Cognitive software: The next standard for supply chain tech

How to Break Down Work into Tasks That Can Be Automated

The foundation of hyper automation is low-code and no-code platforms, which enable non-technical users to create and implement automation workflows without knowing about coding. The platforms democratise automation by allowing more employees to participate in digital transformation projects. Process discovery tools, enhanced by AI and ML, are becoming increasingly sophisticated, permitting companies to map and optimise workflows with minimum human input. AI-powered procedure mining can perceive inefficiencies and endorse optimisations using seamless automation.

• Businesses are increasingly adopting cognitive automation as the next level in process automation.
• Utilizing both in the areas to which they are most suited can exponentially improve businesses.
• Let’s use the automobile industry example of autonomous vehicles as a not-perfect, but suitable parallel for what we’re trying to accomplish here.
• Moreover, AutomationEdge’s data analytics and insight capabilities provide organizations with real-time data insights into their processes.

Once deployed in this type of scenario, Aera predicts variability, market conditions and customer patterns and makes real-time recommendations to the plan such as inventory adjustments, new product introductions, safety stock etc. based on insights derived. Although we are in the infancy of cognitive technologies, it is clear that new capabilities will emerge and compound upon one another, as they did through the information communication boom. It is clear that the future of these systems lies coupled with other emergent technology such as Big Data and cloud computing solutions. Businesses able to utilise these systems in a cooperative space will gain the most value from investments into cognitive technology. Cognitive technologies can be often applied in scenarios where the business engages with customers or end-users.

Prior to joining Deloitte, Hupfer worked for more than 20 years in the technology industry, in roles that included software research and development, strategy consulting, and thought leadership. She has a Bachelor of Science in mathematics and computer science from Trinity College and a Ph.D. in computer science from Yale University. To meet their AI aspirations, companies will likely need the right mix of talent to translate business needs into solution requirements, build and deploy AI systems, integrate AI into processes, and interpret results.

Coupled with this is broader education on AI and helping debunk some of the persistent myths many employees have. Accounts payable (AP) is one of those functions that can be easy to avoid thinking about until you must. It’s also a function that can benefit from the application of automation in some of the most significant ways, ultimately saving on costs and time, which can have a major impact on your business’s bottom line. Ultimately, companies should realize that while RPA can be a costly investment, it’s an investment that should pay itself back.

How Does Intelligent Automation Work?

Founded in 2015, AntWorks has advanced across AI, Machine Learning and NLP technologies to support customers in their work. AntWorks has won awards for its progress, including ‘Intelligent Automation Platforms 2019’, where business consulting company NelsonHall lauded AntWorks’ technology as ‘cutting-edge’ and among the most ‘intriguing competitors’ in cognitive automation. Intelligent automation combines AI and automation technologies to streamline business processes, allowingsystems to make decisions and adapt to new scenarios. Here are our Top 10 companies using intelligent automation to make mundane tasks obsoletee for humans.

Feldman also highlighted Stampli’s core innovation in centralizing the accounts payable process. He explained, “We took the invoice and turned it into a landing page, a central hub where all conversations, documents, and approvals happen in one place.” This approach, according to Feldman, simplifies what has historically been a highly fragmented workflow. Other PO matching tools rely on proximity algorithms to flag simple matches, but these systems achieve success rates of just 20-40%, according to Stampli’s estimates.

With the help of more advanced AI technologies, computers can process vast amounts of information that would prove an impossible task for a human. One of the largest failings, in our estimation, is that organizations aren’t spending the time necessary to deeply understand the work they’re considering automating. They aren’t deconstructing jobs so the specific tasks that can be automated can be identified. And without deconstruction, companies risk significant collateral damage and minimizing their ROI as they attempt to automate entire jobs. Although automation offers a lot of benefits, that doesn’t mean there aren’t a few “gotchas” to be aware of. One of the biggest challenges when it comes to automation in AP is also one of the biggest challenges for automation overall, and that’s making the cultural shift.

The goal is not to replace human experts but to free up their time for the kinds of strategic and nuanced activities that help grow the business. Intelligent automation is a combination of integration, process automation, AI services, and RPA technologies that work together to execute repetitive tasks and augment human decision-making. Intelligent automation can include NLP, ML, cognitive automation, computer vision, intelligent character recognition, and process mining. One of the main ways to expand the capabilities of smart cognitive communication tools is by integrating with chatbots.

Unstructured information processing is barely Level 1 autonomous process capability (see below for more understanding on the levels of process autonomy). “To do it on, a massive scale with 1.2 billion rows of transactional data per day per customer, to handle very complex models and do it in real time,” that’s new. Another challenge is a lack of proper planning, and this is one of the primary reasons automation implementations fail. You can’t just decide to implement automation overnight and expect a radical transformation that fits your needs.

Considerations for AI leaders

SRE.ai addresses this issue by offering a unified platform that integrates seamlessly with standard CI/CD tools like GitHub Actions, reducing the need for additional infrastructure. Kadiyala noted that there’s no need for a separately managed CI/CD server; the platform integrates directly into existing systems, streamlining the process and eliminating redundancy. To accelerate the journey to scale and gain a lasting advantage, organizations must elevate automation across functions and beyond IT as a strategic, board-level priority—a core enabler of an adaptive, future-fit operating model. Everest Group, a leading research and consulting firm, has named Cognizant a Leader in its Intelligent Process Automation (IPA) PEAK Matrix® Assessment for 2023. The assessment evaluated 27 IPA solution providers based on their vision, capabilities, market impact, and value delivered to clients. Once an organization has introduced AI and automation to a process, it should let any time gains and increases in performance be key factors in objectively determining whether the project was a success.

With the shared services and business process outsourcing industry maturing, clients are demanding… However, with the increasing requirement for cognitive forms of automation, vendors are listening and starting to add more aspects of intelligence to their suites, especially the so-called robotic process automation (RPA) vendors. Most of the leading RPA vendors have added unstructured text, image, and in some cases, audio processing.

I believe this represents a major “growing up” of people analytics, moving beyond a “science project” to a focus on operational improvement and focus on giving the business the broad and deep people data managers need to make decisions on a regular basis. The race to the cloud we wrote about several years ago continues to move forward, but as cloud-based HR platforms become more prevalent, companies now realize they need many more applications and a focus on productivity, not “HR” to drive value. We believe the coming year will mark an entirely new identity for HR, refocusing the function on employee productivity, performance, wellness, and engagement, instrumented with data like never before. Just as we focus on the end-to-end customer experience, we must do the same for employees.

These flows can include a series of actions that can perform tasks such as updating data in a database or creating new records in CRM systems. The service includes a wide range of built-in connectors and templates, making connecting to different systems easier. UiPath can help you automate processes with drag-and-drop artificial intelligence and pre-built templates. Additionally, it offers pluggable integration with Active Directory, OAuth, CyberArk, and Azure Key vault and also complies with regulatory standards such as SOC 2 Type 2, ISO 9001, ISO/IEC 27001, and Veracode Verified. As the demand for RPA continues to soar, numerous RPA companies have entered the market, offering their unique blend of AI and software robotics expertise and solutions. With their innovative approaches and proven track records, these companies have set the bar high for RPA excellence.

View All General Business

On the other hand, Natural Language Generation (NLG) technologies convert structured data inside computer systems such as financial reports to a more human readable form, reducing the cognitive load for the user. Speech recognition and Speech Synthesis (Text-to-speech) technologies enhance this further with the ability to communicate verbally. Peritus develops tools for IT operations that automate support delivery and problem resolution, including incident categorization, assignments, and much more. The company, which was founded in 2005, offers RPA solutions that allow customers to automatically log in to a website, extract data from several web pages, and then change it according to their preferences. Finally, you need to understand the business purpose — what you’re trying to accomplish with RPA.

Cognitive automation tools such as employee onboarding bots can help by taking care of many required tasks in a fast, efficient, predictable and error-free manner. This can include automatically creating computer credentials and Slack logins, enrolling new hires into trainings based on their department and scheduling recurring meetings with their managers all before they sit at their desk for the first time. Instead of focusing on complete workflows, organizations can start by optimizing a particular section of a workflow with the maximum data leakage and drop-offs to create an impact. These organizations can also consider low-code or no-code platforms that allow users to create applications with minimal coding, accelerating application development and can be cost-effective. As cognitive automation learns from the data and improves its performance over time, this becomes the go-to option for companies with ever-changing requirements. With all the clutter, getting out of the maze of unstructured data and outdated software seemed impossible back then.

We deploy our deep talent—data scientists, data architects, business and domain specialists who bring a wealth of business-specific knowledge, visualization and design specialists, and of course technology and application engineers—all over the world, at scale. Companies may believe that seeking the best external talent will provide an advantage, but they shouldn’t overlook the option of training their existing employees. Indeed, notwithstanding their desire to replace workers, AI adopters also report training their current workforces to strengthen expertise and narrow their skills gap. The majority are training developers to create AI solutions, IT staff to deploy those solutions, and employees to use AI in their jobs (figure 8).

NLP enables machine translation, enabling systems to automatically translate text from one language to another. Language translation capabilities in cognitive process automation can support multilingual customer interactions, offer real-time translation services, or improve communication in international business operations. Hyper automation continues to trend, with intelligent, autonomous, and scalable business processes more common.

How intelligent automation will change the way we work

Cognitive automation (CA) is a set of technologies and tools that can take business capabilities to a new level by enhancing the functions and accuracy of business processes that rely on ever increasing data loads. These tasks can range from answering complex customer queries to extracting pertinent information from document scans. Some examples of mature cognitive automation use cases include intelligent document processing and intelligent virtual agents. Cognitive automation is a win-win situation for many companies looking to elevate customer experiences and team collaboration. 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%.

Aera continuously improves decision-making, accuracy, automation and augmentation over time. Industry watchers predict that intelligent automation will usher in a workplace where AI not only frees up human workers’ time for more creative work but also helps them set strategies and drive innovation. Most companies are not fully there yet but do have numerous opportunities for business process automation throughout the organization.

In any organization, documentation can be an overwhelming and time-consuming process. This problem statement keeps evolving as companies scale and expand their operations. Hence, the ability to swiftly extract, categorize and analyze data from a voluminous dataset with the same or even a smaller team is a game-changer for many.

Microsoft outperforms Amazon and Google in cloud AI

And the Aera Promotions Skill is agile enough to enable the business to tune the target objective to the needs of the business and lifecycle of the product- from margin optimization, to cost optimization, to revenue optimization and several others. And the tight integration of promotions optimization and execution gives the business data-driven results on performance to use for future marketing strategies and budgeting. As a company’s supply chain expands in size and complexity, efficient planning and execution becomes crucial to their success. Disruptions or delays in production or product deliveries need to be detected and resolved quickly to mitigate any negative impact to the business and their customers.

• This empowers users to customize their RPA processes efficiently, regardless of their technical background.
• NICE this week announced a new framework for integration with cognitive software vendors, enabling organisations to take their customer self-service channels and process automation capabilities to new heights.
• With such systems in place, employees can dedicate the rest of their time to other work that will provide better value to the organisation.
• There are also integration issues, security risks and change management challenges.

Their software, used by freight forwarders, terminals, 3PLs, end manufacturers and retailers, allows them to predict the movement of goods, rather than guessing or approximation. “There’s a lot of manual processes in predicting the time of the ocean carrier’s arrival,” said Bryan Nella, director of marketing at ClearMetal. “Once it learns over time, the level of autonomy will increase. You need the algorithm every step of the way. It gets intelligent data, indexes it, applies the algorithm, analyzes the impact of decisions and provides feedback in the loop,” said Laluyaux. Leveraging the company’s operating system, cognitive software like Aera can learn how the business works from the inside and the outside, making real-time recommendations, predicting business outcomes and enabling autonomous actions.

ClearMetal also says the core thing they do as a provider is data ingestion, cleaning up the customer data. They pull in the previous year’s EDI signals, correcting errors and time sequence gaps, for example, and triangulating data between parties, modes and patterns. That data is fed into the AI machine, where it predicts the path based on past actions, applying it to the future. To build their machine-learning algorithm, they rely on the significant amount of data already at the customer’s disposal, including raw orders and EDI signals. Retail and manufacturers are using ClearMetal to accurately predict inland destination and shipping times for air and ocean freight, to meet inventory demand. With better predictions of partner performance, they can reduce on-hand inventory.

Unlike existing tools, which are often burdened by legacy code and fixed workflows, SRE.ai’s infrastructure uses LLMs to abstract complex decisions. “We’re not relying on pre-configured scripts,” Aryee noted, explaining that the AI’s ability to adapt on the fly to different inputs and contexts offers a revolutionary leap in functionality. The company will use the money to further develop its cognitive automation product suite and hire across sales and marketing. In particular, we will see in this paper how the shift towards Cognitive Automation capabilities is expected to radically accelerate the transition from “people doing the work supported by machine”, to “machine doing the work guided by people”. We will also see “Cognitive Command Centers” have the potential to transform the way supply chains will be designed, operated and managed in the near future.

There’s a drive among these types of AI-centric software platforms towards giving us monitored business decision controls that can also asses their impact so that we can move onward to what is often called ‘continuous improvement’. Demand planning in today’s world is nearly impossible due to faster pace of changes and volatile variables. It’s all part of what the company likes to call the so-called Self-Driving Enterprise (yep, that comes with another ™ too). In less flamboyant terms, the means that it’s software designed to harmonize both internal and external data across the enterprise as it operates. Cognitive automation is generally used to replicate simpler mental processes and activities.

Cognitive insight technologies are capable of altering their algorithms and resultant outputs over multiple iterations (sometimes millions) without human input. Through these iterations the machine will alter its code, optimising the testing process for its next iteration. As this continues, the machine will retain successful processes, while culling failed processes. Many technologies within these categories can be adopted and utilised across almost any industry.

First, not all business processes are encoded in technology – some are purely human-to-human. Second, some of the technology processes are not truly business processes but rather reflections of the way technology systems are setup to deal with various business requirements. Since IT companies are good at handling IT-focused business processes, it makes sense then that the conversations we’ve been having about business process in the context of software tooling is from the technical perspective of business process. Robotic process automation (RPA) leverages software robots – or “bots” – to automate repetitive, rule-based tasks, allowing employees to focus on more strategic and value-added activities. Manufacturing Digital Magazine connects the leading manufacturing executives of the world’s largest brands.

Aera understands how businesses work; makes real-time recommendations; predicts outcomes; and acts autonomously. Using proprietary data crawling, industry models, machine learning and artificial intelligence, Aera is revolutionizing how people relate to data and how organizations function. Headquartered in Mountain View, California, Aera services some of the world’s largest enterprises from its global offices located in San Francisco, Bucharest, Cluj-Napoca, Paris, Munich, London, Pune, and Sydney. By injecting RPA with cognitive computing power, companies can supercharge their automation efforts, says Schatsky, who analyzes the implications of emerging technology and other business trends. By combining RPA with cognitive technologies such as machine learning, speech recognition, and natural language processing, companies can automate higher-order tasks that in the past required the perceptual and judgment capabilities of humans.

“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. A proper needs assessment enables leaders to understand whether cognitive automation can fit their organization well. It’s not an easy path, and there is no perfect solution, but I believe the benefits usually outweigh the risks.

Deloitte Belgium: cognitive technology in client supply chains – Supply Chain Digital

Deloitte Belgium: cognitive technology in client supply chains.

Posted: Mon, 18 May 2020 07:00:00 GMT [source]

The HR software vendors are catching up to this wave, and I believe this will become a major new theme for analytics going forward, as companies have better and more integrated cloud platforms. To make it even harder, the candidate experience now directly drives employment brand and reputation. Research by the Talent Board shows that almost half of job applicants hear nothing from employers for at least two months, indicating how hard it is to manage the flood of resumes companies receive.

These Natural Language Processing (NLP) capabilities are “table stakes” in the intelligence game. If your automation tool can’t process handwritten text or generate transcripts from audio, then you should get rid of that tool immediately. The capabilities for processing unstructured information are widely available from dozens of vendors.

The robotics industry has been expanding for years, and this trend will likely continue into 2020. The International Federation of Robotics predicts that more than 3 million robots will be used in factories worldwide by the end of the year. This significant increase in industrial robotics is not the only growth one can expect. Given that the collection of the data can be offloaded to a robot, which is residing on a server, organisations don’t need to worry about vacation leave, sick leave or office hours.

With automation in place, during peak seasons the system can also be scaled up in order to meet the higher volumes and scaled down again when the demand is less. The emergence of disruptive technologies such as Robotic Process Automation (RPA) and Cognitive Automation has created opportunities for organisations to tackle such challenges. With the introduction of these technologies, organisations can enhance efficiency in terms of both speed and cost reduction whilst fulfilling legal obligations and create positive effect on the business or brand. We’re also beginning to see the frontier of robotics expanding beyond the transactional in health care and radiology, for example, where doctors are now using cognitive computing to support diagnosis of illnesses. But, the potential benefits of RPA go beyond processing transactions faster and more accurately to aggregating data instantly.