AI based workflow automation Archives

Introduction

The technological spectrum is rapidly shifting towards a paradigm where AI-based automation is at the forefront. Not only does it promise operational efficiency, but it also paves the way for businesses to remain agile in a competitive landscape. Integrating AI into workflows today is not just a trend; it is an imperative step for those looking to redefine and upgrade their operational strategies. And that is why intelligent process automation (IPA) market is forecast to expand globally with a CAGR of 14.6% between 2023 and 2032.

Understanding AI-Based Automation

AI-based automation refers to systems or tools that utilize artificial intelligence (AI) to perform tasks without human intervention. Unlike traditional automation that follows predefined rules, AI-driven automation can learn, adapt, and improve over time. Machine learning held 40% of the total AI market share in 2022, for its adoption and forms the foundation of IPA. The essence of this modern automation lies in its efficiency, precision, scalability, and adaptability. It has the prowess to analyze vast data sets swiftly, making real-time decisions, scaling as per demands, and evolving through learning. This has transformed its application as just a cost-saving lever to a tool to create new revenue streams. That is why 69% of respondents in PR Newswire’s study affirmed that their organizations had at least 1 AI project in production.

Prerequisites for Implementing AI Automation

Before delving into AI automation, certain prerequisites need to be in place. A robust infrastructure comprising computing resources, pertinent software, and seamless data pipelines is essential. Moreover, the human touch can’t be ignored. A deep understanding of data science, familiarity with relevant algorithms, and domain-specific knowledge are vital. Above all, the quality and relevance of data can’t be overstated. Ensuring that the data is representative, free from biases, and appropriately preprocessed sets the stage for successful AI integration.

Steps to Implement AI-Based Automation in Your Workflow

a. Identify Automation Opportunities

Begin by mapping out tasks that are repetitive and time-consuming. With a clear view, assess which of these tasks can be automated and the potential return on investment (ROI) of doing so.

b. Choose the Right AI Tool/Platform

Numerous platforms cater to different automation needs. While most AI tools are powerhouses for deep learning, niche tools provide a more straightforward approach for those without deep technical expertise. Factors such as scalability, community support, ease-of-use, and ongoing development should guide your choice.

c. Data Collection and Preprocessing

The foundation of any AI system is data. It’s imperative to gather diverse and representative data sets. Once collected, the data should be cleaned, normalized, and split (typically into training, validation, and test sets) to pave the way for model development.

d. Model Development and Training

Based on the task at hand, select the appropriate algorithms. Train the model using the prepared data sets, adjusting hyperparameters as necessary to enhance its performance.

e. Testing and Validation

Once trained, test the model against a set of unseen data. This phase helps in understanding its real-world efficacy. Based on the outcomes, refinements can be made to further improve accuracy and reliability.

f. Deployment and Integration

Transitioning a trained model into a tool that integrates seamlessly into your workflow is crucial. Ensure that this integration is smooth, with minimal disruptions to existing processes.

g. Monitoring and Maintenance

AI models, once deployed, are not set in stone. They should be continuously monitored for performance. Feedback loops are vital to ensure the model stays updated and relevant to the changing data or environment.

Challenges in Implementing AI-Based Automation

Like any technological implementation, AI-based automation isn’t without its challenges. Here’s what a study by PR Newswire highlighted:

One significant challenge is the potential for skewed or biased data, which can lead to unfair or incorrect outcomes. Ensuring transparency and explainability in AI models is vital to build trust. Furthermore, as data is the backbone of AI, maintaining its privacy and security becomes paramount.

AI-based automation holds a transformative potential that can redefine the operational capacities of businesses across the spectrum. The future is automated, and AI is leading the way. The technology is expected to add $15.7 trillion to the global economy by 2030. Embracing AI isn’t about making a massive leap but taking systematic, informed steps. Start small, understand the nuances, iterate based on feedback, and refine your approach continuously.

In the digital age, meeting customer expectations for convenient access to their preferred journals and eBooks has become crucial. The traditional journal publishing process must adapt to evolving customer demands which necessitates optimizing workflows, and intelligent workflow automation incorporating advanced technologies like AI is the key solution. Real-world statistics highlight the transformative impact of workflow automation on productivity within the publishing sector. According to a Zapier study, 63% of knowledge workers credit automation with helping them combat work burnout, while 65% believe it effectively reduces stress levels. This implies that AI driven workflow automation within journal publishing is aimed to enhance productivity and scale the output for publishers.

Prominent Challenges in the Publishing Industry

From submission of the manuscript to final distribution of the journal, the multiple runs of editing, corrections, revisions, and styling, have their own set of challenges.

Submission and Review– The authoring and editing phase might require numerous runs. Often, these are delayed due to out-of-sync timelines to manage both peer reviews and edits. Additionally, authors are often researchers first and writers second, which means huge gaps in their proficiency with language and writing tools.
Layout Design– Another prominent challenge for publishers comes after the final round of editing. The content design must be optimized for production across screens of all sizes and underlying platforms. All this has to be achieved while ensuring that digital publishing standards are met. Then, there is another round of author and editor proofing.
Content Standardization and Management– Finally, the gigantic task of distribution begins. Despite distribution being just a few swipes away with the digital format, it takes significant publisher effort to have it discovered and referred by scholars. XML-tagging, meta-tagging, managing the digital asset repository and ensuring appropriate credits are critical. This requires high-speed, cloud-based archive and license management.

Making Lives Easier for Publishers with AI-powered Workflow Automation Platform

To address the above at scale, publishers must employ cutting edge and efficient technologies to overcome workflow bottlenecks and deliver faster, better, and cheaper journals. In short, they need technology-driven intelligent workflow management platforms.

Leveraging artificial intelligence for efficient workflows has the potential to disrupt the publishing industry in many ways.

Accelerating turnarounds from content ideation to publishing: Automated grammar checks to assessing language quality and making recommendations for change speed up language quality checks and editing; NLP-based editing tools also facilitate guided editing to improve speed and quality.
Improved citation management with cross-linking and referencing from repositories: AI/ML-powered technical diagnosis helps identify missing information and references much faster and more accurately than human checks. It also aids adequate source-tagging.
Automated XML-tagging: Completely configurable cloud-based style managers can automate XML-tagging to style the documents according to each journal’s style guide. They also ensure compliance with industry standards. Machine-readable stylesheets reduce manual labor and accelerate layout design.

The most pressing challenge is choosing the right technology partner to facilitate efficiency in publishing workflows using AI services, such that the complexities of the publishing process are simplified via technology. Leveraging an intelligent automated publishing platform that comes with analytics capabilities can help publishers make informed decisions on content style and publishing format. It can also help them discover the most in-demand topics. In short, automation enables publishers to focus on strategic and creative aspects of journal publishing rather than tying up resources. Intelligent workflow automation ensures meeting timelines by streamlining and managing every step of the publishing process.

The full-cycle authoring and publishing platform from Integra ensures efficient workflow automation, powered by artificial intelligence. Its intuitive interface and rich functionalities can ease your content production journey. Contact us to streamline your workflows with the latest new technology in book and journal publishing.

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A Step-by-Step Guide to Implementing AI-Based Automation in Your Workflow