Enhancing Platform Leadership: Workiva Integrates Generative AI for Greater Strength

Enhancing Platform Leadership: Workiva Integrates Generative AI for Greater Strength

Introduction:

Workiva Inc., a well-known cloud platform for assured, integrated reporting, has introduced Generative AI to its platform, revolutionizing the business reporting market. With Generative AI deeply integrated across the Workiva platform, users can now leverage its capabilities anywhere in their workflow, empowering them to author, edit, and rewrite content across the company’s suite of solutions. This shift from content producers to content editors enables streamlined workflows and allows users to focus on more value-add tasks. Workiva remains committed to the responsible use of AI, ensuring human judgment, data privacy, and transparency always guide the adoption of AI-generated content.

Full Article: Enhancing Platform Leadership: Workiva Integrates Generative AI for Greater Strength

Workiva Inc., a leading cloud platform for assured, integrated reporting, has announced the inclusion of Generative AI on its cloud platform. This introduction of AI technology aims to revolutionize the business reporting market, increasing productivity and efficiency while enabling better data-driven decisions.

Integration of Generative AI
Generative AI is now deeply integrated across the Workiva platform, offering a seamless user experience. Customers can leverage the new capabilities at any stage of their workflow – from content creation to editing and rewriting. With Generative AI, users are transformed from content producers to content editors, streamlining workflows and allowing more time for value-added tasks. The platform also provides users with a digital thought partner that can answer free-form questions at any point during their work.

Advancements in Innovation
David Haila, EVP and Chief Technology Officer for Workiva, explains that the addition of Generative AI to their platform showcases their commitment to innovation. With increasing scrutiny from stakeholders, the platform and technology have never been more relevant. The integration of off-the-shelf foundational Generative AI models combined with Workiva’s domain knowledge and proprietary data allows for targeted responses, differentiating Workiva in the market. This innovative combination of human expertise, contextual data, and Generative AI technology is set to deliver transformative business value.

Responsible Use of AI
Workiva is dedicated to the responsible use of AI. The company ensures that human judgment, ethical considerations, data privacy, and transparency always guide the adoption of AI-generated content. Workiva’s open ecosystem approach enables customers to choose the industry-leading large language model that best fits their needs, including models from Google Cloud and Microsoft Azure. Importantly, customer data does not need to be moved from the Workiva platform to leverage AI, and neither Workiva nor its technology partners store or use customer data to train the AI models.

The Workiva Platform
Workiva remains the only platform that offers Financial Reporting, ESG, and Governance, Risk, and Compliance in one controlled, secure, and audit-ready environment. With over 15 years of financial reporting leadership, Workiva has been at the forefront of automation to address the fundamental business reporting challenges faced by its customers. The addition of Generative AI to the Workiva platform further strengthens the company’s commitment to providing technology that empowers customers, ensuring data privacy and security.

In conclusion, Workiva’s introduction of Generative AI to its cloud platform presents a significant advancement in business reporting technology. By integrating AI capabilities throughout the workflow, Workiva enhances productivity and efficiency while enabling better and faster data-driven decisions. The company’s commitment to the responsible use of AI ensures the integration of human judgment, ethical considerations, and transparency. Workiva continues to lead the market by providing a comprehensive platform for assured, integrated reporting.

Summary: Enhancing Platform Leadership: Workiva Integrates Generative AI for Greater Strength

Workiva Inc. has introduced Generative AI to its cloud platform, revolutionizing the business reporting market by increasing productivity and efficiency. Generative AI is integrated into the Workiva platform, allowing users to author, edit, and rewrite content across all solutions. Users can also access a digital thought partner that can answer questions during their workflow. Workiva is committed to the responsible use of AI, ensuring human judgment, ethical considerations, data privacy, and transparency. Customers can choose which large language model fits their needs, and neither Workiva nor its partners store or use customer data to train models. Workiva is the only platform that combines Financial Reporting, ESG, and Governance, Risk, and Compliance in a secure environment.

Frequently Asked Questions:

Q1: What is data science?
A1: Data science is an interdisciplinary field that involves extracting actionable insights and knowledge from data through various techniques such as statistical analysis, machine learning, and data visualization. It encompasses the entire data lifecycle, including data collection, cleaning, analysis, and interpretation, with the goal of making data-driven decisions.

Q2: What skills are required to become a data scientist?
A2: To become a data scientist, one needs a combination of technical and analytical skills. Proficiency in programming languages like Python or R, knowledge of statistical concepts, and expertise in machine learning algorithms are essential. Additionally, strong critical thinking, problem-solving, and communication skills are crucial for effectively interpreting data and communicating findings to stakeholders.

Q3: How is data science applied in different industries?
A3: Data science is being widely adopted in various industries to gain insights and make data-driven decisions. For example, in healthcare, data science helps analyze patient records, predict disease outcomes, and derive personalized treatments. In finance, it is used for fraud detection, risk assessment, and optimizing trading strategies. Retail, marketing, transportation, and many other sectors also leverage data science techniques for improving efficiency and decision-making.

Q4: What are the steps involved in the data science process?
A4: The data science process typically involves the following steps:
1. Defining the problem and formulating research questions.
2. Collecting and preparing the relevant data for analysis.
3. Exploratory data analysis to understand the dataset and identify patterns.
4. Developing models and algorithms to analyze the data and make predictions.
5. Evaluating the models’ performance and refining them if necessary.
6. Communicating the findings and insights to stakeholders.

Q5: How is data science different from data analysis and machine learning?
A5: Data science, data analysis, and machine learning are interconnected but distinct disciplines. Data analysis involves examining datasets to extract meaningful information and draw conclusions, often using statistical techniques. Machine learning focuses on developing algorithms that can learn and make predictions based on data without being explicitly programmed. Data science, on the other hand, encompasses both data analysis and machine learning, while also involving other stages of the data lifecycle such as data collection and interpretation.