Workflow automation promises substantial productivity gains in everyday document-related tasks. While prior agentic systems can execute isolated instructions, they struggle with automating multi-step, session-level workflows due to limited control over the operational process. To this end, we introduce AutoDW, a novel execution framework that enables stepwise, rollback-enabled operation orchestration. AutoDW incrementally plans API actions conditioned on user instructions, intent-filtered API candidates, and the evolving states of the document. It further employs robust rollback mechanisms at both the argument and API levels, enabling dynamic correction and fault tolerance. These designs together ensure that the execution trajectory of AutoDW remains aligned with user intent and document context across long-horizon workflows. To assess its effectiveness, we construct a comprehensive benchmark of 250 sessions and 1,708 human-annotated instructions, reflecting realistic document processing scenarios with interdependent instructions. AutoDW achieves 90% and 62% completion rates on instruction- and session-level tasks, respectively, outperforming strong baselines by 40% and 76%. Moreover, AutoDW also remains robust for the decision of backbone LLMs and on tasks with varying difficulty. Code and data will be open-sourced. Code: https://github.com/YJett/AutoDW

Automating enterprise workflows could unlock $4 trillion/year in productivity gains. Despite being of interest to the data management community for decades, the ultimate vision of end-to-end workflow automation has remained elusive. Current solutions rely on process mining and robotic process automation (RPA), in which a bot is hard-coded to follow a set of predefined rules for completing a workflow. Through case studies of a hospital and large B2B enterprise, we find that the adoption of RPA has been inhibited by high set-up costs (12-18 months), unreliable execution (60% initial accuracy), and burdensome maintenance (requiring multiple FTEs). Multimodal foundation models (FMs) such as GPT-4 offer a promising new approach for end-to-end workflow automation given their generalized reasoning and planning abilities. To study these capabilities we propose ECLAIR, a system to automate enterprise workflows with minimal human supervision. We conduct initial experiments showing that multimodal FMs can address the limitations of traditional RPA with (1) near-human-level understanding of workflows (93% accuracy on a workflow understanding task) and (2) instant set-up with minimal technical barrier (based solely on a natural language description of a workflow, ECLAIR achieves end-to-end completion rates of 40%). We identify human-AI collaboration, validation, and self-improvement as open challenges, and suggest ways they can be solved with data management techniques. Code is available at: https://github.com/HazyResearch/eclair-agents

The pandemic has seen accelerated interest in process automation as organizations have scrambled to overhaul business processes and double down on digital transformations in response to disruptions brought about by COVID-19. And for IT leaders stepping into or already steeped in such modernization efforts, artificial intelligence -- mainly in the form of machine learning -- holds the promise to revolutionize automation, pushing them closer to their end-to-end process automation dreams. But for now, AI-powered process automation remains a piecemeal approach, in which AI is involved in individual tasks but not across the entire process chain. Regardless of how vendor's spin it, fully intelligent automation has not yet arrived -- but organizations working to fill the gaps are finding innovative ways to this promising concept closer into being. A typical use case for AI in automation includes the following: instead of requiring someone to manually re-key information from a PDF into a form, an AI is trained to do it for them.

AI also known as Artificial Intelligence is concerned with building smart systems and machines that can learn and perform activities on their own like humans. It also includes learning, planning, speech recognition and problem-solving activities also. Nowadays Artificial Technology has a wide range of uses in business. AI tools can be used for increasing the efficiency of the business by handling administrative tasks as well as customer engagement and broadening the customer base. AI basically processes and analyzes valuable data more quickly than a human brain could do.

Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, iii) delineates tumor tissue subtypes using convolutional neural networks, and iv) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists. Following the implementation of the framework in Docker containers, it was applied to two retrospective glioma datasets collected from the Washington University School of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30) comprising preoperative MRI scans from patients with pathologically confirmed gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA datasets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor segmentation for WUSM and MDA, respectively. This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology datasets and demonstrating a high potential for integration as an assistive tool in clinical practice.

Canopy, the leading cloud-based practice management platform for accounting professionals, announced the availability of new automation enhancements to its Workflow software. Canopy's Workflow automation helps firms streamline the delivery of accounting services by reducing manual, repetitive processes leading to increased efficiency and accuracy. "As accounting firms grow, they experience more complex workflows due to more clients, more internal staff, more projects, and ever-changing regulations. Managing these complexities can be time-consuming, but these new features make that experience significantly easier for firms" By incorporating automation across accounting firms' workflows, professionals have better visibility of their processes, optimize their time to get more done, and are able to use the time saved to focus on higher-value and more profitable efforts. The robust capabilities of Workflow automation are also extremely effective as a holistic practice management solution, working across the firm to ensure information is retained and the user experience is simplified.

Workflow automation is taking over, and it's no wonder why. Modern workflows are full of tedious and repetitive tasks that take time but not necessarily brain power. Automating those workflows can help employees focus on what's most important. The COVID-19 pandemic pushed even more companies toward workflow automation, transforming both their workforce and bottom line. Check out these 40 workflow automation statistics & trends to see why automation is the future of work.

This article was contributed by Sergio Suarez Jr., founder and CEO of TackleAI. Workflow automation, also called data automation, is the new buzzword in the tech industry, but what is it, really? To put it simply, workflow automation means getting information into a system, launching tasks to process the data, and routing it to the correct people. This process is done using rule-based logic, and ideally with little to no human intervention necessary. For some people, a world of automation and artificial intelligence can be overwhelming or even lead to job security concerns.

Cognitive applications run mission-critical business systems in a continuous, self-driving, self-learning, auto-compliant, self-securing, and self-healing approach. These AI driven systems intelligently automate transactional systems and processes such as campaign to lead, order to cash, procure to pay, incident to resolution, concept to market, and hire to retire. The goal of an autonomous enterprise is to continuously automate precision decisions at scale. Why? Transactional applications have run their course. Pressure to reduce margins, technical debt, and investment in core systems create tremendous pressure for automation.

Businesses that have automated important functions are often the ones that experience the most growth and the most success - as well as having the happiest customers. This may have been true for some time, but as we enter an age of digital transformation in 2020 and beyond, it gets even more valuable. As your business automates more, then business runs smoother and is hopefully able reach more customers, as technology becomes an enabler of automation instead of a constant inhibitor or roadblock to success. Workflow automation can adjust and adapt to technology trends as opposed to resisting the changes and improvements - so here's our guide to the technology. Before explaining how workflow automation is changing and evolving, and how it is impacting every aspect of a company, here's a short review of how it works.