Considerations for an Enterprise AI Copilot

In a technology first world, there seems to be a software solution for every problem. But what do we do when too much technology starts to become the issue? Enter the enterprise copilot. Previously known under many names, including digital assistant or virtual assistant, an enterprise copilot utilizes AI and integrations to provide support for employees across different channels, systems, and knowledge repositories within the digital workplace.

The term copilot itself implies how these assistants support employees in the workplace, guiding them to the information and tasks that require their attention. For digital workplace leaders, an enterprise copilot offers numerous benefits including:

• Reducing digital friction

• Streamlining complex, common processes

• Delivering governed AI workflows

• Improving employee productivity

As companies look to invest in enterprise copilot solutions, there are a number of considerations to evaluate. From copilot price to breadth of features and ease of use, it can be challenging to know where to start.

Using conversational AI for the customer experience has been widely adopted across businesses for quite some time. Consider the same solution for your employees. Implementing an AI copilot for the employee experience requires a solution that fits naturally into the employees’ day. It should be an extension of the systems and tasks that are already part of the regular routine.

Guiding attention is a critical part of the user experience when it comes to an enterprise copilot. This technology effectively provides every employee with their own executive assistant to help them get work done, from submitting an IT ticket to completing an expense approval.

While most digital assistants today respond to a request, for example a user types something in and the assistant responds to the query, this is just the beginning.

To get the most out of the experience with an AI copilot, it should also deliver nudges and notifications proactively to help dampen the digital friction employees experience. By bringing information to the employee within the flow of their day and being able to take the next steps on their behalf, a copilot can alleviate friction and improve productivity throughout the day. Looking forward, the future of copilots is this type of advanced agentic AI – the ability for these assistants to not only know and sense what you need in the moment, but to also take those next steps and actions going forward.

A common concern from businesses looking to bring AI to their organization is the challenge of clean data. The standard is clear, if you put clean data in, you get clean data out. But with many legacy systems and continuous digital transformation, it can be challenging to know where to start or have the resources to do so.

With an enterprise copilot, leaders can implement conversational AI workflows based on where they are in their AI journey. Starting small with two or three use cases in which you know the data is clean and accurate, such as time off balances or pay information, allows for fast implementation, and the opportunity to not only prove the value and return on investment, but also encourage adoption and identifying more transformative use cases to add as you scale.

Another major concern when it comes to AI copilots is what happens with your company data?

Across copilots on the market today there is a mix of different ways they handle large language model training. For most, there is peace of mind in using a solution that does not train or tune based on your data.

Some solutions offer a bring your own LLM approach. As organizations dive into AI they may have their own proprietary LLM. This presents a unique opportunity to connect the LLM with an AI Copilot to help deliver powerful AI functions to the entire organization in a governed and secure environment.

The ability to connect an AI copilot within the digital workplace ecosystem is critical for delivering maximum value to employees. Prior to purchasing licenses, it is imperative to understand the resources required to integrate with your systems with your copilot solution.

Questions to consider include:

• What are the resources required to integrate business systems with this solution?

• How long will it take to complete an integration?

• Is this integration accessible to the employees who will benefit from it?

In some cases, there may be limitations to consider when evaluating integration capabilities. In the case of Microsoft Copilot for example, it is tied to the Microsoft 365 ecosystem that requires Copilot Studio for integrations. In contrast, Workgrid offers a platform-agnostic solution that can integrate with any application, API, or system. Similarly, Microsoft Copilot focuses primarily on enhancing Microsoft suite functionality, which may cause delays or require development and professional services for non-Microsoft systems. Workgrid’s collection of pre-built templates alongside a low-code builder approach ensures users are equipped for rapid testing and deployment.

System-based copilot solutions offer a selection of highly specialized and focused use cases. However, as a result these types of copilots are limited in scope and often more expensive in price. For businesses that are looking to start with a targeted selection of AI workflows, an agnostic copilot offers a more scalable and cost-effective solution.

The Workgrid AI Assistant offers high value for enterprise-deployments starting at just $5 per user/month in comparison to Microsoft Copilot’s $30 per user/month price tag.

With the ability to reach across and connect systems, an AI copilot brings siloed application into the flow of work, turning isolated data into actionable insights. By acting as a gateway to all your data, a copilot doesn’t just automate tasks – it streamlines the flow of information, keeping your organization informed and proactive.

Governance is essential at every step of AI’s evolution within an organization. As Artificial Intelligence (AI) shifts from a futuristic buzzword to a driving force behind modern business strategy, its ability to streamline operations, enhance decision-making, and transform employee experiences becomes clear. However, AI’s full potential can only be realized through a structured journey where governance provides the foundation for trust, security, and accountability at every stage of AI maturity. 

AI maturity models provide a valuable framework to help distinguish where an organization is positioned in terms of AI readiness and helps to align the business needs and goals to set expectations. 

From data readiness and talent gaps to ethics and integrations, there is a wide range of operational AI challenges that an organization may need to address based on varying stages of AI maturity.

For those in the Assess or Active stage, an AI copilot enables you to focus first on low-hanging fruit. Implementing use cases that can be quickly stood up while still determining the resources required to achieve the desired AI strategy for employees provides immediate value and can help define future business cases.

Copilots also offer solutions for more advanced AI maturity including those in the operational and transformative stages. By these stages, AI copilots help to deliver more measurable results in driving efficiency and improving workflows. As AI becomes more embedded in the employee experience, isolated data is transformed into actionable insights and provides a gateway for employees beyond just automating tasks by streamlining a flow of information.

Finding the best copilot for your organization will depend on numerous factors from AI maturity and business needs to the existing tech ecosystem and available resources. As you embark on an AI-forward future, it is critical that you invest in a solution that is secure, scalable, adds value to the existing applications your employees use every day. The transformational impact of an enterprise AI copilot cannot be underestimated, empowering employees with solutions that not only support productivity, but also reduce friction and improve knowledge discovery.