Using enterprise intelligent automation for cognitive tasks

By enabling the software bot to handle this common manual task, the accounting team can spend more time analyzing vendor payments and possibly identifying areas to improve the company’s cash flow. Automated process bots are great for handling the kind of reporting tasks that tend to fall between departments. Turning to the future, SaaS revenue is projected to increase at 13.7% annually through 2030, and cloud computing revenue is projected to grow at 14.1% annually during the same period. That gives Microsoft a good shot at low-double-digit sales growth through the end of the decade. Indeed, Wall Street analysts expect the company to grow sales at 14% annually over the next five years. Automation serves as a catalyst for technological progress, inspiring innovation and the evolution of cutting-edge technologies.

Patient investors should consider buying a small position in Microsoft today, whether or not the company splits its stock in the near future. Microsoft reported better-than-expected financial results in the second quarter of fiscal 2024 (ended Dec. 31, 2023). Revenue rose 18% year over year to $62 billion on particularly strong momentum in cloud computing. The most obvious reason is they reduce a company’s share price, making the stock more accessible. To elaborate, forward stock splits are only necessary after substantial share price appreciation, which rarely happens to mediocre businesses.

“As automation becomes even more intelligent and sophisticated, the pace and complexity of automation deployments will accelerate,” predicted Prince Kohli, CTO at Automation Anywhere, a leading RPA vendor. The scope of automation is constantly evolving—and with it, the structures of organizations. Cognitive computing systems become intelligent enough to reason and react without needing pre-written instructions. Once implemented, the solution aids in maintaining a record of the equipment and stock condition.

An example would be robotizing the daily task of a purchasing agent who obtains pricing information from a supplier’s website. “A human traditionally had to make the decision or execute the request, but now the software is mimicking the human decision-making activity,” Knisley said. “Cognitive automation, however, unlocks many of these constraints by being able to more fully automate and integrate across an entire value chain, and in doing so broaden the value realization that can be achieved,” Matcher said. “Cognitive automation multiplies the value delivered by traditional automation, with little additional, and perhaps in some cases, a lower, cost,” said Jerry Cuomo, IBM fellow, vice president and CTO at IBM Automation. CIOs should consider how different flavors of AI can synergize to increase the value of different types of automation.

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That will mark a monumental step forward for AI and robotics in the future. Now, AI and robotics are about to witness another giant leap forward with the brand-new concept of self-replicating, “alive” robots known as xenobots. Data mining and NLP techniques are used to extract policy data and impacts of policy changes to make automated decisions regarding policy changes. Levity is a tool that allows you to train AI models on images, documents, and text data.

Various combinations of artificial intelligence (AI) with process automation capabilities are referred to as cognitive automation to improve business outcomes. The company implemented a cognitive automation application based on established global standards to automate categorization at the local level. The incoming data from retailers and vendors, which consisted of multiple formats such as text and images, are now processed using cognitive automation capabilities. The local datasets are matched with global standards to create a new set of clean, structured data.

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Research from Accenture for the retail banking sector indicates that personalization efforts for customers with the help of cognitive automation tools can increase revenue by 6%. While powerful, cognitive automation, like most artificial intelligence, has limitations and challenges. As the founder of a document processing startup, I’m thrilled by the potential it creates, but I also feel a responsibility to address its risks. Ability to analyze large datasets quickly, cognitive automation provides valuable insights, empowering businesses to make data-driven decisions.

For example, in an accounts payable workflow, cognitive automation could transform PDF documents into machine-readable structure data that would then be handed to RPA to perform rules-based data input into the ERP. Cognitive automation expands the number of tasks that RPA can accomplish, which is good. However, it also increases the complexity of the technology used to perform those tasks, which is bad, argued Chris Nicholson, CEO of Pathmind, a company applying AI to industrial operations. RPA has been around for over 20 years and the technology is generally based on use cases where data is structured, such as entering repetitive information into an ERP when processing invoices. While they are both important technologies, there are some fundamental differences in how they work, what they can do and how CIOs need to plan for their implementation within their organization. “Ultimately, cognitive automation will morph into more automated decisioning as the technology is proven and tested,” Knisley said.

Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO.

Additionally, AI-powered diagnostic tools such as Aidoc’s platform for radiology analyze medical images to identify abnormalities efficiently, aiding radiologists in accurate diagnoses. These automation variations showcase technology’s impact on various sectors, refining operations and spearheading advancements in various facets of our lives and industries. Cognitive automation can act as a shield against compliance risks, which has recently become a huge factor. It enables quick and accurate analysis of vast data by identifying patterns and anomalies within the datasets across industries. There may be a thousand different ways in which procreating robots will impact various sectors. Most importantly, the “living and thinking” nature of this application brings it closer to AGI.

Cognitive automation uses technologies like OCR to enable automation so the processor can supervise and take decisions based on extracted and persisted information. Karev said it’s important to develop a clear ownership strategy with various stakeholders agreeing on the project goals and tactics. For example, if there is a new business opportunity on the table, both the marketing and operations teams should align on its scope. They should also agree on whether the cognitive automation tool should empower agents to focus more on proactively upselling or speeding up average handling time.

RPA is best for straight through processing activities that follow a more deterministic logic. In contrast, cognitive automation excels at automating more complex and less rules-based tasks. RPA is a simple technology that completes repetitive actions from structured digital data inputs.

Cognitive automation performs advanced, complex tasks with its ability to read and understand unstructured data. It has the potential to improve organizations’ productivity by handling repetitive or time-intensive tasks and freeing up your human workforce to focus on more strategic activities. Cognitive automation has a place in most technologies built in the cloud, said John Samuel, executive vice president at CGS, an applications, enterprise learning and business process outsourcing company.

Businesses are increasingly adopting cognitive automation as the next level in process automation. These six use cases show how the technology is making its mark in the enterprise. Automation drives innovation by facilitating the creation of novel technologies and methodologies. Businesses that adopt automation gain a competitive advantage by becoming more adaptable, agile, and inventive. Consider the retail sector, where implementing automated inventory management systems allows companies to innovate in their supply chain strategies, adapting swiftly to changing market demands and customer preferences.

IoT integration enhances connectivity and real-time data exchange, improving efficiency and enabling predictive maintenance across industries. In industries such as marketing, companies use automated systems to analyze consumer behavior and preferences based on data collected from various sources. This data-driven automation helps target specific audiences with personalized advertisements or recommendations, enhancing the overall customer experience. Companies looking for automation functionality will likely consider both Robotic Process Automation (RPA) and cognitive automation systems. While both traditional RPA and cognitive automation provide smart and efficient process automation tools, there are many differences in scope, methodology, processing capabilities, and overall benefits for the business. In domotics, cognitive automation brings innovation in the form of smart kitchens, pervasive computing for elder care and autonomous smart cleaners.

While basic tasks can be automated using RPA, subsequent tasks require context, judgment and an ability to learn. Cognitive automation can use AI techniques in places where document processing, vision, natural language and sound are required, taking automation to the next level. These skills, tools and processes can make more types of unstructured data available in structured format, which enables more complex decision-making, reasoning and predictive analytics.

Still, the enterprise requires humans to choose and apply automation techniques to specific tasks — for now. One area currently under development is the ability for machines to autonomously discover and optimize processes within the enterprise. Some automation tools have started to combine automation and cognitive technologies to figure out how processes are configured or actually operating. And they are automatically able to suggest and modify processes to improve overall flow, learn from itself to figure out better ways to handle process flow and conduct automatic orchestration of multiple bots to optimize processes.

“One of the biggest challenges for organizations that have embarked on automation initiatives and want to expand their automation and digitalization footprint is knowing what their processes are,” Kohli said. Aera releases the full power of intelligent data within the modern enterprise, augmenting business operations while keeping employee skills, knowledge, and legacy expertise intact and more valuable than ever in a new digital era. Most importantly, this platform must be connected outside and in, must operate in real-time, and be fully autonomous.

This assists in resolving more difficult issues and gaining valuable insights from complicated data. TalkTalk received a solution from Splunk that enables the cognitive solution to manage the entire backend, giving customers access to an immediate resolution to their issues. Identifying and disclosing any network difficulties has helped TalkTalk enhance its network. As a result, they have greatly decreased the frequency of major incidents and increased uptime. The issues faced by Postnord were addressed, and to some extent, reduced, by Digitate‘s ignio AIOps Cognitive automation solution. Deliveries that are delayed are the worst thing that can happen to a logistics operations unit.

It ignites advancements in fields such as healthcare, where automated diagnostic tools and AI-powered medical imaging have revolutionized patient care and treatment precision. This perpetual innovation cycle has propelled industries, enhancing their competitive edge and fostering continual development in various sectors. Automation fundamentally alters task completion methods, removing manual stages and integrating advanced technologies to enhance performance. This transformation profoundly impacts various industries, from manufacturing to healthcare and beyond. KlearStack is an AI-based platform that achieves intelligent data extraction from unstructured documents.

You can also learn about other innovations in RPA such as no code RPA from our future of RPA article. Most RPA companies have been investing in various ways to build cognitive capabilities but cognitive capabilities of different tools vary of course. The ideal way would be to test the RPA tool to be procured against the cognitive capabilities required by the process you will automate in your company.

This approach led to 98.5% accuracy in product categorization and reduced manual efforts by 80%. The biggest challenge is that cognitive automation requires customization and integration work specific to each enterprise. This is less of an issue when cognitive automation services are only used for straightforward tasks like using OCR and machine vision to automatically interpret an invoice’s text and structure.

New insights could be revealed thanks to cognitive computing’s capacity to take in various data properties and grasp, analyze, and learn from them. These prospective answers could be essential in various fields, particularly life science and healthcare, which desperately need quick, radical innovation. It now has a new set of capabilities above RPA, thanks to the addition of AI and ML.

In this article, we explore RPA tools in terms of cognitive abilities, what makes them cognitively capable, and which RPA vendors provide such tools. This article dispels fear and provides tools to control AI-enabled automation. The concept alone is good to know but as in many cases, the proof is in the pudding. The next step is, therefore, to determine the ideal cognitive automation approach and thoroughly evaluate the chosen solution. As mentioned above, cognitive automation is fueled through the use of Machine Learning and its subfield Deep Learning in particular.

Get stock recommendations, portfolio guidance, and more from The Motley Fool’s premium services. Further advancements in AI and robotics will bring operations such as the two listed above closer to reality from its current concept stage. We won’t go much deeper into the technicalities of Machine Learning here but if you are new to the subject and want to dive into the matter, have a look at our beginner’s guide to how machines learn. RPA is taught to perform a specific task following rudimentary rules that are blindly executed for as long as the surrounding system remains unchanged.

Cognitive automation works by combining the power of artificial intelligence (AI) and automation to enable systems to perform tasks that typically require human intelligence. This technology uses algorithms to interpret information, make decisions, and execute actions to improve efficiency in various business processes. Moving up the ladder of enterprise intelligent automation can help companies performing increasingly more complex tasks that don’t always follow the same pattern or flow. Dealing with unstructured data and inputs, fixing and validating data as necessary for context or virtual assistants to help with process development all require more cognitive ability from automation systems.

A Four-Part Framework for Explaining The Power of Intelligent Automation

Cognitive automation, or IA, combines artificial intelligence with robotic process automation to deploy intelligent digital workers that streamline workflows and automate tasks. It can also include other automation approaches such as machine learning (ML) and natural language processing (NLP) to read and analyze data in different formats. Robotic process automation involves using software robots, or ‘bots’, to automate repetitive, rule-based tasks traditionally performed by humans. These bots mimic human actions by interacting with digital systems and performing tasks such as data entry, form filling, and data extraction. For instance, in finance, RPA is used to automate invoice processing, reducing errors and speeding up the workflow. Companies such as ‘UiPath’ and ‘Automation Anywhere’ offer RPA solutions that are widely adopted across industries.

And without making it overly technical, we find that a basic knowledge of fundamental concepts is important to understand what can be achieved through such applications. RPA usage has primarily focused on the manual activities of processes and was largely used to drive a degree of process efficiency and reduction of routine manual processing. CIOs also need to address different considerations when working with each of the technologies.

It can carry out various tasks, including determining the cause of a problem, resolving it on its own, and learning how to remedy it. The coolest thing is that as new data is added to a cognitive system, the system can make more and more connections. This allows cognitive automation systems to keep learning unsupervised, and constantly adjusting to the new information they are being fed. Cognitive automation tools are relatively new, but experts say they offer a substantial upgrade over earlier generations of automation software.

As CIOs embrace more automation tools like RPA, they should also consider utilizing cognitive automation for higher-level tasks to further improve business processes. In addition to simple process bots, companies implementing conversational agents such as chatbots further automate processes, including appointments, reminders, inquiries and calls from customers, suppliers, employees and other parties. In its most basic form, machine learning encompasses the ability of machines to learn from data and apply that learning to solve new problems it hasn’t seen yet. Supervised learning is a particular approach of machine learning that learns from well-labeled examples. Companies are using supervised machine learning approaches to teach machines how processes operate in a way that lets intelligent bots learn complete human tasks instead of just being programmed to follow a series of steps. This has resulted in more tasks being available for automation and major business efficiency gains.

Automated systems swiftly respond to shifts in requirements and can efficiently expand operations. You can foun additiona information about ai customer service and artificial intelligence and NLP. Take the hospitality industry, for example, where automated booking systems dynamically adjust room availability and services based on demand fluctuations, streamlining guest experiences and optimizing resources. This adaptability empowers businesses to manage surges in demand or changes in workload without heavy reliance on manual adjustments. It accelerates operations, enabling businesses to achieve greater results in shorter periods. When routine tasks are automated, efficiency soars, leading to boosted productivity. Consider how automation in logistics expedites order processing, allowing for quicker deliveries without sacrificing accuracy.

Meanwhile, Microsoft is gaining market share in cloud computing due in part to strength in artificial intelligence infrastructure and machine learning services. Microsoft Azure accounted for 24% of cloud infrastructure and platform services spending in the fourth quarter, up two percentage points from the prior year. Microsoft is well positioned to maintain that momentum due to its exclusive partnership with OpenAI, which lets Azure clients use models like GPT-4, the cognitive engine that powers ChatGPT Plus, to build custom applications. Inefficient workflows within an organization can bring about delayed payments, document frauds, dataset oversights, time-consuming decision-making processes and more. Cognitive automation leverages natural language processing, computer vision and machine learning algorithms to mimic human cognition. Cognitive automation creates new efficiencies and improves the quality of business at the same time.

“The governance of cognitive automation systems is different, and CIOs need to consequently pay closer attention to how workflows are adapted,” said Jean-François Gagné, co-founder and CEO of Element AI. While technologies have shown strong gains in terms of productivity and efficiency, “CIO was to look way beyond this,” said Tom Taulli author of The Robotic Process Automation Handbook. Cognitive automation will enable them to get more time savings and cost efficiencies from automation. He observed that traditional automation has a limited scope of the types of tasks that it can automate.

It ensures compliance with industry standards, and providing a reliable framework for handling sensitive data, fostering a sense of security among stakeholders. This will involve several tiny robots working to carry products into packaging, transport or other functional lines in a multi-way assembly line. Packages can be directed anywhere within a given assembly line just by the swarm intelligence tools aligning with each other in specific ways. This application will be further optimized by xenobots’ self-replication abilities—allowing the robots that have broken down to be replaced in real-time and keep the assembly line in the factory running continually.

If any are found, it simply adds the issue to the queue for human resolution. The cognitive solution can tackle it independently if it’s a software problem. If not, it alerts a human to address the mechanical problem as soon as possible to minimize downtime. The automation solution also foresees the length of the delay and other follow-on effects.

Change used to occur on a scale of decades, with technology catching up to support industry shifts and market demands. When it comes to automation, tasks performed by simple workflow automation bots are fastest when those tasks can be carried out in a repetitive format. Processes that follow a simple flow and set of rules are most effective for yielding immediately effective results with nonintelligent bots. For example, employees who spend hours every day moving files or copying and pasting data from one source to another will find significant value from task automation.

According to experts, cognitive automation is the second group of tasks where machines may pick up knowledge and make decisions independently or with people’s assistance. For instance, Religare, a well-known health insurance provider, automated its customer service using a chatbot powered by NLP and saved over 80% of its FTEs. The organization can use chatbots to carry out procedures like policy renewal, customer query ticket administration, resolving general customer inquiries at scale, etc. Cognitive automation represents a range of strategies that enhance automation’s ability to gather data, make decisions, and scale automation. It also suggests how AI and automation capabilities may be packaged for best practices documentation, reuse, or inclusion in an app store for AI services. IBM Consulting’s extreme automation consulting services enable enterprises to move beyond simple task automations to handling high-profile, customer-facing and revenue-producing processes with built-in adoption and scale.

Consider the entertainment industry, where automated content recommendation systems swiftly adapt to viewers’ preferences, positioning these companies as pioneers in delivering personalized experiences. This adaptability not only ensures responsiveness but also solidifies their leadership in their respective sectors. Automation profoundly influences economic expansion by bolstering productivity and operational efficiency. It actively contributes to a nation’s GDP growth by fine-tuning resource utilization and refining processes. Consider the tech sector, where automation in software development streamlines workflows, expedites product launches and drives market innovation.

• It must also be able to complete its functions with minimal-to-no human intervention on any level.
• You can also check our article on intelligent automation in finance and accounting for more examples.
• Rather than call our intelligent software robot (bot) product an AI-based solution, we say it is built around cognitive computing theories.
• In a bid to save time and minimize human error, such applications were used by businesses and individuals to automate the tasks that, according to organizations, employees didn’t need to waste their energy on.
• Get stock recommendations, portfolio guidance, and more from The Motley Fool’s premium services.
• “Cognitive automation is not just a different name for intelligent automation and hyper-automation,” said Amardeep Modi, practice director at Everest Group, a technology analysis firm.

Once the life-cycle of a xenobot’s cells is over, they can die like other normal cells. While chatbots are gaining popularity, their impact is limited by how deeply integrated they are into your company’s systems. For example, if they are not integrated into the legacy billing system, a customer will not be able to change her billing period through the chatbot. cognitive automation allows building chatbots that can make changes in other systems with ease. Realizing that they can not build every cognitive solution, top RPA companies are investing in encouraging developers to contribute to their marketplaces where a variety of cognitive solutions from different vendors can be purchased.