refers to the application of advanced technologies, including Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA), to digitize, streamline, and optimize the entire insurance claims lifecycle. Its core purpose is to minimize human intervention in repetitive, rule-based, and data-intensive tasks associated with claims, thereby increasing speed, accuracy, and overall efficiency. This encompasses everything from initial claim intake and fraud detection to adjudication and settlement.
This comprehensive approach transforms traditional claims handling by enabling features such as intelligent document processing for claim forms, automated data extraction, autonomous eligibility verification, and smart workflow management. It moves beyond simple task automation, allowing systems to learn from data, adapt to new claim types, and make intelligent decisions based on predefined parameters and historical patterns. This capability is central to effective automated claims processing insurance, significantly reducing operational burdens and enhancing the customer experience.
Automated claims processing works by integrating various technological components to manage and accelerate the claims journey. The process typically begins with digital intake, where claims can be submitted through various channels like online portals, mobile apps, or even email. AI-powered tools, including Optical Character Recognition (OCR) and Natural Language Processing (NLP), then extract relevant data from unstructured documents such as claim forms, medical reports, or repair estimates. This crucial step eliminates manual data entry, which is a significant source of errors and delays in traditional claims handling.
Once the data is extracted, the system performs automated validation and verification. It cross-references claim details against policy information, coverage limits, and historical data to check for accuracy and eligibility. Machine learning algorithms can be employed here to identify anomalies or potential fraud indicators, flagging suspicious claims for human review. This proactive fraud detection is a key benefit of automated insurance claims processing.
Following validation, the system intelligently routes the claim through an automated workflow for adjudication and approval. Based on predefined rules and learned patterns, minor and straightforward claims can be automatically approved and settled. More complex claims, or those flagged for discrepancies, are seamlessly escalated to human adjusters with all relevant information pre-populated, allowing them to focus on complex decision-making rather than administrative tasks. This dynamic routing accelerates the entire process, making for a highly efficient automated claims processing system. A recent report by Gartner suggests that the focus is shifting from growing revenue to improving customer experience (CX) and operational efficiency, and automation plays an important part in that.
Implementing automated claims processing brings a multitude of benefits that directly enhance an insurance company’s profitability, efficiency, and customer satisfaction. These advantages are crucial for maintaining a competitive edge in a dynamic market.
Improved Compliance and Audit Trails: Automated systems create comprehensive digital records and audit trails for every step of the claims process. This ensures adherence to regulatory requirements, simplifies internal and external audits, and enhances transparency.
Effective automated claims processing systems are built upon a foundation of key features that empower insurers to transform their operations. These functionalities are designed to bring intelligence and efficiency to every stage of the claims journey.
Digital communication capabilities enhance policyholder engagement. Automated systems can send real-time status updates, requests for additional information, and settlement notifications to claimants via their preferred channels, improving transparency and satisfaction. The best insurance claims automation software often includes a robust self-service portal for customers.
While automated claims processing offers transformative benefits, it is also important for insurance companies to understand its limitations and potential challenges during implementation. A realistic view helps in better planning and ensuring that expectations align with capabilities.
The imperative to automate claims processing insurance is clear for today’s insurance companies. By embracing AI and automation, insurers can move beyond outdated, manual processes to achieve remarkable efficiencies, significant cost savings, and profoundly improved customer experiences. The journey involves intelligent data capture, automated validation, smart routing, and robust fraud detection, all contributing to a more agile and responsive claims operation.
While challenges such as initial investment and integration complexities exist, the long-term benefits of a truly automated claims processing system are undeniable. It fosters enhanced accuracy, greater scalability, and stronger compliance, positioning insurers for sustained growth and competitiveness. Kognitos stands as a powerful ally in this transformation, offering an intuitive platform that uses natural language and AI reasoning to seamlessly automate claims processing, empowering insurance companies to unlock new levels of operational excellence and customer satisfaction.
Organizations are relentlessly seeking ways to optimize operations and enhance customer engagement in the rapidly evolving business landscape of today. This pursuit leads many to embrace Digital Process Automation (DPA). DPA represents a sophisticated approach to streamlining business activities, leveraging advanced digital technologies to automate and optimize end-to-end workflows. It goes beyond simple task automation, focusing on creating seamless, intelligent processes that integrate people, applications, and information.
This article aims to provide a comprehensive understanding of Digital Process Automation. It defines DPA as the evolution of process automation, explaining how it orchestrates complex workflows to achieve profound digital transformation. For accounting, finance, and technology leaders, grasping DPA is crucial for driving innovation, enhancing customer experiences, and achieving superior operational agility.
Digital Process Automation signifies a major leap forward from earlier forms of automation. While traditional automation might focus on discrete, repetitive tasks, DPA targets entire end-to-end business operations. It’s about creating a fully digital digital process where information flows effortlessly, decisions are automated where possible, and human intervention is optimized for complex exceptions rather than routine tasks.
This evolution brings unprecedented levels of efficiency and adaptability. It allows businesses to reimagine their operational models, breaking down traditional silos between departments and systems. By embedding intelligence and digital precision into every step, Digital Process Automation empowers organizations to respond more quickly to market changes, meet customer demands with greater agility, and drive continuous innovation across their entire ecosystem. It’s not just about doing things faster; it’s about doing things smarter, end-to-end.
While Digital Process Automation often draws comparisons to Business Process Management (BPM), it represents a distinct evolution in strategic focus and technological capabilities. Understanding Digital process automation vs. business process management is key for effective implementation.
BPM traditionally focused on process discovery, modeling, analysis, and optimization, often with an emphasis on cost reduction and employee productivity through process efficiency. While still valuable, its scope often remained within process improvement.
DPA, conversely, builds upon BPM principles but extends far beyond. It emphasizes:
Therefore, while BPM is about managing processes, Digital Process Automation is about transforming them into fully digital, customer-centric, and innovation-driven engines for the modern enterprise.
Another common comparison is between Digital Process Automation and Robotic Process Automation (RPA). While both aim to automate, their methodologies and scope differ significantly. Understanding What is the difference between DPA and RPA? is crucial for strategic deployment.
While RPA can be a component within a broader DPA strategy, DPA is the overarching framework for digital transformation. It leverages a wider array of technologies, including AI, intelligent forms, and advanced workflow engines, to create a holistic digital process that is customer-centric and highly adaptable. Kognitos, for instance, focuses on AI-driven natural language automation that goes beyond brittle RPA.
Adopting Digital Process Automation brings transformative advantages for enterprises. The benefits of digital process automation extend across operational efficiency, financial performance, and strategic adaptability, making it a pivotal investment for future-forward organizations.
Key advantages include:
These advantages highlight why Digital Process Automation is a cornerstone of modern business strategy.
Digital Process Automation is being applied across various industries and functions, fundamentally transforming how work gets done. These examples of digital automation showcase the versatility and impact of DPA in creating efficient, customer-centric operations. Each instance demonstrates how a streamlined digital process yields tangible benefits.
Consider these impactful applications:
These examples of digital automation highlight DPA’s power to optimize end-to-end processes, improving both operational efficiency and customer satisfaction.
For large enterprises seeking to fully leverage the power of Digital Process Automation, Kognitos offers a fundamentally distinct and uniquely powerful approach. The platform is transforming Digital Process Automation with its patented natural language AI and profound AI reasoning, making enterprise-grade AI natively accessible to business users. This positions Kognitos as a premier digital process automation software.
Kognitos empowers business teams—from accounting and finance leaders to CIOs—to automate complex, end-to-end processes using plain English. This innovative approach bridges the gap between IT and business operations, allowing for greater agility and control over intelligent automations. Our neurosymbolic AI architecture ensures precision and inherently eliminates AI hallucinations, providing robust AI governance and control over every digital process. This makes Kognitos a key tool that helps large companies achieve their DPA objectives effectively and securely.
Kognitos streamlines the journey to digital process automation, making advanced enterprise automation practical, scalable, and inherently secure for large organizations.
Successfully adopting Digital Process Automation requires a strategic approach. Choosing the right digital process automation software and implementing it effectively are paramount for maximizing benefits and ensuring seamless integration with existing enterprise systems. This outlines how to effectively transition to a more automated future.
Consider these strategic steps:
Following these comprehensive strategic steps will empower organizations to effectively implement Digital Process Automation and realize substantial operational gains alongside significant strategic advantages.
The future of business is undeniably digital, and Digital Process Automation is its driving force. As organizations navigate increasing complexity and competition, the ability to seamlessly integrate people, applications, and information through intelligent automation will determine success. The ongoing evolution of digital process automation software promises even greater capabilities.
Kognitos stands at the forefront of this transformation, offering a unique platform that simplifies Digital Process Automation through natural language AI and advanced reasoning. By empowering enterprises to automate end-to-end processes with unparalleled precision and control, Kognitos enables them to unlock vast efficiencies, enhance customer experiences, and secure a lasting competitive advantage in the digital age. Embracing DPA is not just about adopting technology; it’s about fundamentally redefining how your business operates.
In today’s enterprise operations, truly effective automation doesn’t begin with technology; it starts with a profound understanding of existing workflows. This is precisely where Process Discovery plays a pivotal role. What is Process Discovery? It is the systematic methodology of identifying, analyzing, and documenting an organization’s current business processes. This meticulous exploration uncovers inefficiencies, bottlenecks, and, most importantly, critical automation opportunities. It serves as the indispensable first step for any successful business process development initiative.
This article guides businesses through the fundamental stages of Process Discovery and subsequent business process development. We will clarify how organizations can precisely identify, strategically design, and optimally refine their internal processes. Our discussion will highlight key methodologies involved in uncovering significant process automation opportunities, ensuring that every automation effort yields maximum return on investment for corporate leaders.
Central to Process Discovery is the practice of business process mapping. What is business process mapping? It is the visual representation of a process, illustrating its flow from start to finish. This involves documenting each step, decision point, input, output, and the roles responsible for performing specific tasks in the process. A well-crafted process map provides a clear, concise blueprint of how work actually gets done within an organization.
Effective business process mapping transforms abstract workflows into tangible diagrams, whether they are simple flowcharts or more detailed Business Process Model and Notation (BPMN) diagrams. This visual clarity is crucial for all stakeholders to understand complex operations, pinpoint areas of redundancy, and ultimately lay the groundwork for effective automation and optimization. It’s a foundational discipline for designing a business process.
Why do you need business process mapping? Beyond mere documentation, process mapping is a strategic tool that illuminates critical insights and paves the way for substantial improvements and the identification of process automation opportunities.
Key reasons include:
Ultimately, business process mapping is a crucial diagnostic step that informs and guides strategic business process development.
Successfully navigating Process Discovery requires a structured, methodical approach to maximize insights and identify the most impactful process automation opportunities. Following these steps for process discovery for better ROI ensures a comprehensive and effective outcome. This systematic journey moves from broad understanding to granular detail.
These steps ensure a systematic approach to unlocking automation value.
A core objective of Process Discovery is to pinpoint where automation can yield the most significant returns. How to identify and select the right Business Processes for RPA? This question extends to broader process automation opportunities, beyond just RPA, encompassing intelligent automation and automated business process discovery. The ideal candidates possess specific characteristics that make them ripe for transformation.
Look for processes that are:
By focusing on these characteristics, organizations can strategically select process automation opportunities that promise quick wins and substantial ROI, guiding the entire development of business process initiatives for automation.
To execute effective Process Discovery, organizations employ various process discovery methods and leverage specialized process discovery tools. These approaches help gather insights and create a clear picture of existing workflows for creating a process that is optimized.
Key methods include:
Specialized process discovery tools often include features for process mapping, diagramming, simulation, and analysis, aiding in designing a business process effectively.
For enterprises undertaking Process Discovery and subsequent business process development, Kognitos simplifies and accelerates these initial phases through its unique natural language AI, making seamless process definition and development accessible to business users.
Kognitos empowers users to describe and define their process automation opportunities in plain English. This bridges the gap between understanding a process and actually creating a process that is automated. It allows users closest to the work to articulate the tasks in the process, and Kognitos translates that directly into executable automation. This makes process discovery automation an intuitive, collaborative effort.
Kognitos transforms the often-tedious aspects of Process Discovery and business process development into an intuitive, collaborative, and highly efficient undertaking, directly translating insights into impactful automation.
Engaging in strategic Process Discovery yields significant advantages that ripple across an entire organization, optimizing operations and accelerating the journey toward digital transformation. Understanding what are the benefits of Process Discovery reveals its foundational importance.
Key benefits include:
These benefits underscore why Process Discovery is an indispensable investment for any enterprise seeking to thrive in the digital age and capitalize on automation opportunities.
The principles of Process Discovery are applied across various industries to unlock efficiency and drive business process transformation. What are some examples of Process Discovery? These illustrations highlight how structured analysis reveals valuable process automation opportunities.
These examples of Process Discovery underscore its fundamental role in preparing organizations for impactful automation.
Process Discovery is not just a preliminary step; it’s a continuous discipline essential for maintaining agility in an increasingly automated world. As organizations embrace technologies like automated business process discovery and process mining, the initial phase of any automation journey becomes faster and more insightful. The ability to identify, design, and optimize processes with precision will define successful enterprises.
Kognitos fundamentally transforming how Process Discovery insights translate into tangible automation. By enabling the seamless definition and development of business process automation using natural language AI, Kognitos empowers leaders to unlock vast process automation opportunities. This unique approach ensures that businesses can not only identify but also rapidly create business process automations that drive significant ROI and lead to true business process transformation.
The legal profession, traditionally known for its meticulous attention to detail and reliance on extensive documentation, has long grappled with the time-consuming and often repetitive nature of drafting, reviewing, and managing legal documents. From contracts and agreements to pleadings and corporate filings, the sheer volume and complexity of these documents can consume countless hours, impacting efficiency, increasing costs, and diverting legal professionals from higher-value strategic work. However, a significant transformation is underway, making legal work demonstrably easier: Legal Document Automation. For law firm partners, in-house legal counsel, and legal tech innovators, understanding and embracing legal document automation software is no longer a luxury but a strategic imperative.
The challenges are palpable: inconsistent document versions, manual data entry leading to errors, slow turnaround times for clients, and an inability to scale operations efficiently. These issues not only hinder productivity but also impact client satisfaction and a firm’s profitability. Automating legal documents directly addresses these pain points, ushering in an era of enhanced precision, speed, and standardization. It allows legal professionals to focus on the nuanced legal analysis and client-specific advice that truly requires their expertise, rather than getting bogged down in administrative tasks.
The core objective of Legal Document Automation is to streamline and optimize the entire document lifecycle within a legal context. This involves leveraging specialized software to automate the creation, assembly, management, and even analysis of legal documents. From simple non-disclosure agreements to complex merger contracts, a robust legal document automation software ensures consistency, reduces errors, and accelerates delivery, transforming a historically labor-intensive process into a highly efficient and accurate one.
Legal Document Automation is the process of using software and technology to automatically generate, manage, and process legal documents. It involves creating templates that are dynamically populated with specific data, clauses, and conditions based on user input or integrated data sources. The primary goal of legal document automation is to streamline the drafting process, reduce manual effort, minimize errors, ensure consistency, and accelerate the creation of various legal instruments. It’s a critical component of modernizing legal operations, allowing legal professionals to focus on substantive legal work rather than repetitive administrative tasks.
Legal Document Automation operates by leveraging predefined templates and data inputs to generate customized legal documents efficiently and accurately. At its core, the process involves a sophisticated legal document automation software that acts as the central engine.
The typical workflow begins with template creation. Legal experts design master templates for common legal documents—such as contracts, wills, pleadings, or agreements—embedding logic, conditional clauses, and placeholders for variable information. This means the template is not static; it contains rules that determine which sections appear, which clauses are included, and how language is phrased based on specific answers.
Next comes data input. Users, who could be lawyers, paralegals, or even clients (via a secure portal), interact with the legal document automation software by answering a series of questions or inputting relevant data. This data might include client names, specific dates, financial figures, property descriptions, or jurisdiction details. Importantly, the software can often integrate with existing databases or CRM systems to automatically pull in relevant information, further reducing manual entry and potential errors.
Based on the data provided and the embedded logic in the template, the software automatically assembles the complete, tailored legal document. This process instantly populates the placeholders, selects the correct clauses, and ensures all conditions are met. The result is a fully drafted, client-ready document in a fraction of the time it would take to prepare manually.
Beyond just drafting, advanced legal document automation software often includes features for collaboration, version control, and e-signature integration, streamlining the entire document lifecycle. This comprehensive approach to automating legal documents ensures consistency, reduces review cycles, and minimizes the risk of omissions or inconsistencies, making legal work significantly easier and more precise.
The adoption of legal document automation offers profound benefits that transform legal practice, enhancing efficiency, accuracy, and client service. For law firms and in-house legal departments, these advantages are critical for staying competitive and maximizing productivity.
Legal document automation is transforming various aspects of legal practice by streamlining repetitive document-centric tasks. Here are some key examples of how legal document automation software is being used:
These examples illustrate that the meaning and impact of legal document automation extend across diverse legal specialties, proving it to be a versatile and indispensable tool for modern legal practice.
Kognitos significantly helps in automating legal documents by enabling legal professionals to define and execute complex document generation and workflow processes using natural language, effectively eliminating the need for traditional coding. Unlike many other legal document automation software solutions that require specialized scripting or complex template setups, Kognitos’ unique AI reasoning engine understands instructions given in plain English. For example, a lawyer can simply describe the steps for drafting a contract, such as “create NDA, input client name and effective date, add confidentiality clauses, route for review to partner, then send for e-signature.” Kognitos intelligently handles the entire process, from dynamically populating the document template with data to orchestrating the review and approval workflows across various systems. This approach simplifies the creation of sophisticated legal documents, reduces manual effort, minimizes errors, accelerates turnaround times, and provides greater control and transparency, making Kognitos a powerful tool for automating legal documents and streamlining legal operations.
The legal profession is experiencing a paradigm shift, where Legal Document Automation is making work demonstrably easier, faster, and more accurate. By embracing advanced legal document automation software, law firms and in-house legal departments can move beyond the inefficiencies of manual drafting to achieve unprecedented levels of productivity and precision. This technological evolution streamlines the entire document lifecycle, from initial creation to final execution, delivering significant benefits in time savings, error reduction, and enhanced client service.
The strategic implementation of solutions for automating legal documents is no longer a competitive advantage but a fundamental necessity for modern legal practice. It empowers legal professionals to reallocate their valuable expertise from administrative burdens to substantive legal analysis and client counseling. As the demand for efficient and cost-effective legal services continues to grow, firms that leverage the best legal document automation software will be better positioned to scale their operations, manage risk effectively, and provide superior client experiences, truly transforming how legal work is done.
Automation platforms are becoming increasingly more sophisticated with the introduction of AI agents. In fact, even incumbent robotic process automation (RPA) tools are seeking to pivot toward an agentic automation offering through product development and strategic acquisitions.
Speed and scalability often determine competitive advantage for automation platforms, and the ability to run parallel processing is a cornerstone of both. This blog explores run parallelization, parallel or concurrent processing in automation, and its implications on the enterprise.
Parallelization is a foundational requirement for modern computing, enabling software to run multiple tasks at the same time, or in parallel, to avoid persistent bottlenecks. Parallel computing allows each step of a process to execute at the same time, rather than sequentially. This diagram from Lawrence Livermore National Laboratory breaks it down:
In this example, rather than running each instruction one after the other, the instructions are divided into smaller parts that are run concurrently using multiple processors. This concept of parallelization has not effectively been applied to traditional forms of automation including robotic process automation (RPA). Kognitos is now bringing the benefits of parallel processing to automation.
Kognitos is redefining enterprise automation with its parallel processing capabilities. For example, take the common use case of invoice processing. In a traditional RPA environment with a single bot processing invoices, they are run sequentially. For this example, let’s assume that at month-end, there are 100 invoices to process. Each invoice takes 5 minutes to complete. Simple math would dictate that not all of these invoices could be processed in an 8-hour workday.
100 invoices x 5 minutes per invoice = 500 minutes or 8 hours and 20 minutes
Kognitos, on the other hand, is capable of not only breaking down complex documents into smaller, more manageable snippets (parallelization), but also running up to 5,000 processes concurrently with little to no latency increase.
Let’s illustrate this with a concrete scenario similar to the RPA invoice example above. In this instance, month-end invoice processing consists of 100 invoices that have been combined into a single PDF. The Kognitos platform is capable of first breaking down the PDF into 100 separate invoices, then running all of those invoices concurrently. In this example, for comparison’s sake, let’s assume that it also takes the Kognitos platform 5 minutes to process an invoice.
100 invoices x 5 minutes per invoice running concurrently = 5 minutes
Not only do processes run concurrently, parallelization is built into the platform, allowing each stage of the hyperautomation lifecycle to run at the same time. So, while Kognitos is auto-writing new automations, it is simultaneously auto-debugging that same workflow, while also auto-monitoring performance and health of all automations. This is achieved through a serverless infrastructure that dynamically allocates resources without expensive bots.
Parallelization and concurrent processing should be table stakes for AI automation, but has remained a sore spot for RPA and similar traditional automation tools like intelligent document processing (IDP).
Kognitos’ ability to run concurrent processes and incorporate parallelization directly into the platform is a paradigm shift for organizations seeking an enterprise-scale AI automation solution. If you’re looking for an efficient and scalable automation solution, reach out to the Kognitos team today, or sign up for our community version to try it out for yourself.
