Embedded Generative AI in Enterprise Workflows: The Future of Productivity

Enterprises are increasingly adopting generative AI (genAI) to enhance their communication and productivity. Recent findings suggest that by 2025, nearly all enterprises will integrate genAI into their workflows. While standalone genAI tools have their merits, embedding this technology directly into existing enterprise applications offers a more seamless and user-friendly experience.

In a bid to streamline operations and improve communication, enterprises are turning to generative AI (genAI). Unlike standalone genAI tools that often require users to switch to a separate application, embedded genAI promises to seamlessly integrate AI functionality into existing word processing, spreadsheet, email, and other productivity software. This shift is driven by the desire to simplify access to genAI’s capabilities and make them more intuitive for employees.

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Forrester’s insight into GenAI adoption

A recent survey conducted by Forrester, focusing on technology decision-makers in North America and the UK, forecasts a substantial increase in genAI adoption across enterprises. By 2025, it is predicted that nearly all enterprises will leverage generative AI to support their communications, particularly in writing and editing tasks. In fact, 70% of respondents to the survey indicated that they were already using genAI for most or all of their writing and editing needs.

Forrester’s findings suggest that while standalone genAI tools, such as ChatGPT, have their place, they may not fully support cross-functional collaboration and do not seamlessly integrate into employees’ workflow. As a result, genAI’s true potential is more likely to be realized when embedded directly into existing enterprise applications.

Microsoft 365 copilot: A game-changer

One of the most notable developments in embedded genAI is Microsoft 365 Copilot, a generative AI assistant being integrated into various apps within the Microsoft 365 productivity suite. While some genAI features have been introduced to apps like Teams, the general release of Copilot is eagerly awaited. Its potential to transform back-office work processes is generating considerable excitement.

Some enterprises are not waiting for the general availability of embedded genAI solutions and are instead integrating generative AI into their applications themselves. This is often achieved through API calls to AI providers like OpenAI or by implementing locally run large language models (LLMs). These proactive steps help companies enhance their productivity and maintain a competitive edge.

Many vendors have already incorporated genAI features into their applications, allowing enterprises to leverage this technology within their existing workflows. For example, online meeting platforms like Zoom and Microsoft Teams now offer built-in or add-on AI-powered summarization features, significantly improving how meeting content is captured and summarized.

Several companies have already harnessed the power of generative AI within their workflows: Stream Financial Technology uses genAI in their email client to summarize emails and compose messages. They have also adopted Grammarly, which offers AI-powered text creation and editing within applications like Google Docs.

 Insurance broker NFP has been using Jasper AI to create marketing copy. Jasper AI uses GPT-4 models and can be tailored for specific business use cases. NFP has also integrated generative AI into its sales engagement platform, Salesloft, for email creation.

Thomson Reuters has been using AI for years and is actively exploring generative AI’s potential. They use AI to draft communication, create first drafts of work products, and even answer employee questions. This technology is seen as an augmentation of human capabilities rather than a replacement.

While the adoption of generative AI presents numerous benefits, enterprises must be mindful of potential risks. Privacy, security, and the accuracy of training data are key concerns. Enterprises should also be aware of the source of their AI training data to avoid legal issues related to copyright. Choosing AI tools that are user-friendly and integrate seamlessly into existing workflows is essential for successful adoption.

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