Transforming the Workplace: The Evolution of Digital Assistants

An assistant in the workplace is not a new concept. From the early days of apprentices to fictional superhero sidekicks, we’ve long been accustomed to the idea of assistance while at work. While most would associate the term with an executive assistant, typically a person assigned to a high-profile leader within an organization, as digital transformation ushers enterprises into the future of work, new types of assistants have risen to the forefront.

It’s impossible to have an administrative assistant for every employee, but it’s much more feasible to provide employees with a digital right-hand. With many terms, from digital or virtual to online or AI, there are many versions and definitions for workplace assistants. One thing is true regardless of terminology: this technology fills a gap that can greatly impact how employees get work done.

A digital assistant is technology designed to assist users by answering questions and processing simple tasks. They are designed to free users from spending time on routine functions that don’t require human intervention.

In the context of the digital workplace, digital assistants are interfaces that assist employees with finding information and handling requests like processing approvals or submitting and tracking service tickets. They are often driven by logic-based chatbot functionality that helps push employees to information based on a series of manually created prompts and results.

Born out of a need to digitize the workplace, employees found themselves in a sea of apps and systems seemingly overnight. Login here for your benefits information. Use this app to access your time off. Implemented with the best intentions, suddenly the tech stack became a hindrance, not a help to employees. The result is wasted time context switching and a frustrating employee experience.

Digital assistants began to boom in conjunction with remote work and as a solution to the digital friction arising from digital transformation. Without the water-cooler conversations or cross-cubicle collaboration, employees needed a resource to help point them in the right direction toward the information they needed.

Digital assistants, and their logic-based employee chatbot features, provided digital workplaces with the first steps toward a better employee experience. But the AI-boom brings with it the opportunity for even more intelligent enterprise solutions. A conversational AI Assistant solves for digital friction in the same way a digital assistant or virtual assistant might by helping direct employees to the information they seek out. But in addition to this core functionality, an AI Assistant can incorporate conversational AI features to handle more complex queries. Using natural language processing (NLP), conversational AI Assistants can search for data using APIs across multiple sources at once leading to more accurate, contextual information, with answers provided in a cohesive response. By leveraging conversational AI, responses from a digital assistant can be more robust, surfacing full sentence responses, microapps, and more, compared to a list of links or basic one sentence answers.

In addition to more robust search and response, an AI Assistant also has additional practical capabilities for the employee. For digital assistant’s paired with large language models, employees can leverage generative AI to complete more robust tasks such as generating or summarizing content.

The term enterprise copilot can be used interchangeably with AI Assistant to describe the conversational AI solution that connects the digital workplace (including systems, information, and tasks). With a graphical user interface (GUI), some of the more robust copilots help drive the employee experience by providing more than just text-based answers. Consider the components of your work’s most popular system interfaces. What features do they have? Surely, they have features like buttons to answer questions or forms to help intake information. Intelligent assistants bring these features into one centralized interface.

A study done by Asana, the 2022 Anatomy of Work Index, concluded that over half of workers (56%) feel that they have to respond to notifications immediately. Consider the number of pings and dings the average knowledge worker encounters per day. If on average over half of employees are immediately switching their attention at each notification, organizations have a serious context switching problem.

Copilots help with decreasing context switching and improving discoverability by guiding attention to information, improving the ability to access apps and systems, and surfacing generative AI instances for employees when and where they need them. Proactive nudges, anticipating employee's needs, and a consolidated communication feed pair well to reduce the app hopping and context switching the plague the employee experience.

So, who exactly needs an AI Assistant? Let’s consider the problems this type of technology can solve for.

• Context Switching – Regularly moving between tabs and apps hinders employee productivity and creates a frustrating experience. What’s more, many organizations have resulted in adding more technology to the mix, which simply adds to the digital friction or as Gartner defines it: YATTC (yet another thing to check). An AI Assistant reduces context switching by combining apps, notifications, tasks, and information within a single pane of glass. A unified experience where employees can get things done.

• Integrations – With numerous systems across departments and job functions it can seem daunting to introduce another piece of technology. Some technology vendors offer a digital assistant feature within their platform, but this can be met with certain challenges specifically in driving cross-departmental adoption. Finding a system agnostic AI Assistant, that provides cross-domain functionality and connects to multiple business systems, offers a unique solution that provides flexibility when it comes to meeting the needs of different groups across the business. By connecting existing technology to an AI Assistant, teams can deliver modern, user-friendly experiences targeted to the relevant employees, with one back-end system for easy IT management.

• Knowledge Sharing – For those struggling with information finding, a conversational AI Assistant can help provide a single source of truth while leveraging advanced tech benefits that help consolidate, search, and surface responses based on natural-language cues. AI technology such as Retrieval Augmented Generation (RAG) takes a prompt and augments for contextual data, which is then sent to an LLM, which generates an output (or answers to employee questions) using the most appropriate and recent data. With the ability to leverage many data sources including system APIs, knowledge repositories, and documents, AI Assistants can collect and analyze information more efficiently, getting answers into the hands of employees faster.

Building your Conversational AI Assistant in-house can offer some benefits including increased customizations and complete control over the scope, as well as ongoing research and development. But building in-house also brings on the burden of managed support, continuous maintenance, and significant financial investments.

On the other side of the coin, buying an off-the-shelf enterprise conversational AI solution allows for scalability, quick deployment, and ongoing support. While dependence on a vendor can be a challenge, it also means long term support and proven reliability.

For most businesses, purchasing an AI Assistant emerges as the preferable option. Here's why:

Cost and Time Efficiency

Building a bespoke solution is a significant investment. It's not just about the initial development; ongoing maintenance and updates add to perpetual operational cost. In contrast, buying a ready-made enterprise AI solution is more cost-effective, with a predictable expense model.

Access to Advanced Technologies

Purchased solutions often come with the latest technologies and build best-practices. Features are added and regularly updated by the vendor. Building advanced capabilities in-house requires substantial expertise, time, and resources.

Focus on Core Business

By opting to buy, companies can focus their valuable resources on their core business operations instead of diverting attention to developing and maintaining a complex AI system.

Reduced Risk

Off-the-shelf products have been tested across diverse scenarios, reducing the risk of failure. In contrast, a custom-built solution bears the risk of unknown challenges and potential inefficiencies.

Access to Advanced Technologies

Purchased solutions often come with the latest technologies and build best-practices. Features are added and regularly updated by the vendor. Building advanced capabilities in-house requires substantial expertise, time, and resources.

Quick Deployment and Immediate ROI

Time is a crucial factor. Off-the-shelf solutions can be deployed rapidly, enabling businesses to see an immediate return on investment (ROI) of their AI platform in a span of hours vs. months/years leveraging in-house development.

Workgrid’s conversational AI Assistant brings together the ability to find information, perform tasks, and receive alerts all within one user interface. As a system agnostic tool, the client is available where your employees are most. With customizable app experiences, to meet the needs of everyone from frontline workers to IT teams, Workgrid offers an enterprise copilot solution to improve the employee experience.

Contextual and personalized content

Leveraging AI technologies, Workgrid provides employees with contextual answers based on user attributes. For example, if you were to search “car mechanic near me” on Google the expected result would include results related to auto repair. For Workgrid, if an employee were to search “what is the travel per diem?” The Assistant would combine what we know about the employee (i.e. based in the Toronto office) with what we know about the request (i.e. asking about travel) to combine content and transactional items together. Based on this example the Assistant would surface the exact per diem, a link to the Canadian travel policy, as well as an app for the expense system.

Out of the Box Use Cases and Integrations

Whether your team is looking for a faster way to manage SAP Concur approvals or streamline access to ServiceNow IT tickets, Workgrid's catalog of pre-built app templates make it easy to get started.

• 50+ out-of-the-box use case templates focused on streamlining employee productivity. These templates serve as a starting point, allowing you to quickly implement solutions that address your unique business needs.

• Low-code builder to accelerate the creation of custom use cases using an intuitive drag-and-drop interface.

• Advanced query optimization delivering responses with summarized cohesive answers and relevant recommendations.

Data Security

At Workgrid, we prioritize data security. Here's how we ensure your sensitive information remains under your control:

• We do not ingest and retain your data. This means that your data stays within your organization, giving you peace of mind.

• Unlike some other AI Assistants, we do not train our LLM on your data. We believe in maintaining the confidentiality of your people and operations.

• With our platform, you have control over how generative AI solutions are used and who has access. This allows for greater access while ensuring appropriate use.

• We respect that your utterances are your business. Your privacy is important to us.

Our intelligent conversational AI Assistant leverages Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and Natural Language Processing (NLP) to expand our functionality outside of a single platform, connecting to various knowledge chains, and guiding employees attention to what they need to know and take action on.

The best part? Our model is already trained.

We work together with you to connect and make use of your systems' API services. We will help you develop a "land and expand" method to embed use cases such as enhanced information discovery, task automation, and workflow management into your workforce.

Explore Workgrid’s conversational AI experiences with a personalized demo. Schedule yours now.

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